Automated breast cancer detection and classification using ultrasound images: A survey
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paper_title: Breast Cancer in Limited-Resource Countries: An Overview of the Breast Health Global Initiative 2005 Guidelines

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� Abstract: Breast cancer is the most common cause of cancer-related death among women worldwide, with case fatality rates highest in low-resource countries. Despite significant scientific advances in its management, most of the world faces resource constraints that limit the capacity to improve early detection, diagnosis, and treatment of the disease. The Breast Health Global Initiative (BHGI) strives to develop evidence-based, economically feasible, and culturally appropriate guidelines that can be used in nations with limited health care resources to improve breast cancer outcomes. Using an evidence-based consensus panel process, four BHGI expert panels addressed the areas of early detection and access to care, diagnosis and pathology, treatment and resource allocation, and health care systems and public policy as they relate to breast health care in limited-resource settings. To update and expand on the BHGI Guidelines published in 2003, the 2005 BHGI panels outlined a stepwise, systematic approach to health care improvement using a tiered system of resource allotment into four levels—basic, limited, enhanced, and maximal— based on the contribution of each resource toward improving clinical outcomes. Early breast cancer detection improves outcome in a cost-effective fashion assuming treatment is available, but requires public education to foster active patient participation in diagnosis and treatment. Clinical breast examination combined with diagnostic breast imaging (ultrasound ± diagnostic mammography) can facilitate cost-effective tissue sampling techniques for cytologic or histologic diagnosis. Breast-conserving treatment with partial mastectomy and radiation therapy requires more health care resources and infrastructure than mastectomy, but can be provided in a thoughtfully designed limited-resource setting. The availability and administration of systemic therapies are critical to improving breast cancer survival. Estrogen receptor testing allows patient selection for hormonal treatments (tamoxifen, oophorectomy). Chemotherapy, which requires some allocation of resources and infrastructure, is needed to treat node-positive, locally advanced breast cancers, which represent the most common clinical presentation of disease in low-resource countries. When chemotherapy is not available, patients with locally advanced, hormone receptor-negative cancers can only receive palliative therapy. Future research is needed to better determine how these guidelines can best be implemented in limited-resource settings. �
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paper_title: Use of the bootstrap technique with small training sets for computer-aided diagnosis in breast ultrasound.

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The purpose of this study was to test the efficacy of using small training sets in computer-aided diagnostic systems (CAD) and to increase the capabilities of ultrasound (US) technology in the differential diagnosis of solid breast tumors. A total of 263 sonographic images of solid breast nodules, including 129 malignancies and 134 benign nodules, were evaluated by using a bootstrap technique with 10 original training samples. Texture parameters of a region-of-interest (ROI) were resampled with a bootstrap technique and a decision-tree model was used to classify the tumor as benign or malignant. The accuracy was 87.07% (229 of 263 tumors), the sensitivity was 95.35% (123 of 129), the specificity was 79.10% (106 of 134), the positive predictive value was 81.46% (123 of 151), and the negative predictive value was 94.64% (106 of 112). This analysis method provides a second opinion for physicians with high accuracy. The new method shows a potential to be useful in future application of CAD, especially when a large database cannot be obtained for training or a newly developed ultrasonic system has smaller sets of samples.
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paper_title: Application of the compound probability density function for characterization of breast masses in ultrasound B scans

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The compound probability density function (pdf) is investigated for the ability of its parameters to classify masses in ultrasonic B scan breast images. Results of 198 images (29 malignant and 70 benign cases and two images per case) are reported and compared to the classification performance reported by us earlier in this journal. A new parameter, the speckle factor, calculated from the parameters of the compound pdf was explored to separate benign and malignant masses. The receiver operating characteristic curve for the parameter resulted in an Az value of 0.852. This parameter was combined with one of the parameters from our previous work, namely the ratio of the K distribution parameter at the site and away from the site. This combined parameter resulted in an Az value of 0.955. In conclusion, the parameters of the K distribution and the compound pdf may be useful in the classification of breast masses. These parameters can be calculated in an automated fashion. It should be possible to combine the results of the ultrasonic image analysis with those of traditional mammography, thereby increasing the accuracy of breast cancer diagnosis.
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paper_title: Breast lesions on sonograms: computer-aided diagnosis with nearly setting-independent features and artificial neural networks.

