A Review of the Quantification and Classification of Pigmented Skin Lesions: From Dedicated to Hand-Held Devices
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paper_title: A Mobile Automated Skin Lesion Classification System

paper_content:

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: Performance of a dermoscopy-based computer vision system for the diagnosis of pigmented skin lesions compared with visual evaluation by experienced dermatologists

paper_content:

Background: It is often difficult to differentiate early melanomas from benign melanocytic nevi even by expert dermatologists, and the task is even more challenging for primary care physicians untrained in dermatology and dermoscopy. A computer system can provide an objective and quantitative evaluation of skin lesions, reducing subjectivity in the diagnosis. Objective: Our objective is to make a low-cost computer aided diagnostic tool applicable in primary care based on a consumer grade camera with attached dermatoscope, and compare its performance to that of experienced dermatologists. Methods and materials: We propose several new image-derived features computed from automatically segmented dermoscopic pictures. These are related to the asymmetry, color, border, geometry, and texture of skin lesions. The diagnostic accuracy of the system is compared with that of three dermatologists. Results: With a data set of 206 skin lesions, 169 benign and 37 melanomas, the classifier was able to provide competitive sensitivity (86%) and specificity (52%) scores compared with the sensitivity (85%) and specificity (48%) of the most accurate dermatologist using only dermoscopic images. Conclusion: We show that simple statistical classifiers can be trained to provide a recommendation on whether a pigmented skin lesion requires biopsy to exclude skin cancer with a performance that is comparable to and exceeds that of experienced dermatologists.
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paper_title: Clinical and dermatoscopic diagnosis of malignant melanoma. Assessed by expert and non-expert groups.

paper_content:

We investigated the nosographic and diagnostic probabilities and likelihood ratios of dermatoscopy in order to evaluate the method's role in decision-making regarding melanoma. Clinical slides and dermatoscopic photos were obtained from 232 patients referred for dermatoscopy. Four dermatoscopy "experts" and 5 "non-experts" assessed the slides. Diagnoses were compared with histopathology. Sensitivity of the clinical assessments was 0.78 vs. 0.69 ("experts" vs. "non-experts"), sensitivity of dermatoscopy assessment was 0.83 vs. 0.69 (p = 0.04). The expert group demonstrated increased specificity (from 0.89 to 0.94) when applying dermatoscopy compared with clinical assessment alone (p=0.03). Positive likelihood ratios were doubled in the "expert group" and the negative likelihood ratios improved 25% with dermatoscopy compared with clinical assessment.
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paper_title: Accuracy of a smartphone application using fractal image analysis of pigmented moles compared to clinical diagnosis and histological result.

paper_content:

BACKGROUND ::: Lately, various smartphone applications have been introduced as diagnostic self-monitoring tools in the evaluation of pigmented moles, but most of these techniques have not been evaluated systematically. ::: ::: ::: OBJECTIVES ::: The purpose of this study was to evaluate prospectively the sensitivity and specificity of a recently developed smartphone application using fractal image analysis for the risk evaluation algorithm in the diagnosis of malignant melanoma compared to clinical diagnosis and histopathological result. ::: ::: ::: METHODS ::: Consecutive patients with melanocytic lesions were recruited and clinical and dermoscopical diagnosis was documented by two dermatologists independently. Imaging and analysis with the smartphone application was performed prior to excision of lesions. The findings were compared to the histological results as gold standard. ::: ::: ::: RESULTS ::: Of 195 included lesions histopathological analysis revealed 40 melanomas, 42 dysplastic nevi and 113 benign nevi. The sensitivity of the diagnosis melanoma by fractal image analysis using smartphone images was 73%, the specificity was 83% compared to a sensitivity of 88% and specificity of 97% regarding the clinical diagnosis by the dermatologists. ::: ::: ::: CONCLUSION ::: The smartphone application using fractal analysis might be a promising tool in the pre-evaluation of pigmented moles by laypersons, while it is to date inferior to the diagnostic evaluation by a dermatologist.
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paper_title: Quantification of skin lesions with a 3D stereovision camera system: validation and clinical applications.

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BACKGROUND/PURPOSE ::: Three-dimensional (3D) imaging of the skin is a challenging technique. A new 3D digital camera system has been developed that enables 3D reconstruction of the skin and subsequently allows volumetric quantification. Herein we present validation data on calibrated phantoms and the clinical application of this technology. ::: ::: ::: METHODS ::: Absolute and relative geometric 3D measurements were validated with a static imaging phantom manufactured by a metrology institution and a dynamic imaging phantom adjustable for different volume quantities, respectively. Consecutively, in a clinical study, 3D baseline and follow up images from 27 basal cell carcinomas under topical therapy were captured for volumetric analysis. ::: ::: ::: RESULTS ::: Validation experiments have demonstrated an average accuracy for surface position of 55 μm and a precision of 8 μm, as well as excellent correlation (0.999) between injected and measured volumes. The geometric baseline analysis of 27 basal cell carcinomas exhibited a high correlation and agreement between 2D and 3D surface measurements. Under topical therapy, it was possible to gain statistically significant differences between verum- and vehicle-treated basal cell carcinomas when analyzing geometric measurements of 3D volume (P = 0.01) and 3D surface (P = 0.001). ::: ::: ::: CONCLUSION ::: In our study we were able to demonstrate that this newly developed 3D camera system offers a precise objective dimensional representation of the skin. This technique is easily applicable and sensitive enough to measure small differences in area and volume before and after intervention.
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paper_title: Automated skin lesion assessment using mobile technologies and cloud platforms

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This paper presents a smart phone based system for storing digital images of skin areas depicting regions of interest (lesions) and performing self-assessment of these skin lesions within these areas. The system consists of a mobile application that can acquire and identify moles in skin images and classify them according their severity into melanoma, nevus and benign lesions. The proposed system includes also a cloud infrastructure exploiting computational and storage resources. This cloud-based architecture provides interoperability and support of various mobile environments as well as flexibility in enhancing the classification model. Initial evaluation results are quite promising and indicate that the application can be used for the task of skin lesions initial assessment.
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paper_title: DERMA/Care: An Advanced image-Processing Mobile Application for Monitoring Skin Cancer

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This paper describes a mobile hardware/software system (DERMA/care) to help with screening of skin cancer (melanomas). Our system uses an inexpensive apparatus (microscope) and a smart phone (iPhone). These two components standalone are sufficient to capture highly detailed images for use by experts with medical background. However the novelty of our system lies in the fact that we further improved the efficiency of the system by implementing an advanced image-processing framework to detect suspicious areas and help with skin cancer prevention. Our main goal was to demonstrate how smart phones could turn into powerful and intelligent machines and help large populations without expertise in low-resource settings.
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paper_title: State of the art in non-invasive imaging of cutaneous melanoma.

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BACKGROUND ::: This review focuses on looking at recent developments in the non-invasive imaging of skin, in particular at how such imaging may be used at present or in the future to detect cutaneous melanoma. ::: ::: ::: METHODS ::: A MEDLINE search was performed for papers using imaging techniques to evaluate cutaneous melanoma, including melanoma metastasis. ::: ::: ::: RESULTS ::: Nine different techniques were found: dermoscopy, confocal laser scanning microscopy (including multiphoton microscopy), optical coherence tomography, high frequency ultrasound, positron emission tomography, magnetic resonance imaging, and Fourier, Raman, and photoacoustic spectroscopies. This review contrasts the effectiveness of these techniques when seeking to image melanomas in skin. ::: ::: ::: CONCLUSIONS ::: Despite the variety of techniques available for detecting melanoma, there remains a critical need for a high-resolution technique to answer the question of whether tumours have invaded through the basement membrane.
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paper_title: Current technologies for the in vivo diagnosis of cutaneous melanomas

paper_content:

Abstract The rising incidence of cutaneous malignant melanoma has been observed in the past decades.1,2 Currently, there is no cure for metastatic melanoma; only early diagnosis followed by prompt excision of cutaneous lesions ensures a good prognosis. The clinical ABCD rule3 is created as a framework for differentiating melanomas from benign pigmented skin lesions, and it serves as the basis for current clinical diagnosis. The ABCD rule relies on four simple clinical morphologies of melanoma: 1) A symmetry, 2) B order irregularity, 3) C olor variegation, and 4) D iameter greater than 6 mm. Although it is valuable, it has its limitations. Currently, the diagnostic accuracy for physicians is about 65%.4 This statistic implies that 1) melanomas with subtle signs are missed as benign lesions, and 2) benign lesions are over diagnosed as melanomas, which lead to unnecessary biopsies.
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paper_title: SkinScan©: A portable library for melanoma detection on handheld devices

