Computer Aided Diagnostic Support System for Skin Cancer: A Review of Techniques and Algorithms
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paper_title: Computer vision in medicine: color metrics and image segmentation methods for skin cancer diagnosis

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A front-end visual system for an expert system for skin tumor diagnosis will facilitate automatic detection of critical features, enabling the system to be operated with minimal user interaction. This research addresses the problem of using three-dimensional, color information for this purpose. A review of various color spaces and transforms that have appeared in the literature, along with a survey of general types of image segmentation techniques, is provided. ::: The original color space was an RGB (red, green, blue) space created by digitizing color slides of skin tumor images using a monochrome video camera and three filters. Five hundred tumor slides were utilized for this research. Automatic induction software was successfully used to generate classification rules, based on color statistics only, for a set of 238 tumor images. This set contained four possible diagnoses and success rates as high as 84% were achieved. ::: Development of two algorithms for the segmentation of digitized skin tumor images based on color are presented. One segmentation algorithm is based on a spherical coordinate transform of the original RGB data, and the second color image segmentation method is based on the principal components transform of the color data in six different color spaces. It is shown that these color segmentation techniques are successful as aids in the identification of the following features: variegated coloring, tumor, crust, hair, scale, shiny (reflections), and ulcer. Variegated coloring was correctly identified with success rates as high as 92%. The segmentation success rates on the other features approach 95%. The successful results demonstrate the feasibility of the methodology presented to the development of a computer vision/expert system, and specifically the success of the color segmentation techniques, as well as the utility of automatic induction tools.
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paper_title: Accuracy of Computer Diagnosis of Melanoma: A Quantitative Meta-analysis

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Background Recent developments in computer technology have raised expectations that fully automated diagnostic instruments will become available to diagnose cutaneous melanoma without the need of human expertise. Objectives To critically review the contemporary literature on computer diagnosis of melanoma, evaluate the accuracy of such computer diagnosis, analyze the influence of study characteristics, and compare the accuracy of computer diagnosis of melanoma with human diagnosis. Methods Quantitative meta-analysis of published reports. Data Sources Eligible studies were identified by a MEDLINE search covering the period from January 1991 to March 2002, by manual searches of the reference lists of retrieved articles, and by direct communication with experts. Results Thirty studies with substantial differences in methodological quality were deemed eligible for meta-analysis. Five of these complied with the predetermined list of "good quality" requirements, but none met all methodological quality requirements. Ten of these studies compared the performance of computer diagnosis with human diagnosis. The diagnostic accuracy achieved with computer diagnosis was statistically not different from that of human diagnosis (log odds ratios, 3.36 vs 3.51; P = .80). The diagnostic performance of the computer diagnosis was better for studies that used dermoscopic images than for studies that used clinical images (log odds ratios, 4.2 vs 3.4; P = .08). Other study characteristics did not significantly influence the accuracy of the computer diagnosis. Conclusions The computer diagnosis of melanoma is accurate under experimental conditions, but the practical value of automated diagnostic instruments under real-world conditions is currently unknown. We suggest minimum requirements for methodological quality in future experimental studies or, ideally, randomized controlled trials.
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paper_title: Three-point checklist of dermoscopy: an open internet study

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Background In a pilot study, the three-point checklist of dermoscopy has been shown to represent a valid and reproducible tool with high sensitivity for the diagnosis of skin cancer in the hands of a small group of nonexperts.
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paper_title: Addition of dermoscopy to conventional naked-eye examination in melanoma screening: a randomized study

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Abstract Objective We sought to assess the difference in lesion management between combined examination (naked-eye and dermoscopy) and conventional naked-eye examination in evaluations for melanoma; and to assess the impact on patient treatment of facilities for digital follow-up of diagnostically equivocal lesions. Methods We conducted a randomized, controlled trial at a pigmented lesion clinic in a university hospital. A total of 938 consecutive subjects presenting between November 1, 2001, and March 31, 2002, were eligible and 25 were excluded because they were younger than 12 years of age; hence 913 subjects were enrolled. Participants were randomized to combined examination with mandatory excision of equivocal lesion (arm B) and with possibility of digital follow-up according to the clinician's decision (arm C), or to conventional naked-eye examination (mandatory excision of equivocal lesion) (arm A). The same pigmented lesion clinic staff examined all subjects. Results Combined examination determined a significant reduction in the percentage of patients referred for operation (9.0% vs 15.6%) ( P = .013). When facilities for digital follow-up of equivocal lesions were available, the percentage of patients classified as harboring lesions difficult to diagnose increased (group C, 35.8%; group B, 17.8%; P Conclusions the addition of dermoscopy to conventional naked-eye examination is associated with a significant reduction of number of pigmented skin lesions excised for diagnostic verification. The possibility of digital follow-up of equivocal lesions is associated with a not negligible occurrence of initial melanomas left unexcised until the second consultation.
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paper_title: Frequency and Morphologic Characteristics of Invasive Melanomas Lacking Specific Surface Microscopic Features

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Objectives: To create a simple diagnostic method for invasive melanoma with in vivo cutaneous surface microscopy (epiluminescence microscopy, dermoscopy, dermatoscopy) and to analyze the incidence and characteristics of those invasive melanomas that had no diagnostic features by means of hand-held surface microscopes. Design: Pigmented skin lesions were photographed in vivo with the use of immersion oil. All were excised and reviewed for histological diagnosis. A training set of 62 invasive melanomas and 159 atypical nonmelanomas and a test set of 45 invasive melanomas and 119 atypical nonmelanomas were used. Images from the training set were scored for 72 surface microscopic features. Those features with a low sensitivity (0%) and high specificity (>85%) were used to create a simple diagnostic model for invasive melanoma. Setting: All patients were recruited from the Sydney (Australia) Melanoma Unit (a primary case and referral center). Patients: A random sample of patients whose lesions were excised, selected from a larger database. Main Outcome Measures: Sensitivity and specificity of the model for diagnosis of invasive melanona. Results: The model gave a sensitivity of 92% (98/107) and specificity of 71%. Of the 9 "featureless" melanomas the model failed to detect, 6 were pigmented and thin and had a pigment network. The other 3 were thicker, hypomelanotic lesions lacking a pigment network, some with prominent telangiectases, and all with only small areas of pigment. All featureless melanomas noted by the patients had a history of change in color, shape, or size. Conclusions: Surface microscopy does not allow 100% sensitivity in diagnosing invasive melanoma and therefore cannot be used as the sole indicator for excision. Clinical history is an important consideration when featureless lesions are diagnosed. Arch Dermatol. 1996;132:1178-1182
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paper_title: Digital epiluminescence microscopy: usefulness in the differential diagnosis of cutaneous pigmentary lesions. A statistical comparison between visual and computer inspection.

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Epiluminescence light microscopy (ELM) has been confirmed to be a useful tool for the diagnosis of pigmented skin lesions. The application of digital systems to epiluminescence represents the latest attempt to improve the diagnosis of cutaneous melanoma. The aim of this study was to compare the diagnostic accuracy of one of these systems, the DB-Dermo MIPS, with the accuracy of well-trained dermatologists using the ELM technique in order to establish the real usefulness of this instrument and to verify how much it can help the clinician make a diagnosis in a clinical setting. During a campaign for the early diagnosis of cutaneous melanoma, 311 patients with non-melanocytic lesions, common naevi, dysplastic naevi and melanomas underwent clinical diagnosis using ELM, computerized evaluation with DB-Dermo MIPS and skin biopsy. Sensitivity, specificity, true and negative predictive value were evaluated for epiluminescence and digital epiluminescence. Our study revealed that the inspection of pigmented skin lesions by digital epiluminescence has a better diagnostic accuracy than that of a trained dermatologist using the epiluminescence technique only. In our experience, this computerized system can play an essential role in the detection of early melanomas.
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paper_title: Systematic review of dermoscopy and digital dermoscopy/ artificial intelligence for the diagnosis of melanoma

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Summary ::: Background Dermoscopy improves diagnostic accuracy of the unaided eye for melanoma, and digital dermoscopy with artificial intelligence or computer diagnosis has also been shown useful for the diagnosis of melanoma. At present there is no clear evidence regarding the diagnostic accuracy of dermoscopy compared with artificial intelligence. ::: ::: Objectives To evaluate the diagnostic accuracy of dermoscopy and digital dermoscopy/artificial intelligence for melanoma diagnosis and to compare the diagnostic accuracy of the different dermoscopic algorithms with each other and with digital dermoscopy/artificial intelligence for the detection of melanoma. ::: ::: Methods A literature search on dermoscopy and digital dermoscopy/artificial intelligence for melanoma diagnosis was performed using several databases. Titles and abstracts of the retrieved articles were screened using a literature evaluation form. A quality assessment form was developed to assess the quality of the included studies. Heterogeneity among the studies was assessed. Pooled data were analysed using meta-analytical methods and comparisons between different algorithms were performed. ::: ::: Results Of 765 articles retrieved, 30 studies were eligible for meta-analysis. Pooled sensitivity for artificial intelligence was slightly higher than for dermoscopy (91% vs. 88%; P = 0·076). Pooled specificity for dermoscopy was significantly better than artificial intelligence (86% vs. 79%; P < 0·001). Pooled diagnostic odds ratio was 51·5 for dermoscopy and 57·8 for artificial intelligence, which were not significantly different (P = 0·783). There were no significance differences in diagnostic odds ratio among the different dermoscopic diagnostic algorithms. ::: ::: Conclusions Dermoscopy and artificial intelligence performed equally well for diagnosis of melanocytic skin lesions. There was no significant difference in the diagnostic performance of various dermoscopy algorithms. The three-point checklist, the seven-point checklist and Menzies score had better diagnostic odds ratios than the others; however, these results need to be confirmed by a large-scale high-quality population-based study.
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paper_title: In vivo epiluminescence microscopy of pigmented skin lesions. I. Pattern analysis of pigmented skin lesions

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The importance of recognizing early melanoma is generally accepted. Because not all pigmented skin lesions can be diagnosed correctly by their clinical appearance, additional criteria are required for the clinical diagnosis of such lesions. In vivo epiluminescence microscopy provides for a more detailed inspection of the surface of pigmented skin lesions, and, by using the oil immersion technic, which renders the epidermis translucent, opens a new dimension of skin morphology by including the dermoepidermal junction into the macroscopic evaluation of a lesion. In an epiluminescence microscopy study of more than 3000 pigmented skin lesions we have defined morphologic criteria that are not readily apparent to the naked eye but that are detected easily by epiluminescence microscopy and represent relatively reliable markers of benign and malignant pigmented skin lesions. These features include specific patterns, colors, and intensities of pigmentation, as well as the configuration, regularity, and other characteristics of both the margin and the surface of pigmented skin lesions. Pattern analysis of these features permits a distinction between different types of pigmented skin lesions and, in particular, between benign and malignant growth patterns. Epiluminescence microscopy is thus a valuable addition to the diagnostic armamentarium of pigmented skin lesions at a clinical level.
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paper_title: The seven features for melanoma: a new dermoscopic algorithm for the diagnosis of malignant melanoma.

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The clinical diagnosis of melanoma has a mean sensitivity of 67%, dermoscopy or dermatoscopy is a non invasive technique which improves this sensitivity. Our purpose was to create a simple dermoscopic method for the diagnosis of melanoma useful in daily office practice. For this reason a training set of 218 cutaneous pigmented lesions was used and scored for 16 dermoscopic features: for each feature sensitivity, specificity and statistical significance were evaluated. The results were used to create a simple dermoscopic diagnostic method of only seven dermoscopic features (7FFM). The method was used to evaluate a test set of 713 pigmented skin lesions consecutively observed. The diagnostic dermoscopic method developed gave a sensitivity of 94.6%, a specificity of 85.5% and an efficiency of 87.6%. Our method improves the sensitivity in the diagnosis of melanoma and can be used for the screening of pigmented skin lesions.
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paper_title: Diagnosis of cutaneous melanoma: accuracy of a computerized image analysis system (Skin View).

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BACKGROUND/AIMS ::: Early diagnosis is essential for reducing the high mortality of CM (cutaneous melanoma). An expert dermatologist has a diagnostic accuracy of 85% but, in performing mass screening, the problem is to transfer the experience of a specialized centre to peripheral health clinics. Therefore, some computerized analysis systems were made. The aim of this paper is to compare the diagnostic accuracy of one such system, the Skin View System (SVS), to the accuracy of a well trained dermatologist in order to establish the real usefulness of the instrument and to verify how much it can help the diagnostic accuracy of the clinician in a dermatology setting. ::: ::: ::: METHOD ::: During a campaign for the early diagnosis of CM, 176 patients with small and flat pigmentary lesions underwent clinical diagnosis, computerized analysis with SVS and skin biopsy. For clinical and computerized diagnosis, we evaluated sensitivity, specificity, true and negative predictive value. ::: ::: ::: RESULTS ::: The statistical evaluation, in our study, reveals that the computerized system has a diagnostic accuracy lower than that of a trained dermatologist. ::: ::: ::: CONCLUSIONS ::: This computerized system does not help the clinician in the diagnosis of pigmented lesions.
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paper_title: Automated diagnosis of pigmented skin lesions.

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Since advanced melanoma remains practically incurable, early detection is an important step toward a reduction in mortality. High expectations are entertained for a technique known as dermoscopy or epiluminescence light microscopy; however, evaluation of pigmented skin lesions by this method is often extremely complex and subjective. To obviate the problem of qualitative interpretation, methods based on mathematical analysis of pigmented skin lesions, such as digital dermoscopy analysis, have been developed. In the present study, we used a digital dermoscopy analyzer (DBDermo-Mips system) to evaluate a series of 588 excised, clinically atypical, flat pigmented skin lesions (371 benign, 217 malignant). The analyzer evaluated 48 parameters grouped into 4 categories (geometries, colors, textures and islands of color), which were used to train an artificial neural network. To evaluate the diagnostic performance of the neural network and to check it during the training process, we used the error area over the receiver operating characteristic curve. The discriminating power of the digital dermoscopy analyzer plus artificial neural network was compared with histologic diagnosis. A feature selection procedure indicated that as few as 13 of the variables were sufficient to discriminate the 2 groups of lesions, and this also ensured high generalization power. The artificial neural network designed with these variables enabled a diagnostic accuracy of about 94%. In conclusion, the good diagnostic performance and high speed in reading and analyzing lesions (real time) of our method constitute an important step in the direction of automated diagnosis of pigmented skin lesions. © 2002 Wiley-Liss, Inc.
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paper_title: Clinical accuracy of the diagnosis of cutaneous malignant melanoma

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Diagnostic accuracy for melanoma was determined in a dedicated pigmented lesion clinic. We assessed the impact of duration of experience in dermatology and also the relationship between tumour thickness and accuracy of clinical diagnosis. We reviewed the histopathology request forms and reports for all biopsies generated by the Pigmented Lesion Clinic, Western Infirmary. Glasgow during 1992-94 inclusive. The clinic is staffed by two consultants, one senior registrar and one registrar. Diagnostic accuracy, index of suspicion, sensitivity, specificity and positive predictive value were calculated for the clinic overall, and for each grade of staff. One hundred and sixty-three lesions were diagnosed clinically as melanoma. A histopathological diagnosis of melanoma was made for 128 lesions during this period, 113 of which had been correctly diagnosed before surgery. The diagnostic accuracy for two dermatologists each with > 10 years experience in dermatology was 80%, with sensitivity of 91% and positive predictive value of 86%. Diagnostic accuracy rates for two senior registrars (each with 3-5 years experience) and six registrars (each with 1-2 years experience) were 62% and 56%, respectively. Thin and intermediate thickness melanomas generated the greatest inaccuracy irrespective of clinical experience, although registrars failed to recognize melanoma three times more often than the other groups. We report the diagnostic accuracy for melanoma by trained dermatologists to be higher than previously reported. In comparison with trainees, > 10 years experience in dermatology and exposure to more than 10 melanomas per year appears to be associated with greater diagnostic accuracy. Knowledge of the current clinical diagnostic accuracy at varying levels of experience is essential if the impact of training is to be evaluated. As pigmented lesions of virtually all types can be treated within dermatology departments. dermatologists are the appropriate first point of referral for suspected early melanoma.
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paper_title: Increasing burden of melanoma in the United States

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It is controversial whether worldwide increases in melanoma incidence represent a true epidemic. Dramatic increases in incidence in the setting of relatively stable mortality trends have also been attributed to expanded skin screening and detection of biologically indolent tumors with low metastatic potential. To better understand how melanoma incidence trends varied by severity at diagnosis and factors relevant to screening access, we assessed recent United States incidence and mortality trends by histologic type, tumor thickness, and area-level socioeconomic status (SES). We obtained population-based data regarding diagnoses of invasive melanoma among non-Hispanic whites from nearly 291 million person-years of observation by the Surveillance Epidemiology and End Results (SEER) program (1992–2004). Age-adjusted incidence and mortality rates were calculated for SEER and a subset (California) for which small-area SES measure was available. Overall, melanoma incidence increased at 3.1% ( P 4mm. Melanoma incidence rates doubled in all SES groups over a 10-year period whereas melanoma mortality rates did not increase significantly. We conclude that screening-associated diagnosis of thinner melanomas cannot explain the increasing rates of thicker melanomas among low SES populations with poorer access to screening.
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paper_title: The Diagnostic Performance of Expert Dermoscopists vs a Computer-Vision System on Small-Diameter Melanomas

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OBJECTIVE ::: To evaluate the performance of dermoscopists in diagnosing small pigmented skin lesions (diameter </= 6 mm) compared with an automatic multispectral computer-vision system. ::: ::: ::: DESIGN ::: Blinded comparison study. ::: ::: ::: SETTING ::: Dermatologic hospital-based clinics and private practice offices. Patients From a computerized skin imaging database of 990 small (</= 6-mm) pigmented skin lesions, all 49 melanomas from 49 patients were included in this study. Fifty randomly selected nonmelanomas from 46 patients served as a control. ::: ::: ::: MAIN OUTCOME MEASURES ::: Ten dermoscopists independently examined dermoscopic images of 99 pigmented skin lesions and decided whether they identified the lesions as melanoma and whether they would recommend biopsy to rule out melanoma. Diagnostic and biopsy sensitivity and specificity were computed and then compared with the results of the computer-vision system. ::: ::: ::: RESULTS ::: Dermoscopists were able to correctly identify small melanomas with an average diagnostic sensitivity of 39% and a specificity of 82% and recommended small melanomas for biopsy with a sensitivity of 71% and specificity of 49%, with only fair interobserver agreement (kappa = 0.31 for diagnosis and 0.34 for biopsy). In comparison, in recommending biopsy to rule out melanoma, the computer-vision system achieved 98% sensitivity and 44% specificity. ::: ::: ::: CONCLUSIONS ::: Differentiation of small melanomas from small benign pigmented lesions challenges even expert physicians. Computer-vision systems can facilitate early detection of small melanomas and may limit the number of biopsies to rule out melanoma performed on benign lesions.
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paper_title: Dermoscopy of pigmented skin lesions.

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This paper describes the basic concepts of dermoscopy, the various dermoscopic equipments and the standard criteria for diagnosing pigmented skin lesions. In assessing dermoscopic images, both global and local features can be recognized. These features will be systematically described and illustrated in Part I of this article. First, we will focus on 8 morphologically rather distinctive global features that allow a quick, preliminary categorization of a given pigmented skin lesion. Second, we will describe various local features representing the letters of the dermoscopic alphabet. The local features permit a more detailed assessment of pigmented skin lesions.
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paper_title: Overview of Advanced Computer Vision Systems for Skin Lesions Characterization

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During the last years, computer-vision-based diagnosis systems have been used in several hospitals and dermatology clinics, aiming mostly at the early detection of skin cancer, and more specifically, the recognition of malignant melanoma tumour. In this paper, we review the state of the art in such systems by first presenting the installation, the visual features used for skin lesion classification, and the methods for defining them. Then, we describe how to extract these features through digital image processing methods, i.e., segmentation, border detection, and color and texture processing, and we present the most prominent techniques for skin lesion classification. The paper reports the statistics and the results of the most important implementations that exist in the literature, while it compares the performance of several classifiers on the specific skin lesion diagnostic problem and discusses the corresponding findings.
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paper_title: Dermoscopic diagnosis by a trained clinician vs. a clinician with minimal dermoscopy training vs. computer-aided diagnosis of 341 pigmented skin lesions: a comparative study.

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BACKGROUND ::: In the last few years digital dermoscopy has been introduced as an additional tool to improve the clinical diagnosis of pigmented skin lesions. ::: ::: ::: OBJECTIVE ::: To evaluate the validity of digital dermoscopy by comparing the diagnoses of a dermatologist experienced in dermoscopy (5 years of experience) with those of a clinician with minimal training in this field, and then comparing these results with those obtained using computer-aided diagnoses. ::: ::: ::: METHODS ::: Three hundred and forty-one pigmented melanocytic and non-melanocytic skin lesions were included. All lesions were surgically excised and histopathologically examined. Digital dermoscopic images of all lesions were framed and analysed using software based on a trained artificial neural network. Cohen's kappa statistic was calculated to assess the validity with regard to the correct diagnoses of melanoma and non-melanoma. ::: ::: ::: RESULTS ::: Sensitivity was high for the experienced dermatologist and the computer (92%) and lower for the inexperienced clinician (69%). Specificity of the diagnosis by the experienced dermatologist was higher (99%) than that of the inexperienced clinician (94%) and the computer assessment (74%). Notably, computer analysis gave a higher number of false positives (26%) compared with the experienced dermatologist (0.6%) and the inexperienced clinician (5.5%). ::: ::: ::: CONCLUSIONS ::: Our results indicate that analysis either by a trained dermatologist or an artificial neural network-trained computer can improve the diagnostic accuracy of melanoma compared with that of an inexperienced clinician and that the computer diagnosis might represent a useful tool for the screening of melanoma, particularly at centres not experienced in dermoscopy.
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paper_title: Cancer statistics, 2011: the impact of eliminating socioeconomic and racial disparities on premature cancer deaths.

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Each year, the American Cancer Society estimates the numbers of new cancer cases and deaths expected in the United States in the current year and compiles the most recent data on cancer incidence, mortality, and survival based on incidence data from the National Cancer Institute, the Centers for Disease Control and Prevention, and the North American Association of Central Cancer Registries and mortality data from the National Center for Health Statistics. A total of 1,596,670 new cancer cases and 571,950 deaths from cancer are projected to occur in the United States in 2011. Overall cancer incidence rates were stable in men in the most recent time period after decreasing by 1.9% per year from 2001 to 2005; in women, incidence rates have been declining by 0.6% annually since 1998. Overall cancer death rates decreased in all racial/ethnic groups in both men and women from 1998 through 2007, with the exception of American Indian/Alaska Native women, in whom rates were stable. African American and Hispanic men showed the largest annual decreases in cancer death rates during this time period (2.6% and 2.5%, respectively). Lung cancer death rates showed a significant decline in women after continuously increasing since the 1930s. The reduction in the overall cancer death rates since 1990 in men and 1991 in women translates to the avoidance of about 898,000 deaths from cancer. However, this progress has not benefitted all segments of the population equally; cancer death rates for individuals with the least education are more than twice those of the most educated. The elimination of educational and racial disparities could potentially have avoided about 37% (60,370) of the premature cancer deaths among individuals aged 25 to 64 years in 2007 alone. Further progress can be accelerated by applying existing cancer control knowledge across all segments of the population with an emphasis on those groups in the lowest socioeconomic bracket. CA Cancer J Clin 2011. © 2011 American Cancer Society.
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paper_title: The performance of SolarScan: an automated dermoscopy image analysis instrument for the diagnosis of primary melanoma.

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OBJECTIVE ::: To describe the diagnostic performance of SolarScan (Polartechnics Ltd, Sydney, Australia), an automated instrument for the diagnosis of primary melanoma. ::: ::: ::: DESIGN ::: Images from a data set of 2430 lesions (382 were melanomas; median Breslow thickness, 0.36 mm) were divided into a training set and an independent test set at a ratio of approximately 2:1. A diagnostic algorithm (absolute diagnosis of melanoma vs benign lesion and estimated probability of melanoma) was developed and its performance described on the test set. High-quality clinical and dermoscopy images with a detailed patient history for 78 lesions (13 of which were melanomas) from the test set were given to various clinicians to compare their diagnostic accuracy with that of SolarScan. ::: ::: ::: SETTING ::: Seven specialist referral centers and 2 general practice skin cancer clinics from 3 continents. Comparison between clinician diagnosis and SolarScan diagnosis was by 3 dermoscopy experts, 4 dermatologists, 3 trainee dermatologists, and 3 general practitioners. ::: ::: ::: PATIENTS ::: Images of the melanocytic lesions were obtained from patients who required either excision or digital monitoring to exclude malignancy. ::: ::: ::: MAIN OUTCOME MEASURES ::: Sensitivity, specificity, the area under the receiver operator characteristic curve, median probability for the diagnosis of melanoma, a direct comparison of SolarScan with diagnoses performed by humans, and interinstrument and intrainstrument reproducibility. ::: ::: ::: RESULTS ::: The melanocytic-only diagnostic model was highly reproducible in the test set and gave a sensitivity of 91% (95% confidence interval [CI], 86%-96%) and specificity of 68% (95% CI, 64%-72%) for melanoma. SolarScan had comparable or superior sensitivity and specificity (85% vs 65%) compared with those of experts (90% vs 59%), dermatologists (81% vs 60%), trainees (85% vs 36%; P =.06), and general practitioners (62% vs 63%). The intraclass correlation coefficient of intrainstrument repeatability was 0.86 (95% CI, 0.83-0.88), indicating an excellent repeatability. There was no significant interinstrument variation (P = .80). ::: ::: ::: CONCLUSIONS ::: SolarScan is a robust diagnostic instrument for pigmented or partially pigmented melanocytic lesions of the skin. Preliminary data suggest that its performance is comparable or superior to that of a range of clinician groups. However, these findings should be confirmed in a formal clinical trial.
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paper_title: Improvement of the diagnostic accuracy in pigmented skin lesions by epiluminescent light microscopy.

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Epiluminescent light microscopy (ELM) represents a technique which permits to examine details of the surface of the skin and also--in by using the oil immersion technique--of the dermal-epidermal junction zone. Criteria are described which allow the distinction of benign from malignant growth patterns of pigmented skin lesions; thus ELM adds considerably to the diagnostic armamentarium of pigmented skin lesions at the clinical level.
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paper_title: An evaluation of the revised seven-point checklist for the early diagnosis of cutaneous malignant melanoma.

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The seven-point checklist has been widely advocated as a sensitive screening test for malignant melanoma. A number of groups have questioned the sensitivity of this system, especially in the detection of early lesions. We have assessed the sensitivity and specificity of the revised seven-point checklist when applied to lesions seen in our department over a 26-month period and compared it with the American ABCDE evaluation system. All melanomas (n = 65) were detected using the revised seven-point checklist and all were found to have at least one of the three major criteria defined by that system. Five (7.7%) melanomas were not picked up by the ABCDE checklist. Of 100 randomly selected patients who attended the clinic during the same period, with clinically diagnosed benign pigmented lesions, 63 had at least one major feature of the revised seven-point checklist. Forty (62%) of the melanomas, compared with only (4%) of the benign lesions, had more than one major feature. This study confirms the sensitivity of the revised seven-point checklist in the diagnosis of cutaneous malignant melanoma.
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paper_title: Diagnostic accuracy of dermoscopy

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Summary The accuracy of the clinical diagnosis of cutaneous melanoma with the unaided eye is only about 60%. Dermoscopy, a non-invasive, in vivo technique for the microscopic examination of pigmented skin lesions, has the potential to improve the diagnostic accuracy. Our objectives were to review previous publications, to compare the accuracy of melanoma diagnosis with and without dermoscopy, and to assess the influence of study characteristics on the diagnostic accuracy. We searched for publications between 1987 and 2000 and identified 27 studies eligible for meta-analysis. The diagnostic accuracy for melanoma was significantly higher with dermoscopy than without this technique (log odds ratio 4·0 [95% CI 3·0 to 5·1] versus 2·7 [1·9 to 3·4]; an improvement of 49%, p=0·001). The diagnostic accuracy of dermoscopy significantly depended on the degree of experience of the examiners. Dermoscopy by untrained or less experienced examiners was no better than clinical inspection without dermoscopy. The diagnostic performance of dermoscopy improved when the diagnosis was made by a group of examiners in consensus and diminished as the prevalence of melanoma increased. A comparison of various diagnostic algorithms for dermoscopy showed no significant differences in their diagnostic performance. A thorough appraisal of the study characteristics showed that most of the studies were potentially influenced by verification bias. In conclusion, dermoscopy improves the diagnostic accuracy for melanoma in comparison with inspection by the unaided eye, but only for experienced examiners.
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paper_title: Intraobserver agreement in interpretation of digital epiluminescence microscopy.