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PURPOSE ::: To develop a computer-aided diagnosis (CAD) algorithm with setting-independent features and artificial neural networks to differentiate benign from malignant breast lesions. ::: ::: ::: MATERIALS AND METHODS ::: Two sets of breast sonograms were evaluated. The first set contained 160 lesions and was stored directly on the magnetic optic disks from the ultrasonographic (US) system. Four different boundaries were delineated by four persons for each lesion in the first set. The second set comprised 111 lesions that were extracted from the hard-copy images. Seven morphologic features were used, five of which were newly developed. A multilayer feed-forward neural network was used as the classifier. Reliability, extendability, and robustness of the proposed CAD algorithm were evaluated. Results with the proposed algorithm were compared with those with two previous CAD algorithms. All performance comparisons were based on paired-samples t tests. ::: ::: ::: RESULTS ::: The area under the receiver operating characteristic curve (A(z)) was 0.952 +/- 0.014 for the first set, 0.982 +/- 0.004 for the first set as the training set and the second set as the prediction set, 0.954 +/- 0.016 for the second set as the training set and the first set as the prediction set, and 0.950 +/- 0.005 for all 271 lesions. At the 5% significance level, the performance of the proposed CAD algorithm was shown to be extendible from one set of US images to the other set and robust for both small and large sample sizes. Moreover, the proposed CAD algorithm was shown to outperform the two previous CAD algorithms in terms of the A(z) value. ::: ::: ::: CONCLUSION ::: The proposed CAD algorithm could effectively and reliably differentiate benign and malignant lesions. The proposed morphologic features were nearly setting independent and could tolerate reasonable variation in boundary delineation.
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paper_title: Computerized lesion detection on breast ultrasound

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We investigated the use of a radial gradient index (RGI) filtering technique to automatically detect lesions on breast ultrasound. After initial RGI filtering, a sensitivity of 87% at 0.76 false-positive detections per image was obtained on a database of 400 patients (757 images). Next, lesion candidates were segmented from the background by maximizing an average radial gradient (ARD) index for regions grown from the detected points. At an overlap of 0.4 with a radiologist lesion outline, 75% of the lesions were correctly detected. Subsequently, round robin analysis was used to assess the quality of the classification of lesion candidates into actual lesions and false-positives by a Bayesian neural network. The round robin analysis yielded an Az value of 0.84, and an overall performance by case of 94% sensitivity at 0.48 false-positives per image. Use of computerized analysis of breast sonograms may ultimately facilitate the use of sonography in breast cancer screening programs.
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paper_title: Malignant and Benign Breast Masses on 3D US Volumetric Images: Effect of Computer-aided Diagnosis on Radiologist Accuracy

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Purpose: To retrospectively investigate the effect of using a custom-designed computer classifier on radiologists' sensitivity and specificity for discriminating malignant masses from benign masses on three-dimensional (3D) volumetric ultrasonographic (US) images, with histologic analysis serving as the reference standard. Materials and Methods: Informed consent and institutional review board approval were obtained. Our data set contained 3D US volumetric images obtained in 101 women (average age, 51 years; age range, 25–86 years) with 101 biopsy-proved breast masses (45 benign, 56 malignant). A computer algorithm was designed to automatically delineate mass boundaries and extract features on the basis of segmented mass shapes and margins. A computer classifier was used to merge features into a malignancy score. Five experienced radiologists participated as readers. Each radiologist read cases first without computer-aided diagnosis (CAD) and immediately thereafter with CAD. Observers' malignancy rating da...
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paper_title: Computer aided diagnosis (CAD) of breast mass on ultrasonography and scintimammography

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Both breast ultrasonography (US) and scintimammography (SMM) shows limited diagnostic accuracy in the differential diagnosis of breast cancer. We investigated whether the diagnostic performance of computer-aided diagnosis (CAD) of US was improved by adding SMM results. We retrospectively reviewed 40 breast US images and corresponding SMM images from 40 patients who presented with breast masses (21 malignant and 19 benign tumors). The morphologic features of the breast lesions on US were extracted and quantitated using the automated CAD software program (So no Eye, CAD Impact, Inc., Seoul, Korea), which calculated the probability of malignancy based on the stored database. The quantitative data of SMM were obtained as the uptake ratio of lesion to contralateral normal breast on SMM SPECT. The diagnostic accuracy of CAD of US and SMM was determined using ROC curve analysis, respectively. The best discriminating function combining the CAD results of US and SMM uptake value was created using linear discriminant analysis and the diagnostic performance was compared to that using only one diagnostic modality. Both US and SMM showed a relatively good diagnostic accuracy (area under curve=0.831 and 0.846, respectively). Combining CAD results of US and SMM resulted in improved diagnostic accuracy (area under curve =0.860), but it was not statistically significant. The diagnostic performance of CAD of breast US in the differential diagnosis of the breast mass was not significantly improved by adding SMM. However, SMM SPECT may be complementary to CAD of US in differential diagnosis of breast cancer.
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paper_title: Diagnosis of breast tumors with ultrasonic texture analysis using support vector machines

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This study presents a computer-aided diagnosis (CAD) system with textural features for classifying benign and malignant breast tumors on medical ultrasound systems. A series of pathologically proven breast tumors were evaluated using the support vector machine (SVM) in the differential diagnosis of breast tumors. The proposed CAD system utilized facile textural features, i.e., block difference of inverse probabilities, block variation of local correlation coefficients and auto-covariance matrix, to identify breast tumor. An SVM classifier using the textual features classified the tumor as benign or malignant. The proposed system identifies breast tumors with a comparatively high accuracy. This can help inexperienced physicians avoid misdiagnosis. The main advantage of the proposed system is that the training and diagnosis procedure of SVM are faster and more stable than that of multilayer perception neural networks. With the expansion of the database, new cases can easily be gathered and used as references. This study dramatically reduces the training and diagnosis time. The SVM is a reliable choice for the proposed CAD system because it is fast and excellent in ultrasound image classification.
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paper_title: Ultrasound as a complement to mammography and breast examination to characterize breast masses.