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We have developed a portable library for automated detection of melanoma termed SkinScan© that can be used on smartphones and other handheld devices. Compared to desktop computers, embedded processors have limited processing speed, memory, and power, but they have the advantage of portability and low cost. In this study we explored the feasibility of running a sophisticated application for automated skin cancer detection on an Apple iPhone 4. Our results demonstrate that the proposed library with the advanced image processing and analysis algorithms has excellent performance on handheld and desktop computers. Therefore, deployment of smartphones as screening devices for skin cancer and other skin diseases can have a significant impact on health care delivery in underserved and remote areas.
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paper_title: Melanoma and Other Skin Lesion Detection Using Smart Handheld Devices

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Smartphones of the latest generation featuring advanced multicore processors, dedicated microchips for graphics, high-resolution cameras, and innovative operating systems provide a portable platform for running sophisticated medical screening software and delivering point-of-care patient diagnostic services at a very low cost. In this chapter, we present a smartphone digital dermoscopy application that can analyze high-resolution images of skin lesions and provide the user with feedback about the likelihood of malignancy. The same basic procedure has been adapted to evaluate other skin lesions, such as the flesh-eating bacterial disease known as Buruli ulcer. When implemented on the iPhone, the accuracy and speed achieved by this application are comparable to that of a desktop computer, demonstrating that smartphone applications can combine portability and low cost with high performance. Thus, smartphone-based systems can be used as assistive devices by primary care physicians during routine office visits, and they can have a significant impact in underserved areas and in developing countries, where health-care infrastructure is limited.
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paper_title: Noninvasive imaging of skin tumors.

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In this article, the authors review different approaches to the diagnosis of skin tumors using noninvasive diagnostic tools, which are becoming increasingly reliable and, as a consequence, increasingly popular among physicians and patients. Especially in the realm of pigmented skin lesions, dermoscopy and sonography may add useful information to the clinical constellation, improving the diagnostic performance for early diagnosis of melanoma and for differentiating various melanocytic and nonmelanocytic pigmented lesions. More recently, confocal scanning laser microscopy was introduced as a novel technique that enables the in vivo study of the skin at a nearly histologic resolution, being of diagnostic value in various skin disorders, including basal cell carcinoma and pigmented skin lesions. These modalities have various other potential applications besides diagnosis, including lesion's selection for biopsy, determination of appropriate therapeutic modalities, verification of treatment efficacy, and decision of surgical margins. Finally, a hint to the use of cytodiagnosis for basal cell carcinoma is provided.
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paper_title: Lesion border detection in dermoscopy images using bilateral filter

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This paper proposes an efficient way to effectively segment malignant melanoma in color dermoscopy images. A combination of methods are used in the proposed technique, including smoothing filters, PSNR, Spline, edge detection, morphological operations and segmentation. The pre-processing step eliminates noise, smoothes the image and employs the spline function to improve edge detection, while morphological operations are used to segment the lesion from image. Manual boundary selection is used as benchmark to test the accuracy of the automatic boundary selection by the proposed algorithm. The evaluation results show that the proposed method (Bilt-Sp) is able to achieve a good accuracy of 96.26%, supporting the effectiveness of the proposed method in automatically detecting skin lesions.
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paper_title: Bilateral filtering for gray and color images

paper_content:

Bilateral filtering smooths images while preserving edges, by means of a nonlinear combination of nearby image values. The method is noniterative, local, and simple. It combines gray levels or colors based on both their geometric closeness and their photometric similarity, and prefers near values to distant values in both domain and range. In contrast with filters that operate on the three bands of a color image separately, a bilateral filter can enforce the perceptual metric underlying the CIE-Lab color space, and smooth colors and preserve edges in a way that is tuned to human perception. Also, in contrast with standard filtering, bilateral filtering produces no phantom colors along edges in color images, and reduces phantom colors where they appear in the original image.
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paper_title: Multilateral filtering: A novel framework for generic similarity-based image denoising

paper_content:

We present a novel iterative nonlinear filtering framework, termed multilateral filtering, based on the idea of generic local similarity. A set of local features is computed for each pixel using its local neighborhood. Two pixels are considered to be similar if the Euclidean distance between their corresponding feature vectors is small and vice versa. Multilateral filtering results in image smoothing while preserving edge and textural features. Our experimental results show that the proposed method produces comparable and often better results than the state-of-the-art denoising methods.
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paper_title: Scale-space and edge detection using anisotropic diffusion

paper_content:

A new definition of scale-space is suggested, and a class of algorithms used to realize a diffusion process is introduced. The diffusion coefficient is chosen to vary spatially in such a way as to encourage intraregion smoothing rather than interregion smoothing. It is shown that the 'no new maxima should be generated at coarse scales' property of conventional scale space is preserved. As the region boundaries in the approach remain sharp, a high-quality edge detector which successfully exploits global information is obtained. Experimental results are shown on a number of images. Parallel hardware implementations are made feasible because the algorithm involves elementary, local operations replicated over the image. >
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paper_title: Image Processing, Analysis and Machine Vision

paper_content:

List of Algorithms. Preface. Possible Course Outlines. 1. Introduction. 2. The Image, Its Representations and Properties. 3. The Image, Its Mathematical and Physical Background. 4. Data Structures for Image Analysis. 5. Image Pre-Processing. 6. Segmentation I. 7. Segmentation II. 8. Shape Representation and Description. 9. Object Recognition. 10. Image Understanding. 11. 3d Geometry, Correspondence, 3d from Intensities. 12. Reconstruction from 3d. 13. Mathematical Morphology. 14. Image Data Compression. 15. Texture. 16. Motion Analysis. Index.
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paper_title: Snakes , Shapes , and Gradient Vector Flow

paper_content:

Snakes, or active contours, are used extensively in computer vision and image processing applications, particularly to locate object boundaries. Problems associated with initialization and poor convergence to boundary concavities, however, have limited their utility. This paper presents a new external force for active contours, largely solving both problems. This external force, which we call gradient vector flow (GVF), is computed as a diffusion of the gradient vectors of a gray-level or binary edge map derived from the image. It differs fundamentally from traditional snake external forces in that it cannot be written as the negative gradient of a potential function, and the corresponding snake is formulated directly from a force balance condition rather than a variational formulation. Using several two-dimensional (2-D) examples and one three-dimensional (3-D) example, we show that GVF has a large capture range and is able to move snakes into boundary concavities.
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paper_title: Bilateral filtering for gray and color images

paper_content:

Bilateral filtering smooths images while preserving edges, by means of a nonlinear combination of nearby image values. The method is noniterative, local, and simple. It combines gray levels or colors based on both their geometric closeness and their photometric similarity, and prefers near values to distant values in both domain and range. In contrast with filters that operate on the three bands of a color image separately, a bilateral filter can enforce the perceptual metric underlying the CIE-Lab color space, and smooth colors and preserve edges in a way that is tuned to human perception. Also, in contrast with standard filtering, bilateral filtering produces no phantom colors along edges in color images, and reduces phantom colors where they appear in the original image.
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paper_title: Type-2 Fuzzy Sets Made Simple

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Type-2 fuzzy sets let us model and minimize the effects of uncertainties in rule-base fuzzy logic systems. However, they are difficult to understand for a variety of reasons which we enunciate. In this paper, we strive to overcome the difficulties by: (1) establishing a small set of terms that let us easily communicate about type-2 fuzzy sets and also let us define such sets very precisely, (2) presenting a new representation for type-2 fuzzy sets, and (3) using this new representation to derive formulas for union, intersection and complement of type-2 fuzzy sets without having to use the Extension Principle.
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paper_title: An Improved Iterative Segmentation Algorithm using Canny Edge Detector with Iterative Median Filter for Skin Lesion Border Detection

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system for the computer-aided diagnosis of melanoma, provides quantitative and objective evaluation of the skin lesion, as opposed to visual assessment, which is subjective in nature, is comprised of four major components: skin image acquisition, lesion segmentation, feature extraction, and lesion classification. Automatic segmentation of lesions in color skin images, which is the main focus of this paper, is one of the most important steps towards the automated analysis and evaluation of dermoscopy images in the computer aided diagnosis of melanoma. The accuracy of segmentation is highly dependent on the success or failure of each computerized analysis procedure. An improved iterative segmentation algorithm using canny edge detector with iterative median filter, for border detection of real skin lesions is presented ,which helps in early detection of malignant melanoma and its performance is compared with the segmentation algorithm using canny detector (1) developed by us previously for border detection of real skin lesions. The experimental results demonstrate the successful border detection of noisy real skin lesions by the proposed segmentation algorithm. We conclude that the proposed, improved iterative segmentation algorithm using canny detector with iterative filtering, segments the lesion from the image even in the presence of noise for a variety of lesions, and skin types and its performance is better than the segmentation algorithm (1) that we have developed previously that uses canny detector, for border detection of real skin lesions.
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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: Toward a combined tool to assist dermatologists in melanoma detection from dermoscopic images of pigmented skin lesions

paper_content:

Highlights? Classification of melanocytic lesions as malignant or benign from dermoscopic images. ? Border, texture, color and structure features inspired by the clinical practice. ? Method designed and tested on a database of 655 images of melanocytic lesions. ? Automatic lesion segmentation. Classification based on AdaBoost with decision trees. ? Sensitivity/specificity: 90%/77% (90%/85%) for automatic (manual) segmentation. In this paper we propose a machine learning approach to classify melanocytic lesions as malignant or benign, using dermoscopic images. The lesion features used in the classification framework are inspired on border, texture, color and structures used in popular dermoscopy algorithms performed by clinicians by visual inspection. The main weakness of dermoscopy algorithms is the selection of a set of weights and thresholds, that appear not to be robust or independent of population. The use of machine learning techniques allows to overcome this issue. The proposed method is designed and tested on an image database composed of 655 images of melanocytic lesions: 544 benign lesions and 111 malignant melanoma. After an image pre-processing stage that includes hair removal filtering, each image is automatically segmented using well known image segmentation algorithms. Then, each lesion is characterized by a feature vector that contains shape, color and texture information, as well as local and global parameters. The detection of particular dermoscopic patterns associated with melanoma is also addressed, and its inclusion in the classification framework is discussed. The learning and classification stage is performed using AdaBoost with C4.5 decision trees. For the automatically segmented database, classification delivered a specificity of 77% for a sensitivity of 90%. The same classification procedure applied to images manually segmented by an experienced dermatologist yielded a specificity of 85% for a sensitivity of 90%.
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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: Hybrid genetic algorithm — Artificial neural network classifier for skin cancer detection

paper_content:

Skin cancer is a deadly form of cancer, affecting skin. It is one of the very deadly form of cancer. It occurs on the melanocytic cells of skin. So skin cancer is also known as Melanoma. Due to the malignancy feature it is also known as malignant melanoma. Melanoma causes the abnormal growth of melanocytic cells which produces the sun protective pigment melanin. Due to that reason melanoma appears as black or brown in color. Main factors that cause melanoma are over exposure to sunlight and genetic factors. Ultraviolet (UV) radiation from the sun is important for maintaining vitamin D levels in the body, but too much UV can cause sunburn, premature ageing, skin and damage and ultimately skin cancer. Early detection can cure skin cancer completely. The probability of cure decreases if it is not treated at early stages. Diagnosing methods like Biopsy, SIAscopy are painful and time consuming. An economical, less time consuming, painless Computer Aided Skin Cancer detection is proposed here. This methodology uses Digital Image processing and Artificial intelligence for skin cancer detection. This methodology involves no direct contact with skin. Only the dermoscopic image is used here. The image after certain image processing techniques is subjected to segmentation. After segmentation, the unique features are extracted from the image using feature extraction techniques. The feature extraction technique used here is GLCM(Gray Level Co-occurrence Matrix) and RGB color feature. These features are used for classification. Artificial neural network classifier is used for classification. In order to improve the accuracy of classification, the ANN is optimized by Genetic Algorithm.
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paper_title: Multilateral filtering: A novel framework for generic similarity-based image denoising

paper_content:

We present a novel iterative nonlinear filtering framework, termed multilateral filtering, based on the idea of generic local similarity. A set of local features is computed for each pixel using its local neighborhood. Two pixels are considered to be similar if the Euclidean distance between their corresponding feature vectors is small and vice versa. Multilateral filtering results in image smoothing while preserving edge and textural features. Our experimental results show that the proposed method produces comparable and often better results than the state-of-the-art denoising methods.
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paper_title: Image segmentation based on Edge detection using boundary code

paper_content:

Image segmentation is an important basic process in image analysis. It is used in several processes, which receive the input of more advanced processes, the result of which will affect the accuracy of overall results significantly. Edge detection is the basic process of image segmentation. The Sobel filter, Derivative of Gaussian and Laplace of Gaussian are well-known methods of edge detection. However, these methods cannot detect edge details and it remains difficult to segment thin areas because the edge pixels obliterate details. For example, in a text image, the details of the characters are obliterated by using these methods. We propose an image segmentation method by using boundary code to solve this problem. Thin areas are segmented continuously by using our proposed segmentation method. In an experiment, we compare the proposed method with existing methods. We use binarization with a discriminant analysis method and a Sobel filter to compare our proposed method. According to the experimental result, the proposed method segments more accurately than existing methods in images that have a narrow elongated object, shading and blurring.
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paper_title: Region-based segmentation and object detection

paper_content:

Object detection and multi-class image segmentation are two closely related tasks that can be greatly improved when solved jointly by feeding information from one task to the other [10, 11]. However, current state-of-the-art models use a separate representation for each task making joint inference clumsy and leaving the classification of many parts of the scene ambiguous. ::: ::: In this work, we propose a hierarchical region-based approach to joint object detection and image segmentation. Our approach simultaneously reasons about pixels, regions and objects in a coherent probabilistic model. Pixel appearance features allow us to perform well on classifying amorphous background classes, while the explicit representation of regions facilitate the computation of more sophisticated features necessary for object detection. Importantly, our model gives a single unified description of the scene—we explain every pixel in the image and enforce global consistency between all random variables in our model. ::: ::: We run experiments on the challenging Street Scene dataset [2] and show significant improvement over state-of-the-art results for object detection accuracy.
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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: Skin lesion segmentation using an improved snake model

paper_content:

Accurate identification of lesion borders is an important task in the analysis of dermoscopy images since the extraction of skin lesion borders provides important cues for accurate diagnosis. Snakes have been used for segmenting a variety of medical imagery including dermoscopy, however, due to the compromise of internal and external energy forces they can lead to under- or over-segmentation problems. In this paper, we introduce a mean shift based gradient vector flow (GVF) snake algorithm that drives the internal/external energies towards the correct direction. The proposed segmentation method incorporates a mean shift operation within the standard GVF cost function. Experimental results on a large set of diverse dermoscopy images demonstrate that the presented method accurately determines skin lesion borders in dermoscopy images.
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paper_title: Border detection in dermoscopy images using hybrid thresholding on optimized color channels

paper_content:

Automated border detection is one of the most important steps in dermoscopy image analysis. Although numerous border detection methods have been developed, few studies have focused on determining the optimal color channels for border detection in dermoscopy images. This paper proposes an automatic border detection method which determines the optimal color channels and performs hybrid thresholding to detect the lesion borders. The color optimization process is tested on a set of 30 dermoscopy images with four sets of dermatologist-drawn borders used as the ground truth. The hybrid border detection method is tested on a set of 85 dermoscopy images with two sets of ground truth using various metrics including accuracy, precision, sensitivity, specificity, and border error. The proposed method, which is comprised of two stages, is designed to increase specificity in the first stage and sensitivity in the second stage. It is shown to be highly competitive with three state-of-the-art border detection methods and potentially faster, since it mainly involves scalar processing as opposed to vector processing performed in the other methods. Furthermore, it is shown that our method is as good as, and in some cases more effective than a dermatology registrar.
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paper_title: Scale-space and edge detection using anisotropic diffusion

paper_content:

A new definition of scale-space is suggested, and a class of algorithms used to realize a diffusion process is introduced. The diffusion coefficient is chosen to vary spatially in such a way as to encourage intraregion smoothing rather than interregion smoothing. It is shown that the 'no new maxima should be generated at coarse scales' property of conventional scale space is preserved. As the region boundaries in the approach remain sharp, a high-quality edge detector which successfully exploits global information is obtained. Experimental results are shown on a number of images. Parallel hardware implementations are made feasible because the algorithm involves elementary, local operations replicated over the image. >
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paper_title: Image Segmentation by Using Threshold Techniques

paper_content:

This paper attempts to undertake the study of segmentation image techniques by using five threshold methods as Mean method, P-tile method, Histogram Dependent Technique (HDT), Edge Maximization Technique (EMT) and visual Technique and they are compared with one another so as to choose the best technique for threshold segmentation techniques image. These techniques applied on three satellite images to choose base guesses for threshold segmentation image.
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paper_title: A Color and Texture Based Hierarchical K-NN Approach to the Classification of Non-melanoma Skin Lesions

paper_content:

This chapter proposes a novel hierarchical classification system based on the K-Nearest Neighbors (K-NN) model and its application to non-melanoma skin lesion classification. Color and texture features are extracted from skin lesion images. The hierarchical structure decomposes the classification task into a set of simpler problems, one at each node of the classification. Feature selection is embedded in the hierarchical framework that chooses the most relevant feature subsets at each node of the hierarchy. The accuracy of the proposed hierarchical scheme is higher than 93 % in discriminating cancer and potential at risk lesions from benign lesions, and it reaches an overall classification accuracy of 74 % over five common classes of skin lesions, including two non-melanoma cancer types. This is the most extensive known result on non-melanoma skin cancer classification using color and texture information from images acquired by a standard camera (non-dermoscopy).
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paper_title: Image Processing, Analysis and Machine Vision

paper_content:

List of Algorithms. Preface. Possible Course Outlines. 1. Introduction. 2. The Image, Its Representations and Properties. 3. The Image, Its Mathematical and Physical Background. 4. Data Structures for Image Analysis. 5. Image Pre-Processing. 6. Segmentation I. 7. Segmentation II. 8. Shape Representation and Description. 9. Object Recognition. 10. Image Understanding. 11. 3d Geometry, Correspondence, 3d from Intensities. 12. Reconstruction from 3d. 13. Mathematical Morphology. 14. Image Data Compression. 15. Texture. 16. Motion Analysis. Index.
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paper_title: A Computational Approach to Edge Detection

paper_content:

This paper describes a computational approach to edge detection. The success of the approach depends on the definition of a comprehensive set of goals for the computation of edge points. These goals must be precise enough to delimit the desired behavior of the detector while making minimal assumptions about the form of the solution. We define detection and localization criteria for a class of edges, and present mathematical forms for these criteria as functionals on the operator impulse response. A third criterion is then added to ensure that the detector has only one response to a single edge. We use the criteria in numerical optimization to derive detectors for several common image features, including step edges. On specializing the analysis to step edges, we find that there is a natural uncertainty principle between detection and localization performance, which are the two main goals. With this principle we derive a single operator shape which is optimal at any scale. The optimal detector has a simple approximate implementation in which edges are marked at maxima in gradient magnitude of a Gaussian-smoothed image. We extend this simple detector using operators of several widths to cope with different signal-to-noise ratios in the image. We present a general method, called feature synthesis, for the fine-to-coarse integration of information from operators at different scales. Finally we show that step edge detector performance improves considerably as the operator point spread function is extended along the edge.
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paper_title: Neuro-fuzzy analysis of dermatological images

paper_content:

The employment of image processing techniques appears to be wide-spreading in several application areas, with special reference to the medical context. A neuro-fuzzy approach for pixel classification is detailed and its application is proposed to the analysis of dermatological images of nevi. An ensemble of experimental results is provided to demonstrate the usefulness of the introduced methodology and to show the possible benefits deriving from its employment in clinical diagnosis.
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paper_title: Perceptual image segmentation using fuzzy-based hierarchical algorithm and its application to dermoscopy images

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This paper proposes perceptual segmentation of natural color images using a fuzzy-based hierarchical algorithm and its application to the segmentation of dermoscopy images. A fuzzy-based homogeneity measure makes a fusion of the color features and the texture features. The proposed hierarchical segmentation method is performed in four stages: simple splitting, local merging, global merging and boundary refinement. The effectiveness of the proposed method is confirmed through computer simulations that demonstrate the applicability of the proposed method to the segmentation of natural color images and dermoscopy images.
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paper_title: Fronts propagating with curvature dependent speed: algorithms based on Hamilton–Jacobi formulations

paper_content:

We devise new numerical algorithms, called PSC algorithms, for following fronts propagating with curvature-dependent speed. The speed may be an arbitrary function of curvature, and the front also can be passively advected by an underlying flow. These algorithms approximate the equations of motion, which resemble Hamilton-Jacobi equations with parabolic right-hand sides, by using techniques from hyperbolic conservation laws. Non-oscillatory schemes of various orders of accuracy are used to solve the equations, providing methods that accurately capture the formation of sharp gradients and cusps in the moving fronts. The algorithms handle topological merging and breaking naturally, work in any number of space dimensions, and do not require that the moving surface be written as a function. The methods can be also used for more general Hamilton-Jacobi-type problems. We demonstrate our algorithms by computing the solution to a variety of surface motion problems.
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paper_title: Snakes: Active contour models

paper_content:

A snake is an energy-minimizing spline guided by external constraint forces and influenced by image forces that pull it toward features such as lines and edges. Snakes are active contour models: they lock onto nearby edges, localizing them accurately. Scale-space continuation can be used to enlarge the capture region surrounding a feature. Snakes provide a unified account of a number of visual problems, including detection of edges, lines, and subjective contours; motion tracking; and stereo matching. We have used snakes successfully for interactive interpretation, in which user-imposed constraint forces guide the snake near features of interest.
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paper_title: Number-Driven Perceptual Segmentation of Natural Color Images for Easy Decision of Optimal Result

paper_content:

This paper proposes number-driven perceptual segmentation of natural color images using a fuzzy-based hierarchical algorithm for an easy decision of the optimal segmentation result. A fuzzy-based homogeneity measure makes a fusion of the L*a*b* color features and the SGF texture features. Proposed hierarchical segmentation method is performed in four stages: simple splitting, local merging, global merging and boundary refinement. The effectiveness of the proposed method is confirmed through computer simulations that demonstrate an easy determination of the optimal segmentation result.
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paper_title: Active Contours without Edges

paper_content:

We propose a new model for active contours to detect objects in a given image, based on techniques of curve evolution, Mumford-Shah (1989) functional for segmentation and level sets. Our model can detect objects whose boundaries are not necessarily defined by the gradient. We minimize an energy which can be seen as a particular case of the minimal partition problem. In the level set formulation, the problem becomes a "mean-curvature flow"-like evolving the active contour, which will stop on the desired boundary. However, the stopping term does not depend on the gradient of the image, as in the classical active contour models, but is instead related to a particular segmentation of the image. We give a numerical algorithm using finite differences. Finally, we present various experimental results and in particular some examples for which the classical snakes methods based on the gradient are not applicable. Also, the initial curve can be anywhere in the image, and interior contours are automatically detected.
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paper_title: An approach to edge detection in images of skin lesions by Chan-Vese model

paper_content:

Nowadays there is a great interest in the application of computational systems for the analysis of skin lesions. These systems allow the dermatologist to prevent the development of malignant lesions. The development of the systems has occurred due to the increase of skin cancer cases. In the characterization of skin lesions it is necessary to segment the images accurately. Thus the features and edges information of the lesion can be extracted and used by a classifier or by a dermatologist for a better classification. When images are acquired in a non-systematic and non-controlled way there may be a segmentation problem. In this case the skin lesion of images can have different sizes and various type of noises, such as the hair. These factors can affect the detection of the lesion edges and complicate its characterization. One solution would be to apply a smoothing filter to reduce noise before the segmentation step. Segmentation techniques adapted to each type of image can be used to solve the problem of diversified images, such as images with different sizes lesions, reflexions and light intensities. In this paper is proposed a computational method to assist the dermatologists in the diagnosis of skin lesions by digital images. It was used the anisotropic diffusion technique for the preprocessing of the images in order to remove the noises. The Chan-Vese model was used to segment the lesions. The next step consists of the application of morphological filters to eliminate outside and inside noises from the object, that remained in the segmented images, and also to smooth their edges. This approach allowed to minimize noise problems and edge detection to different cases of skin lesions images, such as melanoma, melanocytic nevi and seborrheic keratosis. The segmentation achieved 94.36% of accuracy for the three types of skin lesions. Keywords—Skin Lesions; Anisotropic Diffusion Filter; ChanVese Model; Morphological Filters .
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paper_title: Photoelectric Color Difference Meter

paper_content:

The color difference meter has three photodetectors, each with a separate tristimulus filter, and each receiving some of the light reflected from the specimen. Signals from the photodetectors are measured by analog circuits that give rectangular coordinates for surface colors in close correspondence to their positions in uniform color space. The first model described in 1948 uses barrier-layer photocells and three tristimulus filters. Recently, a model employing vacuum phototubes and four filters has been built. Use of vacuum phototubes makes it possible to substitute a dc amplifier and pivot meter for the suspension galvanometer necessary with barrier-layer photocells. By thermostatting the phototube chamber, excellent stability is obtained. A light pipe in the viewing beam provides a more stable and efficient mixer of light to the different photodetectors than the white-lined sphere used previously.
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paper_title: Two Systems for the Detection of Melanomas in Dermoscopy Images Using Texture and Color Features

paper_content:

Melanoma is one of the deadliest forms of cancer; hence, great effort has been put into the development of diagnosis methods for this disease. This paper addresses two different systems for the detection of melanomas in dermoscopy images. The first system uses global methods to classify skin lesions, whereas the second system uses local features and the bag-of-features classifier. This paper aims at determining the best system for skin lesion classification. The other objective is to compare the role of color and texture features in lesion classification and determine which set of features is more discriminative. It is concluded that color features outperform texture features when used alone and that both methods achieve very good results, i.e., Sensitivity = 96% and Specificity = 80% for global methods against Sensitivity = 100% and Specificity = 75% for local methods. The classification results were obtained on a data set of 176 dermoscopy images from Hospital Pedro Hispano, Matosinhos.
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paper_title: Snakes , Shapes , and Gradient Vector Flow

paper_content:

Snakes, or active contours, are used extensively in computer vision and image processing applications, particularly to locate object boundaries. Problems associated with initialization and poor convergence to boundary concavities, however, have limited their utility. This paper presents a new external force for active contours, largely solving both problems. This external force, which we call gradient vector flow (GVF), is computed as a diffusion of the gradient vectors of a gray-level or binary edge map derived from the image. It differs fundamentally from traditional snake external forces in that it cannot be written as the negative gradient of a potential function, and the corresponding snake is formulated directly from a force balance condition rather than a variational formulation. Using several two-dimensional (2-D) examples and one three-dimensional (3-D) example, we show that GVF has a large capture range and is able to move snakes into boundary concavities.
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paper_title: A fast parallel algorithm for thinning digital patterns

paper_content:

A fast parallel thinning algorithm is proposed in this paper. It consists of two subiterations: one aimed at deleting the south-east boundary points and the north-west corner points while the other one is aimed at deleting the north-west boundary points and the south-east corner points. End points and pixel connectivity are preserved. Each pattern is thinned down to a skeleton of unitary thickness. Experimental results show that this method is very effective. 12 references.
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paper_title: Principal Axes-Based Asymmetry Assessment Methodology for Skin Lesion Image Analysis

paper_content:

Skin cancer is the most common of all cancer types and Malignant Melanoma is the most dangerous form of it, thus prevention is vital. Risk assessment of skins lesions is usually done through the ABCD rule (asymmetry, border, color and differential structures) that classifies the lesion as benign, suspicious or highly suspicious of Malignant Melanoma. A methodology to assess the asymmetry of a skin lesion image in relation to each axis of inertia, for both dermoscopic and mobile acquired images, is presented. It starts by extracting a set of 310 of asymmetry features, followed by testing several feature selection and machine learning classification methods in order to minimize the classification error. For dermoscopic images, the developed methodology achieves an accuracy of 87% regarding asymmetry classification while, for mobile acquired images the accuracy reaches 73.1%.
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paper_title: Fractional Brownian Motion: A Maximum Likelihood Estimator and Its Application to Image Texture

paper_content:

Fractals have been shown to be useful in characterizing texture in a variety of contexts. Use of this methodology normally involves measurement of a parameter H, which is directly related to fractal dimension. In this work the basic theory of fractional Brownian motion is extended to the discrete case. It is shown that the power spectral density of such a discrete process is only approximately proportional to |f|a instead of in direct proportion as in the continuous case. An asymptotic Cramer-Rao bound is derived for the variance of an estimate of H. Subsequently, a maximum likelihood estimator (MLE) is developed to estimate H. It is shown that the variance of this estimator nearly achieves the minimum bound. A generation algorithm for discrete fractional motion is presented and used to demonstrate the capabilities of the MLE when the discrete fractional Brownian process is contaminated with additive Gaussian noise. The results show that even at signal-to-noise ratios of 30 dB, significant errors in estimation of H can result when noise is present. The MLE is then applied to X-ray images of the human calcaneus to demonstrate how the line-to-line formulation can be applied to the two-dimensional case. These results indicate that it has strong potential for quantifying texture.
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paper_title: Melanoma Classification Based on Ensemble Classification of Dermoscopy Image Features

paper_content:

Malignant melanoma is the deadliest form of skin cancer and is one of the most rapidly increasing cancers in the world. If diagnosed early, it can be easily cured, and consequently early diagnosis of melanoma is of vital importance. In this paper, we present an effective approach to melanoma classification from dermoscopy images of skin lesions. First, we perform automatic border detection to delineate the lesion from the background skin. We then extract shape features from the border, while colour and texture features are obtained based on a division of the image into clinically significant regions using a Euclidean distance transform. The derived features are then used in a pattern classification stage for which we employ a dedicated ensemble learning approach to address the class imbalance in the training data. In particular, we employ a committee of one-class classifiers for that purpose. One-class classification uses samples from a single distribution to derive a decision boundary, and employing this method on the minority class can significantly boost its recognition rate and hence the sensitivity of our approach. We combine several one-class classifiers using a random subspace approach and a diversity measure to select members of the committee. Experimental results on a large dataset of dermoscopic skin lesion images show our approach to work well, the employed classifier selection stage to be crucial for achieving this performance, and the classifier ensembles to perform statistically better compared to several state-of-the-art ensembles.
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paper_title: Textural Features for Image Classification

paper_content:

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: Texture Analysis Methods for Medical Image Characterisation

paper_content:

Texture analysis refers to the branch of imaging science that is concerned with the description of characteristic image properties by textural features. However, there is no universally agreed-upon definition of what image texture is and in general different researchers use different definitions depending upon the particular area of application (Tuceryan & Jain, 1998). In this chapter texture is defined as the spatial variation of pixel intensities, which is a definition that is widely used and accepted in the field. The main image processing disciplines in which texture analysis techniques are used are classification, segmentation and synthesis. In image classification the goal is to classify different images or image regions into distinct groups (Pietikainen, 2000). Texture analysis methods are well suited to this because they provide unique information on the texture, or spatial variation of pixels, of the region where they are applied. In image segmentation problems the aim is to establish boundaries between different image regions (Mirmehdi et al., 2008). By applying texture analysis methods to an image, and determining the precise location where texture feature values change significantly, boundaries between regions can be established. Synthesising image texture is important in three-dimensional (3D) computer graphics applications where the goal is to generate highly complex and realistic looking surfaces. Fractals have proven to be a mathematically elegant means of generating textured surfaces through the iteration of concise equations (Pentland, 1984). Conversely the ability to accurately represent a textured surface by a concise set of fractal equations has led to significant advances in image compression applications using fractal methods (Distani et al., 2006)
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paper_title: Color Feature Detection

paper_content:

The detection and classification of local structures (i.e., edges, corners, an T-junctions) in color images is important for many applications, such as image segmentation, image matching, object recognition, and visual tracking in the fields of image processing and computer vision. In general, those local image structures are detected by differential operators that are commonly restricted to luminance information. However, most of the images recorded today are in color. Therefore, in this chapter, the focus is on the use of color information to detect and classify local image features.
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paper_title: Automated Quantification of Clinically Significant Colors in Dermoscopy Images and Its Application to Skin Lesion Classification

paper_content:

Dermoscopy is a noninvasive skin imaging technique, which permits visualization of features of pigmented melanocytic neoplasms that are not discernable by examination with the naked eye. Color information is indispensable for the clinical diagnosis malignant melanoma, the most deadly form of skin cancer. For this reason, most of the currently accepted dermoscopic scoring systems either directly or indirectly incorporate color as a diagnostic criterion. For example, both the asymmetry, border, colors, and dermoscopic (ABCD) rule of dermoscopy and the more recent color, architecture, symmetry, and homogeneity (CASH) algorithm include the number of clinically significant colors in their calculation of malignancy scores. In this paper, we present a machine learning approach to the automated quantification of clinically significant colors in dermoscopy images. Given a true-color dermoscopy image with N colors, we first reduce the number of colors in this image to a small number K, i.e., K <; N, using the K-means clustering algorithm incorporating a spatial term. The optimal K value for the image is estimated separately using five commonly used cluster validity criteria. We then train a symbolic regression algorithm using the estimates given by these criteria, which are calculated on a set of 617 images. Finally, the mathematical equation given by the regression algorithm is used for two-class (benign versus malignant) classification. The proposed approach yields a sensitivity of 62% and a specificity of 76% on an independent test set of 297 images.
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paper_title: Computer applications in the diagnosis and prognosis of malignant melanoma.

paper_content:

Recent advances in computer technology have begun to make computers a more effective tool in the diagnosis and evaluation of malignant melanoma. Preliminary computer-aided diagnosis programs have been developed. Histologic evaluation applications in both diagnosis and prognosis are also evolving. Further advances in computers may make them an integral part of the diagnosis and prognosis of melanoma in the future.
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paper_title: A Novel Approach to Segment Skin Lesions in Dermoscopic Images Based on a Deformable Model

paper_content:

Dermoscopy is an imaging technique that has been widely used in the diagnosis of skin lesions. However, its accuracy largely depends on the dermatologist's experience; thus, computer-aided diagnosis techniques are required. In this paper, a novel approach based on a deformable model is proposed to handle the segmentation of skin lesions in dermoscopic images. The RGB color space is converted so that the color information contained in the images can be used effectively to differentiate normal skin and skin lesions; and the differences in the color channels are combined together to define the speed function and the stopping criterion of the deformable model. This novel approach is robust against the noise, and provides an effective and flexible segmentation. Two image databases were used to test the performance of the novel approach and the segmentation results obtained were satisfactory. Quantitative analysis on 250 dermoscopic images showed that the novel algorithm outperformed other state-of-the-art algorithms. Also, using comparative data, the reliability and the implementation issues of the approach are discussed in this paper.
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paper_title: Statistical shape discrimination and clustering using an efficient set of moments

paper_content:

Abstract This paper refers to the use of a modified set of moments for the development of efficient shape discrimination and classification techniques. These moments may be applied in general for the discrimination of other patterns as well, such as geological, medical and robotic vision signals. A new set of efficient moments is introduced, which is invariant under rotation, translation, and scale of the image. These moments are normalized with respect to standard deviation, they appear to have better classification performance over the existing sets of moments, and they are less sensitive to noise. Application of the proposed technique to the classification of silhouettes of airplanes is given. The discrimination of an unknown shape is achieved by testing a weighted least square function.
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paper_title: Quantification of skin lesions with a 3D stereovision camera system: validation and clinical applications.

paper_content:

BACKGROUND/PURPOSE ::: Three-dimensional (3D) imaging of the skin is a challenging technique. A new 3D digital camera system has been developed that enables 3D reconstruction of the skin and subsequently allows volumetric quantification. Herein we present validation data on calibrated phantoms and the clinical application of this technology. ::: ::: ::: METHODS ::: Absolute and relative geometric 3D measurements were validated with a static imaging phantom manufactured by a metrology institution and a dynamic imaging phantom adjustable for different volume quantities, respectively. Consecutively, in a clinical study, 3D baseline and follow up images from 27 basal cell carcinomas under topical therapy were captured for volumetric analysis. ::: ::: ::: RESULTS ::: Validation experiments have demonstrated an average accuracy for surface position of 55 μm and a precision of 8 μm, as well as excellent correlation (0.999) between injected and measured volumes. The geometric baseline analysis of 27 basal cell carcinomas exhibited a high correlation and agreement between 2D and 3D surface measurements. Under topical therapy, it was possible to gain statistically significant differences between verum- and vehicle-treated basal cell carcinomas when analyzing geometric measurements of 3D volume (P = 0.01) and 3D surface (P = 0.001). ::: ::: ::: CONCLUSION ::: In our study we were able to demonstrate that this newly developed 3D camera system offers a precise objective dimensional representation of the skin. This technique is easily applicable and sensitive enough to measure small differences in area and volume before and after intervention.
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paper_title: Skin lesion feature vectors classification in models of a Riemannian manifold

paper_content:

This study is a continuation of a work published by Mete, Ou, and Sirakov (2012), where a model of a 4D manifold of feature vectors was developed. The present paper introduces an improved metric in the 4D manifold first and then extends both the size of the sample space and the dimension (to 6D) of the manifold model in which the sample space lies. As a result, we not only overcame the issue of one single vector representing multiple skin lesions, which occurred in the work of Mete, Ou, and Sirakov (2012), but also improved the accuracy of classification. Furthermore, a statistical evaluation of our support vector machine (SVM) classification method was performed. The intervals of confidence were calculated for the mean of classification of a large sample set in the 6D model. Comparison results of classification with our SVM in 4D and 6D models using 10-fold cross-validation are given at the end of the paper. It is found that the 6D model improves the classification results of the previous study suggesting that two newly introduced features contributed to the increase of the classification accuracy.
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paper_title: Texture Analysis Based Automated Decision Support System for Classification of Skin Cancer Using SA-SVM

paper_content:

Early diagnosis of skin cancer is one of the greatest challenges due to lack of experience of general practitioners (GPs). This paper presents a clinical decision support system aimed to save lives, time and resources in the early diagnostic process. Segmentation, feature extraction, and lesion classification are the important steps in the proposed system. The system analyses the images to extract the affected area using a novel proposed segmentation method H-FCM-LS. A set of 45 texture based features is used. These underlying features which indicate the difference between melanoma and benign images are obtained through specialized texture analysis methods. For classification purpose, self-advising SVM is adapted which showed improved classification rate as compared to standard SVM. The diagnostic accuracy obtained through the proposed system is around 90% with sensitivity 91% and specificity 89%.
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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: SCALED CONJUGATE GRADIENT BASED DECISION SUPPORT SYSTEM FOR AUTOMATED DIAGNOSIS OF SKIN CANCER

paper_content:

Melanoma is the most deathful form of skin cancer but early diagnosis can ensure a high rate of survival. Early diagnosis is one of the greatest challenges due to lack of experience of general practitioners (GPs). This paper presents a clinical decision support system designed for the use of general practitioners, aiming to save time and resources in the diagnostic process. Segmentation, pattern recognition, and lesion detection are the important steps in the proposed decision support system. The system analyses the images to extract the affected area using a novel proposed segmentation method. It determinates the underlying features which indicate the difference between melanoma and benign images and makes a decision. Considering the efficiency of neural networks in classification of complex data, scaled conjugate gradient based neural network is used for classification. The presented work also considers analyzed performance of other efficient neural network training algorithms on the specific skin lesion diagnostic problem and discussed the corresponding findings. The best diagnostic rates obtained through the proposed decision support system are around 92%.
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paper_title: Visual pattern recognition by moment invariants

paper_content:

In this paper a theory of two-dimensional moment invariants for planar geometric figures is presented. A fundamental theorem is established to relate such moment invariants to the well-known algebraic invariants. Complete systems of moment invariants under translation, similitude and orthogonal transformations are derived. Some moment invariants under general two-dimensional linear transformations are also included. Both theoretical formulation and practical models of visual pattern recognition based upon these moment invariants are discussed. A simple simulation program together with its performance are also presented. It is shown that recognition of geometrical patterns and alphabetical characters independently of position, size and orientation can be accomplished. It is also indicated that generalization is possible to include invariance with parallel projection.
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paper_title: Estimating fractal dimension with fractal interpolation function models

paper_content:

Fractal dimension (fd) is a feature which is widely used to characterize medical images. Previously, researchers have shown that fd separates important classes of images and provides distinctive information about texture. We analyze limitations of two principal methods of estimating fd: box-counting (BC) and power spectrum (PS). BC is ineffective when applied to data-limited, low-resolution images; PS is based on a fractional Brownian motion (fBm) model-a model which is not universally applicable. We also present background information on the use of fractal interpolation function (FIF) models to estimate fd of data which can be represented in the form of a function. We present a new method of estimating fd in which multiple FIF models are constructed. The mean of the fd's of the FIF models is taken as the estimate of the fd of the original data. The standard deviation of the fd's of the FIF models is used as a confidence measure of the estimate. We demonstrate how the new method can be used to characterize fractal texture of medical images. In a pilot study, we generated plots of curvature values around the perimeters of images of red blood cells from normal and sickle cell subjects. The new method showed improved separation of the image classes when compared to BC and PS methods.
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paper_title: Four-Class Classification of Skin Lesions With Task Decomposition Strategy

paper_content:

This paper proposes a new computer-aided method for the skin lesion classification applicable to both melanocytic skin lesions (MSLs) and nonmelanocytic skin lesions (NoMSLs). The computer-aided skin lesion classification has drawn attention as an aid for detection of skin cancers. Several researchers have developed methods to distinguish between melanoma and nevus, which are both categorized as MSL. However, most of these studies did not focus on NoMSLs such as basal cell carcinoma (BCC), the most common skin cancer and seborrheic keratosis (SK) despite their high incidence rates. It is preferable to deal with these NoMSLs as well as MSLs especially for the potential users who are not enough capable of diagnosing pigmented skin lesions on their own such as dermatologists in training and physicians with different expertise. We developed a new method to distinguish among melanomas, nevi, BCCs, and SKs. Our method calculates 828 candidate features grouped into three categories: color, subregion, and texture. We introduced two types of classification models: a layered model that uses a task decomposition strategy and flat models to serve as performance baselines. We tested our methods on 964 dermoscopy images: 105 melanomas, 692 nevi, 69 BCCs, and 98 SKs. The layered model outperformed the flat models, achieving detection rates of 90.48%, 82.51%, 82.61%, and 80.61% for melanomas, nevi, BCCs, and SKs, respectively. We also identified specific features effective for the classification task including irregularity of color distribution. The results show promise for enhancing the capability of the computer-aided skin lesion classification.
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paper_title: Development of Automated Diagnostic System for Skin Cancer: Performance Analysis of Neural Network Learning Algorithms for Classification

paper_content:

Melanoma is the most deathly of all skin cancers but early diagnosis can ensure a high degree of survival. Early diagnosis is one of the greatest challenges due to lack of experience of general practitioners (GPs). In this paper we present a clinical decision support system designed for general practitioners, aimed at saving time and resources in the diagnostic process. Segmentation, pattern recognition, and change detection are the important steps in our approach. This paper also investigates the performance of Artificial Neural Network (ANN) learning algorithms for skin cancer diagnosis. The capabilities of three learning algorithms i.e. Levenberg-Marquardt (LM), Resilient Back propagation (RP), Scaled Conjugate Gradient (SCG) algorithms in differentiating melanoma and benign lesions are studied and their performances are compared. The results show that Levenberg-Marquardt algorithm was quick and efficient in figuring out benign lesions with specificity 95.1%, while SCG algorithm gave better results in detecting melanoma at the cost of more number of epochs with sensitivity 92.6%.
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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: Computer-Supported Diagnosis of Melanoma in Profilometry

paper_content:

Laser profilometry offers new possibilities to improve non-invasive tumor diagnostics in dermatology. In this paper, a new approach to computer-supported analysis and interpretation of high-resolution skin-surface profiles of melanomas and nevocellular nevi is presented. Image analysis methods are used to describe the profile's structures by texture parameters based on co-occurrence matrices, features extracted from the Fourier power spectrum, and fractal features. Different feature selection strategies, including genetic algorithms, are applied to determine the best possible subsets of features for the classification task. Several architectures of multilayer perceptrons with error back-propagation as learning paradigm are trained for the automatic recognition of melanomas and nevi. Further-more, network-pruning algorithms are applied to optimize the network topology. In the study, the best neural classifier showed an error rate of 4.5% and was obtained after network pruning. The smallest error rate in all, of 2.3%, was achieved with nearest neighbor classification.
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paper_title: Dermoscopy: alternative melanocytic algorithms-the ABCD rule of dermatoscopy, Menzies scoring method, and 7-point checklist.

paper_content:

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: Wavelet transforms and filter banks

paper_content:

Abstract. Wavelet and short-time Fourier analysis is introduced in the context of frequency decompositions. Wavelet type frequency decompositions are associated with filter banks, and using this fact, filter bank theory is used to construct multiplicity M wavelet frames and tight frames.
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paper_title: A hybrid thinning algorithm for 3D medical images

paper_content:

Thinning is a frequently used method for extracting skeletons in discrete spaces. This paper presents an efficient parallel algorithm for thinning elongated 3D binary objects (e.g., bony structures, vessel trees, or airway trees). The proposed algorithm directly extracts medial lines as shape features from 3D binary objects by applying a brand- new class of thinning strategy called hybrid method. Our topology preserving algorithm makes easy implementation possible and gives satisfactory results for synthetic data tests and for MR angiography brain studies.
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paper_title: Thinning methodologies−a comprehensive survey

paper_content:

A comprehensive survey of thinning methodologies is presented. A wide range of thinning algorithms, including iterative deletion of pixels and nonpixel-based methods, is covered. Skeletonization algorithms based on medial axis and other distance transforms are not considered. An overview of the iterative thinning process and the pixel-deletion criteria needed to preserve the connectivity of the image pattern is given first. Thinning algorithms are then considered in terms of these criteria and their modes of operation. Nonpixel-based methods that usually produce a center line of the pattern directly in one pass without examining all the individual pixels are discussed. The algorithms are considered in great detail and scope, and the relationships among them are explored. >
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paper_title: Fronts propagating with curvature dependent speed: algorithms based on Hamilton–Jacobi formulations

paper_content:

We devise new numerical algorithms, called PSC algorithms, for following fronts propagating with curvature-dependent speed. The speed may be an arbitrary function of curvature, and the front also can be passively advected by an underlying flow. These algorithms approximate the equations of motion, which resemble Hamilton-Jacobi equations with parabolic right-hand sides, by using techniques from hyperbolic conservation laws. Non-oscillatory schemes of various orders of accuracy are used to solve the equations, providing methods that accurately capture the formation of sharp gradients and cusps in the moving fronts. The algorithms handle topological merging and breaking naturally, work in any number of space dimensions, and do not require that the moving surface be written as a function. The methods can be also used for more general Hamilton-Jacobi-type problems. We demonstrate our algorithms by computing the solution to a variety of surface motion problems.
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paper_title: Snakes: Active contour models

paper_content:

A snake is an energy-minimizing spline guided by external constraint forces and influenced by image forces that pull it toward features such as lines and edges. Snakes are active contour models: they lock onto nearby edges, localizing them accurately. Scale-space continuation can be used to enlarge the capture region surrounding a feature. Snakes provide a unified account of a number of visual problems, including detection of edges, lines, and subjective contours; motion tracking; and stereo matching. We have used snakes successfully for interactive interpretation, in which user-imposed constraint forces guide the snake near features of interest.
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paper_title: Efficient skeletonization of volumetric objects

paper_content:

Skeletonization promises to become a powerful tool for compact shape description, path planning and other applications. However, current techniques can seldom efficiently process real, complicated 3D data sets, such as MRI and CT data of human organs. In this paper, we present an efficient voxel coding-based algorithm for the skeletonization of 3D voxelized objects. The skeletons are interpreted as connected center lines, consisting of sequences of medial points of consecutive clusters. These center lines are initially extracted as paths of voxels, followed by medial point replacement, refinement, smoothing and connection operations. The voxel-coding techniques have been proposed for each of these operations in a uniform and systematic fashion. In addition to preserving basic connectivity and centeredness, the algorithm is characterized by straightforward computation, no sensitivity to object boundary complexity, explicit extraction of ready-to-parameterize and branch-controlled skeletons, and efficient object hole detection. These issues are rarely discussed in traditional methods. A range of 3D medical MRI and CT data sets were used for testing the algorithm, demonstrating its utility.
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paper_title: Designing Fourier Descriptor-Based Geometric Models for Object Interpretation in Medical Images Using Genetic Algorithms

paper_content:

In previous work we have modeled simple 3D anatomical objects using deformed superquadrics and established their optimal position with the aid of genetic algorithms (GAs). Here we extend the complexity of the search object using 3D Fourier descriptor (FD) representations and allow GAs once again to optimize the object's shape and position. Using magnetic resonance image data, we perform an approximate segmentation on one lateral ventricle in the brain and use the FDs from this as seeding values for the GAs to search for the left and right lateral ventricles in seven 3D data sets. We show that the method is capable of coping with normal biological variation. Finally, we compare FD/GA-guided segmentation with a manually guided interactive region growing method and find an agreement of 78 � 10% in voxel classification with a corresponding average edge placement error of 2.2 � 0.4 mm.
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paper_title: Fractal Dimension Based Texture Analysis of Digital Images

paper_content:

Abstract Fractal dimension is an important parameter of Fractal geometry that finds significant applications in various fields including image processing. Image analysis is a high-level image processing technique to identify the image features such as texture, roughness, smoothness, area and solidity. This paper proposes an algorithm to calculate the fractal dimension of digital images, does compare the fractal dimension of such images and proves that fractal dimension is an ideal tool for measuring the roughness/texture of an image
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paper_title: Active Contours without Edges

paper_content:

We propose a new model for active contours to detect objects in a given image, based on techniques of curve evolution, Mumford-Shah (1989) functional for segmentation and level sets. Our model can detect objects whose boundaries are not necessarily defined by the gradient. We minimize an energy which can be seen as a particular case of the minimal partition problem. In the level set formulation, the problem becomes a "mean-curvature flow"-like evolving the active contour, which will stop on the desired boundary. However, the stopping term does not depend on the gradient of the image, as in the classical active contour models, but is instead related to a particular segmentation of the image. We give a numerical algorithm using finite differences. Finally, we present various experimental results and in particular some examples for which the classical snakes methods based on the gradient are not applicable. Also, the initial curve can be anywhere in the image, and interior contours are automatically detected.
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paper_title: An ensemble classification approach for melanoma diagnosis

paper_content:

Malignant melanoma is the deadliest form of skin cancer, and has, among cancer types, one of the most rapidly increasing incidence rates in the world. Early diagnosis is crucial, since if detected early, its cure is simple. In this paper, we present an effective approach to melanoma identification from dermoscopic images of skin lesions based on ensemble classification. First, we perform automatic border detection to segment the lesion from the background skin. Based on the extracted border, we extract a series of colour, texture and shape features. The derived features are then employed in a pattern classification stage for which we employ a novel, dedicated ensemble learning approach to address the class imbalance in the training data and to yield improved classification performance. Our classifier committee trains individual classifiers on balanced subspaces, removes redundant predictors based on a diversity measure and combines the remaining classifiers using a neural network fuser. Experimental results on a large dataset of dermoscopic skin lesion images show our approach to work well, to provide both high sensitivity and specificity, and our presented classifier ensemble to lead to statistically better recognition performance compared to other dedicated classification algorithms.
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paper_title: Can early malignant melanoma be differentiated from atypical melanocytic nevus by in vivo techniques?.

paper_content:

BACKGROUND/AIMS ::: Differentiation between early (Breslow thickness less than 1 mm) malignant melanoma (MM) and atypical melanocytic nevus (AMN) remains a challenge even to trained clinicians. The purpose of this study is to determine the feasibility of reliable discrimination between early MM and AMN with noninvasive, objective, automatic machine vision techniques. ::: ::: ::: METHODS ::: A data base of 104 digitized dermoscopic color transparencies of melanocytic lesions was used to develop and test our computer-based algorithms for classification of such lesions as malignant (MM) or benign (AMN). Histopathologic diagnoses (30 MM and 74 AMN) were used as the "gold standard" for training and testing the algorithms. ::: ::: ::: RESULTS ::: A fully automatic, objective technique for differentiating between early MM and AMN from their dermoscopic digital images was developed. The multiparameter linear classifier was trained to provide 100% sensitivity for MM. In the blind test, this technique did not miss a single MM and its specificity was comparable to that of skilled dermatologists. ::: ::: ::: CONCLUSIONS ::: Reliable differentiation between early MM and AMN with high sensitivity is possible using machine vision techniques to analyze digitized dermoscopic lesion images.
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paper_title: Early Detection of Malignant Melanoma: The Role of Physician Examination and Self-Examination of the Skin

paper_content:

The combination of routine physician examination of the skin coupled with self-examination provides a realistic opportunity for the identification of early malignant melanomas. Removal of such thin lesions can significantly reduce the mortality rate from this potentially serious form of cutaneous cancer.
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paper_title: A Mobile Automated Skin Lesion Classification System

paper_content:

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: Accuracy of a smartphone application using fractal image analysis of pigmented moles compared to clinical diagnosis and histological result.

paper_content:

BACKGROUND ::: Lately, various smartphone applications have been introduced as diagnostic self-monitoring tools in the evaluation of pigmented moles, but most of these techniques have not been evaluated systematically. ::: ::: ::: OBJECTIVES ::: The purpose of this study was to evaluate prospectively the sensitivity and specificity of a recently developed smartphone application using fractal image analysis for the risk evaluation algorithm in the diagnosis of malignant melanoma compared to clinical diagnosis and histopathological result. ::: ::: ::: METHODS ::: Consecutive patients with melanocytic lesions were recruited and clinical and dermoscopical diagnosis was documented by two dermatologists independently. Imaging and analysis with the smartphone application was performed prior to excision of lesions. The findings were compared to the histological results as gold standard. ::: ::: ::: RESULTS ::: Of 195 included lesions histopathological analysis revealed 40 melanomas, 42 dysplastic nevi and 113 benign nevi. The sensitivity of the diagnosis melanoma by fractal image analysis using smartphone images was 73%, the specificity was 83% compared to a sensitivity of 88% and specificity of 97% regarding the clinical diagnosis by the dermatologists. ::: ::: ::: CONCLUSION ::: The smartphone application using fractal analysis might be a promising tool in the pre-evaluation of pigmented moles by laypersons, while it is to date inferior to the diagnostic evaluation by a dermatologist.
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paper_title: Automated skin lesion assessment using mobile technologies and cloud platforms

paper_content:

This paper presents a smart phone based system for storing digital images of skin areas depicting regions of interest (lesions) and performing self-assessment of these skin lesions within these areas. The system consists of a mobile application that can acquire and identify moles in skin images and classify them according their severity into melanoma, nevus and benign lesions. The proposed system includes also a cloud infrastructure exploiting computational and storage resources. This cloud-based architecture provides interoperability and support of various mobile environments as well as flexibility in enhancing the classification model. Initial evaluation results are quite promising and indicate that the application can be used for the task of skin lesions initial assessment.
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paper_title: DERMA/Care: An Advanced image-Processing Mobile Application for Monitoring Skin Cancer

paper_content:

This paper describes a mobile hardware/software system (DERMA/care) to help with screening of skin cancer (melanomas). Our system uses an inexpensive apparatus (microscope) and a smart phone (iPhone). These two components standalone are sufficient to capture highly detailed images for use by experts with medical background. However the novelty of our system lies in the fact that we further improved the efficiency of the system by implementing an advanced image-processing framework to detect suspicious areas and help with skin cancer prevention. Our main goal was to demonstrate how smart phones could turn into powerful and intelligent machines and help large populations without expertise in low-resource settings.
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paper_title: Mobile teledermoscopy--melanoma diagnosis by one click?

paper_content:

Mobile telemedicine integrates wireless communications for different telemedical applications, such as mobile phones and personal digital assistants, and with the implementation of modern wireless telecommunication, wireless local area network and satellite communication is a reality. New generation cellular phones or personal digital assistants have overcome limitations of image quality seen in older devices and, with dermatology being a visual profession, mobile teledermatology is perhaps the most recent development in this field. Mobile teledermatology may provide a triage service aimed toward management of patients with emergent skin disease or for follow-up with patients requiring systemic treatment. Teledermoscopy enables rapid transmission of dermoscopic images via e-mail or specific web-application and studies have demonstrated a high, 91%, concordance between face-to-face diagnosis and remote diagnosis of such images. Further to this, telediagnosis of melanocytic skin neoplasms achieved a diagnostic accuracy of 83% versus the conventional histopathologic diagnosis. Mobile teledermoscopy is the combination of such approaches enabling transfer of images captured with cellular phones coupled with a pocket dermatoscope and preliminary studies have demonstrated the feasibility and potential of its use in triage of pigmented lesions. Such applications are of benefit to physicians in enabling easy storage of data for follow-up or referral of images for expert second opinion and may facilitate a "person-centered health system" for patients with numerous moles and pigmented skin lesions who could forward images for evaluation. The incidence of skin cancers has reached epidemic proportions among whites and the trend is still going upward. Mobile teledermatology and teledermoscopy may be implemented as a triage or screening tool for malignant tumors to facilitate early detection and diagnosis, which is crucial for improved patient outcomes. While the legal aspects concerning teleconsultations need to be evaluated, the communications technologies provide a unique opportunity for physicians and patients alike and we foresee a place for these tools in dermatology soon.
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paper_title: SkinScan©: A portable library for melanoma detection on handheld devices

paper_content:

We have developed a portable library for automated detection of melanoma termed SkinScan© that can be used on smartphones and other handheld devices. Compared to desktop computers, embedded processors have limited processing speed, memory, and power, but they have the advantage of portability and low cost. In this study we explored the feasibility of running a sophisticated application for automated skin cancer detection on an Apple iPhone 4. Our results demonstrate that the proposed library with the advanced image processing and analysis algorithms has excellent performance on handheld and desktop computers. Therefore, deployment of smartphones as screening devices for skin cancer and other skin diseases can have a significant impact on health care delivery in underserved and remote areas.
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Title: A Review of the Quantification and Classification of Pigmented Skin Lesions: From Dedicated to Hand-Held Devices
Section 1: Introduction
Description 1: Write about the nature and types of skin lesions, the prevalence of skin cancer, and the need for computer-assisted diagnostic methods.

Section 2: Image acquisition
Description 2: Discuss various noninvasive techniques used for imaging skin lesions, including clinical images, dermoscopy, and portable devices.

Section 3: Image pre-processing
Description 3: Outline the pre-processing steps involved in enhancing acquired images by reducing noise, smoothing, and increasing contrast.

Section 4: Segmentation
Description 4: Cover the methods used for segmenting the region of interest in images, such as edge-based, threshold-based, and region-based algorithms.

Section 5: Quantification and classification
Description 5: Explain the process of characterizing and classifying skin lesions using features such as size, color, texture, and shape, and the use of machine-learning algorithms for diagnosis.

Section 6: Hand-held device applications
Description 6: Review the recent advancements in mobile applications for skin lesion analysis and their performance metrics.

Section 7: Summary
Description 7: Provide an overview of the findings, including the efficacy of computer-assisted methods and the role of hand-held devices in skin lesion diagnosis.