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BACKGROUND ::: Although a major problem with the classification of epiluminescence microscopy (ELM) findings is the lack of standard definitions, reproducibility of the criteria proposed has never been investigated. ::: ::: ::: OBJECTIVES ::: Our purposes were (1) to perform a review of four major published classifications to obtain a set of apparently well-defined ELM variables and descriptors and (2) to evaluate the ability of one of us to report consistently ELM findings in melanocytic lesions according to these criteria. ::: ::: ::: METHODS ::: Intraobserver agreement (with a set of 44 selected descriptors) between two readings of 150 digital ELM images was evaluated with the kappa (kappa) statistic. Subgroups of descriptors were compared for kappa value distribution. ::: ::: ::: RESULTS ::: The median kappa value for the whole series of descriptors was 0.66. Median kappa did not vary significantly among the four classification systems (kappa = 0.61 to 0.67). Agreement was significantly better as to the presence or absence of ELM findings (kappa range, 0.39 to 1.00; median kappa, 0.77) compared with agreement as to their distribution (kappa range, 0.10 to 0.79; median kappa, 0.47; p = 0.0007) and their width, thickness, and size (kappa range, 0.06 to 0.83; median kappa, 0.39; p = 0.0075). ::: ::: ::: CONCLUSION ::: Although nothing can be inferred from a single study, descriptors associated with low intraobserver agreement are likely to be inadequately defined.
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paper_title: Nevoscopy: Three-Dimensional Computed Tomography of Nevi and Melanomas in Situ by Transillumination

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The thickness of a malignant nevus has been found to be an important prognostic factor for patients with melanoma. We have designed a new method of imaging nevi that permits their thickness to be measured in situ. Using fiber optics directed into the surrounding skin, we transilluminate the nevus. Three images are picked up by a digitizing TV camera: the vertical image (90 degrees ), a glancing image (180 degrees ), and one at 45 degrees , obtained by using two front-silvered mirrors held next to the nevus in a "nevoscope." The digitized images are used in a computed tomography algorithm to calculate approximate vertical cross sections of the nevus. The algorithm is one we recently developed to permit reconstruction from a very few projections. Our method is completely noninvasive. It may be used to check all the nevi on a patient. Without excisions, we could establish a baseline three-dimensional shape for each nevus, follow any changes in time, and obtain an early warning of increase in thickness and possible malignancy.
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paper_title: A possible new tool for clinical diagnosis of melanoma: the computer.

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The analysis of cutaneous melanoma images by two coupled computers (IBM 7350/4361) was carried out on twenty color slides. Each color slide was digitized with a spatial reduction of 25 X 25 microns. Classic technics of digital image analysis and new algorithms were used to improve the contrast on the full image or a portion of it, contrast a skin lesion with statistical information deduced from another lesion, evaluate the shape of the lesion, the roughness of the surface, and the transition region from the lesion to the normal skin, and analyze a lesion from the chromatic point of view. The theoretical reasons of interest are to have an objective method that is easy to standardize and reliably repeatable and to be able to analyze details not perceivable by the human eye. If the same technic are used in the evaluation of histologic characteristics of the lesions, a chance of making much more sophisticated clinicopathologic correlations will be available. The system needs to be improved at the technical level so that the response time of acquisition of the digitized images is shortened by the use of a digital television camera and the development of new computer programs to be run on a small computer. Evaluation of the system's sensitivity and specificity and an adequate clinical trial are needed.
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paper_title: Dermoscopy: alternative melanocytic algorithms-the ABCD rule of dermatoscopy, Menzies scoring method, and 7-point checklist.

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In the year 2000, the Second Consensus Meeting on Dermoscopy was held over the Internet. Experts from around the world were asked to evaluate melanocytic, nonmelanocytic, benign, and malignant skin lesions using pattern analysis, the ABCD rule of dermatoscopy, Menzies scoring method, and the 7-point checklist. One hundred twenty-eight digital dermoscopic images were randomly subdivided into a training set of 20 images in which the unifying concepts of pattern analysis, the ABCD rule of dermatoscopy, Menzies scoring method, and the 7-point check list were presented. Those melanocytic algorithms were then used to evaluate another 108 cases. The results of the Second Consensus Meeting were presented at the First World Congress of Dermoscopy held in Rome, Italy, March 2001 and in an atlas that outlined the results and illustrated all of the cases.1,2 One of the main conclusions from the Second Consensus Meeting was that the four melanocytic algorithms studied were all valid ways to evaluate pigmented skin lesions with dermoscopy. This article will present the unifying concepts of the ABCD rule of dermatoscopy, Menzies scoring method, and the 7-point checklist. Other less-well-known and studied dermoscopic algorithms will not be presented.3–5
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paper_title: Automated Diagnostic Instruments for Cutaneous Melanoma

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The objective of this review is to report and discuss the evidence for fully automated diagnostic instruments for cutaneous melanoma tested in a real-world clinical setting directly compared with human diagnosis. A systematic review was performed and articles excluded when studies did not report sensitivity or specificity for melanoma directly compared with humans on an independent test set. Only 3 instruments have had their diagnostic accuracy compared with a human diagnosis in the clinical field with a meaningful sample size that could allow some generalization with the wider clinical arena. Two of these instruments showed a significantly inferior specificity for the diagnosis of melanoma compared with specialists. In one of these studies, the sensitivity for diagnosis, although superior to the specialist diagnosis, did not reach statistical significance. In contrast, one instrument had an equivalent specificity and trended superior but not significantly for sensitivity for the diagnosis of melanoma. Other image based nonclinic studies and studies comparing clinical management as the endpoint rather than diagnosis are also reviewed.
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paper_title: Accuracy in the clinical diagnosis and pattern of malignant melanoma at a dermatological clinic.

paper_content:

The diagnostic pattern of malignant melanoma and clinical suspicion rate has been investigated at a dermatological university clinic. Of 174 histologically proven malignant melanomas, 60 (34%) were not clinically suspected as melanomas. The accuracy of clinical diagnosis increased with level of experience. Physicians with < 1 year experience in dermatology were able to detect malignant melanoma as the first diagnosis in 31% of the cases in contrast to 63% for those with > 10 years experience. Of 50 patients immediately referred from the dermatologic clinic to surgery departments, 12 did not have melanoma.
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paper_title: Computer-aided diagnosis of melanocytic lesions

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Background: The clinical diagnosis of melanoma could be difficult for a general practitioner and, in some cases, for dermatologists. To enhance and support the clinical evaluation of pigmented skin lesions a computer-aided diagnosis has been introduced. Materials and Methods: Images of melanocytic lesions (477 total, 42 melanomas and 435 melanocytic nevi) evaluated in epiluminescence microscopy and recorded with x16 magnification were selected. A training set of 22 melanomas and 218 nevi was randomized from the dataset. The test set was formed by the complement (the remaining 20 melanomas and 217 nevi). Furthermore, a set of images consisting of 31 melanomas and 103 nevi was selected to compare the discrimination capacity of three general practitioners and three dermatologists with experience in dermoscopy (2 years), and with the automatic data analysis for the melanoma early detection system (ADAM). Sensitivity and specificity were estimated for observer assessments and computer diagnosis. Results: The entire dataset used to test the implementation of the diagnostic algorithms ADAM showed a good sensitivity and specificity performance. Compared with the physicians, the ADAM system showed a slightly higher diagnostic performance in terms of sensitivity and a lower one in terms of specificity. Dermatologists showed higher levels of specificity, but lower levels in terms of sensitivity, when compared with the general practitioners. Conclusion: Image analysis has the potential to distinguish nevi and melanomas and to support the clinical diagnosis of melanocytic lesions by the general practitioner.
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paper_title: Computerized digital image analysis: an aid for melanoma diagnosis--preliminary investigations and brief review.

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Both digital imaging and epiluminescence microscopy hold promise for improved early detection of cutaneous melanoma. Several centers have been actively working in these areas during the past decade. These experiences and preliminary work based on the image capture of 83 pigmented lesions at our center using a prototype digital imaging system (SKINVIEW) are described. This system is based, in part, on the analysis of lesional morphologic features, such as shape, border, and radii. Histopathologic correlation was matched against these features to assess the efficacy of diagnosis. At our center, these parameters alone were not sufficient to discriminate between benign and malignant lesions, in part, because the melanomas were, in general, early lesions and many of the nevi were sufficiently clinically atypical to require removal for discrimination from melanoma. In addition, technical improvements in the image capturing and processing mechanism are needed. Rapid progress in this area is anticipated.
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paper_title: Reliability and inter-observer agreement of dermoscopic diagnosis of melanoma and melanocytic naevi

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The aim of this study was to analyse the reliability and the inter-observer agreement of dermoscopy in the diagnosis of melanocytic skin lesions. Nine dermatologists, with a different training experience and who routinely used dermoscopy in different hospitals in Italy, evaluated clinical and dermoscopy photographs of 15 melanocytic lesions (four invasive melanomas, four histologically common naevi, and seven naevi with histological atypia). A further series of dermoscopic photographs of 40 melanocytic lesions was evaluated to quantify inter-observer concordance in recognizing dermoscopic criteria. Compared to the true (histological) diagnosis, clinical diagnosis (categories: melanoma, common naevus, atypical naevus) was correct in 40% of cases (range, 27-53%). The percentage raised to 55% (40-73%) by the use of dermoscopy, with an average improvement of 15.6%. Concerning melanoma, clinical diagnosis resulted in a sensitivity of 41.9%, specificity of 77.8%, positive predictive value (PPV) of 36.1%, negative predictive value (NPV) of 81.8%. By using dermoscopy, an improvement of diagnostic performance was found (sensitivity 75%, specificity 88.8%, VPP 71.0%, VPN 90.7%). The inter-observer agreement in melanoma diagnosis, by using dermoscopy, was similar to that obtained by clinical examination (k statistics = 0.54 and 0.52, respectively). Concerning dermoscopic criteria, the best agreement among observers was found for pseudopods, a dermoscopic parameter related to the radial growth phase of melanoma. We conclude that dermoscopy is an useful tool for a non-invasive diagnosis of melanocytic skin lesions, improving the diagnostic performance compared to clinical examination.
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paper_title: Skills Training to Learn Discrimination of ABCDE Criteria by Those at Risk of Developing Melanoma

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OBJECTIVE ::: To develop skin self-examination skills by improving the use of the ABCDE criteria by those at risk of developing melanoma. ::: ::: ::: DESIGN ::: Ten single-sex groups of 10 unrelated adults received a knowledge-based intervention followed by skills training. ::: ::: ::: SETTING ::: Classroom in the ambulatory care area of a hospital. Patients One hundred unrelated adults with either a personal or family history of melanoma and no previous skin self-examination counseling. Interventions A dermatologist presented information, and each subject read a brochure and reviewed a bookmark. The facilitator led the group discussions about scoring each feature and the decision to seek medical care. Measurement of moles with a ruler and a pencil eraser and the use of an illuminated magnifying lens were demonstrated. Preintervention and postintervention assessments were performed for each intervention. ::: ::: ::: MAIN OUTCOME MEASURE ::: Recognition of A, B, C, D, and E traits. ::: ::: ::: RESULTS ::: The subjects preferred the bookmark (87% of subjects), opaque ruler (94% of subjects), and lighted magnifying lens (81% of subjects) to reading the brochure, which was felt to take too long. Recognition of border irregularity, color variation, and diameter improved significantly after the intervention. Women recognized color variation more often than men (94% vs 62%; odds ratio [OR], 23.7; 95% confidence interval [CI], 16.1-50.5). Men identified irregular borders more often than women (91% vs 75%; OR, 16.8; 95% CI, 4.5-22.6). Women appropriately decided to see a physician more often than men (81% vs 57%; OR, 13.5; 95% CI, 7.1-28.4). ::: ::: ::: CONCLUSIONS ::: Determining the presence of the A, B, C, and E criteria is challenging for the novice. Skills training aided performance of skin self-examination. Differences in proclivities according to sex suggest that if men and women form partnerships for skin checks, they may learn from each other, and their combined strengths will promote detection of early melanomas.
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paper_title: Digital image analysis for diagnosis of skin tumors.

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Between 1987 and 2007, different groups developed digital image analysis systems for the diagnosis of benign and malignant skin tumors. As the result of significant differences in the technical devices, the number, the nature and benign/malignant ratio of included skin tumors, different variables and statistical methods any comparison of these different systems and their results is difficult. For the use and comparison of the diagnostic performance of different digital image analysis systems in the future, some principle basic conditions are required: All used systems should have a standardized recording system and calibration. First, melanocytic and nonmelanocytic lesions should be included for the development of the diagnostic algorithms. Critical analyses of the results should answer the question if in future only melanocytic lesions should be analyzed or all pigmented and nonpigmented lesions. This will also lead to the answer if only dermatologists or all specialities of medical doctors will use such a system. All artifacts (eg, hairs, air bubbles) should be removed. The number of variables should be chosen according to the number of included melanomas. A high number of benign skin lesions should be included. Of all lesions only 10% or better less should be invasive melanomas. Each system should be developed by a training-set and controlled by an independent test-set. Each system should be controlled by the user with the final decision and responsibility and tested by independent users without any conflict of financial interest.
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paper_title: In Vivo Epiluminescence Microscopy: Improvement of Early Diagnosis of Melanoma

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The majority of pigmented skin lesions can be diagnosed correctly on the basis of clinical criteria; however, there remain a surprisingly high number of small pigmented lesions in which the distinction between melanocytic and non-melanocytic and benign and malignant lesions, and thus between melanoma and non-melanoma, is difficult or impossible to make with the naked eye. Epiluminescence microscopy is a non-invasive technique that, by use of oil immersion, makes sub-surface structures of skin accessible for in vivo microscopic examination and thus provides additional criteria for the diagnosis of pigmented lesions. The technique of epiluminescence microscopy is reviewed, and the significant improvement in the clinical diagnosis of pigmented skin lesions and, in particular, melanoma by this technique is documented.
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paper_title: Improvement of malignant/benign ratio in excised melanocytic lesions in the ‘dermoscopy era’: a retrospective study 1997–2001

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Summary ::: ::: Background Because of the many limitations of studies based on the diagnostic setting of excised lesions, the impact of dermoscopy (epiluminescence microscopy, dermatoscopy) in melanoma screening during practice remains to be established. ::: ::: ::: ::: Objectives We assumed that effects of the use of dermoscopy on some indicators of diagnostic performance in melanoma screening should be traceable retrospectively; therefore, we analysed the impact of routine dermoscopy use on the malignant/benign ratio in excised melanocytic lesions. ::: ::: ::: ::: Methods Preoperative and histological diagnosis of 3053 melanocytic lesions [319 melanomas (10·4%)] consecutively diagnosed and excised at the Department of Dermatology, University of Florence in the period 1997–2001 inclusive were retrieved. Six dermatologists who selected the lesions to excise and who performed preoperative diagnosis were divided into two groups according to their use of dermoscopy in routine activity (n = 2 dermoscopy users and n = 4 nonusers). The study period was divided into a predermoscopy period (1997), a shift phase (1998) and a dermoscopy period (1999–2001). ::: ::: ::: ::: Results During the study period, the malignant/benign ratio improved in dermoscopy users only (from 1 : 18 to 1 : 4·3, P = 0·037). No significant difference was found for nonusers (from 1 : 11·8 to 1 : 14·4). Dermoscopy users were more likely to have a melanoma diagnosed within a series of excised lesions than nonusers, even taking into account potential confounders such as sex, age and study period by means of multivariate analysis (odds ratio 1·55, 95% confidence interval 1·17–2·01). The percentage of ‘problem’ naevi (naevi with architectural disorder with or without cytological atypia and Spitz or Reed naevi) over the total number of excised lesions was higher in dermoscopy users than in nonusers (year 2001, 51·6% vs. 40·9%, P = 0·014). Similar findings were obtained after exclusion from the data set of lesions excised for cosmetic reasons. ::: ::: ::: ::: Conclusions The adoption of dermoscopy in routine melanoma screening is followed by an improvement of the malignant/benign ratio in excised lesions, suggesting a more appropriate selection of pigmented lesions referred to surgery. Because of the possible limitations of a retrospective study design, future confirmation of this finding by means of a prospective, randomized study is advisable. The introduction of dermoscopy in routine practice may have major implications in large-scale melanoma screening with cost savings and a reduction of the dermosurgery workload.
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paper_title: Computer screening for early detection of melanoma--is there a future?

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Summary ::: ::: Computer image analysis in the study of pigmented lesions is critically examined and discussed in the light of the current published data. The potential for objective analysis by computers as a possible screening aid for the inexperienced clinician is discussed. The future for this technology is exciting if handled with care.
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paper_title: Comparison of Feature Selection Techniques for Detection of Malignant Tumor in Brain Images

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This paper presents and compares feature selection algorithms for the detection of glioblastoma multiforme in brain images. Texture features are extracted from normal and tumor regions (ROI) using spatial gray level dependence method and wavelet transform. An artificial neural network has been used for classification. A very difficult problem in classification. techniques is the choice of features to distinguish between classes. The feature optimization problem is addressed using a genetic algorithm (GA) as a search method. Principal component analysis, classical sequential methods and floating search algorithm are compared against the genetic approach in terms of the best recognition rate achieved and the optimal number of features. The classification performance of 97.3% is achieved in GA with optimal features compared to sequential methods and Principal component analysis.
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paper_title: Improved resolution of magnetic resonance microscopy in examination of skin tumors.

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Magnetic resonance imaging has become increasingly important for visualization and tissue differentiation of internal organs. Because of limited resolution, investigation of skin has been of little diagnostic value so far. We combined a homogeneous magnetic field of 9.4 T, as used in magnetic resonance spectroscopy, with gradient fields of 11.7 G/cm and an imaging unit to obtain a voxel resolution of 40 × 40 × 300 μm 3 . With this magnetic resonance microscopy unit, we studied normal skin, 12 nevocellular nevi, 20 basal cell carcinomas, 8 melanomas, and 8 seborrheic keratoses after excision in vitro . The specimens were visualized in spin-echo images. The proton relaxation times T1 and T2 were determined for the different skin layers and tumor tissues. Interpretation of the spin-echo images was based on comparison with the correlating histology. Epidermis, dermis, subcutaneous tissue, and hair follicle complexes could be distinguished. Stratum corneum and hairs emitted no signal. All tumors presented as distinct, signal-rich, homogeneous structures within the dark, signal-poor dermis. Their shape corresponded to their outline in the histologic sections. Buds of superficial basal cell carcinomas could be resolved. The proton relaxation times T1 and T2 were significantly different among all skin layers and tumors. Our results demonstrate that with sufficient resolution, differentiation of skin tumors is possible using magnetic resonance imaging.
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paper_title: Role of high-resolution magnetic resonance imaging for differentiating melanin-containing skin tumors.

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RATIONALE AND OBJECTIVES ::: The aim of the study was to evaluate the use of high resolution magnetic resonance imaging (MRI) for differentiation of melanin-containing skin tumors. ::: ::: ::: METHODS ::: Twenty-seven melanocytic nevi and 18 malignant melanomas were examined by high resolution MRI. Signal intensities and signal-to-noise (SNR) and contrast-to-noise ratios (CNR) of the tumors were determined in enhanced (T1, T2, water-suppression, and fat-suppression sequences) and contrast-enhanced images (T1 and fat-suppression sequences). The differences were tested for significance by a Wilcoxon test. ::: ::: ::: RESULTS ::: Malignant melanomas differed from melanocytic nevi in that they displayed a higher SNR in T2-weight and unenhanced and contrast-enhanced fat-suppression scan. Malignant melanomas exhibited a higher CNR than did benign lesions in unenhanced and contrast-enhanced fat-suppression sequences with dermis as reference tissue (P < 0.05) and in enhanced fat-suppression sequences with subcutis as reference tissue (P < 0.05). ::: ::: ::: CONCLUSIONS ::: The usefulness of SNR and CNR analysis on MRI for the differentiation of malignant skin tumors from benign skin tumors of the melanin-containing system is limited. Clinical and histologic examinations are, further, the important step in evaluation of melanin-containing skin tumors.
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paper_title: High frequency ultrasound in the preoperative staging of primary melanoma: a statistical analysis.

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High frequency sonography has been shown to be a useful tool in the preoperative staging of malignant melanoma. In the present study sonometric and histometric data concerning tumour thickness were compared, using appropriate statistical methods, in order to assess the accuracy of ultrasonography. F
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paper_title: Instruments and new technologies for the in vivo diagnosis of melanoma.

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The principal objective of screening individuals at risk for melanoma is detection of cutaneous melanoma during the curable stages of its early evolution. Unaided visual inspection of the skin is often suboptimal at diagnosing melanoma. Improving the diagnostic accuracy for melanoma remains an area of active research. These research efforts have focused on both the detection of early melanoma and the in-depth evaluation of suspicious pigmented lesions for the presence or absence of melanoma. Numerous instruments are under investigation to determine their usefulness in imaging and ascertaining a correct in vivo diagnosis of melanoma. It is anticipated that some of these tools, alone or in combination, will improve our ability to differentiate, in vivo, melanoma from its simulators. Ultimately, these advances may prevent unnecessary biopsies (increased specificity) while increasing the sensitivity for diagnosing melanoma. This article reviews the current instruments and new technologies for the in vivo diagnosis of melanoma. Learning objective At the conclusion of this learning activity, participants should be acquainted with the instruments designed to facilitate the early detection of melanoma. They should also be familiar with the basic technology behind these instruments and should recognize the potential benefits and limitations inherent in each.
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paper_title: Improved forward floating selection algorithm for feature subset selection

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We present results on two new databases for a new improved forward floating selection (IFFS) algorithm for selecting a subset of features. The algorithm is an improvement upon the state-of-the-art sequential forward floating selection algorithm that includes a new search strategy to check whether removing any feature in the selected feature set and adding a new one at each sequential step can improve the resultant feature set. We find that this method provides the optimal or quasi-optimal (close to optimal) solutions for many selected subsets and requires significantly less computational load than an exhaustive search optimal feature selection algorithm. Our experimental results for two different databases demonstrate that our algorithm consistently selects better subsets than other quasi-optimal feature selection algorithms do.
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paper_title: Ultrasound Mapping of Lymph Node and Subcutaneous Metastases in Patients with Cutaneous Melanoma: Results of a Prospective Multicenter Study

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Background: Ultrasound (sonography, B-mode sonography, ultrasonography) examination improves the sensitivity in more than 25% compared to the clinical palpation, especially after surgery on the regional lymph node area. Objective: To evaluate the distribution of metastases during follow-up in the draining lymph node areas from the scar of primary to regional lymph nodes (head and neck, supraclavicular, axilla, infraclavicular, groin) in patients with cutaneous melanoma with or without sentinel lymph node biopsy (SLNB) or former elective or consecutive complete lymph node dissection in case of positive sentinel lymph node (CLND). Methods: Prospective multicenter study of the Departments of Dermatology of the Universities of Homburg/Saar, Tübingen and Munich (Germany) in which the distribution of lymph node and subcutaneous metastases were mapped from the scar of primary to the lymphatic drainage region in 53 melanoma patients (23 women, 30 men; median age: 64 years; median tumor thickness: 1.99 mm) with known primary, visible lymph nodes or subcutaneous metastases proven by ultrasound and histopathology during the follow-up. Results: Especially in the axilla, infraclavicular region and groin the metastases were not limited to the anatomic lymph node regions. In 5 patients (9.4%) (4 of them were in stage IV) lymph node metastases were not located in the corresponding lymph node area. 32 patients without former SLNB had a time range between melanoma excision and lymph node metastases of 31 months (median), 21 patients with SLNB had 18 months (p < 0.005). In 11 patients with positive SLNB the time range was 17 months, in 10 patients with negative SLNB 21 months (p < 0.005); in 32 patients with CLND the time range was 31 months and in 21 patients without CLND 18 months (p < 0.005). In thinner melanomas lymph node metastases occurred later (p < 0.05). Conclusions: After surgery of cutaneous melanoma, SLNB and CLND the lymphatic drainage can show significant changes which should be considered in clinical and ultrasound follow-up examinations. Especially for high-risk melanoma patients follow-up examinations should be performed at intervals of 3 months in the first years. Patients at stage IV should be examined in all regional lymph node areas clinically and by ultrasound.
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paper_title: Current and emerging technologies in melanoma diagnosis: the state of the art

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Relative to other specialties, dermatologists have been slow to adopt advanced technologic diagnostic aids. Most skin disease can be diagnosed by simple visual inspection, and the skin is readily accessible for a diagnostic biopsy. Diagnostic aids, such as total body photography and dermoscopy, improve the clinician's ability to diagnose melanoma beyond unaided visual inspection, however, and are now considered mainstream methods for early detection. Emerging technologies such as in vivo reflectance confocal microscopy are currently being investigated to determine their utility for noninvasive diagnosis of melanoma. This review summarizes the currently available cutaneous imaging devices and new frontiers in noninvasive diagnosis of skin disease. We anticipate that multimodal systems that combine different imaging technologies will further improve our ability to detect, at the bedside, melanoma at an earlier stage.
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paper_title: Magnetic resonance imaging for diagnosing skin tumors.

paper_content:

The depth and three-dimensional morphology of skin tumors were studied with MR imaging. This technology was found to be an effective tool not only for evaluating depth but also for determining the degree of invasion of malignant tumors in the skin and soft tissue. Of the various tumors examined, this method was particularly useful for visualizing malignant melanoma and malignant fibrous histiocytoma. Thus, it is likely that MR imaging has great potential as an auxiliary tool for the diagnosis of other types of malignant skin tumors. By means of three-dimensional displays, tumors were more clearly visualized with respect to their relationship to the surrounding tissue and the three-dimensional state of infiltration, which was particularly useful in deciding on an appropriate resection area prior to surgery. The authors expect further technologic improvements in MR imaging in the future which will serve to increase the benefits obtained in its clinical applications.
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paper_title: Automated Diagnostic Instruments for Cutaneous Melanoma

paper_content:

The objective of this review is to report and discuss the evidence for fully automated diagnostic instruments for cutaneous melanoma tested in a real-world clinical setting directly compared with human diagnosis. A systematic review was performed and articles excluded when studies did not report sensitivity or specificity for melanoma directly compared with humans on an independent test set. Only 3 instruments have had their diagnostic accuracy compared with a human diagnosis in the clinical field with a meaningful sample size that could allow some generalization with the wider clinical arena. Two of these instruments showed a significantly inferior specificity for the diagnosis of melanoma compared with specialists. In one of these studies, the sensitivity for diagnosis, although superior to the specialist diagnosis, did not reach statistical significance. In contrast, one instrument had an equivalent specificity and trended superior but not significantly for sensitivity for the diagnosis of melanoma. Other image based nonclinic studies and studies comparing clinical management as the endpoint rather than diagnosis are also reviewed.
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paper_title: The evolution of melanoma diagnosis: 25 years beyond the ABCDs.

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Early detection of malignant melanoma remains the key factor in lowering mortality from this cancer. Recognizing the importance of this issue 25 years ago, our group at New York University published in CA: A Cancer Journal for Clinicians the mnemonic "ABCD" to facilitate the early diagnosis of melanoma. Studies have demonstrated the usefulness of this paradigm in enhancing early melanoma diagnosis as a part of clinical examinations, mass screenings, and public education programs. Approaches to melanoma diagnosis have dynamically evolved during the ensuing quarter century. In the 1990s, dermoscopy enabled the recognition of new subsurface features to differentiate between malignant and benign pigmented lesions. During the last decade, new computer-based technologies have improved diagnostic sensitivity and specificity and may result in optimizing lesion selection for biopsy and pathology review. Despite all of the advances in melanoma diagnosis, timely recognition, detection, and rapid treatment of melanoma remain critical. Although pathologic examination remains the gold standard for diagnosis, this cancer has the potential to be diagnosed through noninvasive approaches because of its cutaneous location. From the development of the ABCDs through current attempts that use complex computer algorithms and genetic markers, a clinician's ability to detect melanoma in its earliest form has been augmented. However, a "good clinical eye" is still fundamental to selecting the lesions for evaluation among the sea of those that are prevalent. As current approaches are refined and new techniques are developed, the improved ability to diagnose this cancer will hopefully enhance reaching the goal of reducing melanoma mortality.
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paper_title: Evaluation of different image acquisition techniques for a computer vision system in the diagnosis of malignant melanoma

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Background: Digital image analysis was found to be a useful technique for improved accuracy of preoperative diagnosis of melanocytic lesions. In previous studies digitized color slides were used as input for digital image analysis. New technologies and smaller video cameras made it possible to develop a camera system that allows the digitization of skin lesions directly from the patient. Objective: We investigated whether conventional color slides or directly digitized images should be used for a reliable recognition of malignant melanoma. Methods: Computer features describing characteristics of the lesions were computed for 404 digitized color slides and for 309 directly acquired lesions. Statistical analysis and classifier construction was performed by the commercial statistical classification program CART. Results: With the data set derived either from the color slides or from the directly digitized lesions a sensitivity of about 90% for the recognition of malignant melanoma could be obtained. Conclusion: Both image acquisition techniques allow a reliable detection of malignant melanoma and both are appropriate as input for an image analysis system regarding its efficiency as a diagnostic tool. However, none of the classifiers can be applied with reasonable significance to both techniques.
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paper_title: A color clustering technique for image segmentation

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Abstract This paperr describes a clustering algorithm for segmenting the color images of natural scenes. The proposed method operates in the 1976 CIE (L∗, a∗, b∗)-uniform color coordinate system. It detects image clusters in some circular-cylindrical decision elements of the color space. This estimates the clusters' color distributions without imposing any constraints on their forms. Surfaces of the decision elements are formed with constant lightness and constant chromaticity loci. Each surface is obtained using only 1D histogramsof the L∗, H°, C∗ cylindrical coordinates of the image data or the extracted feature vector. The Fisher linear discriminant method is then used to project simultaneously the detected color clusters onto a line for 1D thresholding. This permits utilization of all the color properties for segmentation and inherently recognizes their respective cross correlation. In this respect, the proposed algorithm also differs from the multiple histogram-based thresholding schemes.
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paper_title: Border detection in dermoscopy images using statistical region merging

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Malignant melanoma has consistently had one of the most rapidly increasing incidence of all cancers, with 59,940 new cases and 8110 deaths estimated in the United States in 2007 (1). Early diagnosis is particularly important because melanoma can be cured with a simple excision if detected early. ::: ::: Dermoscopy is a non-invasive skin imaging technique that uses optical magnification and either liquid immersion and low angle-of-incidence lighting or cross-polarized lighting to make the contact area translucent, making subsurface structures more easily visible when compared with conventional macroscopic (clinical) images. Dermoscopy allows the identification of dozens of morphological features such as pigment networks, dots/globules, streaks, blue-white areas, and blotches (2). This reduces screening errors, and provides greater differentiation between difficult lesions such as pigmented Spitz nevi and small, clinically equivocal lesions (3). However, it has been demonstrated that dermoscopy may actually lower the diagnostic accuracy in the hands of inexperienced dermatologists (4). Therefore, due to the lack of reproducibility and subjectivity of human interpretation, the development of computerized image analysis techniques is of paramount importance (5). ::: ::: The first step in the computerized analysis of skin lesion images is the detection of the lesion borders. The importance of border detection for the analysis is two-fold. First, the border structure provides important information for accurate diagnosis. Many clinical features such as asymmetry, border irregularity, and abrupt border cutoff are calculated from the border. Second, the extraction of other important clinical features such as atypical pigment networks, globules, and blue-white areas critically depends on the accuracy of border detection. Automated border detection in dermoscopy images is a challenging task due to several reasons: (i) low contrast between the lesion and the surrounding skin; (ii) irregular and fuzzy lesion borders; (iii) artifacts such as skin lines, air bubbles and hairs; and (iv) variegated coloring inside the lesion. ::: ::: Numerous methods have been developed for border detection in pigmented skin lesion images earlier; most of these dealt with clinical images (6). However, recent research has focused more on dermoscopy images. Gao et al. (7) proposed two methods: one based on stabilized inverse diffusion equations, a form of non-linear diffusion and another one based on Markov random fields in which the model parameters are estimated using the mean field theory. Pagadala (8) described a method based on optimized histogram thresholding. Schmid (6) developed a technique based on color clustering. First, a 2D histogram is calculated from the first two principal components of the CIE L*u*v* color space. The histogram is then smoothed and initial cluster centers are determined from the peaks using a perceptron classifier. Finally, the lesion image is segmented using a modified version of the fuzzy c-means (FCM) clustering algorithm. Donadey et al. (9) presented a supervised method based on intensity radial profiles calculated from the I (intensity) component of the HSI space. Cucchiara et al. (10) presented a recursive FCM clustering technique that augments Schmid’s method using topological information. Erkol et al. (11) proposed a method based on the gradient vector flow (GVF) snakes with an automatic initialization. Iyatomi et al. (12) described a method called the dermatologist-like tumor extraction algorithm (DTEA) that is based on thresholding followed by iterative region growing. Melli et al. (13) compared four different color clustering algorithms: median cut, k-means, FCM, and meanshift. They concluded that the meanshift algorithm gave the best results. Celebi et al. (14) developed a method based on the JSEG segmentation algorithm. Their method involves an algorithm for approximate lesion localization that reduces the computational time and improves the accuracy by focusing the border detection process on the immediate neighborhood of the lesion rather than the whole image. ::: ::: In this paper, we present an unsupervised approach to border detection in dermoscopy images based on the statistical region merging (SRM) algorithm (15). The SRM algorithm is adapted to this problem due to its simplicity, computational efficiency, and excellent performance in a variety of image domains.
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paper_title: Qualitative asymmetry measure for melanoma detection

paper_content:

Size functions and support vector machines are used to implement a new automatic classifier of melanocytic lesions. This is mainly based on a qualitative assessment of asymmetry. The program is used, at clinical level, with two thresholds, so that comparison of the two outputs produces a report of low-middle-high risk. Experimental results with cross-validation are reported.
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paper_title: Color Science: Concepts and Methods, Quantitative Data and Formulas

paper_content:

Physical Data. The Eye. Colorimetry. Photometry. Visual Equivalence and Visual Matching. Uniform Color Scales. Visual Thresholds. Theories and Models of Color Vision. Appendix. References. Author and Subject Indexes.
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paper_title: Dermascopic hair disocclusion using inpainting

paper_content:

Inpainting, a technique originally used to restore film and photographs, is used to disocclude hair from dermascopic images of skin lesions. The technique is compared to the conventional software DullRazor, which uses linear interpolation to perform disocclusion. Comparison was performed by simulating occluding hair on a dermascopic image, applying DullRazor and inpainting and calculating the error induced. Inpainting is found to perform approximately 33% better than DullRazor's linear interpolation, and is more stable under heavy occlusion. The results are also compared to published results from two other alternatives: auto-regressive (AR) model signal extrapolation and band-limited (BL) signal interpolation.
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paper_title: Dark line detection with line width extraction

paper_content:

Automated line detection is a classical image processing topic with many applications such as road detection in remote images and vessel detection in medical images. Many traditional line detectors, such as Gabor filter, the second order derivative of Gaussian and Radon transform will response not only to lines but also to edges, e.g. they will give high responses to the edges of bright lines or blobs when only dark lines are required. To reduce false detections when extracting only dark (or bright) lines, in this paper we propose a line detector by using the first derivative of Gaussian. It can detect dark lines without much false detection on blobs or bright lines. Meanwhile, the proposed method can estimate line width simultaneously. Experiments on various images are performed to test the proposed algorithm.
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paper_title: Anisotropic Mean Shift Based Fuzzy C-Means Segmentation of Dermoscopy Images

paper_content:

Image segmentation is an important task in analysing dermoscopy images as the extraction of the borders of skin lesions provides important cues for accurate diagnosis. One family of segmentation algorithms is based on the idea of clustering pixels with similar characteristics. Fuzzy c-means has been shown to work well for clustering based segmentation, however due to its iterative nature this approach has excessive computational requirements. In this paper, we introduce a new mean shift based fuzzy c-means algorithm that requires less computational time than previous techniques while providing good segmentation results. The proposed segmentation method incorporates a mean field term within the standard fuzzy c-means objective function. Since mean shift can quickly and reliably find cluster centers, the entire strategy is capable of effectively detecting regions within an image. Experimental results on a large dataset of diverse dermoscopy images demonstrate that the presented method accurately and efficiently detects the borders of skin lesions.
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paper_title: Level set segmentation of dermoscopy images

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This paper presents a method for the segmentation of skin lesions in dermoscopy images. The proposed technique uses region based level sets and adopts a mixture of Gaussian densities as the probabilistic model for both the lesion and the skin regions. The estimation-maximization (EM) algorithm is used to estimate the probability density functions in each region. Experimental results which demonstrate the effectiveness of the proposed technique are presented.
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paper_title: Butterworth equations for homomorphic filtering of images

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Abstract In digital image processing, the homomorphic filtering approach is derived from an illumination–reflectance model of the image. Homomorphic filtering can perform simultaneous dynamic range compression and contrast enhancement. Crucial for the success of the homomorphic approach is the selection of an appropriate frequency-domain filter function in order to modify the illumination and reflectance components of an image differently. The author found Butterworth type highpass equations far superior to other frequency-domain filter functions, including Gaussian equations, making the Butterworth highpass suitable for use with the homomorphic filtering approach. The program was written in Microsoft (MS) Visual C++™ (filter) as well as MS Visual Basic™ (user interface) to run as a module under the image processing software package Image-Pro Plus™.
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paper_title: Lesion Border Detection in Dermoscopy Images

paper_content:

BACKGROUND ::: Dermoscopy is one of the major imaging modalities used in the diagnosis of melanoma and other pigmented skin lesions. Due to the difficulty and subjectivity of human interpretation, computerized analysis of dermoscopy images has become an important research area. One of the most important steps in dermoscopy image analysis is the automated detection of lesion borders. ::: ::: ::: METHODS ::: In this article, we present a systematic overview of the recent border detection methods in the literature paying particular attention to computational issues and evaluation aspects. ::: ::: ::: CONCLUSION ::: Common problems with the existing approaches include the acquisition, size, and diagnostic distribution of the test image set, the evaluation of the results, and the inadequate description of the employed methods. Border determination by dermatologists appears to depend upon higher-level knowledge, therefore it is likely that the incorporation of domain knowledge in automated methods will enable them to perform better, especially in sets of images with a variety of diagnoses.
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paper_title: Techniques for a structural analysis of dermatoscopic imagery

paper_content:

Techniques were developed for automated detection and characterization of dermatoscopic structures, including the pigment network and brown globules. These techniques incorporate algorithms for grayscale shape extraction based on differential geometry developed by Steger, a snake algorithm, and a modification of the region competition strategy of Zhu and Yuille. A novel approach was developed for global segmentation of pigmented lesions, based on stabilized inverse diffusion equations. Procedures for detection of air bubbles and hairs in dermatoscopic images are also reported.
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paper_title: Lesion detection in dermatoscopic images using anisotropic diffusion and morphological flooding

paper_content:

A new technique for the unsupervised detection of multiple objects in noisy background is presented in this paper, with application to digitized dermatoscopic images. The proposed method uses anisotropic diffusion in the L*u*v* uniform color space to suppress background noise and spurious structures while preserving the object boundaries. A simple and fast hair removal scheme based on morphological operators and luminance thresholding is also introduced for the application to pigmented skin lesions. The luminance component is then used to extract iso-level closed contours by morphological flooding. These curves are considered as contour candidates and the selection is based on the minimization of a gradient based energy functional. The main advantages of this technique is the low processing time and the possibility, if necessary, to scroll through the different level curves without additional processing.
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paper_title: Segmenting skin lesions with partial-differential-equations-based image processing algorithms

paper_content:

A PDE-based system for detecting the boundary of skin lesions in digital clinical skin images is presented. The image is first pre-processed via contrast-enhancement and anisotropic diffusion. If the lesion is covered by hairs, a PDE-based continuous morphological filter that removes them is used as an additional pre-processing step. Following these steps, the skin lesion is segmented either by the geodesic active contours model or the geodesic edge tracing approach. These techniques are based on computing, again via PDE's, a geodesic curve in a space defined by the image content. Examples showing the performance of the algorithm are given.
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paper_title: Segmentation of digitized dermatoscopic images by two-dimensional color clustering

paper_content:

In this paper a color-based segmentation scheme applied to dermatoscopic images is proposed. The RGB image is processed in the L* u* v* color space. A two-dimensional (2-D) histogram is computed with the two principal components and then smoothed with a Gaussian low-pass filter. The maxima location and a set of features are computed from the histogram contour lines. These features are the number of enclosed pixels, the surface of the base, and the height of the maximum. They allow for the selection of valid clusters which determine the number of classes. The image is then segmented using a modified version of the fuzzy c-means (FCM) clustering technique that takes into account the cluster orientation. Finally, the segmented image is cleaned using mathematical morphology, the region borders are smoothed, and small components are removed.
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paper_title: Unsupervised skin lesions border detection via two-dimensional image analysis

paper_content:

The skin cancer was analyzed by dermoscopy helpful for dermatologists. The classification of melanoma and carcinoma such as basal cell, squamous cell, and merkel cell carcinomas tumors can be increased the sensitivity and specificity. The detection of an automated border is an important step for the correctness of subsequent phases in the computerized melanoma recognition systems. The artifacts such as, dermoscopy-gel, specular reflection and outline (skin lines, blood vessels, and hair or ruler markings) were also contained in the dermoscopic images. In this paper, we present an unsupervised approach for multiple lesion segmentation, modification of Region-based Active Contours (RACs) as well as artifact diminution steps. Iterative thresholding is applied to initialize level set automatically; the stability of curves is enforced by maximum smoothing constraints on Courant-Friedreichs-Lewy (CFL) function. The work has been tested on dermoscopic database of 320 images. The border detection error is quantified by five distinct statistical metrics and manually used to determine the borders from a dermatologist as the ground truth. The segmentation results were compared with other state-of-the-art methods along with the evaluation criteria. The unsupervised border detection system increased the true detection rate (TDR) is 4.31% and reduced the false positive rate (FPR) of 5.28%.
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paper_title: Automatic Lesion Boundary Detection in Dermoscopy Images Using Gradient Vector Flow Snakes

paper_content:

Dermoscopy, otherwise known as skin-surface microscopy or dermatoscopy was originally introduced in 1921 (1) and reintroduced in 1987 (2). One common form of dermoscopy integrates oil immersion under glass with standard × 10 magnifying optics and incident surface lighting permit in vivo visualization of certain features of pigmented melanocytic neoplasms that cannot be observed by visual inspection. Dermoscopy using various rule- or feature-based schemes improves pigmented lesion diagnostic accuracy over the accuracy obtained without this aid for those dermatologists trained in the technique (3–8). Accurate segmentation of the lesion from the background skin is important for computer-assisted diagnostic techniques, both for the determination of lesion shape and lesion color, compared with the color of the surrounding skin (9). ::: ::: In this paper, a method of automatic lesion boundary detection using gradient vector flow (GVF) snakes is presented. Snakes can be defined as curves within an image domain that can move under the influence of internal forces defined within the curve, from features such as smoothness, and external forces computed from the image data (10). Snake algorithms are often sensitive to initialization for effective object segmentation. Initial points for a snake algorithm can be selected by an operator or automatically determined. For this research, an automatic initialization method is used to select an initial set of skin lesion border points for the dermoscopy image data set examined. ::: ::: The remainder of this paper is organized as follows. In the following section, an overview of traditional snake algorithms is presented, along with limitations of traditional snakes in the domain of pigmented images. In the third section, GVF snakes are presented. In the fourth section, the application of GVF snakes for skin lesion border determination in dermoscopy images is given. The automatic initialization method and the two-step operation of GVF snakes are described. In the fifth section, experimental results and discussion are presented comparing the GVF snake algorithm and its comparison with the histogram analysis technique developed by Pagadala (11). Finally, conclusions from this research are provided.
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paper_title: Intelligent segmentation and classification of pigmented skin lesions in dermatological images

paper_content:

During the last years, computer vision-based diagnostic systems have been used in several hospitals and dermatology clinics, aiming mostly at the early detection of malignant melanoma tumor, which is among the most frequent types of skin cancer, versus other types of non-malignant cutaneous diseases. In this paper we discuss intelligent techniques for the segmentation and classification of pigmented skin lesions in such dermatological images. A local thresholding algorithm is proposed for skin lesion separation and border, texture and color based features, are then extracted from the digital images. Extracted features are used to construct a classification module based on Support Vector Machines (SVM) for the recognition of malignant melanoma versus dysplastic nevus.
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paper_title: Nonlinear Vector Filtering for Impulsive Noise Removal from Color Images

paper_content:

A comprehensive survey of 48 filters for impulsive noise removal from color images is presented. The filters are formulated using a uniform notation and categorized into 8 families. The performance of these filters is compared on a large set of images that cover a variety of domains using three effectiveness and one efficiency criteria. In order to ensure a fair efficiency comparison, a fast and accurate approximation for the inverse cosine function is introduced. In addition, commonly used distance measures (Minkowski, angular, and directional-distance) are analyzed and evaluated. Finally, suggestions are provided on how to choose a filter given certain requirements.
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paper_title: Hair removal methods: A comparative study for dermoscopy images

paper_content:

Abstract Removal and restoration of hair and hair-like regions within skin lesion images is needed so features within lesions can be more effectively analyzed for benign lesions, cancerous lesions, and for cancer discrimination. This paper refers to “melanoma texture” as a rationale for supporting the need for the proposed hair detection and repair techniques, which incompletely represents why hair removal is an important operation for skin lesion analysis. A comparative study of the state-of-the-art hair-repaired methods with a novel algorithm is also proposed by morphological and fast marching schemes. The hair-repaired techniques are evaluated in terms of computational, performance and tumor-disturb patterns ( TDP ) aspects. The comparisons have been done among (i) linear interpolation, inpainting by (ii) non-linear partial differential equation ( PDE ) and (iii) exemplar-based repairing techniques. The performance analysis of hair detection quality, was based on the evaluation of the hair detection error ( HDE ), quantified by statistical metrics and manually used to determine the hair lines from a dermatologist as the ground truth. The results are presented on a set of 100 dermoscopic images. For the two characteristics measured in the experiments the best method is the fast marching hair removal algorithm ( HDE : 2.98%, TDP : 4.21%). This proposed algorithm repaired the texture of the melanoma, which becomes consistent with human vision. The comparisons results obtained, indicate that hair-repairing algorithm based on the fast marching method achieve an accurate result.
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paper_title: Dullrazor®: A software approach to hair removal from images

paper_content:

Recently, there has been a growing number of studies applying image processing techniques to analyze melanocytic lesions for atypia and possible malignancy and for total-body mole mapping. However, such lesions can be partially obscured by body hairs. None of these studies has fully addressed the problem of human hairs occluding the imaged lesions. In our previous study we designed an automatic segmentation program to differentiate skin lesions from the normal healthy skin, and learned that the program performed well with most of the images, the exception being those with hairs, especially dark thick hairs, covering part of the lesions. These thick dark hairs confused the program, resulting in unsatisfactory segmentation results. In this paper, we present a method to remove hairs from an image using a pre-processing program we have called DullRazor®. This pre-processing step enables the segmentation program to achieve satisfactory results. DullRazor® can be downloaded as shareware from http://www.derm.ubc.ca.
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paper_title: Application of an artificial neural network in epiluminescence microscopy pattern analysis of pigmented skin lesions: a pilot study.

paper_content:

In vivo epiluminescence microscopy (ELM) is a non-invasive technique which improves the clinical diagnosis of naevi and malignant melanoma by providing diagnostic criteria that cannot be appreciated by the naked eye. The present study investigated whether ELM criteria pattern analysis can be employed in an objective, observer-trained, computer-aided diagnostic system, and whether artificial neural networks (ANN) can be applied to the diagnosis of pigmented skin lesions (PSL). The ELM criteria patterns of 200 PSL oil immersion images (60 common naevi, 60 dysplastic naevi, and 80 malignant melanomas) were analysed using a standardized questionnaire. One hundred randomly assigned PSL were used as a training set for an ANN, the remaining 100 PSL serving as the test set. The ANN was trained by backward propagation according to the histological diagnosis, and its performance was compared with that of human investigators. Out of the test set the human investigators correctly diagnosed 88% of PSL and the ANN 86%. In a dichotomized model comparing common, compound, and dysplastic naevi vs. malignant melanoma, i.e. benign vs. malignant PSL, the sensitivity and specificity of human diagnosis was 95 and 90%, respectively, whereas the sensitivity and specificity of the ANN diagnosis was 95 and 88%. Our data indicate that artificial neural networks can be trained to diagnose PSL at a human expert level, based on patterns provided by ELM criteria. We suggest that this technique offers a new approach to the diagnosis of PSL.
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paper_title: Unsupervised border detection of skin lesion images

paper_content:

As a result of the advances in skin imaging technology and the development of suitable image processing/computer vision algorithms, during the last decade, there has been a significant increase of interest in the computer-aided diagnosis of skin cancer. Automated border extraction is one of the most important steps in this procedure since the accuracy of the subsequent steps crucially depends on the accuracy of this very first step. In this paper, we present an unsupervised approach to border detection in skin lesion (tumor) images based on a modified version of the JSEG algorithm [Y. Deng et al., (2001)]. The segmentation results are visually examined by an expert dermatologist and are found to be highly accurate.
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paper_title: Independent Histogram Pursuit for Segmentation of Skin Lesions

paper_content:

In this paper, an unsupervised algorithm, called the Independent Histogram Pursuit (IHP), for segmenting dermatological lesions is proposed. The algorithm estimates a set of linear combinations of image bands that enhance different structures embedded in the image. In particular, the first estimated combination enhances the contrast of the lesion to facilitate its segmentation. Given an N-band image, this first combination corresponds to a line in N dimensions, such that the separation between the two main modes of the histogram obtained by projecting the pixels onto this line, is maximized. The remaining combinations are estimated in a similar way under the constraint of being orthogonal to those already computed. The performance of the algorithm is tested on five different dermatological datasets. The results obtained on these datasets indicate the robustness of the algorithm and its suitability to deal with different types of dermatological lesions. The boundary detection precision using k-means segmentation was close to 97%. The proposed algorithm can be easily combined with the majority of classification algorithms.
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paper_title: A novel adaptive Recursive Median Filter in image noise reduction based on using the entropy

paper_content:

The problem of image noise reduction is discussed in this paper. One of the most efficient filters in this area is Recursive Median Filter (RMF). To improve the performance of RMF, a new adaptive structure for RMF is introduced using the concept of entropy. In the proposed filter, which is called Adaptive Recursive Median Filter (ARMF), the size of moving window is changed adaptively based on the entropy value of moving window. Simulation results show that the ARMF provides improved performance over the RMF in noise reduction with preserving small details.
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paper_title: Lesion border detection in dermoscopy images using dynamic programming

paper_content:

Background/purpose: Automated border detection is an important and challenging task in the computerized analysis of dermoscopy images. However, dermoscopic images often contain artifacts such as illumination, dermoscopic gel, and outline (hair, skin lines, ruler markings, and blood vessels). As a result, there is a need for robust methods to remove artifacts and detect lesion borders in dermoscopy images. ::: ::: ::: ::: Methods: This automated method consists of three main steps: (1) preprocessing, (2) edge candidate point detection, and (3) tumor outline delineation. First, algorithms to reduce artifacts were used. Second, a least-squares method (LSM) was performed to acquire edge points. Third, dynamic programming (DP) technique was used to find the optimal boundary of the lesion. Statistical measures based on dermatologist-drawn borders were utilized as ground-truth to evaluate the performance of the proposed method. ::: ::: ::: ::: Results: The method is tested on a total of 240 dermoscopic images: 30 benign melanocytic, 50 malignant melanomas, 50 basal cell carcinomas, 20 Merkel cell carcinomas, 60 seborrheic keratosis, and 30 atypical naevi. We obtained mean border detection error of 8.6%, 5.04%, 9.0%, 7.02%, 2.01%, and 3.24%, respectively. ::: ::: ::: ::: Conclusions: The results demonstrate that border detection combined with artifact removal increases sensitivity and specificity for segmentation of lesions in dermoscopy images.
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paper_title: Feature-preserving artifact removal from dermoscopy images

paper_content:

Dermoscopy, also called surface microscopy, is a non-invasive imaging procedure developed for early screening ::: of skin cancer. With recent advance in skin imaging technologies and image processing techniques, there has ::: been increasing interest in computer-aided diagnosis of skin cancer from dermoscopy images. Such diagnosis ::: requires the identification of over one hundred cutaneous morphological features. However, computer procedures ::: designed for extracting and classifying these intricate features can be distracted by the presence of artifacts like ::: hair, ruler markings, and air bubbles. Therefore, reliable artifact removal is an important pre-processing step for ::: improving the performance of computer-aided diagnosis of skin cancer. In this paper, we present a new scheme ::: that automatically detects and removes hairs and ruler markings from dermoscopy images. Moreover, our method ::: also addresses the issue of preserving morphological features during artifact removal. The key components of this ::: method include explicit curvilinear structure detection and modeling, as well as feature guided exemplar-based ::: inpainting. We experiment on a number of dermoscopy images and demonstrate that our method produces ::: superior results compared to existing techniques.
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paper_title: Border Detection of Skin Lesion Images Based on Fuzzy C-Means Thresholding

paper_content:

The accurate location of the border of skin lesions is an important first step in the automatic diagnosis of malignant melanoma. In this paper, we propose a new method of segmentation to locate the skin lesion. The method consists of two stages; image pre-processing and image segmentation. As the first step of image analysis, pre-processing techniques are implemented to remove noise and undesired structures for the images using median filtering. In the second step, the fuzzy c-means (FCM) thresholding technique is used to segment and localize the lesion. The border detection results are visually examined by an expert dermatologist and are found to be highly accurate.
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paper_title: Detection of blue-white veil areas in dermoscopy images using machine learning techniques

paper_content:

As a result of the advances in skin imaging technology and the development of suitable image processing techniques, during the last decade, there has been a significant increase of interest in the computer-aided diagnosis of skin cancer. Dermoscopy is a non-invasive skin imaging technique which permits visualization of features of pigmented melanocytic neoplasms that are not discernable by examination with the naked eye. One of the useful features in dermoscopic diagnosis is the blue-white veil (irregular, structureless areas of confluent blue pigmentation with an overlying white "ground-glass" film) which is mostly associated with invasive melanoma. In this preliminary study, a machine learning approach to the detection of blue-white veil areas in dermoscopy images is presented. The method involves pixel classification based on relative and absolute color features using a decision tree classifier. Promising results were obtained on a set of 224 dermoscopy images.
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paper_title: Lesion Border Detection in Dermoscopy Images

paper_content:

BACKGROUND ::: Dermoscopy is one of the major imaging modalities used in the diagnosis of melanoma and other pigmented skin lesions. Due to the difficulty and subjectivity of human interpretation, computerized analysis of dermoscopy images has become an important research area. One of the most important steps in dermoscopy image analysis is the automated detection of lesion borders. ::: ::: ::: METHODS ::: In this article, we present a systematic overview of the recent border detection methods in the literature paying particular attention to computational issues and evaluation aspects. ::: ::: ::: CONCLUSION ::: Common problems with the existing approaches include the acquisition, size, and diagnostic distribution of the test image set, the evaluation of the results, and the inadequate description of the employed methods. Border determination by dermatologists appears to depend upon higher-level knowledge, therefore it is likely that the incorporation of domain knowledge in automated methods will enable them to perform better, especially in sets of images with a variety of diagnoses.
---
paper_title: Intelligent segmentation and classification of pigmented skin lesions in dermatological images

paper_content:

During the last years, computer vision-based diagnostic systems have been used in several hospitals and dermatology clinics, aiming mostly at the early detection of malignant melanoma tumor, which is among the most frequent types of skin cancer, versus other types of non-malignant cutaneous diseases. In this paper we discuss intelligent techniques for the segmentation and classification of pigmented skin lesions in such dermatological images. A local thresholding algorithm is proposed for skin lesion separation and border, texture and color based features, are then extracted from the digital images. Extracted features are used to construct a classification module based on Support Vector Machines (SVM) for the recognition of malignant melanoma versus dysplastic nevus.
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paper_title: Comparison of Segmentation Methods for Melanoma Diagnosis in Dermoscopy Images

paper_content:

In this paper, we propose and evaluate six methods for the segmentation of skin lesions in dermoscopic images. This set includes some state of the art techniques which have been successfully used in many medical imaging problems (gradient vector flow (GVF) and the level set method of Chan et al.[(C-LS)]. It also includes a set of methods developed by the authors which were tailored to this particular application (adaptive thresholding (AT), adaptive snake (AS), EM level set (EM-LS), and fuzzy-based split-and-merge algorithm (FBSM)]. The segmentation methods were applied to 100 dermoscopic images and evaluated with four different metrics, using the segmentation result obtained by an experienced dermatologist as the ground truth. The best results were obtained by the AS and EM-LS methods, which are semi-supervised methods. The best fully automatic method was FBSM, with results only slightly worse than AS and EM-LS.
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paper_title: Lesion border detection in dermoscopy images using dynamic programming

paper_content:

Background/purpose: Automated border detection is an important and challenging task in the computerized analysis of dermoscopy images. However, dermoscopic images often contain artifacts such as illumination, dermoscopic gel, and outline (hair, skin lines, ruler markings, and blood vessels). As a result, there is a need for robust methods to remove artifacts and detect lesion borders in dermoscopy images. ::: ::: ::: ::: Methods: This automated method consists of three main steps: (1) preprocessing, (2) edge candidate point detection, and (3) tumor outline delineation. First, algorithms to reduce artifacts were used. Second, a least-squares method (LSM) was performed to acquire edge points. Third, dynamic programming (DP) technique was used to find the optimal boundary of the lesion. Statistical measures based on dermatologist-drawn borders were utilized as ground-truth to evaluate the performance of the proposed method. ::: ::: ::: ::: Results: The method is tested on a total of 240 dermoscopic images: 30 benign melanocytic, 50 malignant melanomas, 50 basal cell carcinomas, 20 Merkel cell carcinomas, 60 seborrheic keratosis, and 30 atypical naevi. We obtained mean border detection error of 8.6%, 5.04%, 9.0%, 7.02%, 2.01%, and 3.24%, respectively. ::: ::: ::: ::: Conclusions: The results demonstrate that border detection combined with artifact removal increases sensitivity and specificity for segmentation of lesions in dermoscopy images.
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paper_title: Comparison of Segmentation Methods for Automatic Diagnosis of Dermoscopy Images

paper_content:

Dermoscopy is a non-invasive diagnostic technique for the in vivo observation of pigmented skin lesions used in dermatology. There is currently a great interest in the prospects of automatic image analysis methods for dermoscopy, both to provide quantitative information about a lesion, which can be of relevance for the clinician, and as a stand alone early warning tool. The effective implementation of such a tool could lead to a reduction in the number of cases selected for exeresis, with obvious benefits both to the patients and to the health care system. The standard approach in automatic dermoscopic image analysis has usually three stages: (i) image segmentation, (ii) feature extraction and feature selection, (iii) lesion classification. This paper presents a comparison of segmentation methods applied to 50 dermoscopic image analysis, along with a clinical evaluation of each segmentation result performed by an experienced dermatologist.
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paper_title: Automatic Detection of Blue-White Veil and Related Structures in Dermoscopy Images

paper_content:

Dermoscopy is a non-invasive skin imaging technique, which permits visualization of features of pigmented melanocytic neoplasms that are not discernable by examination with the naked eye. One of the most important features for the diagnosis of melanoma in dermoscopy images is the blue-white veil (irregular, structureless areas of confluent blue pigmentation with an overlying white "ground-glass" film). In this article, we present a machine learning approach to the detection of blue-white veil and related structures in dermoscopy images. The method involves contextual pixel classification using a decision tree classifier. The percentage of blue-white areas detected in a lesion combined with a simple shape descriptor yielded a sensitivity of 69.35% and a specificity of 89.97% on a set of 545 dermoscopy images. The sensitivity rises to 78.20% for detection of blue veil in those cases where it is a primary feature for melanoma recognition.
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paper_title: Identification of Malignant Melanoma by Wavelet Analysis

paper_content:

This paper presents a different method for classifying benign and malignant melanoma skin lesions from digital images. The features used for classification were collected from coefficients created by Wavelet decompositions. Wavelet analysis, like Fourier transformation, is used for a number of operations in signal analysis. Images can also be treated as signals with their color values constituting the signal. The mean and variance of wavelet coefficients for a two level decomposition were calculated and used in a neural network to make a classification. The ability to correctly discriminate between benign and malignant lesions was about 83%.
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paper_title: Detection of atypical texture features in early malignant melanoma

paper_content:

BACKGROUND ::: The presence of an atypical (irregular) pigment network (APN) can indicate a diagnosis of melanoma. This study sought to analyze the APN with texture measures. ::: ::: ::: METHODS ::: For 106 dermoscopy images including 28 melanomas and 78 benign dysplastic nevi, the areas of APN were selected manually. Ten texture measures in the CVIPtools image analysis system were applied. ::: ::: ::: RESULTS ::: Of the 10 texture measures used, correlation average provided the highest discrimination accuracy, an average of 95.4%. Discrimination of melanomas was optimal at a pixel distance of 20 for the 768 x 512 images, consistent with a melanocytic lesion texel size estimate of 4-5 texels per mm. ::: ::: ::: CONCLUSION ::: Texture analysis, in particular correlation average at an optimized pixel spacing, may afford automatic detection of an irregular pigment network in early malignant melanoma.
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paper_title: Morphologic changes of pigmented skin lesions : A useful extension of the ABCD rule for dermatoscopy

paper_content:

Abstract Background: Epiluminescence microscopy (ELM) significantly increases the early diagnosis of pigmented skin lesions (PSL) using established criteria and pattern analysis. The ABCD rule for dermatoscopy (ie, ELM) provides a simplified approach to the interpretation of ELM images on the basis of asymmetry (A), border (B), color (C), and dermatoscopic structure (D). Objective: We set out to determine whether the diagnostic accuracy of the ABCD scoring algorithm can be significantly improved by incorporating information about morphologic changes of the lesion observed and provided by the patient. Methods: We prospectively collected 356 small pigmented skin lesions ( Results: The frequency of reported changes was significantly higher for melanomas than benign PSL (65.8% vs 29.7%, P P P ABCD = 0.87 vs AUC ABCD-E = 0.90; P = .006). Conclusion: Information about morphologic changes of PSL as reported by the patient is a useful extension of the ABCD rule for dermatoscopy. (J Am Acad Dermatol 1999;40:558-62.)
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paper_title: Qualitative asymmetry measure for melanoma detection

paper_content:

Size functions and support vector machines are used to implement a new automatic classifier of melanocytic lesions. This is mainly based on a qualitative assessment of asymmetry. The program is used, at clinical level, with two thresholds, so that comparison of the two outputs produces a report of low-middle-high risk. Experimental results with cross-validation are reported.
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paper_title: A study of the value of the seven-point checklist in distinguishing benign pigmented lesions from melanoma

paper_content:

Summary ::: ::: A seven-point scoring system has been adopted by the Cancer Research Campaign to help non-dermatologists recognize melanoma (MM). Its value is reviewed in the light of increasing referrals of pigmented lesions. One-hundred and ninety-five patients (M:F, 43:152; mean age=43 years, s.d. = 19) were asked whether their lesions possessed the seven points before diagnosis. The dermatologist assessed the signs. Six patients were unable to comply and some had multiple lesions; thus, 216 lesions were fully assessed by patient and dermatologist, and six by the dermatologist alone. Histology was obtained where appropriate. There were eight MMs, 95 naevi, 80 seborrhoeic warts, three dysplastic naevi, and 36 other lesions. Seven of the eight MMs were diagnosed clinically; the other was biopsied because of suspicious features and was a nodular MM. Four lesions suspected to be MMs proved benign. The predictive value (PV) of a clinical diagnosis of MM was 64% and of non-MM was 99%. Using accepted cutpoints for the seven-point system (refer if score 3) patients' scores gave a PV for MM of 7% and for non-MM of 99%. Two MMs scored less than three. Dermatologists' scores gave a PV for MM of 8% and for non-MM of 99%. One MM scored 3. Univariate analyses showed that enlargement (P<0.05), dermatologists' assessments of an irregular margin (P <0.001), size (P<0.05) and pigmentary irregularity (P<0.05), and patients' assessments of size (P<0.05) were statistically significant. Five patients with MM did not recognize the irregular margin. After stepwise logistic regression analysis of the dermatologists' assessments, only irregularity of the margin remained significant. This single sign gave a PV for MM of 13% and for non-MM of 100%. No MMs were missed. Fifty-five (26%) of 214 non-MM lesions had an irregular margin. No useful discrimination was obtained from the patients’assessments. We conclude that if these results are confirmed then considerable emphasis should be placed on educating the public to recognize the single feature of irregularity of the margin in order to improve the early detection of melanoma.
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paper_title: Frequency and Morphologic Characteristics of Invasive Melanomas Lacking Specific Surface Microscopic Features

paper_content:

Objectives: To create a simple diagnostic method for invasive melanoma with in vivo cutaneous surface microscopy (epiluminescence microscopy, dermoscopy, dermatoscopy) and to analyze the incidence and characteristics of those invasive melanomas that had no diagnostic features by means of hand-held surface microscopes. Design: Pigmented skin lesions were photographed in vivo with the use of immersion oil. All were excised and reviewed for histological diagnosis. A training set of 62 invasive melanomas and 159 atypical nonmelanomas and a test set of 45 invasive melanomas and 119 atypical nonmelanomas were used. Images from the training set were scored for 72 surface microscopic features. Those features with a low sensitivity (0%) and high specificity (>85%) were used to create a simple diagnostic model for invasive melanoma. Setting: All patients were recruited from the Sydney (Australia) Melanoma Unit (a primary case and referral center). Patients: A random sample of patients whose lesions were excised, selected from a larger database. Main Outcome Measures: Sensitivity and specificity of the model for diagnosis of invasive melanona. Results: The model gave a sensitivity of 92% (98/107) and specificity of 71%. Of the 9 "featureless" melanomas the model failed to detect, 6 were pigmented and thin and had a pigment network. The other 3 were thicker, hypomelanotic lesions lacking a pigment network, some with prominent telangiectases, and all with only small areas of pigment. All featureless melanomas noted by the patients had a history of change in color, shape, or size. Conclusions: Surface microscopy does not allow 100% sensitivity in diagnosing invasive melanoma and therefore cannot be used as the sole indicator for excision. Clinical history is an important consideration when featureless lesions are diagnosed. Arch Dermatol. 1996;132:1178-1182
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paper_title: Automatic detection of asymmetry in skin tumors

paper_content:

Asymmetry, a critical feature in the diagnosis of malignant melanoma, is analyzed using a new algorithm to find a major axis of asymmetry and calculate the degree of asymmetry of the tumor outline. The algorithm provides a new objective definition of asymmetry. A dermatologist classified 86 tumors as symmetric or asymmetric. Borders of tumors were found either manually or automatically using a radial search method. With either method, asymmetry determination by the asymmetry algorithm agreed with the dermatologist's determination of asymmetry in about 93% of cases.
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paper_title: A Novel Hybrid System for Skin Lesion Detection

paper_content:

In this paper, a new intelligent method of classifying benign and malignant melanoma lesions is implemented. The system consists of four stages; image pre-processing, image segmentation, feature extraction, and image classification. As the first step of the image analysis, pre-processing techniques are implemented to remove noise and undesired structures from the images using techniques such as median filtering and contrast enhancement. In the second step, a simple thresholding method is used to segment and localise the lesion, a boundary tracing algorithm is also implemented to validate the segmentation. Then, a wavelet approach is used to extract the features, more specifically wavelet packet transform (WPT). Finally, the dimensionality of the selected features is reduced with principal component analysis (PCA) and later supplied to an artificial neural network and support vector machine classifiers for classification. The ability to correctly discriminate between benign and malignant lesions was about 95% for the Artificial Neural Network and 85% for the Support Vector Machine classifier.
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paper_title: Detection of blue-white veil areas in dermoscopy images using machine learning techniques

paper_content:

As a result of the advances in skin imaging technology and the development of suitable image processing techniques, during the last decade, there has been a significant increase of interest in the computer-aided diagnosis of skin cancer. Dermoscopy is a non-invasive skin imaging technique which permits visualization of features of pigmented melanocytic neoplasms that are not discernable by examination with the naked eye. One of the useful features in dermoscopic diagnosis is the blue-white veil (irregular, structureless areas of confluent blue pigmentation with an overlying white "ground-glass" film) which is mostly associated with invasive melanoma. In this preliminary study, a machine learning approach to the detection of blue-white veil areas in dermoscopy images is presented. The method involves pixel classification based on relative and absolute color features using a decision tree classifier. Promising results were obtained on a set of 224 dermoscopy images.
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paper_title: In vivo epiluminescence microscopy of pigmented skin lesions. I. Pattern analysis of pigmented skin lesions

paper_content:

The importance of recognizing early melanoma is generally accepted. Because not all pigmented skin lesions can be diagnosed correctly by their clinical appearance, additional criteria are required for the clinical diagnosis of such lesions. In vivo epiluminescence microscopy provides for a more detailed inspection of the surface of pigmented skin lesions, and, by using the oil immersion technic, which renders the epidermis translucent, opens a new dimension of skin morphology by including the dermoepidermal junction into the macroscopic evaluation of a lesion. In an epiluminescence microscopy study of more than 3000 pigmented skin lesions we have defined morphologic criteria that are not readily apparent to the naked eye but that are detected easily by epiluminescence microscopy and represent relatively reliable markers of benign and malignant pigmented skin lesions. These features include specific patterns, colors, and intensities of pigmentation, as well as the configuration, regularity, and other characteristics of both the margin and the surface of pigmented skin lesions. Pattern analysis of these features permits a distinction between different types of pigmented skin lesions and, in particular, between benign and malignant growth patterns. Epiluminescence microscopy is thus a valuable addition to the diagnostic armamentarium of pigmented skin lesions at a clinical level.
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paper_title: A decision support system for the diagnosis of melanoma: A comparative approach

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Melanoma is the most deathful of all skin cancers and the number of cases grows every year. The extirpation in early phases implies a high degree of survival so it is fundamental to diagnose it as soon as possible. In this paper we present a clinical decision support system for melanoma diagnosis using as input an image set of the skin lesion to be diagnosed. The system analyses the image sequence to extract the affected area, determinates the characteristics which indicate the degree of damage and, according to them, it makes a decision. Several methods of classification are proposed: a multilayered perceptron, a Bayesian classifier and the algorithm of the K nearest neighbours. These methods work independently and also in combination making a collaborative decision support system. The classification rates obtained are around 87%.
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paper_title: Computerized evaluation of pigmented skin lesion images recorded by a videomicroscope: comparison between polarizing mode observation and oil/slide mode observation.

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BACKGROUND/AIMS ::: It has now been established that ELM significantly improves the clinical diagnosis of pigmented skin lesions, particularly when associated with image analysis on digital pictures. Besides surface microscopes, so called "videomicroscopes" are nowadays commercially available. Thanks to their practicality and to their reasonable price and small volume, they could be usefully employed by general practitioners or peripheral dermatologists in their practice. The aim of our study was to evaluate the usefulness and the handiness of a videomicroscope provided with polarized light, and to compare images recorded in the polarizing mode to those of the same lesions recorded after application of oil and a glass slide. ::: ::: ::: METHODS ::: 114 images of benign pigmented skin lesions were recorded both in polarizing mode and in oil/slide mode observation. Digital images were then analyzed by means of a dedicated software, evaluating 34 different parameters. ::: ::: ::: RESULTS ::: The instrument is easy to handle and the acquisition of images is simple and quick. Polarizing mode observation enables the recognition of subsurface structures, like pigment network and capillaries. Red and blue components in the images tend to be more pronounced, when images are acquired by the polarizing system, whereas green components are prominent in oil images. Values referring to dark areas are also higher for oil images. However, the 2 observation modes provide digital images, whose values, in most cases, do not differ statistically. ::: ::: ::: CONCLUSION ::: Videomicroscopes are cheap, space saving, and enable a quick examination of skin areas to be imaged. The possibility of a rapid switch from surface to subsurface polarizing mode observation, makes the examination procedure even faster. Our data demonstrate that polarizing mode observation and recording can be employed as a basis for clinical and computerized epiluminescence, particularly in situations, where a rapid screening of numerous patients and lesions is necessary.
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paper_title: Detection of asymmetric blotches (asymmetric structureless areas) in dermoscopy images of malignant melanoma using relative color

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BACKGROUND ::: Dermoscopy, also known as dermatoscopy or epiluminescence microscopy (ELM), is a non-invasive, in vivo technique, which permits visualization of features of pigmented melanocytic neoplasms that are not discernable by examination with the naked eye. One prominent feature useful for melanoma detection in dermoscopy images is the asymmetric blotch (asymmetric structureless area). ::: ::: ::: METHOD ::: Using both relative and absolute colors, blotches are detected in this research automatically by using thresholds in the red and green color planes. Several blotch indices are computed, including the scaled distance between the largest blotch centroid and the lesion centroid, ratio of total blotch areas to lesion area, ratio of largest blotch area to lesion area, total number of blotches, size of largest blotch, and irregularity of largest blotch. ::: ::: ::: RESULTS ::: The effectiveness of the absolute and relative color blotch features was examined for melanoma/benign lesion discrimination over a dermoscopy image set containing 165 melanomas (151 invasive melanomas and 14 melanomas in situ) and 347 benign lesions (124 nevocellular nevi without dysplasia and 223 dysplastic nevi) using a leave-one-out neural network approach. Receiver operating characteristic curve results are shown, highlighting the sensitivity and specificity of melanoma detection. Statistical analysis of the blotch features are also presented. ::: ::: ::: CONCLUSION ::: Neural network and statistical analysis showed that the blotch detection method was somewhat more effective using relative color than using absolute color. The relative-color blotch detection method gave a diagnostic accuracy of about 77%.
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paper_title: Automatic Diagnosis of Melanoma: A Software System Based on the 7-Point Check-List

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Early detection of melanoma is one of the greatest challenges of dermatologic practice today. A new diagnostic method, the "ELM 7 point checklist", defines a set of seven features, based on colour and texture parameters, which describe the malignancy of a lesion. It has been presented as faster and with the same accuracy than the traditional ABCD criteria in the diagnosis of melanoma. In this paper a new system for automated diagnosis of melanocytic skin lesions, based on ELM 7 point checklist, is introduced.
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paper_title: Comparative Performance of 4 Dermoscopic Algorithms by Nonexperts for the Diagnosis of Melanocytic Lesions

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OBJECTIVE ::: To assess 4 dermoscopy methods in a nonexpert setting. ::: ::: ::: DESIGN ::: Sixty-one medical practitioners, mainly primary care physicians in Australia, were trained in 4 dermoscopy algorithms. Participants then assessed macroscopic and dermoscopic images of 40 melanocytic skin lesions. Each of the dermoscopic images was assessed with pattern analysis, the 7-point checklist, the ABCD rule, and the Menzies method. ::: ::: ::: RESULTS ::: The Menzies method showed the highest sensitivity, 84.6%, for the diagnosis of melanoma, followed by the 7-point checklist (81.4%), the ABCD rule (77.5%), pattern analysis (68.4%), and assessment of a macroscopic image (60.9%). Pattern analysis and assessment of the macroscopic image showed the highest specificity, 85.3% and 85.4%, respectively. The ABCD rule showed a specificity of 80.4%; the Menzies method, 77.7%; and the 7-point checklist, 73%. The Menzies method had a diagnostic accuracy of 81.1%; the ABCD rule, 79.0%; the 7-point checklist, 77.2%; pattern analysis, 76.8%; and clinical assessment, 73.2%. ::: ::: ::: CONCLUSIONS ::: All algorithms performed well in the hands of relatively inexpert practitioners who had undertaken self-guided training provided on compact disc. The Menzies method showed the highest diagnostic accuracy and sensitivity for melanoma diagnosis and was preferred by study participants.
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paper_title: Techniques for a structural analysis of dermatoscopic imagery

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Techniques were developed for automated detection and characterization of dermatoscopic structures, including the pigment network and brown globules. These techniques incorporate algorithms for grayscale shape extraction based on differential geometry developed by Steger, a snake algorithm, and a modification of the region competition strategy of Zhu and Yuille. A novel approach was developed for global segmentation of pigmented lesions, based on stabilized inverse diffusion equations. Procedures for detection of air bubbles and hairs in dermatoscopic images are also reported.
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paper_title: Epiluminescence Image Processing for Melanocytic Skin Lesion Diagnosis Based on 7-Point Check-List: A Preliminary Discussion on Three Parameters

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Epiluminescence microscopy (ELM) is a noninvasive technique used to enhance visualization of microscopic structures of pigmented lesions for the early detection of melanoma. The 7� point checklist is a diagnostic method that requires the identification of only seven dermoscopic criteria, defining the image through the use of algorithms. This paper describes an experimental automated diagnosis setup of melanocytic skin lesions through an image processing methodology focused on finding the presence of different epiluminescence parameters. In this paper the image processing setup allows the automatic detection of some specific dermoscopic criteria. We analyze the blue whitish veil, the regression, and the irregular streaks. The procedure developed was tested by considering a set of about 200 ELM images. A good concordance between ELM 7� point checklist parameters detected and the new method of image processing was achieved by kappa analysis. Although ELM doesn't substitute histological evaluation, it could be a reliable instrument to enhance clinical accuracy of skin pigmented lesions diagnosis.
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paper_title: A Mobile Automated Skin Lesion Classification System

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Melanoma skin cancer accounts for less than 5% of skin cancer cases but causes the most deaths due to skin cancer. Convenient automated diagnosis of skin lesions and melanoma recognition can greatly improve early detection of melanomas. This paper presents a prototype of an image-based automated melanoma recognition system on Android smart phones. The system consists of three major components: image segmentation, feature calculation, and classification. It is designed to run on a mobile device with a camera, such as a smart phone or a tablet PC. A skin lesion image is converted to a monochrome image for outline contour detection. Color and shape features of the lesion are extracted and used as input to a kNN classifier. Initial experimental result shows that the system is efficient and works well on well-lighted test images, achieving an average accuracy of 66.7%, with average malignant class recall/sensitivity of 60.7% and specificity of 80.5%.
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paper_title: Dermoscopic image-analysis system: estimation of atypical pigment network and atypical vascular pattern

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Early detection of melanoma is one of the greatest challenges of dermatologic practice today. A new diagnostic method, the "ELM 7 point checklist", defines a set of seven features, based on color and texture parameters, which describe the malignancy of a lesion. This paper describes image processing algorithms developed for the detection of the occurrence of "Atypical Pigment Network" and "Atypical Vascular Pattern" that are two important criteria of the "7-point checklist" method
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paper_title: Detection of granularity in dermoscopy images of malignant melanoma using color and texture features

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Granularity, also called peppering and multiple blue-grey dots, is defined as an accumulation of tiny, blue-grey granules in dermoscopy images. Granularity is most closely associated with a diagnosis of malignant melanoma. This study analyzes areas of granularity with color and texture measures to discriminate granularity in melanoma from similar areas in non-melanoma skin lesions. The granular areas in dermoscopy images of 74 melanomas and 14 melanomas in situ were identified and manually selected. For 200 non-melanoma dermoscopy images, those areas which most closely resembled granularity in color and texture were similarly selected. Ten texture and twenty-two color measures were studied. The texture measures consisted of the average and range of energy, inertia, correlation, inverse difference, and entropy. The color measures consisted of absolute and relative RGB averages, absolute and relative RGB chromaticity averages, absolute and relative G/B averages, CIE X, Y, Z, X/Y, X/Z and Y/Z averages, R variance, and luminance. These measures were calculated for each granular area of the melanomas and the comparable areas in the non-melanoma images. Receiver operating characteristic (ROC) curve analysis showed that the best separation of melanoma images from non-melanoma images by granular area features was obtained with a combination of color and texture measures. Comparison of ROC results showed greater separation of melanoma from benign lesions using relative color than using absolute color. Statistical analysis showed that the four most significant measures of granularity in melanoma are two color measures and two texture measures averaged over the spots: relative blue, relative green, texture correlation, and texture energy range. The best feature set, utilizing texture and relative color measures, achieved an accuracy of 96.4% based on area under the receiver operating characteristic curve.
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paper_title: Fuzzy logic techniques for blotch feature evaluation in dermoscopy images

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Blotches, also called structureless areas, are critical in differentiating malignant melanoma from benign lesions in dermoscopy skin lesion images. In this paper, fuzzy logic techniques are investigated for the automatic detection of blotch features for malignant melanoma discrimination. Four fuzzy sets representative of blotch size and relative and absolute blotch colors are used to extract blotchy areas from a set of dermoscopy skin lesion images. Five previously reported blotch features are computed from the extracted blotches as well as four new features. Using a neural network classifier, malignant melanoma discrimination results are optimized over the range of possible alpha-cuts and compared with results using crisp blotch features. Features computed from blotches using the fuzzy logic techniques based on three plane relative color and blotch size yield the highest diagnostic accuracy of 81.2%.
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paper_title: Three-Point Checklist of Dermoscopy A New Screening Method for Early Detection of Melanoma

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Background: Dermoscopy used by experts has been demonstrated to improve the diagnostic accuracy for melanoma. However, little is known about the diagnostic validity of dermoscopy wh
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paper_title: A computer aided diagnostic system for malignant melanomas

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We describe a system for automatic diagnosis of malignant melanoma based on digital dermoscopic images. The tool is designed for use with general practitioners, saving time and resources in the diagnostic process. A variety of indicative features are described mimicking the human approach for diagnosis. Segmentation, pattern recognition, and change detection are the important steps in our approach.
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paper_title: Results obtained by using a computerized image analysis system designed as an aid to diagnosis of cutaneous melanoma

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Results obtained using a computerized image analysis system as an aid to clinical diagnosis of melanoma are reported. The system comprises a colour television camera connected through a digitizing board to a 386 personal computer. By means of original algorithms able to measure the shape, the colours and texture of a pigmented lesion of the skin, the system provides eight on/off indicators that are matched with the histological diagnosis to identify benign and malignant pigmented lesions. The chances that a given lesion is malignant increase with the increasing number of positive indicators. The training field of the system was constituted of images and data of 169 cutaneous lesions in 165 patients. Taking two positive indicators as the threshold between pigmented benign and malignant lesions, the efficiency of the system is 0.98, the positive predictive value is 0.45 and the negative predictive value is 0.95. These values were confirmed in a series of 44 pigmented lesions, 10 of which were melanoma, that constitute the present test series. The authors conclude that this computerized image analysis system should be regarded as a useful aid to diagnosis for a non-expert clinician. The system limit is transformation within a naevus.
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paper_title: An evaluation of the revised seven-point checklist for the early diagnosis of cutaneous malignant melanoma.

paper_content:

The seven-point checklist has been widely advocated as a sensitive screening test for malignant melanoma. A number of groups have questioned the sensitivity of this system, especially in the detection of early lesions. We have assessed the sensitivity and specificity of the revised seven-point checklist when applied to lesions seen in our department over a 26-month period and compared it with the American ABCDE evaluation system. All melanomas (n = 65) were detected using the revised seven-point checklist and all were found to have at least one of the three major criteria defined by that system. Five (7.7%) melanomas were not picked up by the ABCDE checklist. Of 100 randomly selected patients who attended the clinic during the same period, with clinically diagnosed benign pigmented lesions, 63 had at least one major feature of the revised seven-point checklist. Forty (62%) of the melanomas, compared with only (4%) of the benign lesions, had more than one major feature. This study confirms the sensitivity of the revised seven-point checklist in the diagnosis of cutaneous malignant melanoma.
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paper_title: Dermoscopic interest point detector and descriptor

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Dermoscopy is an imaging technique dermatologists use to better visualize pigmented skin lesions (PSLs) and determine their malignancy. Dermoscopic features revealed by this technique have been shown to correlate with histopathology features, and are used as diagnosis indicators by many dermatologists. Hence, automated detection and classification of these features is the first step toward computer-aided diagnosis of melanoma in dermoscopy. In this paper, we present a novel scale- and rotation-invariant feature detector and descriptor specifically designed as a general visual vocabulary of dermoscopic features. We compare our feature detector and descriptor to the popular interest point detectors in the vision community, namely, SIFT, and a more recent fast variant, SURF. We demonstrate that our feature detector is more discriminative and reliable for dermoscopic features.
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paper_title: Automated Extraction and Description of Dark Areas in Surface Microscopy Melanocytic Lesion Images

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Background: Identification of dark areas inside a melanocytic lesion (ML) is of great importance for melanoma diagnosis, both during clinical examination and employing programs for
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paper_title: Decision boundary feature extraction for neural networks

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In this paper, we propose a new feature extraction method for feedforward neural networks. The method is based on the recently published decision boundary feature extraction algorithm which is based on the fact that all the necessary features for classification can be extracted from the decision boundary. The decision boundary feature extraction algorithm can take advantage of characteristics of neural networks which can solve complex problems with arbitrary decision boundaries without assuming underlying probability distribution functions of the data. To apply the decision boundary feature extraction method, we first give a specific definition for the decision boundary in a neural network. Then, we propose a procedure for extracting all the necessary features for classification from the decision boundary. Experiments show promising results.
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paper_title: I rregularity index : A new border irregularity measure for cutaneous melanocytic lesions

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Abstract One of the important clinical features that differentiates benign melanocytic nevi from malignant melanomas is the irregularity of the lesion border. There are two types of border irregularities: texture irregularities, the small variations along the border, and structure irregularities, the global indentations and protrusions. Texture irregularities are subject to noise, whereas structure irregularities may suggest excessive cell growth or regression of a melanoma. We have designed a new algorithm for measuring the structure irregularities in the border. Our algorithm first locates all the local and global indentations and protrusions and organizes them in a hierarchical structure. Then an area-based index, called the irregularity index, is computed for each indentation and protrusion along the border. From the individual irregularity indices, two important new measures, the most significant irregularity index and the overall irregularity index are derived. These two new indices provide a measure of the degree of irregularity along the lesion border. A double-blinded test was performed to examine the effectiveness of these two new indices. Fourteen experienced dermatologists were asked to evaluate the borders of 40 pigmented lesions. The clinical evaluation result was then compared with the two new indices and other published shape measurements. The user study showed that both of the new indices vastly outperformed the other shape descriptors. Moreover, our algorithm captured the knowledge of expert dermatologists in analysing malignancy of a lesion based on its shape alone, indicating that the new measures may be useful for diagnosing melanomas.
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paper_title: Developing a predictive model of human skin coloring

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A model of color formation within human skin has been developed to aid the characterization of pigmented skin lesions from their digitized color images. The model is based on the Kublenka-Munk theory of scattering and absorption within inhomogeneous materials and the physics pertaining to their color properties. By considering the skin to be a layered construction of such materials, the stratum corneum, epidermis, papillary dermis and reticular dermis, and by exploiting the physics related to the optical interface between these layers, the model generates all possible colors occurring within normal human skin. In particular, the model predicts that all skin colors have to lie on a simple curved surface patch within a three- dimensional color space bounded by two physiologically meaningful axes, one corresponding to the amount of melanin within the epidermis and the other to the amount of blood within the dermis. These predictions were verified by comparing the CIE LMS coordinates of a representative, cross-racial sample of fifty skin images with the LMS coordinates predicted by the model. The results show that, within the predicted error bounds, the coordinates for normal skin colors do indeed lie on the curved surface generated by the model. Several possible applications of this representation are outlined, including images representing the melanin and blood components separately, as well as the possibility of measuring the Breslow thickness of melanocytic invasion within malignant melanoma.© (1996) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
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paper_title: Symmetry axis computation for almost-symmetrical and asymmetrical objects: Application to pigmented skin lesions

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The detection of symmetry axes through the optimization of a given symmetry measure, computed as a function of the mean-square error between the original and reflected images, is investigated in this paper. A genetic algorithm and an optimization scheme derived from the self-organizing maps theory are presented. The notion of symmetry map is then introduced. This transform allows us to map an object into a symmetry space where its symmetry properties can be analyzed. The locations of the different axes that globally and locally maximize the symmetry value can be obtained. The input data are assumed to be vector-valued, which allow to focus on either shape. color or texture information. Finally, the application to skin cancer diagnosis is illustrated and discussed.
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paper_title: Analysis of skin line pattern for lesion classification

paper_content:

Background/purpose: It has been observed that skin patterning tends to be disrupted by malignant but not by benign skin lesions. This suggests that measurements of skin pattern disruption on simply captured white light optical skin images could be a useful contribution to a diagnostic feature set. Previous work using a measurement of line strength by a consistent high-value profiling technique followed by local variance measurement or a region agglomerative classifier to measure skin line pattern disruption was extremely promising but computationally intensive, suggesting that the idea of measuring skin pattern disruption was useful but a simpler method was required. ::: ::: ::: ::: Methods: The skin pattern was extracted by high-pass filtration and enhanced by adaptive anisotropic (spatial variant) filtering which smoothes along skin lines but not across them. The skin line main direction and direction variance were estimated using a local image gradient matrix and the difference of these measures across the lesion image boundary was used as a lesion classifier. ::: ::: ::: ::: Results: A set of images of malignant melanoma and benign naevi were processed as above and the scatter plot of results in a two-dimensional feature (line direction and line variation difference) space showed excellent separation of benign and malignant lesions. An ROC plot enclosed an area of 0.88. ::: ::: ::: ::: Conclusions: The experimental results showed that the local line direction and the local line variation were promising features for distinguishing malignant melanoma from benign lesion and the methods used were effective and computationally low-cost.
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paper_title: An analysis of co-occurrence texture statistics as a function of grey level quantization

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In this paper, the effect of grey level quantization on the ability of co-occurrence probability statistics to classify natural textures is studied. Generally, as a function of increasing grey levels, many of the statistics demonstrate a decrease in classification ability while a few maintain constant classification accuracy. None of the individual statistics show increasing classification accuracy throughout all grey levels. Correlation analysis is used to rationalize a preferred subset of statistics. The preferred statistics set (contrast, correlation, and entropy) is demonstrated to be an improvement over using single statistics or using the entire set of statistics. If the feature space dimension only allows for a single statistic, one of contrast, dissimilarity, inverse difference normalized, or inverse difference moment normalized, is recommended. Testing that compares (using all orientations separately), the average of all orientations and look direction averaging, when determining the co-occurrenc...
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paper_title: Textural Features for Image Classification

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Texture is one of the important characteristics used in identifying objects or regions of interest in an image, whether the image be a photomicrograph, an aerial photograph, or a satellite image. This paper describes some easily computable textural features based on gray-tone spatial dependancies, and illustrates their application in category-identification tasks of three different kinds of image data: photomicrographs of five kinds of sandstones, 1:20 000 panchromatic aerial photographs of eight land-use categories, and Earth Resources Technology Satellite (ERTS) multispecial imagery containing seven land-use categories. We use two kinds of decision rules: one for which the decision regions are convex polyhedra (a piecewise linear decision rule), and one for which the decision regions are rectangular parallelpipeds (a min-max decision rule). In each experiment the data set was divided into two parts, a training set and a test set. Test set identification accuracy is 89 percent for the photomicrographs, 82 percent for the aerial photographic imagery, and 83 percent for the satellite imagery. These results indicate that the easily computable textural features probably have a general applicability for a wide variety of image-classification applications.
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paper_title: Intelligent segmentation and classification of pigmented skin lesions in dermatological images

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During the last years, computer vision-based diagnostic systems have been used in several hospitals and dermatology clinics, aiming mostly at the early detection of malignant melanoma tumor, which is among the most frequent types of skin cancer, versus other types of non-malignant cutaneous diseases. In this paper we discuss intelligent techniques for the segmentation and classification of pigmented skin lesions in such dermatological images. A local thresholding algorithm is proposed for skin lesion separation and border, texture and color based features, are then extracted from the digital images. Extracted features are used to construct a classification module based on Support Vector Machines (SVM) for the recognition of malignant melanoma versus dysplastic nevus.
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paper_title: The Morphologic Criteria of the Pseudopod in Surface Microscopy

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Background and Design: In vivo cutaneous surface microscopy (oil epiluminescence, dermatoscopy, and dermoscopy) has been shown to greatly enhance the clinical diagnosis of melanoma. The pseudopod is a morphologic feature seen on surface microscopy that corresponds to the radial growth of tumor in melanoma. While it is one of the most specific surface microscopic features of invasive melanoma, it has remained poorly defined. We studied 239 pigmented lesions, 80 melanomas (62 invasive and 18 in situ) and 159 randomly selected pigmented nonmelanomas. We photographed these lesions in vivo using immersion oil and a Heine Dermaphot camera (Heine Ltd, Herrsching, Germany). We then scored the lesions in a ''blinded'' fashion for the presence of pseudopods based on strictly defined morphologic criteria. Results: We defined the morphologic criteria of the pseudopod. As defined, the pseudopod retained a 97% specificity and 23% sensitivity for invasive melanoma. No difference was seen in the mean Breslow thickness between melanomas with and without pseudopods. None of the in situ melanomas were observed to have pseudopods. Conclusion: We suggest morphologic criteria for a highly specific in vivo cutaneous surface microscopic feature of invasive melanoma, the pseudopod. (Arch Dermatol. 1995;131:436-440)
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paper_title: Statistical Techniques Applied to the Automatic Diagnosis of Dermoscopic Images

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An image based system implementing a well-known diagnostic method is disclosed for the automatic detection of melanomas as support to clinicians. The software procedure is able to recognize automatically the skin lesion within the digital image, measure morphological and chromatic feature, carry out a suitable classification for the detection of structural dermoscopic criteria provided by the 7-Point Check. Statistical techniques are introduced and adopted for border detection, feature extraction and classification as well as the resulting diagnostic score are described with reference to a large image set of pigmented lesions.
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paper_title: Shape analysis for classification of malignant melanoma.

paper_content:

Melanoma is a malignant skin tumour. If detected and surgically removed early whilst residing in the superficial part of the skin the prognosis is excellent. A seven-point check-list of signs and symptoms has been adopted by the Cancer Research Campaign to help non-dermatologists distinguish benign pigmented lesions from melanoma. The presence of irregularity in shape or outline of a mole is one of these important signs. However, it has recently been shown that not only patients, but also clinicians have difficulty in agreeing upon whether a mole exhibits irregularity or not. Computer image analysis methods have been developed to derive quantitative measures of those shape parameters which dermatologists appear to use in their assessment of shape irregularity. The overall shape of the lesion is expressed by the 'bulkiness' measure. Irregularity of the border is expressed by two fractal dimension measures, one for the 'structural' aspect of the shape and the other for the 'textural' aspect. These measures were used in combination to classify melanomas in the study containing silhouettes of 43 melanomas and 45 benign lesions producing correct classification with 91% sensitivity and 69% specificity. This paper describes computer image analysis aspects of the study.
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paper_title: Lacunarity Analysis: A Promising Method for the Automated Assessment of Melanocytic Naevi and Melanoma

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The early diagnosis of melanoma is critical to achieving reduced mortality and increased survival. Although clinical examination is currently the method of choice for melanocytic lesion assessment, there is a growing interest among clinicians regarding the potential diagnostic utility of computerised image analysis. Recognising that there exist significant shortcomings in currently available algorithms, we are motivated to investigate the utility of lacunarity, a simple statistical measure previously used in geology and other fields for the analysis of fractal and multi-scaled images, in the automated assessment of melanocytic naevi and melanoma. Digitised dermoscopic images of 111 benign melanocytic naevi, 99 dysplastic naevi and 102 melanomas were obtained over the period 2003 to 2008, and subject to lacunarity analysis. We found the lacunarity algorithm could accurately distinguish melanoma from benign melanocytic naevi or non-melanoma without introducing many of the limitations associated with other previously reported diagnostic algorithms. Lacunarity analysis suggests an ordering of irregularity in melanocytic lesions, and we suggest the clinical application of this ordering may have utility in the naked-eye dermoscopic diagnosis of early melanoma.
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paper_title: Automatic Detection of Melanoma Skin Cancer using Texture Analysis

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Melanoma is considered the most dangerous type of skin cancer. Early and accurate diagnosis depends mainly on important issues, accuracy of feature extracted and efficiency of classifier method. This paper presents an automated method for melanoma diagnosis applied on a set of dermoscopy images. Features extracted are based on gray level Co-occurrence matrix (GLCM) and Using Multilayer perceptron classifier (MLP) to classify between Melanocytic Nevi and Malignant melanoma. MLP classifier was proposed with two different techniques in training and testing process: Automatic MLP and Traditional MLP. Results indicated that texture analysis is a useful method for discrimination of melanocytic skin tumors with high accuracy. The first technique, Automatic iteration counter is faster but the second one, Default iteration counter gives a better accuracy, which is 100 % for the training set and 92 % for the test set.
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paper_title: Dermoscopy: alternative melanocytic algorithms-the ABCD rule of dermatoscopy, Menzies scoring method, and 7-point checklist.

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In the year 2000, the Second Consensus Meeting on Dermoscopy was held over the Internet. Experts from around the world were asked to evaluate melanocytic, nonmelanocytic, benign, and malignant skin lesions using pattern analysis, the ABCD rule of dermatoscopy, Menzies scoring method, and the 7-point checklist. One hundred twenty-eight digital dermoscopic images were randomly subdivided into a training set of 20 images in which the unifying concepts of pattern analysis, the ABCD rule of dermatoscopy, Menzies scoring method, and the 7-point check list were presented. Those melanocytic algorithms were then used to evaluate another 108 cases. The results of the Second Consensus Meeting were presented at the First World Congress of Dermoscopy held in Rome, Italy, March 2001 and in an atlas that outlined the results and illustrated all of the cases.1,2 One of the main conclusions from the Second Consensus Meeting was that the four melanocytic algorithms studied were all valid ways to evaluate pigmented skin lesions with dermoscopy. This article will present the unifying concepts of the ABCD rule of dermatoscopy, Menzies scoring method, and the 7-point checklist. Other less-well-known and studied dermoscopic algorithms will not be presented.3–5
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paper_title: FEATURE EXTRACTION BASED ON DECISION BOUNDARIES

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A novel approach to feature extraction for classification based directly on the decision boundaries is proposed. It is shown how discriminantly redundant features and discriminantly informative features are related to decision boundaries. A procedure to extract discriminantly informative features based on a decision boundary is proposed. The proposed feature extraction algorithm has several desirable properties: (1) it predicts the minimum number of features necessary to achieve the same classification accuracy as in the original space for a given pattern recognition problem; and (2) it finds the necessary feature vectors. The proposed algorithm does not deteriorate under the circumstances of equal class means or equal class covariances as some previous algorithms do. Experiments show that the performance of the proposed algorithm compares favorably with those of previous algorithms. >
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paper_title: Texture Features in the Classification of Melanocytic Lesions

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The use of different texture features for the classification of melanocytic lesions is studied in an attempt to develop a computerized method for the early detection of melanoma. The computer laboratory at the University of Oulu has a strong tradition in applications of computer vision to visual inspection for industrial quality control, and some of the methods learnt in these applications are being transferred and experimented with in this medical context. This include the utilization of texture distributions for classification purposes.
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paper_title: Reliability of computer image analysis of pigmented skin lesions of Australian adolescents.

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BACKGROUND ::: The diagnosis of melanomas at an early stage is associated with improved survival, so the recognition of changes in pigmented skin lesions over time is important. We have developed a computer imaging system with the aim of assisting clinicians in differentiating early melanomas from benign pigmented skin lesions. The objective of this study was to investigate the system's reliability over time in measuring diagnostic characteristics of pigmented skin lesions, including their color, size, shape, and distinctness of boundary. ::: ::: ::: METHODS ::: We captured video images of 5 lesions, all larger than 2 mm in greatest dimension, on each of 66 Australian adolescents on 2 occasions approximately 1 month apart. Features extracted by computer image analysis included area, perimeter, and regularity of outline of the lesions, the mean and standard deviation of reflectance at red, green, and blue wavelengths, and the mean and standard deviation of the gradients of red, green, and blue reflectance at the lesion boundary. ::: ::: ::: RESULTS ::: All measurements showed moderate to high reliability (intraclass correlation coefficients 0.66-0.94), except for the standard deviations of the color gradients, whose reliability improved to moderate levels (0.68-0.71) when the mean of 5 lesions was considered. For most outcomes, reasonable within subject reliability was achieved when five lesions per subject were measured. ::: ::: ::: CONCLUSIONS ::: These results, in combination with previous work demonstrating the reasonable ability of this computer imaging system to discriminate between malignant melanomas and other pigmented lesions, indicates that the system has the potential to become a useful tool for clinicians in following people with pigmented lesions over time to detect early malignant changes.
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paper_title: Classification of melanoma using tree structured wavelet transforms.

paper_content:

This paper presents a wavelet transform based tree structure model developed and evaluated for the classification of skin lesion images into melanoma and dysplastic nevus. The tree structure model utilizes a semantic representation of the spatial-frequency information contained in the skin lesion images including textural information. Results show that the presented method is effective in discriminating melanoma from dysplastic nevus. The results are also compared with those obtained using another method of developing tree structures utilizing the maximum channel energy criteria with a fixed energy ratio threshold.
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paper_title: Learning features for streak detection in dermoscopic color images using localized radial flux of principal intensity curvature

paper_content:

Malignant melanoma (MM) is one of the most frequent types of cancers among the world's white population. Dermoscopy is a noninvasive method for early recognition of MM by which physicians assess the skin lesion according to the skin subsurface features. The presence or absence of “streaks” is one of the most important dermoscopic criteria for the diagnosis of MM. We develop a machine-learning approach for identifying streaks in dermoscopic images using a novel melanoma feature, which captures the quaternion tubularness in the color dermoscopic images, is sensitive to the radial features of streaks, and is localized to different lesion bands (e.g. the most periphery band where streaks commonly appear). We validate the classification accuracy of SVM using our novel features on 99 dermoscopic images (including images in the absence, presence of regular, and presence of irregular streaks). Compared to state-of-the-art, we obtain improved classification results by up to 9% in terms of area under ROC curves.
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paper_title: Automatic detection of irregular borders in melanoma and other skin tumors

paper_content:

Abstract An irregularity index previously developed is applied to detect irregular borders automatically in skin tumor images, particularly malignant melanoma. The irregularity index is used to classify various tumor borders as irregular or regular. This procedure processes tumor images with borders automatically determined by a radial search algorithm previously described. Potential use of this algorithm in an in vivo skin cancer detection system and errors expected in the use of the algorithm are discussed.
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paper_title: A methodological approach to the classification of dermoscopy images

paper_content:

In this paper a methodological approach to the classification of pigmented skin lesions in dermoscopy images is presented. First, automatic border detection is performed to separate the lesion from the background skin. Shape features are then extracted from this border. For the extraction of color and texture related features, the image is divided into various clinically significant regions using the Euclidean distance transform. This feature data is fed into an optimization framework, which ranks the features using various feature selection algorithms and determines the optimal feature subset size according to the area under the ROC curve measure obtained from support vector machine classification. The issue of class imbalance is addressed using various sampling strategies, and the classifier generalization error is estimated using Monte Carlo cross validation. Experiments on a set of 564 images yielded a specificity of 92.34% and a sensitivity of 93.33%.
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paper_title: Neural network diagnosis of malignant melanoma from color images

paper_content:

Malignant melanoma is the deadliest form of all skin cancers. Approximately 32,000 new cases of malignant melanoma were diagnosed in 1991 in the United States, with approximately 80% of patients expected to survive five years [1]. Fortunately, if detected early, even malignant melanoma may be treated successfully. Thus, in recent years, there has been rising interest in the automated detection and diagnosis of skin cancer, particularly malignant melanoma [2]. In this paper, we present a novel neural network approach for the automated separation of melanoma from three benign categories of tumors which exhibit melanoma-like characteristics. Our approach uses discriminant features, based on tumor shape and relative tumor color, that are supplied to an artificial neural network for classification of tumor images as malignant or benign. With this approach, for reasonably balanced training/testing sets, we are able to obtain above 80% correct classification of the malignant and benign tumors on real skin tumor images.
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paper_title: Pattern Classification of Nevus with Texture Analysis

paper_content:

Melanoma is a skin tumor with high malignancy. Cases of melanoma have recently been reported from many hospitals around the world. Since melanoma is analogous with benign nevus, discriminating between melanoma and benign nevus is difficult for nonspecialists. A screening system for melanoma and benign nevus is desired. Melanoma is mainly diagnosed by features of specific shape and color. However, texture patterns within the tumor region can be diagnostically important. The purpose of this research was to classify patterns on the tumor surface. Digital images of a tumor were classified into 3 patterns by texture analysis: homogeneous pattern; globular pattern; and reticular pattern. The tumor part in the image was specified first, and the specified tumor part was divided into sub-images. Texture features of each sub-image were then calculated. Patterns were classified by discriminant analysis based on the number of texture features. As a result, patterns could be classified correctly into the three categories at a ratio of 94%. Copyright © 2007 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
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paper_title: The Clinical Features of Malignant Melanoma

paper_content:

Currently the most important means of reducing the number of deaths from malignant melanoma is by detecting the lesion and removing it at an early and thus curable stage. With this in mind, this article is devoted to a presentation of the clinical features of early, small, macular malignant melanoma in situ. The evolution of this macular lesion to more advanced malignant melanoma and simulators of this cutaneous malignancy are also discussed.
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paper_title: The automatic identification of melanoma by wavelet and curvelet analysis: Study based on neural network classification

paper_content:

This paper proposes an automatic skin cancer (melanoma) classification system. The input for the prosed system is a collected data images, it followed by different image processing procedures to enhance the image properties. Two segmentation methods used to identify the normal skin cancer from malignant skin and to extract the useful information from these images that passed to the classifier for training and testing. The features used for classification is the coefficients created by Wavelet decompositions and simple wrapper curvelet. Curvelet is suitable for the image that contains oriented texture and cartoon edges. Recognition accuracy of the three layers back-propagation neural network classifier with wavelet is 51.1% and with curvelet is 75. 6% in digital images database.
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paper_title: Classification of melanoma and Clark nevus skin lesions based on medical image processing techniques

paper_content:

According to the statistics, melanoma accounts for just 11 % of all types of skin cancer, it is responsible for most of the deaths. Melanoma is visually difficult for clinicians to differentiate from Clark nevus lesions which are benign. The application of image processing techniques to these lesions may be useful as an educational tool for teaching physicians to differentiate lesions, as well as for contributing information about the essential optical characteristics for identifying them. This research tried find the most effective features to extract from melanoma, melanoma in situ and Clark nevus lesions, and to find the most effective pattern-classification criteria and algorithms for differentiating those lesions. The color differences between images that occur because of differences in ambient lighting during the photographic process were minimized by the use of dermoscopic images. Differences in skin color between patients was minimized by using normalizing them by means of converting them to relative-color images, and differences in ambient lighting during photography, and the photographic and digitization processes, original color images were normalized by converting them into relative-color images. Tumors in the relative-color images were then segmented out and morphologically filtered. The filtered-tumor features were then extracted and various pattern-classification schemes were applied. Experimentation resulted in four useful pattern classification methods, the best of which was a classification rate of 100% for melanoma and melanoma in situ (grouped) and 65% for Clark nevus.
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paper_title: Different Learning Paradigms for the Classification of Melanoid Skin Lesions Using Wavelets

paper_content:

We use the wavelet-based decomposition to generate the multiresolution representation of dermatoscopic images of potentially malignant pigmented lesions. Three different machine learning methods are experimentally applied, namely neural networks, support vector machines, and Attributional Calculus. The obtained results confirm that neighborhood properties of pixels in dermatoscopic images are a sensitive probe of the melanoma progression and together with the selected machine learning methods may be an important diagnostic tool.
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paper_title: Computer image analysis in the diagnosis of melanoma.

paper_content:

BACKGROUND ::: It is often difficult to differentiate early melanoma from benign pigmented lesions of similar clinical appearance. ::: ::: ::: OBJECTIVE ::: Our purpose was to develop a computer image analysis system that has the potential for use as an adjunct to the clinical distinction of melanoma from less serious pigmented lesions. ::: ::: ::: METHODS ::: The system, consisting of a hand-held device incorporating a color video camera and color frame grabber mounted in a microcomputer, was used in a pigmented lesion clinic. Analysis software extracted features relevant to the size, color, shape, and boundary of each lesion, and these features were correlated with clinical and histologic characteristics on which standard diagnoses of skin tumors are based. For discriminant analysis based on image analysis measurements, equal prior probabilities were assigned to two specified diagnostic groups, namely melanoma and "other pigmented lesions," most of which were melanocytic nevi. ::: ::: ::: RESULTS ::: In a 20-month period, video images of 164 unselected pigmented lesions for which complete diagnostic data were available were successfully captured using the camera. Sixteen of 18 melanomas, and 89% of pigmented lesions overall, were correctly classified by the image analysis system, compared with 83% based on clinical gradings of lesion characteristics. ::: ::: ::: CONCLUSION ::: Computer image analysis has the potential to provide a valuable diagnostic aid that could enable clinicians to make highly sensitive and specific diagnoses of early, curable melanoma.
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paper_title: Digital dermoscopy analysis for the differentiation of atypical nevi and early melanoma: a new quantitative semiology.

paper_content:

OBJECTIVES ::: To use a digital dermoscopy analyzer with a series of "borderline" pigmentary skin lesions (ie, clinically atypical nevi and early melanoma) to find correlation between the studied variables and to determine their discriminating power with respect to histological diagnosis. ::: ::: ::: DESIGN ::: A total of 147 pigmentary skin lesions were histologically examined by 3 experienced dermatopathologists and identified as nevi (n = 90) and melanomas (n = 57). The system evaluated 36 variables to be studied as possible discriminant variables, grouped into 4 categories: geometries, colors, textures, and islands of color. ::: ::: ::: SETTING ::: University medical department. ::: ::: ::: PATIENTS ::: A sample of patients with excised pigmentary skin lesions (nevi and melanomas). ::: ::: ::: MAIN OUTCOME MEASURES ::: Sensitivity, specificity, and accuracy of the model for evaluating "borderline" pigmentary skin lesions. ::: ::: ::: RESULTS ::: After multivariate stepwise discriminant analysis, only 13 variables were selected to compute the canonical discriminant function. ::: ::: ::: CONCLUSION ::: The present method made it possible to determine which objective variables are important for distinguishing atypical benign pigmentary skin lesions and early melanoma.
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paper_title: Computer screening for early detection of melanoma--is there a future?

paper_content:

Summary ::: ::: Computer image analysis in the study of pigmented lesions is critically examined and discussed in the light of the current published data. The potential for objective analysis by computers as a possible screening aid for the inexperienced clinician is discussed. The future for this technology is exciting if handled with care.
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paper_title: A Direct Method of Nonparametric Measurement Selection

paper_content:

A direct method of measurement selection is proposed to determine the best subset of d measurements out of a set of D total measurements. The measurement subset evaluation procedure directly employs a nonparametric estimate of the probability of error given a finite design sample set. A suboptimum measurement subset search procedure is employed to reduce the number of subsets to be evaluated. Teh primary advantage of the approach is the direct but nonparametric evaluation of measurement subsets, for the M class problem.
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paper_title: A decision support system for the diagnosis of melanoma: A comparative approach

paper_content:

Melanoma is the most deathful of all skin cancers and the number of cases grows every year. The extirpation in early phases implies a high degree of survival so it is fundamental to diagnose it as soon as possible. In this paper we present a clinical decision support system for melanoma diagnosis using as input an image set of the skin lesion to be diagnosed. The system analyses the image sequence to extract the affected area, determinates the characteristics which indicate the degree of damage and, according to them, it makes a decision. Several methods of classification are proposed: a multilayered perceptron, a Bayesian classifier and the algorithm of the K nearest neighbours. These methods work independently and also in combination making a collaborative decision support system. The classification rates obtained are around 87%.
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paper_title: Feature selection in melanoma recognition

paper_content:

Melanoma, one of the most aggressive types of cancer, can be healed, if recognized in early stages. In order to automate the early recognition of skin cancer; a system that analyses digital epiluminescence microscopic images is used. After segmentation, 33 features representing shape and radiometric properties are calculated. In the paper the quality of the features is evaluated by applying several feature selection methods. The results show that with each selection method the feature set can be reduced to dimension four with nearly no loss of information. Results with classification rates of up to 75% are achieved and relations between selected features and medical criteria are observed.
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paper_title: Automated melanoma recognition

paper_content:

A system for the computerized analysis of images obtained from epiluminescence microscopy (ELM) has been developed to enhance the early recognition of malignant melanoma. As an initial step, the binary mask of the skin lesion is determined by several basic segmentation algorithms together with a fusion strategy. A set of features containing shape and radiometric features as well as local and global parameters is calculated to describe the malignancy of a lesion. Significant features are then selected from this set by application of statistical feature subset selection methods. The final kNN classification delivers a sensitivity of 87% with a specificity of 92%.
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paper_title: Improved forward floating selection algorithm for feature subset selection

paper_content:

We present results on two new databases for a new improved forward floating selection (IFFS) algorithm for selecting a subset of features. The algorithm is an improvement upon the state-of-the-art sequential forward floating selection algorithm that includes a new search strategy to check whether removing any feature in the selected feature set and adding a new one at each sequential step can improve the resultant feature set. We find that this method provides the optimal or quasi-optimal (close to optimal) solutions for many selected subsets and requires significantly less computational load than an exhaustive search optimal feature selection algorithm. Our experimental results for two different databases demonstrate that our algorithm consistently selects better subsets than other quasi-optimal feature selection algorithms do.
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paper_title: Floating search methods in feature selection

paper_content:

Sequential search methods characterized by a dynamically changing number of features included or eliminated at each step, henceforth "floating" methods, are presented. They are shown to give very good results and to be computationally more effective than the branch and bound method.
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paper_title: Correlation-based Feature Selection for Machine Learning

paper_content:

A central problem in machine learning is identifying a representative set of features from which to construct a classification model for a particular task. This thesis addresses the problem of feature selection for machine learning through a correlation based approach. The central hypothesis is that good feature sets contain features that are highly correlated with the class, yet uncorrelated with each other. A feature evaluation formula, based on ideas from test theory, provides an operational definition of this hypothesis. CFS (Correlation based Feature Selection) is an algorithm that couples this evaluation formula with an appropriate correlation measure and a heuristic search strategy. CFS was evaluated by experiments on artificial and natural datasets. Three machine learning algorithms were used: C4.5 (a decision tree learner), IB1 (an instance based learner), and naive Bayes. Experiments on artificial datasets showed that CFS quickly identifies and screens irrelevant, redundant, and noisy features, and identifies relevant features as long as their relevance does not strongly depend on other features. On natural domains, CFS typically eliminated well over half the features. In most cases, classification accuracy using the reduced feature set equaled or bettered accuracy using the complete feature set. Feature selection degraded machine learning performance in cases where some features were eliminated which were highly predictive of very small areas of the instance space. Further experiments compared CFS with a wrapper—a well known approach to feature selection that employs the target learning algorithm to evaluate feature sets. In many cases CFS gave comparable results to the wrapper, and in general, outperformed the wrapper on small datasets. CFS executes many times faster than the wrapper, which allows it to scale to larger datasets. Two methods of extending CFS to handle feature interaction are presented and experimentally evaluated. The first considers pairs of features and the second incorporates iii feature weights calculated by the RELIEF algorithm. Experiments on artificial domains showed that both methods were able to identify interacting features. On natural domains, the pairwise method gave more reliable results than using weights provided by RELIEF.
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paper_title: Automatic Detection of Melanoma Skin Cancer using Texture Analysis

paper_content:

Melanoma is considered the most dangerous type of skin cancer. Early and accurate diagnosis depends mainly on important issues, accuracy of feature extracted and efficiency of classifier method. This paper presents an automated method for melanoma diagnosis applied on a set of dermoscopy images. Features extracted are based on gray level Co-occurrence matrix (GLCM) and Using Multilayer perceptron classifier (MLP) to classify between Melanocytic Nevi and Malignant melanoma. MLP classifier was proposed with two different techniques in training and testing process: Automatic MLP and Traditional MLP. Results indicated that texture analysis is a useful method for discrimination of melanocytic skin tumors with high accuracy. The first technique, Automatic iteration counter is faster but the second one, Default iteration counter gives a better accuracy, which is 100 % for the training set and 92 % for the test set.
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paper_title: An Accelerated System for Melanoma Diagnosis Based on Subset Feature Selection

paper_content:

In this paper we present an optimised system for diagnosing skin lesions based on digitized dermatoscopic color images. This system is composed mainly of three levels : lesion detection, lesion description (features selection) and decision. The preprocessing of the lesion image is used to remove the undesired objects from the original image and the extraction of the lesion is done by separating it from the healthy surrounding skin. The classification scheme is based on the extraction of a set of features modeling clinical signs of malignancy. The produced vector of features scores is used as input to a multi-layer perceptron classifier in order to assign the lesion to the class of benign lesions or to the one of malignant melanomas. We focus particularly in this paper on the critical step of the features selection allowing to select a reasonable reduced number of useful features while removing redundant information and approximating the properties of melanoma recognition. This permits to reduce the dimension of the lesion's vector, and consequently the calculation time, without a significant loss of information. In fact, a large set of features was investigated by the application of relevant features selection techniques. Then, the number of features for classification was optimized and only five well-selected features were used to cover the discriminatory information about lesions malignancy. With this approach, for reasonably balanced training/test sets, we record a good classification rate of 77.7% in a very promising cpu time.
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paper_title: Using mutual information for selecting features in supervised neural net learning

paper_content:

This paper investigates the application of the mutual information criterion to evaluate a set of candidate features and to select an informative subset to be used as input data for a neural network classifier. Because the mutual information measures arbitrary dependencies between random variables, it is suitable for assessing the "information content" of features in complex classification tasks, where methods bases on linear relations (like the correlation) are prone to mistakes. The fact that the mutual information is independent of the coordinates chosen permits a robust estimation. Nonetheless, the use of the mutual information for tasks characterized by high input dimensionality requires suitable approximations because of the prohibitive demands on computation and samples. An algorithm is proposed that is based on a "greedy" selection of the features and that takes both the mutual information with respect to the output class and with respect to the already-selected features into account. Finally the results of a series of experiments are discussed. >
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paper_title: A methodological approach to the classification of dermoscopy images

paper_content:

In this paper a methodological approach to the classification of pigmented skin lesions in dermoscopy images is presented. First, automatic border detection is performed to separate the lesion from the background skin. Shape features are then extracted from this border. For the extraction of color and texture related features, the image is divided into various clinically significant regions using the Euclidean distance transform. This feature data is fed into an optimization framework, which ranks the features using various feature selection algorithms and determines the optimal feature subset size according to the area under the ROC curve measure obtained from support vector machine classification. The issue of class imbalance is addressed using various sampling strategies, and the classifier generalization error is estimated using Monte Carlo cross validation. Experiments on a set of 564 images yielded a specificity of 92.34% and a sensitivity of 93.33%.
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paper_title: Regression modelling strategies for improved prognostic prediction.

paper_content:

Regression models such as the Cox proportional hazards model have had increasing use in modelling and estimating the prognosis of patients with a variety of diseases. Many applications involve a large number of variables to be modelled using a relatively small patient sample. Problems of overfitting and of identifying important covariates are exacerbated in analysing prognosis because the accuracy of a model is more a function of the number of events than of the sample size. We used a general index of predictive discrimination to measure the ability of a model developed on training samples of varying sizes to predict survival in an independent test sample of patients suspected of having coronary artery disease. We compared three methods of model fitting: (1) standard 'step-up' variable selection, (2) incomplete principal components regression, and (3) Cox model regression after developing clinical indices from variable clusters. We found regression using principal components to offer superior predictions in the test sample, whereas regression using indices offers easily interpretable models nearly as good as the principal components models. Standard variable selection has a number of deficiencies.
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paper_title: Feature selection for optimized skin tumor recognition using genetic algorithms

paper_content:

Abstract In this paper, a new approach to computer supported diagnosis of skin tumors in dermatology is presented. High resolution skin surface profiles are analyzed to recognize malignant melanomas and nevocytic nevi (moles), automatically. In the first step, several types of features are extracted by 2D image analysis methods characterizing the structure of skin surface profiles: texture features based on cooccurrence matrices, Fourier features and fractal features. Then, feature selection algorithms are applied to determine suitable feature subsets for the recognition process. Feature selection is described as an optimization problem and several approaches including heuristic strategies, greedy and genetic algorithms are compared. As quality measure for feature subsets, the classification rate of the nearest neighbor classifier computed with the leaving-one-out method is used. Genetic algorithms show the best results. Finally, neural networks with error back-propagation as learning paradigm are trained using the selected feature sets. Different network topologies, learning parameters and pruning algorithms are investigated to optimize the classification performance of the neural classifiers. With the optimized recognition system a classification performance of 97.7% is achieved.
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paper_title: Feature Extraction, Foundations and Applications

paper_content:

An Introduction to Feature Extraction.- An Introduction to Feature Extraction.- Feature Extraction Fundamentals.- Learning Machines.- Assessment Methods.- Filter Methods.- Search Strategies.- Embedded Methods.- Information-Theoretic Methods.- Ensemble Learning.- Fuzzy Neural Networks.- Feature Selection Challenge.- Design and Analysis of the NIPS2003 Challenge.- High Dimensional Classification with Bayesian Neural Networks and Dirichlet Diffusion Trees.- Ensembles of Regularized Least Squares Classifiers for High-Dimensional Problems.- Combining SVMs with Various Feature Selection Strategies.- Feature Selection with Transductive Support Vector Machines.- Variable Selection using Correlation and Single Variable Classifier Methods: Applications.- Tree-Based Ensembles with Dynamic Soft Feature Selection.- Sparse, Flexible and Efficient Modeling using L 1 Regularization.- Margin Based Feature Selection and Infogain with Standard Classifiers.- Bayesian Support Vector Machines for Feature Ranking and Selection.- Nonlinear Feature Selection with the Potential Support Vector Machine.- Combining a Filter Method with SVMs.- Feature Selection via Sensitivity Analysis with Direct Kernel PLS.- Information Gain, Correlation and Support Vector Machines.- Mining for Complex Models Comprising Feature Selection and Classification.- Combining Information-Based Supervised and Unsupervised Feature Selection.- An Enhanced Selective Naive Bayes Method with Optimal Discretization.- An Input Variable Importance Definition based on Empirical Data Probability Distribution.- New Perspectives in Feature Extraction.- Spectral Dimensionality Reduction.- Constructing Orthogonal Latent Features for Arbitrary Loss.- Large Margin Principles for Feature Selection.- Feature Extraction for Classification of Proteomic Mass Spectra: A Comparative Study.- Sequence Motifs: Highly Predictive Features of Protein Function.
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paper_title: The Use of Resampling Methods to Simplify Regression Models in Medical Statistics

paper_content:

Summary. The number of variables in a regression model is often too large and a more parsimonious model may be preferred. Selection strategies (e.g. all-subset selection with various penalties for model complexity, or stepwise procedures) are widely used, but there are few analytical results about their properties. The problems of replication stability, model complexity, selection bias and an overoptimistic estimate of the predictive value of a model are discussed together with several proposals based on resampling methods. The methods are applied to data from a case-control study on atopic dermatitis and a clinical trial to compare two chemotherapy regimes by using a logistic regression and a Cox model. A recent proposal to use shrinkage factors to reduce the bias of parameter estimates caused by model building is extended to parameterwise shrinkage factors and is discussed as a further possibility to illustrate problems of models which are too complex. The results from the resampling approaches favour greater simplicity of the final regression model.
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paper_title: Computer image analysis in the diagnosis of melanoma.

paper_content:

BACKGROUND ::: It is often difficult to differentiate early melanoma from benign pigmented lesions of similar clinical appearance. ::: ::: ::: OBJECTIVE ::: Our purpose was to develop a computer image analysis system that has the potential for use as an adjunct to the clinical distinction of melanoma from less serious pigmented lesions. ::: ::: ::: METHODS ::: The system, consisting of a hand-held device incorporating a color video camera and color frame grabber mounted in a microcomputer, was used in a pigmented lesion clinic. Analysis software extracted features relevant to the size, color, shape, and boundary of each lesion, and these features were correlated with clinical and histologic characteristics on which standard diagnoses of skin tumors are based. For discriminant analysis based on image analysis measurements, equal prior probabilities were assigned to two specified diagnostic groups, namely melanoma and "other pigmented lesions," most of which were melanocytic nevi. ::: ::: ::: RESULTS ::: In a 20-month period, video images of 164 unselected pigmented lesions for which complete diagnostic data were available were successfully captured using the camera. Sixteen of 18 melanomas, and 89% of pigmented lesions overall, were correctly classified by the image analysis system, compared with 83% based on clinical gradings of lesion characteristics. ::: ::: ::: CONCLUSION ::: Computer image analysis has the potential to provide a valuable diagnostic aid that could enable clinicians to make highly sensitive and specific diagnoses of early, curable melanoma.
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paper_title: Nearest Neighbor Pattern Classification

paper_content:

The nearest neighbor decision rule assigns to an unclassified sample point the classification of the nearest of a set of previously classified points. This rule is independent of the underlying joint distribution on the sample points and their classifications, and hence the probability of error R of such a rule must be at least as great as the Bayes probability of error R^{\ast} --the minimum probability of error over all decision rules taking underlying probability structure into account. However, in a large sample analysis, we will show in the M -category case that R^{\ast} \leq R \leq R^{\ast}(2 --MR^{\ast}/(M-1)) , where these bounds are the tightest possible, for all suitably smooth underlying distributions. Thus for any number of categories, the probability of error of the nearest neighbor rule is bounded above by twice the Bayes probability of error. In this sense, it may be said that half the classification information in an infinite sample set is contained in the nearest neighbor.
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paper_title: A Comparison of Machine Learning Methods for the Diagnosis of Pigmented Skin Lesions

paper_content:

We analyze the discriminatory power of k-nearest neighbors, logistic regression, artificial neural networks (ANNs), decision tress, and support vector machines (SVMs) on the task of classifying pigmented skin lesions as common nevi, dysplastic nevi, or melanoma. Three different classification tasks were used as benchmarks: the dichotomous problem of distinguishing common nevi from dysplastic nevi and melanoma, the dichotomous problem of distinguishing melanoma from common and dysplastic nevi, and the trichotomous problem of correctly distinguishing all three classes. Using ROC analysis to measure the discriminatory power of the methods shows that excellent results for specific classification problems in the domain of pigmented skin lesions can be achieved with machine-learning methods. On both dichotomous and trichotomous tasks, logistic regression, ANNs, and SVMs performed on about the same level, with k-nearest neighbors and decision trees performing worse.
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paper_title: Detection of suspected malignant patterns in three-dimensional magnetic resonance breast images

paper_content:

In this article, a Boolean Neural Network (BNN) is used for the detection of suspected malignant regions in 3D breast magnetic resonance (MR) images. The BNN is characterized by fast learning and classification, guaranteed convergence, and simple, integer weight calculations. The BNN learning algorithm is incremental, which allows the addition and deletion of training patterns without unclearning those already learned. The incremental learning algorithm automatically reduces the training set and trains the network only with those examples estimated to be useful. The architecture is suitable for parallel hardware implementation using available Very Large Scale Integration (VLSI) technology. The BNN was trained by using a set of malignant, benign, and false-positive patterns, extracted by experts, from selected MR studies, by using an incremental learning algorithm. After training, the network was tested by means of a consistency checking test, cross validation techniques, and patterns from actual MR breast images. During the consistency test, the BNN was tested by using the same patterns used for training. The BNN classification accuracy in this case was 99.75%, proving the ability of the BNN to select useful patterns from the training set. Then, a leave one out cross-validation (LOOCV) test was done by using patterns from the training set and the classification accuracy was 90%. Next, an extended training set was created by shifting the original patterns in different directions. A cross-validation test was then performed by dividing the set of patterns into a training and a test set. Classification accuracy was compared to the nearest neighbor classifier. Results showed that the BNN achieved an average of 77% classification accuracy while requiring only 34% of the original training set. On the other hand, the nearest neighbor classifier achieved an accuracy of 57.9% while retaining the whole training set. Another test using actual MR slices different from the training set was done and results compared favorably to a radiologist’s findings. Test results show the BNN’s capability to detect suspected malignant regions in 3D MR images of the breast. The proposed BNN architecture can save the radiologist a great deal of time browsing MR slices searching for suspected malignancies.
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paper_title: Feature selection for optimized skin tumor recognition using genetic algorithms

paper_content:

Abstract In this paper, a new approach to computer supported diagnosis of skin tumors in dermatology is presented. High resolution skin surface profiles are analyzed to recognize malignant melanomas and nevocytic nevi (moles), automatically. In the first step, several types of features are extracted by 2D image analysis methods characterizing the structure of skin surface profiles: texture features based on cooccurrence matrices, Fourier features and fractal features. Then, feature selection algorithms are applied to determine suitable feature subsets for the recognition process. Feature selection is described as an optimization problem and several approaches including heuristic strategies, greedy and genetic algorithms are compared. As quality measure for feature subsets, the classification rate of the nearest neighbor classifier computed with the leaving-one-out method is used. Genetic algorithms show the best results. Finally, neural networks with error back-propagation as learning paradigm are trained using the selected feature sets. Different network topologies, learning parameters and pruning algorithms are investigated to optimize the classification performance of the neural classifiers. With the optimized recognition system a classification performance of 97.7% is achieved.
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paper_title: C4.5: Programs for Machine Learning

paper_content:

From the Publisher: ::: Classifier systems play a major role in machine learning and knowledge-based systems, and Ross Quinlan's work on ID3 and C4.5 is widely acknowledged to have made some of the most significant contributions to their development. This book is a complete guide to the C4.5 system as implemented in C for the UNIX environment. It contains a comprehensive guide to the system's use , the source code (about 8,800 lines), and implementation notes. The source code and sample datasets are also available on a 3.5-inch floppy diskette for a Sun workstation. ::: ::: C4.5 starts with large sets of cases belonging to known classes. The cases, described by any mixture of nominal and numeric properties, are scrutinized for patterns that allow the classes to be reliably discriminated. These patterns are then expressed as models, in the form of decision trees or sets of if-then rules, that can be used to classify new cases, with emphasis on making the models understandable as well as accurate. The system has been applied successfully to tasks involving tens of thousands of cases described by hundreds of properties. The book starts from simple core learning methods and shows how they can be elaborated and extended to deal with typical problems such as missing data and over hitting. Advantages and disadvantages of the C4.5 approach are discussed and illustrated with several case studies. ::: ::: This book and software should be of interest to developers of classification-based intelligent systems and to students in machine learning and expert systems courses.
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paper_title: Large Datasets Lead to Overly Complex Models: An Explanation and a Solution

paper_content:

This paper explores unexpected results that lie at the intersection of two common themes in the KDD community: large datasets and the goal of building compact models. Experiments with many different datasets and several model construction algorithms (including tree learning algorithms such as c4.5 with three different pruning methods, and rule learning algorithms such as C4.5 RULES and RIPPER) show that increasing the amount of data used to build a model often results in a linear increase in model size, even when that additional complexity results in no significant increase in model accuracy. Despite the promise of better parameter estimation held out by large datasets, as a practical matter, models built with large amounts of data are often needlessly complex and cumbersome. In the case of decision trees, the cause of this pathology is identified as a bias inherent in several common pruning techniques. Pruning errors made low in the tree, where there is insufficient data to make accurate parameter estimates, are propagated and magnified higher in the tree, working against the accurate parameter estimates that are made possible there by abundant data. We propose a general solution to this problem based on a statistical technique known as randomization testing, and empirically evaluate its utility.
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paper_title: Classification and regression trees

paper_content:

Classification and regression trees are machine-learning methods for constructing prediction models from data. The models are obtained by recursively partitioning the data space and fitting a simple prediction model within each partition. As a result, the partitioning can be represented graphically as a decision tree. Classification trees are designed for dependent variables that take a finite number of unordered values, with prediction error measured in terms of misclassification cost. Regression trees are for dependent variables that take continuous or ordered discrete values, with prediction error typically measured by the squared difference between the observed and predicted values. This article gives an introduction to the subject by reviewing some widely available algorithms and comparing their capabilities, strengths, and weakness in two examples. © 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 14-23 DOI: 10.1002/widm.8 ::: ::: This article is categorized under: ::: ::: Technologies > Classification ::: Technologies > Machine Learning ::: Technologies > Prediction ::: Technologies > Statistical Fundamentals
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paper_title: The Limitations of Decision Trees and Automatic Learning in Real World Medical Decision Making

paper_content:

The decision tree approach is one of the most common approaches in automatic learning and decision making. The automatic learning of decision trees and their use usually show very good results in various “ theoretical” environments. But in real life it is often impossible to find the desired number of representative training objects for various reasons. The lack of possibilities to measure attribute values, high cost and complexity of such measurements, and unavailability of all attributes at the same time are the typical representatives. For this reason we decided to use the decision trees not for their primary task—the decision making—but for outlining the most important attributes. This was possible by using a well-known property of the decision trees—their knowledge representation, which can be easily understood by humans. In a delicate field of medical decision making, we cannot allow ourselves to make any inaccurate decisions and the “tips,” provided by the decision trees, can be of a great assistance. Our main interest was to discover a predisposition to two forms of acidosis: the metabolic acidosis and respiratory acidosis, which can both have serious effects on child's health. We decided to construct different decision trees from a set of training objects. Instead of using a test set for evaluation of a decision tree, we asked medical experts to take a closer look at the generated trees. They examined and evaluated the decision trees branch by branch. Their comments show that trees generated from the available training set mainly have surprisingly good branches, but on the other hand, for some, no medical explanation could be found.
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paper_title: Introduction to Neural Networks

paper_content:

Modelled on the cerebral cortex of the human brain, Artificial Neural Networks are presently being applied to solve elusive problems of AI viz, speech and image recognition, computer vision and adaptive control. The neural net topology, interconnections and ‘learning’ algorithms are topics of current research. This paper introduces this exciting and fast expanding field by reviewing some of the important neural networks of proven performance. A detailed explanation and simulation examples are presented for Hopfield net, Hamming net, Bidirectional Associative Memory, Temporal Associative Memory and Kohonen's network. Perceptions and multilayer perceptions are also explained in the light of their class discrimination capabilities. The classical pattern recognition algorithms vis-a-vis their neural network equivalents are also discussed.
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paper_title: A Comparison of Machine Learning Methods for the Diagnosis of Pigmented Skin Lesions

paper_content:

We analyze the discriminatory power of k-nearest neighbors, logistic regression, artificial neural networks (ANNs), decision tress, and support vector machines (SVMs) on the task of classifying pigmented skin lesions as common nevi, dysplastic nevi, or melanoma. Three different classification tasks were used as benchmarks: the dichotomous problem of distinguishing common nevi from dysplastic nevi and melanoma, the dichotomous problem of distinguishing melanoma from common and dysplastic nevi, and the trichotomous problem of correctly distinguishing all three classes. Using ROC analysis to measure the discriminatory power of the methods shows that excellent results for specific classification problems in the domain of pigmented skin lesions can be achieved with machine-learning methods. On both dichotomous and trichotomous tasks, logistic regression, ANNs, and SVMs performed on about the same level, with k-nearest neighbors and decision trees performing worse.
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paper_title: Probabilistic prediction in patient management and clinical trials.

paper_content:

It is argued that the provision of accurate and useful probabilistic assessments of future events should be a fundamental task for biostatisticians collaborating in clinical or experimental medicine, and we explore two aspects of obtaining and evaluating such predictions. When covariate information on patients is available, logistic regression and other multivariate techniques are often used to select prognostic factors and create predictive models. An example shows how the explicit aim of prediction needs to be taken into account in such modelling, and how predictive performance may be assessed by decomposition of a scoring rule. Secondly, results from a program that provides pretrial and interim predictions in clinical trials are displayed, bringing together the use of subjective opinion, Bayesian methodology and techniques for evaluating and criticizing predictions.
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paper_title: Regression modelling strategies for improved prognostic prediction.

paper_content:

Regression models such as the Cox proportional hazards model have had increasing use in modelling and estimating the prognosis of patients with a variety of diseases. Many applications involve a large number of variables to be modelled using a relatively small patient sample. Problems of overfitting and of identifying important covariates are exacerbated in analysing prognosis because the accuracy of a model is more a function of the number of events than of the sample size. We used a general index of predictive discrimination to measure the ability of a model developed on training samples of varying sizes to predict survival in an independent test sample of patients suspected of having coronary artery disease. We compared three methods of model fitting: (1) standard 'step-up' variable selection, (2) incomplete principal components regression, and (3) Cox model regression after developing clinical indices from variable clusters. We found regression using principal components to offer superior predictions in the test sample, whereas regression using indices offers easily interpretable models nearly as good as the principal components models. Standard variable selection has a number of deficiencies.
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paper_title: Dermoscopic image segmentation and classification using machine learning algorithms

paper_content:

Dermoscopy is the method of examining the skin lesions. It is especially used for diagnosing melanoma, a type of skin cancer. Image segmentation and classification are important tools to provide the information about the Dermoscopic images clinically in terms of its size and shape. Many algorithms were developed for classification and segmentation of Dermoscopic images. This work proposes the tasks of extracting, classifying and segmenting the Dermoscopic image using the machine learning algorithms. The algorithms such as Back Propagation network (BPN), Radial Basis Function Network (RBF) and Extreme Learning Machine (ELM) are used. The features are extracted from the Dermoscopic image and these features are used to train the classifiers. The trained networks are used for segmentation. The results are compared with the ground truth images and their performance is evaluated. The results proved that the ELM has better accuracy, faster training period and it provides better segmentation than the BPN and RBF neural networks.
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paper_title: BAYESIAN LEARNING FOR NEURAL NETWORKS

paper_content:

From the Publisher: ::: Artificial "neural networks" are now widely used as flexible models for regression classification applications, but questions remain regarding what these models mean, and how they can safely be used when training data is limited. Bayesian Learning for Neural Networks shows that Bayesian methods allow complex neural network models to be used without fear of the "overfitting" that can occur with traditional neural network learning methods. Insight into the nature of these complex Bayesian models is provided by a theoretical investigation of the priors over functions that underlie them. Use of these models in practice is made possible using Markov chain Monte Carlo techniques. Both the theoretical and computational aspects of this work are of wider statistical interest, as they contribute to a better understanding of how Bayesian methods can be applied to complex problems. Presupposing only the basic knowledge of probability and statistics, this book should be of interest to many researchers in statistics, engineering, and artificial intelligence. Software for Unix systems that implements the methods described is freely available over the Internet.
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paper_title: Fundamentals of neural networks: architectures, algorithms, and applications

paper_content:

1. Introduction. 2. Simple Neural Nets for Pattern Classification. 3. Pattern Association. 4. Neural Networks Based on Competition. 5. Adaptive Resonance Theory. 6. Backpropagation Neural Net. 7. A Sampler of Other Neural Nets. Glossary. References. Index.
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paper_title: Statistical learning theory

paper_content:

A comprehensive look at learning and generalization theory. The statistical theory of learning and generalization concerns the problem of choosing desired functions on the basis of empirical data. Highly applicable to a variety of computer science and robotics fields, this book offers lucid coverage of the theory as a whole. Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.
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paper_title: The Nature of Statistical Learning Theory

paper_content:

Setting of the learning problem consistency of learning processes bounds on the rate of convergence of learning processes controlling the generalization ability of learning processes constructing learning algorithms what is important in learning theory?.
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paper_title: A methodological approach to the classification of dermoscopy images

paper_content:

In this paper a methodological approach to the classification of pigmented skin lesions in dermoscopy images is presented. First, automatic border detection is performed to separate the lesion from the background skin. Shape features are then extracted from this border. For the extraction of color and texture related features, the image is divided into various clinically significant regions using the Euclidean distance transform. This feature data is fed into an optimization framework, which ranks the features using various feature selection algorithms and determines the optimal feature subset size according to the area under the ROC curve measure obtained from support vector machine classification. The issue of class imbalance is addressed using various sampling strategies, and the classifier generalization error is estimated using Monte Carlo cross validation. Experiments on a set of 564 images yielded a specificity of 92.34% and a sensitivity of 93.33%.
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paper_title: Extreme learning machine: Theory and applications

paper_content:

Abstract It is clear that the learning speed of feedforward neural networks is in general far slower than required and it has been a major bottleneck in their applications for past decades. Two key reasons behind may be: (1) the slow gradient-based learning algorithms are extensively used to train neural networks, and (2) all the parameters of the networks are tuned iteratively by using such learning algorithms. Unlike these conventional implementations, this paper proposes a new learning algorithm called e xtreme l earning m achine (ELM) for s ingle-hidden l ayer f eedforward neural n etworks (SLFNs) which randomly chooses hidden nodes and analytically determines the output weights of SLFNs. In theory, this algorithm tends to provide good generalization performance at extremely fast learning speed. The experimental results based on a few artificial and real benchmark function approximation and classification problems including very large complex applications show that the new algorithm can produce good generalization performance in most cases and can learn thousands of times faster than conventional popular learning algorithms for feedforward neural networks. 1
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paper_title: Extreme learning machine: a new learning scheme of feedforward neural networks

paper_content:

It is clear that the learning speed of feedforward neural networks is in general far slower than required and it has been a major bottleneck in their applications for past decades. Two key reasons behind may be: 1) the slow gradient-based learning algorithms are extensively used to train neural networks, and 2) all the parameters of the networks are tuned iteratively by using such learning algorithms. Unlike these traditional implementations, this paper proposes a new learning algorithm called extreme learning machine (ELM) for single-hidden layer feedforward neural networks (SLFNs) which randomly chooses the input weights and analytically determines the output weights of SLFNs. In theory, this algorithm tends to provide the best generalization performance at extremely fast learning speed. The experimental results based on real-world benchmarking function approximation and classification problems including large complex applications show that the new algorithm can produce best generalization performance in some cases and can learn much faster than traditional popular learning algorithms for feedforward neural networks.
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paper_title: Dermoscopic image segmentation and classification using machine learning algorithms

paper_content:

Dermoscopy is the method of examining the skin lesions. It is especially used for diagnosing melanoma, a type of skin cancer. Image segmentation and classification are important tools to provide the information about the Dermoscopic images clinically in terms of its size and shape. Many algorithms were developed for classification and segmentation of Dermoscopic images. This work proposes the tasks of extracting, classifying and segmenting the Dermoscopic image using the machine learning algorithms. The algorithms such as Back Propagation network (BPN), Radial Basis Function Network (RBF) and Extreme Learning Machine (ELM) are used. The features are extracted from the Dermoscopic image and these features are used to train the classifiers. The trained networks are used for segmentation. The results are compared with the ground truth images and their performance is evaluated. The results proved that the ELM has better accuracy, faster training period and it provides better segmentation than the BPN and RBF neural networks.
---
paper_title: Decision Support System for Skin Cancer Diagnosis

paper_content:

Computer aided medical diagnosis is mostly based on very advanced analysis of huge amounts of measured data. Application of computational intelligence methods helps medical doctors to process data faster, and give better, more reliable diagnoses. In dermatology computer analysis is getting more important every year. Differentiation of malignant melanoma images requires very fast image processing and feature extraction/classification algorithms. Applying artificial intelligence algorithms to explore and search large database of dermatoscopic images allows doctors to semantically filter out images with specified characteristics. This paper describes a decision-support system which is based on semantic analysis of melanoma images and further classification of characteristic objects commonly found in pigmented skin lesions. For classification Artificial Neural Networks and Support Vector Machines are used and their performance is evaluated and compared.
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paper_title: A multiple classifier system for early melanoma diagnosis

paper_content:

Melanoma is the most dangerous skin cancer and early diagnosis is the key factor in its successful treatment. Well-trained dermatologists reach a diagnosis via visual inspection, and reach sensitivity and specificity levels of about 80%. Several computerised diagnostic systems were reported in the literature using different classification algorithms. In this paper, we will illustrate a novel approach by which a suitable combination of different classifiers is used in order to improve the diagnostic performances of single classifiers. We used three different kinds of classifiers, namely linear discriminant analysis (LDA), k-nearest neighbour (k-NN) and a decision tree, the inputs of which are 38 geometric and colorimetric features automatically extracted from digital images of skin lesions. Multiple classifiers were generated by combining the diagnostic outputs of single classifiers with appropriate voting schemata. This approach was evaluated on a set of 152 digital skin images. We compared the performances of multiple classifiers (2- and 3-classifier groups) between them and with respect to single ones (1-classifier group). We further compared the classifiers' performances with those of eight dermatologists. Classifiers' performances were measured in terms of distance from the ideal classifier. Compared with 1- and 2-classifier groups, performances of 3-classifier systems were significantly higher (P<0.0005 and P<0.001, respectively). No statistically significant differences were found between the 1- and 2-classifier groups (P=0.352). While the dermatologists group showed a level of performances significantly higher than the 1-classifier systems (P<0.020), no differences were found between the multiple classifier groups and the dermatologists groups, indicating comparable performances. This work suggests that a suitable combination of different kinds of classifiers can improve the performances of an automatic diagnostic system.
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paper_title: Class Imbalances versus Class Overlapping: an Analysis of a Learning System Behavior

paper_content:

Several works point out class imbalance as an obstacle on applying machine learning algorithms to real world domains. However, in some cases, learning algorithms perform well on several imbalanced domains. Thus, it does not seem fair to directly correlate class imbalance to the loss of performance of learning algorithms. In this work, we develop a systematic study aiming to question whether class imbalances are truly to blame for the loss of performance of learning systems or whether the class imbalances are not a problem by themselves. Our experiments suggest that the problem is not directly caused by class imbalances, but is also related to the degree of overlapping among the classes.
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paper_title: Resampling and cross-validation techniques: a tool to reduce bias caused by model building?

paper_content:

The process of model building involved in the analysis of many medical studies may lead to a considerable amount of over-optimism with respect to the predictive ability of the 'final' regression model. In this paper we illustrate this phenomenon in a simple cutpoint model and explore to what extent bias can be reduced by using cross-validation and bootstrap resampling. These computer intensive methods are compared to an ad hoc approach and to a heuristic method. Besides illustrating all proposals with the data from a breast cancer study we perform a simulation study in order to assess the quality of the methods.
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paper_title: Learning from Imbalanced Data Sets: A Comparison of Various Strategies

paper_content:

Although the majority of concept-learning systems previously designed usually assume that their training sets are well-balanced, this assumption is not necessarily correct. Indeed, there exists many domains for which one class is represented by a large number of examples while the other is represented by only a few. The purpose of this paper is 1) to demonstrate experimentally that, at least in the case of connectionist systems, class imbalances hinder the performance of standard classifiers and 2) to compare the performance of several approaches previously proposed to deal with the problem.
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paper_title: AUC: a Better Measure than Accuracy In Comparing Learning Algorithms

paper_content:

Predictive accuracy has been widely used as the main criterion for comparing the predictive ability of classification systems (such as C4.5, neural networks, and Naive Bayes). Most of these classifiers also produce probability estimations of the classification, but they are completely ignored in the accuracy measure. This is often taken for granted because both training and testing sets only provide class labels. In this paper we establish rigourously that, even in this setting, the area under the ROC (Receiver Operating Characteristics) curve, or simply AUC, provides a better measure than accuracy. Our result is quite significant for three reasons. First, we establish, for the first time, rigourous criteria for comparing evaluation measures for learning algorithms. Second, it suggests that AUC should replace accuracy when measuring and comparing classification systems. Third, our result also prompts us to reevaluate many well-established conclusions based on accuracy in machine learning. For example, it is well accepted in the machine learning community that, in terms of predictive accuracy, Naive Bayes and decision trees are very similar. Using AUC, however, we show experimentally that Naive Bayes is significantly better than the decision-tree learning algorithms.
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paper_title: Classification of melanoma and Clark nevus skin lesions based on medical image processing techniques

paper_content:

According to the statistics, melanoma accounts for just 11 % of all types of skin cancer, it is responsible for most of the deaths. Melanoma is visually difficult for clinicians to differentiate from Clark nevus lesions which are benign. The application of image processing techniques to these lesions may be useful as an educational tool for teaching physicians to differentiate lesions, as well as for contributing information about the essential optical characteristics for identifying them. This research tried find the most effective features to extract from melanoma, melanoma in situ and Clark nevus lesions, and to find the most effective pattern-classification criteria and algorithms for differentiating those lesions. The color differences between images that occur because of differences in ambient lighting during the photographic process were minimized by the use of dermoscopic images. Differences in skin color between patients was minimized by using normalizing them by means of converting them to relative-color images, and differences in ambient lighting during photography, and the photographic and digitization processes, original color images were normalized by converting them into relative-color images. Tumors in the relative-color images were then segmented out and morphologically filtered. The filtered-tumor features were then extracted and various pattern-classification schemes were applied. Experimentation resulted in four useful pattern classification methods, the best of which was a classification rate of 100% for melanoma and melanoma in situ (grouped) and 65% for Clark nevus.
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paper_title: Using AUC and accuracy in evaluating learning algorithms

paper_content:

The area under the ROC (receiver operating characteristics) curve, or simply AUC, has been traditionally used in medical diagnosis since the 1970s. It has recently been proposed as an alternative single-number measure for evaluating the predictive ability of learning algorithms. However, no formal arguments were given as to why AUC should be preferred over accuracy. We establish formal criteria for comparing two different measures for learning algorithms and we show theoretically and empirically that AUC is a better measure (defined precisely) than accuracy. We then reevaluate well-established claims in machine learning based on accuracy using AUC and obtain interesting and surprising new results. For example, it has been well-established and accepted that Naive Bayes and decision trees are very similar in predictive accuracy. We show, however, that Naive Bayes is significantly better than decision trees in AUC. The conclusions drawn in this paper may make a significant impact on machine learning and data mining applications.
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paper_title: Decision Support System for Skin Cancer Diagnosis

paper_content:

Computer aided medical diagnosis is mostly based on very advanced analysis of huge amounts of measured data. Application of computational intelligence methods helps medical doctors to process data faster, and give better, more reliable diagnoses. In dermatology computer analysis is getting more important every year. Differentiation of malignant melanoma images requires very fast image processing and feature extraction/classification algorithms. Applying artificial intelligence algorithms to explore and search large database of dermatoscopic images allows doctors to semantically filter out images with specified characteristics. This paper describes a decision-support system which is based on semantic analysis of melanoma images and further classification of characteristic objects commonly found in pigmented skin lesions. For classification Artificial Neural Networks and Support Vector Machines are used and their performance is evaluated and compared.
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paper_title: A decision support system for the diagnosis of melanoma: A comparative approach

paper_content:

Melanoma is the most deathful of all skin cancers and the number of cases grows every year. The extirpation in early phases implies a high degree of survival so it is fundamental to diagnose it as soon as possible. In this paper we present a clinical decision support system for melanoma diagnosis using as input an image set of the skin lesion to be diagnosed. The system analyses the image sequence to extract the affected area, determinates the characteristics which indicate the degree of damage and, according to them, it makes a decision. Several methods of classification are proposed: a multilayered perceptron, a Bayesian classifier and the algorithm of the K nearest neighbours. These methods work independently and also in combination making a collaborative decision support system. The classification rates obtained are around 87%.
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paper_title: Overview of Advanced Computer Vision Systems for Skin Lesions Characterization

paper_content:

During the last years, computer-vision-based diagnosis systems have been used in several hospitals and dermatology clinics, aiming mostly at the early detection of skin cancer, and more specifically, the recognition of malignant melanoma tumour. In this paper, we review the state of the art in such systems by first presenting the installation, the visual features used for skin lesion classification, and the methods for defining them. Then, we describe how to extract these features through digital image processing methods, i.e., segmentation, border detection, and color and texture processing, and we present the most prominent techniques for skin lesion classification. The paper reports the statistics and the results of the most important implementations that exist in the literature, while it compares the performance of several classifiers on the specific skin lesion diagnostic problem and discusses the corresponding findings.
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paper_title: A Comparison of Machine Learning Methods for the Diagnosis of Pigmented Skin Lesions

paper_content:

We analyze the discriminatory power of k-nearest neighbors, logistic regression, artificial neural networks (ANNs), decision tress, and support vector machines (SVMs) on the task of classifying pigmented skin lesions as common nevi, dysplastic nevi, or melanoma. Three different classification tasks were used as benchmarks: the dichotomous problem of distinguishing common nevi from dysplastic nevi and melanoma, the dichotomous problem of distinguishing melanoma from common and dysplastic nevi, and the trichotomous problem of correctly distinguishing all three classes. Using ROC analysis to measure the discriminatory power of the methods shows that excellent results for specific classification problems in the domain of pigmented skin lesions can be achieved with machine-learning methods. On both dichotomous and trichotomous tasks, logistic regression, ANNs, and SVMs performed on about the same level, with k-nearest neighbors and decision trees performing worse.
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paper_title: Different Learning Paradigms for the Classification of Melanoid Skin Lesions Using Wavelets

paper_content:

We use the wavelet-based decomposition to generate the multiresolution representation of dermatoscopic images of potentially malignant pigmented lesions. Three different machine learning methods are experimentally applied, namely neural networks, support vector machines, and Attributional Calculus. The obtained results confirm that neighborhood properties of pixels in dermatoscopic images are a sensitive probe of the melanoma progression and together with the selected machine learning methods may be an important diagnostic tool.
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paper_title: Digital computer analysis of dermatoscopical images of 260 melanocytic skin lesions; perimeter/area ratio for the differentiation between malignant melanomas and melanocytic nevi

paper_content:

BACKGROUND ::: Digital computer analysis of dermatoscopical images has been reported to facilitate the differential diagnosis of pigmented skin lesions in recent years. ::: ::: ::: OBJECTIVE ::: The aim of our study was to perform digital computer analysis of a set of different melanocytic lesions and compare the objective results. ::: ::: ::: METHODS ::: The set of 260 melanocytic lesions (150 excised difficult cases (46 melanomas, 47 atypical nevi, 57 common nevi and 110 unexcised common nevi) was automatically analysed by the digital dermatoscopical system microDERM. We searched for differences in asymmetry, size, compactness and colour distribution. Perimeter/area ratio was calculated. ::: ::: ::: RESULTS ::: The perimeter/area ratio was detected as the most important criterion for differentiation between malignant and benign melanocytic lesions (sensitivity 91.3% and specificity 90.7% for malignant melanomas vs. all benign nevi; sensitivity 91.3% and specificity 80.8% for melanomas vs. clinically atypical nevi). Differences in size of the lesion, shape and asymmetry of colour were found and statistically verified. Using step-wise logistic regression the formula for calculation of probability of malignant nature of every analysed lesion was constructed. ::: ::: ::: CONCLUSION ::: The perimeter/area ratio is a simple parameter for the differential diagnosis of melanocytic skin lesions.
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paper_title: Decision Support System for Skin Cancer Diagnosis

paper_content:

Computer aided medical diagnosis is mostly based on very advanced analysis of huge amounts of measured data. Application of computational intelligence methods helps medical doctors to process data faster, and give better, more reliable diagnoses. In dermatology computer analysis is getting more important every year. Differentiation of malignant melanoma images requires very fast image processing and feature extraction/classification algorithms. Applying artificial intelligence algorithms to explore and search large database of dermatoscopic images allows doctors to semantically filter out images with specified characteristics. This paper describes a decision-support system which is based on semantic analysis of melanoma images and further classification of characteristic objects commonly found in pigmented skin lesions. For classification Artificial Neural Networks and Support Vector Machines are used and their performance is evaluated and compared.
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paper_title: A multiple expert-based melanoma recognition system for dermoscopic images of pigmented skin lesions

paper_content:

This paper presents an integrated decision support system for an automated melanoma recognition of dermoscopic images based on multiple expert fusion. In this context, the ultimate aim is to support decision making by predicting image categories (e.g., melanoma, benign and dysplastic nevi) by combining outputs from different classifiers. A fast and automatic segmentation method to detect the lesion from the background healthy skin is proposed and lesion-specific local color and texture-related features are extracted. For the classification, combining experts which are classifiers with different structures, are examined as alternative solution instead of an individual classifier. In this approach, probabilistic outputs of the experts are combined based on the combination rules that are derived by following Bayespsila theorem. The category label with the highest confidence score is considered to be the class of a test image. Experimental results on a collection of 358 dermoscopic images demonstrate the effectiveness of the proposed expert fusion-based approach.
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paper_title: Automatic Detection of Blue-White Veil and Related Structures in Dermoscopy Images

paper_content:

Dermoscopy is a non-invasive skin imaging technique, which permits visualization of features of pigmented melanocytic neoplasms that are not discernable by examination with the naked eye. One of the most important features for the diagnosis of melanoma in dermoscopy images is the blue-white veil (irregular, structureless areas of confluent blue pigmentation with an overlying white "ground-glass" film). In this article, we present a machine learning approach to the detection of blue-white veil and related structures in dermoscopy images. The method involves contextual pixel classification using a decision tree classifier. The percentage of blue-white areas detected in a lesion combined with a simple shape descriptor yielded a sensitivity of 69.35% and a specificity of 89.97% on a set of 545 dermoscopy images. The sensitivity rises to 78.20% for detection of blue veil in those cases where it is a primary feature for melanoma recognition.
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paper_title: In vivo spectrophotometric evaluation of neoplastic and non-neoplastic skin pigmented lesions. II: Discriminant analysis between nevus and melanoma.

paper_content:

Reflectance spectrophotometry from 420 to 780 nm on 31 primary melanoma and 31 benign nevi has been performed by using an external integrating sphere coupled to a spectrophotometer. Measurements show that reflectance spectra of melanoma and nevi manifest dissimilar patterns. From these spectra four variables, whose physical and/or physiological meanings remain to be investigated, have been derived. All of them are significantly different when compared between melanoma and nevi. A discriminant function between the two groups of lesions has been determined by using a stepwise discriminant analysis, resulting in a test with a sensitivity of 90.3% and a specificity of 77.4%. This method of discrimination between melanoma and nevi seems to have a discriminating power almost equal to that of a clinical judgement from a specialized medical doctor, thus suggesting a new method for screening skin pigmented lesions.
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paper_title: Qualitative asymmetry measure for melanoma detection

paper_content:

Size functions and support vector machines are used to implement a new automatic classifier of melanocytic lesions. This is mainly based on a qualitative assessment of asymmetry. The program is used, at clinical level, with two thresholds, so that comparison of the two outputs produces a report of low-middle-high risk. Experimental results with cross-validation are reported.
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paper_title: A Novel Hybrid System for Skin Lesion Detection

paper_content:

In this paper, a new intelligent method of classifying benign and malignant melanoma lesions is implemented. The system consists of four stages; image pre-processing, image segmentation, feature extraction, and image classification. As the first step of the image analysis, pre-processing techniques are implemented to remove noise and undesired structures from the images using techniques such as median filtering and contrast enhancement. In the second step, a simple thresholding method is used to segment and localise the lesion, a boundary tracing algorithm is also implemented to validate the segmentation. Then, a wavelet approach is used to extract the features, more specifically wavelet packet transform (WPT). Finally, the dimensionality of the selected features is reduced with principal component analysis (PCA) and later supplied to an artificial neural network and support vector machine classifiers for classification. The ability to correctly discriminate between benign and malignant lesions was about 95% for the Artificial Neural Network and 85% for the Support Vector Machine classifier.
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paper_title: Detection of blue-white veil areas in dermoscopy images using machine learning techniques

paper_content:

As a result of the advances in skin imaging technology and the development of suitable image processing techniques, during the last decade, there has been a significant increase of interest in the computer-aided diagnosis of skin cancer. Dermoscopy is a non-invasive skin imaging technique which permits visualization of features of pigmented melanocytic neoplasms that are not discernable by examination with the naked eye. One of the useful features in dermoscopic diagnosis is the blue-white veil (irregular, structureless areas of confluent blue pigmentation with an overlying white "ground-glass" film) which is mostly associated with invasive melanoma. In this preliminary study, a machine learning approach to the detection of blue-white veil areas in dermoscopy images is presented. The method involves pixel classification based on relative and absolute color features using a decision tree classifier. Promising results were obtained on a set of 224 dermoscopy images.
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paper_title: A decision support system for the diagnosis of melanoma: A comparative approach

paper_content:

Melanoma is the most deathful of all skin cancers and the number of cases grows every year. The extirpation in early phases implies a high degree of survival so it is fundamental to diagnose it as soon as possible. In this paper we present a clinical decision support system for melanoma diagnosis using as input an image set of the skin lesion to be diagnosed. The system analyses the image sequence to extract the affected area, determinates the characteristics which indicate the degree of damage and, according to them, it makes a decision. Several methods of classification are proposed: a multilayered perceptron, a Bayesian classifier and the algorithm of the K nearest neighbours. These methods work independently and also in combination making a collaborative decision support system. The classification rates obtained are around 87%.
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paper_title: A multiple classifier system for early melanoma diagnosis

paper_content:

Melanoma is the most dangerous skin cancer and early diagnosis is the key factor in its successful treatment. Well-trained dermatologists reach a diagnosis via visual inspection, and reach sensitivity and specificity levels of about 80%. Several computerised diagnostic systems were reported in the literature using different classification algorithms. In this paper, we will illustrate a novel approach by which a suitable combination of different classifiers is used in order to improve the diagnostic performances of single classifiers. We used three different kinds of classifiers, namely linear discriminant analysis (LDA), k-nearest neighbour (k-NN) and a decision tree, the inputs of which are 38 geometric and colorimetric features automatically extracted from digital images of skin lesions. Multiple classifiers were generated by combining the diagnostic outputs of single classifiers with appropriate voting schemata. This approach was evaluated on a set of 152 digital skin images. We compared the performances of multiple classifiers (2- and 3-classifier groups) between them and with respect to single ones (1-classifier group). We further compared the classifiers' performances with those of eight dermatologists. Classifiers' performances were measured in terms of distance from the ideal classifier. Compared with 1- and 2-classifier groups, performances of 3-classifier systems were significantly higher (P<0.0005 and P<0.001, respectively). No statistically significant differences were found between the 1- and 2-classifier groups (P=0.352). While the dermatologists group showed a level of performances significantly higher than the 1-classifier systems (P<0.020), no differences were found between the multiple classifier groups and the dermatologists groups, indicating comparable performances. This work suggests that a suitable combination of different kinds of classifiers can improve the performances of an automatic diagnostic system.
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paper_title: Detection of asymmetric blotches (asymmetric structureless areas) in dermoscopy images of malignant melanoma using relative color

paper_content:

BACKGROUND ::: Dermoscopy, also known as dermatoscopy or epiluminescence microscopy (ELM), is a non-invasive, in vivo technique, which permits visualization of features of pigmented melanocytic neoplasms that are not discernable by examination with the naked eye. One prominent feature useful for melanoma detection in dermoscopy images is the asymmetric blotch (asymmetric structureless area). ::: ::: ::: METHOD ::: Using both relative and absolute colors, blotches are detected in this research automatically by using thresholds in the red and green color planes. Several blotch indices are computed, including the scaled distance between the largest blotch centroid and the lesion centroid, ratio of total blotch areas to lesion area, ratio of largest blotch area to lesion area, total number of blotches, size of largest blotch, and irregularity of largest blotch. ::: ::: ::: RESULTS ::: The effectiveness of the absolute and relative color blotch features was examined for melanoma/benign lesion discrimination over a dermoscopy image set containing 165 melanomas (151 invasive melanomas and 14 melanomas in situ) and 347 benign lesions (124 nevocellular nevi without dysplasia and 223 dysplastic nevi) using a leave-one-out neural network approach. Receiver operating characteristic curve results are shown, highlighting the sensitivity and specificity of melanoma detection. Statistical analysis of the blotch features are also presented. ::: ::: ::: CONCLUSION ::: Neural network and statistical analysis showed that the blotch detection method was somewhat more effective using relative color than using absolute color. The relative-color blotch detection method gave a diagnostic accuracy of about 77%.
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paper_title: Automatic Diagnosis of Melanoma: A Software System Based on the 7-Point Check-List

paper_content:

Early detection of melanoma is one of the greatest challenges of dermatologic practice today. A new diagnostic method, the "ELM 7 point checklist", defines a set of seven features, based on colour and texture parameters, which describe the malignancy of a lesion. It has been presented as faster and with the same accuracy than the traditional ABCD criteria in the diagnosis of melanoma. In this paper a new system for automated diagnosis of melanocytic skin lesions, based on ELM 7 point checklist, is introduced.
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paper_title: A Mobile Automated Skin Lesion Classification System

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Melanoma skin cancer accounts for less than 5% of skin cancer cases but causes the most deaths due to skin cancer. Convenient automated diagnosis of skin lesions and melanoma recognition can greatly improve early detection of melanomas. This paper presents a prototype of an image-based automated melanoma recognition system on Android smart phones. The system consists of three major components: image segmentation, feature calculation, and classification. It is designed to run on a mobile device with a camera, such as a smart phone or a tablet PC. A skin lesion image is converted to a monochrome image for outline contour detection. Color and shape features of the lesion are extracted and used as input to a kNN classifier. Initial experimental result shows that the system is efficient and works well on well-lighted test images, achieving an average accuracy of 66.7%, with average malignant class recall/sensitivity of 60.7% and specificity of 80.5%.
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paper_title: Diagnosis of cutaneous melanoma: accuracy of a computerized image analysis system (Skin View).

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BACKGROUND/AIMS ::: Early diagnosis is essential for reducing the high mortality of CM (cutaneous melanoma). An expert dermatologist has a diagnostic accuracy of 85% but, in performing mass screening, the problem is to transfer the experience of a specialized centre to peripheral health clinics. Therefore, some computerized analysis systems were made. The aim of this paper is to compare the diagnostic accuracy of one such system, the Skin View System (SVS), to the accuracy of a well trained dermatologist in order to establish the real usefulness of the instrument and to verify how much it can help the diagnostic accuracy of the clinician in a dermatology setting. ::: ::: ::: METHOD ::: During a campaign for the early diagnosis of CM, 176 patients with small and flat pigmentary lesions underwent clinical diagnosis, computerized analysis with SVS and skin biopsy. For clinical and computerized diagnosis, we evaluated sensitivity, specificity, true and negative predictive value. ::: ::: ::: RESULTS ::: The statistical evaluation, in our study, reveals that the computerized system has a diagnostic accuracy lower than that of a trained dermatologist. ::: ::: ::: CONCLUSIONS ::: This computerized system does not help the clinician in the diagnosis of pigmented lesions.
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paper_title: Model Predicting Survival in Stage I Melanoma Based on Tumor Progression

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We used the lesional steps in tumor progression and multivariable logistic regression to develop a prognostic model for primary, clinical stage I cutaneous melanoma. This model is 89% accurate in predicting survival. Using histologic criteria, we assigned melanomas to tumor progression steps by ascertaining their particular growth phase. These phases were the in situ and invasive radial growth phase and the vertical growth phase (the focal formation of a dermal tumor nodule or dermal tumor plaque within the radial growth phase or such dermal growth without an evident radial growth phase). After a minimum follow-up of 100.6 months and a median follow-up of 150.2 months, 122 invasive radial-growth-phase tumors were found to be without metastases. Eight-year survival among the 264 patients whose tumors had entered the vertical growth phase was 71.2%. Survival prediction in these patients was enhanced by the use of a multivariable logistic regression model. Twenty-three attributes were tested for entry into this model. Six had independently predictive prognostic information: (a) mitotic rate per square millimeter, (b) tumor-infiltrating lymphocytes, (c) tumor thickness, (d) anatomic site of primary melanoma, (e) sex of the patient, and (f) histologic regression. When mitotic rate per square millimeter, tumor-infiltrating lymphocytes, primary site, sex, and histologic regression are added to a logistic regression model containing tumor thickness alone, they are independent predictors of 8-year survival (P less than .0005).
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paper_title: Overview of Advanced Computer Vision Systems for Skin Lesions Characterization

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During the last years, computer-vision-based diagnosis systems have been used in several hospitals and dermatology clinics, aiming mostly at the early detection of skin cancer, and more specifically, the recognition of malignant melanoma tumour. In this paper, we review the state of the art in such systems by first presenting the installation, the visual features used for skin lesion classification, and the methods for defining them. Then, we describe how to extract these features through digital image processing methods, i.e., segmentation, border detection, and color and texture processing, and we present the most prominent techniques for skin lesion classification. The paper reports the statistics and the results of the most important implementations that exist in the literature, while it compares the performance of several classifiers on the specific skin lesion diagnostic problem and discusses the corresponding findings.
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paper_title: Results obtained by using a computerized image analysis system designed as an aid to diagnosis of cutaneous melanoma

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Results obtained using a computerized image analysis system as an aid to clinical diagnosis of melanoma are reported. The system comprises a colour television camera connected through a digitizing board to a 386 personal computer. By means of original algorithms able to measure the shape, the colours and texture of a pigmented lesion of the skin, the system provides eight on/off indicators that are matched with the histological diagnosis to identify benign and malignant pigmented lesions. The chances that a given lesion is malignant increase with the increasing number of positive indicators. The training field of the system was constituted of images and data of 169 cutaneous lesions in 165 patients. Taking two positive indicators as the threshold between pigmented benign and malignant lesions, the efficiency of the system is 0.98, the positive predictive value is 0.45 and the negative predictive value is 0.95. These values were confirmed in a series of 44 pigmented lesions, 10 of which were melanoma, that constitute the present test series. The authors conclude that this computerized image analysis system should be regarded as a useful aid to diagnosis for a non-expert clinician. The system limit is transformation within a naevus.
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paper_title: Dysplastic naevus vs. in situ melanoma: digital dermoscopy analysis.

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BACKGROUND ::: To date, much confusion exists about the biological significance of dysplastic naevi and about the relationship between melanocytic dysplasia and clinical atypia. ::: ::: ::: OBJECTIVES ::: To use a digital dermoscopy analyser with a series of 'borderline' pigmented skin lesions (i.e. dysplastic naevi and in situ melanomas) to find correlation between the studied variables and to determine their discriminating power with respect to histological diagnosis. ::: ::: ::: METHODS ::: The pigmented skin lesions (n = 174) were histologically examined by three experienced dermatopathologists and identified as in situ melanomas (n = 38) and dysplastic naevi (n = 136). The system evaluated 48 parameters as possible discriminant variables, grouped into four categories: geometry, colours, textures and islands of colour. Once the lesions were analysed (stepwise discriminant analysis), sensitivity, specificity and accuracy were calculated. ::: ::: ::: RESULTS ::: At the end of the stepwise procedure the percentage of cases classified correctly was 71.8%. Of 136 dysplastic naevi only 98 were classified correctly, while 27 of 38 in situ melanomas were recognized correctly. ::: ::: ::: CONCLUSIONS ::: We conclude that there are so far no digital dermoscopic criteria that can clearly distinguish dysplastic naevi from in situ melanomas.
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paper_title: A Comparison of Machine Learning Methods for the Diagnosis of Pigmented Skin Lesions

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We analyze the discriminatory power of k-nearest neighbors, logistic regression, artificial neural networks (ANNs), decision tress, and support vector machines (SVMs) on the task of classifying pigmented skin lesions as common nevi, dysplastic nevi, or melanoma. Three different classification tasks were used as benchmarks: the dichotomous problem of distinguishing common nevi from dysplastic nevi and melanoma, the dichotomous problem of distinguishing melanoma from common and dysplastic nevi, and the trichotomous problem of correctly distinguishing all three classes. Using ROC analysis to measure the discriminatory power of the methods shows that excellent results for specific classification problems in the domain of pigmented skin lesions can be achieved with machine-learning methods. On both dichotomous and trichotomous tasks, logistic regression, ANNs, and SVMs performed on about the same level, with k-nearest neighbors and decision trees performing worse.
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paper_title: A fuzzy-based histogram analysis technique for skin lesion discrimination in dermatology clinical images.

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A fuzzy logic-based color histogram analysis technique is presented for discriminating benign skin lesions from malignant melanomas in dermatology clinical images. The approach utilizes a fuzzy set for benign skin lesion color, and alpha-cut and support set cardinality for quantifying a fuzzy ratio skin lesion color feature. Skin lesion discrimination results are reported for the fuzzy ratio and fusion with a previously determined percent melanoma color feature over a data set of 258 clinical images. For the fusion technique, alpha-cuts for the fuzzy ratio can be chosen to recognize over 93.30% of melanomas with approximately 15.67% false positive lesions.
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paper_title: Towards an automatic diagnosis system for skin lesions: Estimation of blue-whitish veil and regression structures

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This paper deals with ELM image processing for automatic analysis of pigmented skin lesions which represents one of the greatest challenges of dermatologic practice today. The “ELM 7 point checklist” defines a set of seven features, based on colour and texture parameters, which describe the malignancy of a lesion. It has been revealed as faster and with the same accuracy than the traditional ABCD criteria in the diagnosis of melanoma. A preliminary approach to the automated diagnosis of melanocytic skin lesions, based on ELM 7 point checklist is proposed. In particular, the image processing algorithms and classification techniques involved in the automatic detection of the occurrence of two criteria (Blue-whitish Veil and Regression structures) are introduced and the experimental results are reported.
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paper_title: A support vector machine for decision support in melanoma recognition

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The early diagnosis of melanoma is critical to achieving reduced mortality and increased survival. Although clinical examination is currently the method of choice for melanocytic lesion assessment, difficulties may arise in the diagnosis of atypical lesions. From both the naked eye and dermoscopic perspective, dysplastic naevi often exhibit a prominent heterogeneity of structure that renders their clinical distinction from melanoma difficult. To address these problems in diagnosis, there exists a heightened interest among researchers regarding the utility of machine learning techniques in computerised image analysis. Here we report on the utility, for dermatologists, of support vector machine (SVM) technology in melanoma diagnosis, using an archive of 199 digital dermoscopic images of excised atypical melanocytic lesions. Our best validation models achieved an average sensitivity and specificity for melanoma diagnosis of 0.86 and 0.72, respectively. Applying the best model to our test set yielded a sensitivity of 0.89, a diagnostic odds ratio of 14.09 and an area under the receiver operated characteristic curve (AUC) of 0.76. Advantages of the procedure implemented are the simplicity of feature extraction and the computationally cheap and efficient nature of SVMs. The derived model generalises well with outcomes that compare favourably with competing algorithms and expert assessment. In line with the concept of the utility of decision support systems in clinical practice, we propose that to reduce the risk of missed melanomas, both the dermatologists' assessment and the SVM diagnosis be incorporated into the clinical decision-making process.
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paper_title: Classification of melanocytic lesions with color and texture analysis using digital image processing.

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The incidence of malignant melanoma, the most dangerous skin cancer, has increased rapidly during the last decade, and the figures are still rising. Since well-trained and experienced dermatologists are able to reach only a diagnostic accuracy of about 75% in visual preoperative classification, the discriminating ability of digital image analysis was evaluated in more than 350 malignant melanoma and benign melanocytic lesions that had all been confirmed histologically. Color slides of melanocytic lesions were scanned and digitized. Computer algorithms were programmed in FORTRAN on a DECstation 5000/200. A feature set was calculated describing the texture, color and their distributions as well as asymmetry, size and border of each lesion. These features, together with the histologic diagnosis, were the input in a commercial statistical classification program. In contrast to the accuracy of 75% achievable by the human eye, a correct classification rate of about 92% was reached with the mathematical classifier as compared with the histologic diagnosis.
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paper_title: Intelligent segmentation and classification of pigmented skin lesions in dermatological images

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During the last years, computer vision-based diagnostic systems have been used in several hospitals and dermatology clinics, aiming mostly at the early detection of malignant melanoma tumor, which is among the most frequent types of skin cancer, versus other types of non-malignant cutaneous diseases. In this paper we discuss intelligent techniques for the segmentation and classification of pigmented skin lesions in such dermatological images. A local thresholding algorithm is proposed for skin lesion separation and border, texture and color based features, are then extracted from the digital images. Extracted features are used to construct a classification module based on Support Vector Machines (SVM) for the recognition of malignant melanoma versus dysplastic nevus.
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paper_title: Statistical Techniques Applied to the Automatic Diagnosis of Dermoscopic Images

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An image based system implementing a well-known diagnostic method is disclosed for the automatic detection of melanomas as support to clinicians. The software procedure is able to recognize automatically the skin lesion within the digital image, measure morphological and chromatic feature, carry out a suitable classification for the detection of structural dermoscopic criteria provided by the 7-Point Check. Statistical techniques are introduced and adopted for border detection, feature extraction and classification as well as the resulting diagnostic score are described with reference to a large image set of pigmented lesions.
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paper_title: Digital Dermoscopy Analysis and Artificial Neural Network for the Differentiation of Clinically Atypical Pigmented Skin Lesions: A Retrospective Study

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Noninvasive diagnostic methods such as dermoscopy or epiluminescence light microscopy have been developed in an attempt to improve diagnostic accuracy of pigmented skin lesions. The evaluation of the many morphologic characteristics of pigmented skin lesions observable by epiluminescence light microscopy, however, is often extremely complex and subjective. With the aim of obviating these problems of qualitative interpretation, methods based on mathematical analysis of pigmented skin lesions have recently been designed. These methods are based on computerized analysis of digital images obtained by epiluminescence light microscopy. In this study we used a digital dermoscopy analyzer with 147 clinically atypical pigmented skin lesions (90 nevi and 57 melanomas) to determine its discriminating power with respect to histologic diagnosis. The system evaluated 48 objective parameters used to train an artificial neural network. Using the artificial neural network with 10 variables selected by a stepwise procedure, we obtained a maximum accuracy in distinguishing melanoma from benign lesions of about 93%. Comparing this result with those of the many studies using classical epiluminescence light microscopy, it emerges that the method proposed is equal or even superior in diagnostic accuracy and has the advantage of not depending on the expertise of the clinician who examines the lesion.
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paper_title: Colour Analysis of Skin Lesion Regions for Melanoma Discrimination in Clinical Images

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Rules most clinicians use for melanoma detection are generally derived from the ABCD criteria: asymmetry, border irregularity, color variegation and diameter of the skin lesion (1). There are many computer-based techniques incorporating features that are variations of the ABCD rules that have been applied to detecting malignant melanoma in clinical images (2–20). A computer-based color analysis technique is explored in this research. Certain colors are generally associated with melanocytic lesions, including shades of tan, brown or black and occasional patches of red, white or blue. Numerous color descriptors have been applied to melanoma detection, including variation of hues (2), red, green and blue (RGB) color channel statistical parameters (16–18), color variegation detection using analytical color techniques (11), spherical color coordinates and (L, a*, b*) color coordinate features (5), percentage of the skin lesion containing absolute shades of reddish, bluish, grayish and blackish areas and the number of those color shades present within the skin lesion in dermatoscopy images (8). Melanoma detection has also been performed based on probability analysis for three classes of colors (benign, melanoma and other colors) found from relative color histograms (19). ::: ::: In prior research, a data-driven relative color histogram analysis technique was investigated for determining colors characteristic of melanomas, i.e., ‘melanoma colors’ (19). For that study, relative color was used to compensate for skin lesion images that were digitized using a Nikon Coolscan® from 35 mm slides collected from three different sources. Relative color subtracts the average surrounding skin color from the lesion color. The relative color technique attempts to correct for different film types, other acquired photograph variations and lighting and different skin coloration (pigmentation) for different patients. Color histogram analysis was performed using relative color to determine melanoma colors. Quantifying these melanoma colors, a percentage melanoma color feature was computed for individual skin lesions as the total number of pixels within the skin lesion that were labeled as melanoma colors divided by the lesion area. This feature could discriminate 84.6% of a test set of malignant melanomas and 83.0% of benign lesions including seborrheic keratoses and nevocellular nevi (no dysplastic nevi or melanomas in situ were in the set of lesions). ::: ::: In this research, the relative color histogram analysis technique is extended to explore color feature analysis in different regions of the skin lesion to identify the region(s) that may enhance skin lesion discrimination. The color features examined include the percent melanoma color and a novel color clustering ratio. The color clustering ratio is a natural extension of the percent melanoma color feature to quantify the grouping of skin lesion pixels with colors characteristic of melanoma and to associate the physiological interpretation of that grouping with skin lesion discrimination. The color clustering ratio is the ratio of the total melanoma-colored eight-connected neighbors of melanoma-colored pixels within the skin lesion to the total number of eight-connected neighbors for all melanoma-colored pixels within the lesion. The remaining sections of this paper include: (1) the techniques used to calculate the percent melanoma color and color clustering ratio features, (2) experiments performed for melanoma and benign lesion discrimination based on feature calculations from different regions of the skin lesion, (3) experimental results and discussion and (4) conclusions from the study.
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paper_title: A new algorithm for border description of polarized light surface microscopic images of pigmented skin lesions

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The aim of the study was to provide mathematical descriptors for the border of pigmented skin lesion images and to assess their efficacy for distinction among different lesion groups. New descriptors such as lesion slope and lesion slope regularity are introduced and mathematically defined. A new algorithm based on the Catmull-Rom spline method and the computation of the gray-level gradient of points extracted by interpolation of normal direction on spline points was employed. The efficacy of these new descriptors was tested on a data set of 510 pigmented skin lesions, composed by 85 melanomas and 425 nevi, by employing statistical methods for discrimination between the two populations.
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paper_title: Computer-aided diagnosis of melanocytic lesions

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Background: The clinical diagnosis of melanoma could be difficult for a general practitioner and, in some cases, for dermatologists. To enhance and support the clinical evaluation of pigmented skin lesions a computer-aided diagnosis has been introduced. Materials and Methods: Images of melanocytic lesions (477 total, 42 melanomas and 435 melanocytic nevi) evaluated in epiluminescence microscopy and recorded with x16 magnification were selected. A training set of 22 melanomas and 218 nevi was randomized from the dataset. The test set was formed by the complement (the remaining 20 melanomas and 217 nevi). Furthermore, a set of images consisting of 31 melanomas and 103 nevi was selected to compare the discrimination capacity of three general practitioners and three dermatologists with experience in dermoscopy (2 years), and with the automatic data analysis for the melanoma early detection system (ADAM). Sensitivity and specificity were estimated for observer assessments and computer diagnosis. Results: The entire dataset used to test the implementation of the diagnostic algorithms ADAM showed a good sensitivity and specificity performance. Compared with the physicians, the ADAM system showed a slightly higher diagnostic performance in terms of sensitivity and a lower one in terms of specificity. Dermatologists showed higher levels of specificity, but lower levels in terms of sensitivity, when compared with the general practitioners. Conclusion: Image analysis has the potential to distinguish nevi and melanomas and to support the clinical diagnosis of melanocytic lesions by the general practitioner.
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paper_title: Primary cutaneous melanoma : identification of prognostic groups and estimation of individual prognosis for 5093 patients

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Background. Numerous investigations have examined prognostic factors for patients with primary cutaneous melanoma. However, only a few studies have been published on the definition of prognostic groups. The first aim of the present study was to determine the relative importance of different prognostic factors in a large collective study. The second aim was to define prognostic groups of patients based on combinations of prognostic factors and to define a model that allows the estimation of individual survival probability. Methods. Long term follow-up of 5264 patients with invasive primary cutaneous melanoma was performed from 1970 to 1988 at four German University Departments of Dermatology (Berlin-Steglitz, Miinster-Hornheide, Tiibingen, and Wurzburg). The multivariate Cox model was used to analyze 5093 patients, and 4371 patients with complete information were included in a classification and regression tree analysis (CART). Results. Tumor thickness, sex, anatomic location, and level of invasion were highly significant prognostic factors according to the multivariate analysis (P < 0.0001). However, histologic subtype and age influenced prognosis less significantly (P < 0.05). The CART analysis resulted in 12 groups defined mainly by tumor thickness, sex, and anatomic location, which were combined into five prognostic groups. The prognostic stratification defined by the five groups was superior compared with the standard TNM model. Ten-year survival rates of the five groups ranged from 97% to 14% (P < 0.0001), and an equation was used to calculate individual survival probabilities based on the significant factors of the Cox model. Conclusions. Consideration of all significant prognostic factors of patients with primary cutaneous melanoma investigated in the present study allows for the definition of prognostic groups with a more reliable estimation of prognosis than by previous staging systems and also enables calculation of individual survival probabilities. Cancer 199575 :2484-91.
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paper_title: Reliability of computer image analysis of pigmented skin lesions of Australian adolescents.

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BACKGROUND ::: The diagnosis of melanomas at an early stage is associated with improved survival, so the recognition of changes in pigmented skin lesions over time is important. We have developed a computer imaging system with the aim of assisting clinicians in differentiating early melanomas from benign pigmented skin lesions. The objective of this study was to investigate the system's reliability over time in measuring diagnostic characteristics of pigmented skin lesions, including their color, size, shape, and distinctness of boundary. ::: ::: ::: METHODS ::: We captured video images of 5 lesions, all larger than 2 mm in greatest dimension, on each of 66 Australian adolescents on 2 occasions approximately 1 month apart. Features extracted by computer image analysis included area, perimeter, and regularity of outline of the lesions, the mean and standard deviation of reflectance at red, green, and blue wavelengths, and the mean and standard deviation of the gradients of red, green, and blue reflectance at the lesion boundary. ::: ::: ::: RESULTS ::: All measurements showed moderate to high reliability (intraclass correlation coefficients 0.66-0.94), except for the standard deviations of the color gradients, whose reliability improved to moderate levels (0.68-0.71) when the mean of 5 lesions was considered. For most outcomes, reasonable within subject reliability was achieved when five lesions per subject were measured. ::: ::: ::: CONCLUSIONS ::: These results, in combination with previous work demonstrating the reasonable ability of this computer imaging system to discriminate between malignant melanomas and other pigmented lesions, indicates that the system has the potential to become a useful tool for clinicians in following people with pigmented lesions over time to detect early malignant changes.
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paper_title: Classification of melanoma using tree structured wavelet transforms.

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This paper presents a wavelet transform based tree structure model developed and evaluated for the classification of skin lesion images into melanoma and dysplastic nevus. The tree structure model utilizes a semantic representation of the spatial-frequency information contained in the skin lesion images including textural information. Results show that the presented method is effective in discriminating melanoma from dysplastic nevus. The results are also compared with those obtained using another method of developing tree structures utilizing the maximum channel energy criteria with a fixed energy ratio threshold.
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paper_title: SVM-based texture classification and application to early melanoma detection.

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We have recently developed a decision support system for early skin cancer detection that relies on analysis of the pigmentation characteristics of a skin lesion, detected using crosspolarization imaging, and the increased vasculature associated with malignant lesions that is detected using transillumination imaging. Current system uses size difference based on lesion physiology and achieves great overall accuracy (86.9%). In this paper, we explore texture information, one of the criteria dermatologists use in the diagnosis of skin cancer, but has been found very difficult to utilize in an automatic manner. The overarching goal is to improve the overall decision support capability of the DSS. The objective is to use texture information ONLY to classify the benign and malignancy of the skin lesion. A three-layer mechanism that inherent to the support vector machine (SVM) methodology is employed to improve the generalization error rate and the computational efficiency. The performance of the algorithm is validated with a series of benchmark texture images and then tested on 22 pairs of real clinical skin lesion images. Our experimental results show that a 4 th -order polynomial kernel can reach an average accuracy of 70% in determining the malignancy of any pixel within any given skin lesion image. Further study will look at whether multi-channel filtering based feature extraction algorithm will improve the accuracy rate, and the performance comparison between SVM-based texture classification and decision tree-based texture classification in both the spatial and frequency domain.
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paper_title: A methodological approach to the classification of dermoscopy images

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In this paper a methodological approach to the classification of pigmented skin lesions in dermoscopy images is presented. First, automatic border detection is performed to separate the lesion from the background skin. Shape features are then extracted from this border. For the extraction of color and texture related features, the image is divided into various clinically significant regions using the Euclidean distance transform. This feature data is fed into an optimization framework, which ranks the features using various feature selection algorithms and determines the optimal feature subset size according to the area under the ROC curve measure obtained from support vector machine classification. The issue of class imbalance is addressed using various sampling strategies, and the classifier generalization error is estimated using Monte Carlo cross validation. Experiments on a set of 564 images yielded a specificity of 92.34% and a sensitivity of 93.33%.
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paper_title: Feature of malignant melanoma based on color information

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This research aims at finding the features that contribute to diagnose the illness as the malignant melanoma, based on the color information of images. We divide RGB space into 4096 pieces and choose the spaces including colors peculiar to malignant melanoma. By using these spaces, about 26% of images of melanoma could be taken out only by the color information. This is very useful to malignant melanoma distinction accuracy.
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paper_title: A basis function feature-based approach for skin lesion discrimination in dermatology dermoscopy images.

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BACKGROUND ::: Skin lesion color is an important feature for diagnosing malignant melanoma. New basis function correlation features are proposed for discriminating malignant melanoma lesions from benign lesions in dermoscopy images. The proposed features are computed based on correlating the luminance histogram of melanoma or benign labeled relative colors from a specified portion of the skin lesion with a set of basis functions. These features extend previously developed statistical and fuzzy logic-based relative color histogram analysis techniques for automated mapping of colors representative of melanoma and benign skin lesions from a training set of lesion images. ::: ::: ::: METHODS ::: Using the statistical and fuzzy logic-based approaches for relative color mapping, melanoma and benign color features are computed over skin lesion region of interest, respectively. Luminance histograms are obtained from the melanoma and benign mapped colors within the lesion region of interest and are correlated with a set of basis functions to quantify the distribution of colors. The histogram analysis techniques and feature calculations are evaluated using a data set of 279 malignant melanomas and 442 benign dysplastic nevi images. ::: ::: ::: RESULTS ::: Experimental test results showed that combining existing melanoma and benign color features with the proposed basis function features found from the melanoma mapped colors yielded average correct melanoma and benign lesion discrimination rates as high as 86.45% and 83.35%, respectively. ::: ::: ::: CONCLUSIONS ::: The basis function features provide an alternative approach to melanoma discrimination that quantifies the variation and distribution of colors characteristic of melanoma and benign skin lesions.
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paper_title: Pattern Classification of Nevus with Texture Analysis

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Melanoma is a skin tumor with high malignancy. Cases of melanoma have recently been reported from many hospitals around the world. Since melanoma is analogous with benign nevus, discriminating between melanoma and benign nevus is difficult for nonspecialists. A screening system for melanoma and benign nevus is desired. Melanoma is mainly diagnosed by features of specific shape and color. However, texture patterns within the tumor region can be diagnostically important. The purpose of this research was to classify patterns on the tumor surface. Digital images of a tumor were classified into 3 patterns by texture analysis: homogeneous pattern; globular pattern; and reticular pattern. The tumor part in the image was specified first, and the specified tumor part was divided into sub-images. Texture features of each sub-image were then calculated. Patterns were classified by discriminant analysis based on the number of texture features. As a result, patterns could be classified correctly into the three categories at a ratio of 94%. Copyright © 2007 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
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paper_title: Multi-spectral image analysis and classification of melanoma using fuzzy membership based partitions

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Abstract The sensitivity and specificity of melanoma diagnosis can be improved by adding the lesion depth and structure information obtained from the multi-spectral, trans-illumination images to the surface characteristic information obtained from the epi-illumination images. Wavelet transform based bi-modal channel energy features obtained from the images are used in the analysis. Methods using both crisp and fuzzy membership based partitioning of the feature space are evaluated. For this purpose, the ADWAT classification method that uses crisp partitioning is extended to handle multi-spectral image data. Also, multi-dimensional fuzzy membership functions with Gaussian and Bell profiles are proposed for classification. Results show that the fuzzy membership functions with Bell profile are more effective than the extended ADWAT method in discriminating melanoma from dysplastic nevus.
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paper_title: Classification of melanoma and Clark nevus skin lesions based on medical image processing techniques

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According to the statistics, melanoma accounts for just 11 % of all types of skin cancer, it is responsible for most of the deaths. Melanoma is visually difficult for clinicians to differentiate from Clark nevus lesions which are benign. The application of image processing techniques to these lesions may be useful as an educational tool for teaching physicians to differentiate lesions, as well as for contributing information about the essential optical characteristics for identifying them. This research tried find the most effective features to extract from melanoma, melanoma in situ and Clark nevus lesions, and to find the most effective pattern-classification criteria and algorithms for differentiating those lesions. The color differences between images that occur because of differences in ambient lighting during the photographic process were minimized by the use of dermoscopic images. Differences in skin color between patients was minimized by using normalizing them by means of converting them to relative-color images, and differences in ambient lighting during photography, and the photographic and digitization processes, original color images were normalized by converting them into relative-color images. Tumors in the relative-color images were then segmented out and morphologically filtered. The filtered-tumor features were then extracted and various pattern-classification schemes were applied. Experimentation resulted in four useful pattern classification methods, the best of which was a classification rate of 100% for melanoma and melanoma in situ (grouped) and 65% for Clark nevus.
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paper_title: Dermoscopic image segmentation and classification using machine learning algorithms

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Dermoscopy is the method of examining the skin lesions. It is especially used for diagnosing melanoma, a type of skin cancer. Image segmentation and classification are important tools to provide the information about the Dermoscopic images clinically in terms of its size and shape. Many algorithms were developed for classification and segmentation of Dermoscopic images. This work proposes the tasks of extracting, classifying and segmenting the Dermoscopic image using the machine learning algorithms. The algorithms such as Back Propagation network (BPN), Radial Basis Function Network (RBF) and Extreme Learning Machine (ELM) are used. The features are extracted from the Dermoscopic image and these features are used to train the classifiers. The trained networks are used for segmentation. The results are compared with the ground truth images and their performance is evaluated. The results proved that the ELM has better accuracy, faster training period and it provides better segmentation than the BPN and RBF neural networks.
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paper_title: Different Learning Paradigms for the Classification of Melanoid Skin Lesions Using Wavelets

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We use the wavelet-based decomposition to generate the multiresolution representation of dermatoscopic images of potentially malignant pigmented lesions. Three different machine learning methods are experimentally applied, namely neural networks, support vector machines, and Attributional Calculus. The obtained results confirm that neighborhood properties of pixels in dermatoscopic images are a sensitive probe of the melanoma progression and together with the selected machine learning methods may be an important diagnostic tool.
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paper_title: An integrated computer supported acquisition, handling, and characterization system for pigmented skin lesions in dermatological images

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This paper describes an integrated prototype computer-based system for the characterization of skin digital images. The first stage includes an image acquisition arrangement designed for capturing skin images, under reproducible conditions. The system processes the captured images and performs unsupervised image segmentation and image registration utilizing an efficient algorithm based on the log-polar transform of the images' Fourier spectrum. Border- and color-based features, extracted from the digital images of skin lesions, were used to construct a classification module for the recognition of malignant melanoma versus dysplastic nevus. Different methods, drawn from the fields of artificial intelligence (neural networks) and statistical modeling (discriminant analysis), were used in order to find the best classification rules and to compare the results of different approaches to the problem.
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paper_title: A simple digital image processing system to aid in melanoma diagnosis in an everyday melanocytic skin lesion unit: a preliminary report.

paper_content:

Background For early melanoma diagnosis, experienced dermatologists have an accuracy of 64–80% using clinical diagnostic criteria, usually the ABCD rule, while automated melanoma diagnosis systems are still considered to be experimental and serve as adjuncts to the naked-eye expert prediction. In an attempt to aid in early melanoma diagnosis, we developed an image processing program with the aim to discriminate melanoma from melanocytic nevi, establishing a mathematical model to come up with a melanoma probability. ::: ::: ::: ::: Methods Digital images of 132 melanocytic skin lesions (23 melanomas and 109 melanocytic nevi) were studied in features of geometry, color, and color texture. A total of 43 variables were studied for all lesions, e.g., geometry, color texture, sharpness of border, and color variables. Univariate logistic regression analysis followed by “−2 log likelihood” test and Spearman's rank correlation coefficient were used to eliminate inappropriate variables, as the presence of multicollinearity among variables could cause severe problems in any stepwise variable selection method. Initially, “−2 log likelihood” and nonparametric Spearman's rho picked five variables to be included in a multivariate model of prediction. The five-variable model was then reduced to three variables and the performance of each model was tested. The “jackknife” method was performed in order to validate the model with the three variables and its accuracy was weighed vs. the five-variable model by receiver-operating characteristics (ROC) curve plotting. It was concluded that the reduced model did not compromise discriminatory power. ::: ::: ::: ::: Results Not all variables contributed much to the model, therefore they were progressively eliminated and the model was finally reduced to three covariates of significance. A predictive equation was calculated, incorporating parameters of geometry, color, and color texture as independent covariates for the prediction of melanoma. The proposed model provides melanoma probability with a 60.9% sensitivity and 95.4% specificity of prediction, an overall accuracy of 89.4% (probability level 0.5), and 8% false-negative results. ::: ::: ::: ::: Conclusions Through a digital image processing system and the development of a mathematical model of prediction, discrimination between melanomas and melanocytic nevi seems feasible with a high rate of accuracy using multivariate logistic regression analysis. The proposed model is an alternative method to aid in early melanoma diagnosis. Expensive and sophisticated equipment is not required and it can be easily implemented in a reasonably priced portable programmable computer, in order to predict previously undiagnosed skin melanoma before histopathology results confirm diagnosis.
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paper_title: Computer image analysis in the diagnosis of melanoma.

paper_content:

BACKGROUND ::: It is often difficult to differentiate early melanoma from benign pigmented lesions of similar clinical appearance. ::: ::: ::: OBJECTIVE ::: Our purpose was to develop a computer image analysis system that has the potential for use as an adjunct to the clinical distinction of melanoma from less serious pigmented lesions. ::: ::: ::: METHODS ::: The system, consisting of a hand-held device incorporating a color video camera and color frame grabber mounted in a microcomputer, was used in a pigmented lesion clinic. Analysis software extracted features relevant to the size, color, shape, and boundary of each lesion, and these features were correlated with clinical and histologic characteristics on which standard diagnoses of skin tumors are based. For discriminant analysis based on image analysis measurements, equal prior probabilities were assigned to two specified diagnostic groups, namely melanoma and "other pigmented lesions," most of which were melanocytic nevi. ::: ::: ::: RESULTS ::: In a 20-month period, video images of 164 unselected pigmented lesions for which complete diagnostic data were available were successfully captured using the camera. Sixteen of 18 melanomas, and 89% of pigmented lesions overall, were correctly classified by the image analysis system, compared with 83% based on clinical gradings of lesion characteristics. ::: ::: ::: CONCLUSION ::: Computer image analysis has the potential to provide a valuable diagnostic aid that could enable clinicians to make highly sensitive and specific diagnoses of early, curable melanoma.
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paper_title: Digital dermoscopy analysis for the differentiation of atypical nevi and early melanoma: a new quantitative semiology.

paper_content:

OBJECTIVES ::: To use a digital dermoscopy analyzer with a series of "borderline" pigmentary skin lesions (ie, clinically atypical nevi and early melanoma) to find correlation between the studied variables and to determine their discriminating power with respect to histological diagnosis. ::: ::: ::: DESIGN ::: A total of 147 pigmentary skin lesions were histologically examined by 3 experienced dermatopathologists and identified as nevi (n = 90) and melanomas (n = 57). The system evaluated 36 variables to be studied as possible discriminant variables, grouped into 4 categories: geometries, colors, textures, and islands of color. ::: ::: ::: SETTING ::: University medical department. ::: ::: ::: PATIENTS ::: A sample of patients with excised pigmentary skin lesions (nevi and melanomas). ::: ::: ::: MAIN OUTCOME MEASURES ::: Sensitivity, specificity, and accuracy of the model for evaluating "borderline" pigmentary skin lesions. ::: ::: ::: RESULTS ::: After multivariate stepwise discriminant analysis, only 13 variables were selected to compute the canonical discriminant function. ::: ::: ::: CONCLUSION ::: The present method made it possible to determine which objective variables are important for distinguishing atypical benign pigmentary skin lesions and early melanoma.
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paper_title: What do we mean by validating a prognostic model?

paper_content:

Prognostic models are used in medicine for investigating patient outcome in relation to patient and disease characteristics. Such models do not always work well in practice, so it is widely recommended that they need to be validated. The idea of validating a prognostic model is generally taken to mean establishing that it works satisfactorily for patients other than those from whose data it was derived. In this paper we examine what is meant by validation and review why it is necessary. We consider how to validate a model and suggest that it is desirable to consider two rather different aspects - statistical and clinical validity - and examine some general approaches to validation. We illustrate the issues using several case studies.
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paper_title: Validity of prognostic models: when is a model clinically useful?

paper_content:

Prognostic models combine patient characteristics to predict medical outcomes. Unfortunately, such models do not always perform as well for other patients as those from whose data the models were derived. Therefore, validity of prognostic models needs to be assessed in new patients. Predicted probabilities can be calculated with the model and compared with the actually observed outcomes. We may distinguish several aspects of validity: (1) agreement between predicted probabilities and observed probabilities (calibration), (2) ability of the model to distinguish subjects with different outcomes (discrimination), and (3) ability of the model to improve the decision-making process (clinical usefulness). We discuss those aspects and show some measures by using models for testicular and prostate cancer. We conclude that good calibration and discriminative ability are not sufficient for a model to be clinically useful. Application of a prognostic model is sensible, if the model is able to provide useful additional information for clinical decision making.
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Title: Computer Aided Diagnostic Support System for Skin Cancer: A Review of Techniques and Algorithms

Section 1: Introduction
Description 1: Summarize the background, importance, and current practices in skin cancer diagnosis. 

Section 2: Computer-Aided Diagnosis System
Description 2: Describe the general approach and components of a Computer-Aided Diagnosis (CAD) system for skin cancer.

Section 3: Image Acquisition/Methods for Screening Skin Lesions
Description 3: Provide an overview of the different imaging modalities used for screening skin lesions, including their pros and cons.

Section 4: Preprocessing
Description 4: Discuss the preprocessing techniques applied to dermoscopy images to remove artifacts and prepare for segmentation.

Section 5: Segmentation
Description 5: Outline the segmentation techniques used to delineate the skin lesions from surrounding tissue.

Section 6: Feature Extraction
Description 6: Describe the various methods used to extract important features from dermoscopic images for classification.

Section 7: Feature Selection
Description 7: Discuss the methods used for selecting the most relevant features to improve classification performance.

Section 8: Classification
Description 8: Present the primary classification algorithms used in skin cancer diagnosis, including k-NN, decision trees, logistic regression, ANN, SVM, and ELM.

Section 9: Evaluation of Classification Performance
Description 9: Explain the metrics and procedures used to evaluate the performance of classification models in skin cancer diagnosis.

Section 10: Selection of Suitable Classification Method
Description 10: Provide guidelines and criteria for selecting appropriate classification methods for specific contexts or datasets.

Section 11: Model Validation
Description 11: Discuss the importance of validating diagnostic algorithms/models and present the common criteria for quality assessment.

Section 12: Concluding Comments
Description 12: Summarize the key contributions of the study, its implications for future research, and the potential of automated systems in improving clinical diagnostic accuracy.