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Abstract This study was designed to determine if complementary ultrasound (US) imaging and Doppler could decrease the number of biopsies for benign masses. A total of 761 breast masses were sequentially scored on a level of suspicion (LOS) of 1–5, where 1 represented low, and 5 was a high suspicion of malignancy, for mammography, US, and color flow with pulse Doppler (DUS). After biopsy, the results were analyzed using 2 × 2 contingency tables and ROC analysis, for mammography alone and in combination with US and DUS. The addition of US increased the specificity from 51.4% to 66.4% at a prevalence of 31.3% malignancy. ROC analysis showed that the addition of US significantly improved the performance over mammography alone in women p = 0.049); > 55 years old ( p = 0.029); masses p = 0.016) and masses > 1 cm ( p = 0.016). These results show that the addition of US to mammography alone could substantially reduce the number of breast biopsies for benign disease. (E-mail: Kenneth.Taylor@yale.edu)
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paper_title: Breast mass lesions: computer-aided diagnosis models with mammographic and sonographic descriptors.

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PURPOSE ::: To retrospectively develop and evaluate computer-aided diagnosis (CAD) models that include both mammographic and sonographic descriptors. ::: ::: ::: MATERIALS AND METHODS ::: Institutional review board approval was obtained for this HIPAA-compliant study. A waiver of informed consent was obtained. Mammographic and sonographic examinations were performed in 737 patients (age range, 17-87 years), which yielded 803 breast mass lesions (296 malignant, 507 benign). Radiologist-interpreted features from mammograms and sonograms were used as input features for linear discriminant analysis (LDA) and artificial neural network (ANN) models to differentiate benign from malignant lesions. An LDA with all the features was compared with an LDA with only stepwise-selected features. Classification performances were quantified by using receiver operating characteristic (ROC) analysis and were evaluated in a train, validate, and retest scheme. On the retest set, both LDAs were compared with radiologist assessment score of malignancy. ::: ::: ::: RESULTS ::: Both the LDA and ANN achieved high classification performance with cross validation (area under the ROC curve [A(z)] = 0.92 +/- 0.01 [standard deviation] and (0.90)A(z) = 0.54 +/- 0.08 for LDA, A(z) = 0.92 +/- 0.01 and (0.90)A(z) = 0.55 +/- 0.08 for ANN). Results of both models generalized well to the retest set, with no significant performance differences between the validate and retest sets (P > .1). On the retest set, there were no significant performance differences between LDA with all features and LDA with only the stepwise-selected features (P > .3) and between either LDA and radiologist assessment score (P > .2). ::: ::: ::: CONCLUSION ::: Results showed that combining mammographic and sonographic descriptors in a CAD model can result in high classification and generalization performance. On the retest set, LDA performance matched radiologist classification performance.
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paper_title: Approaches For Automated Detection And Classification Of Masses In Mammograms

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Breast cancer is one of the most common cancer among women around the world. Several techniques are available for detection of breast cancer. Mammography is one of the most effective tools for early detection. The goal of this research is to increase the diagnostic accuracy of image processing and machine learning techniques for optimum classification between normal and abnormalities in digital mammograms. GLCM texture feature extractions are known to be the most common and powerful techniques for texture analysis. This paper presents an evaluation and comparison of the performance of two different classification methods used to classify the normal and abnormal patterns. The experimental result suggest that Artificial Neural Network is outperformed the other method.
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paper_title: Breast cancer diagnosis using image retrieval for different ultrasonic systems

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This paper employs the image retrieval technique to classify breast tumors as benign or malignant lesions. We evaluated 600 ultrasound (US) images of pathologically proven solid breast nodules including 230 malignant and 370 benign tumors. The US images were acquired from four different ultrasound systems. Firstly, the physician located regions-of-interest (ROI) of ultrasound images. The textual features from ROI sub-image are utilized to classify breast tumors. The principal component analysis (PCA) is used to reduce the dimension of textual feature vector and then the image retrieval technique was utilized to differentiate between benign and malignant tumors. Historical cases can be directly added into the database and training of the diagnosis system again is not needed. The accuracy of the proposed computer-aided diagnosis (CAD) system was 91.2%, the sensitivity was 97.0% and the specificity was 87.6%. This system differentiates solid breast nodules with a relatively high accuracy in the different US systems and helps inexperienced operators avoid misdiagnosis.
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paper_title: Computer-aided detection and classification of microcalcifications in mammograms: a survey

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Abstract Breast cancer continues to be a significant public health problem in the world. Approximately, 182,000 new cases of breast cancer are diagnosed and 46,000 women die of breast cancer each year in the United States. Even more disturbing is the fact that one out of eight women in US will develop breast cancer at some point during her lifetime. Primary prevention seems impossible since the causes of this disease still remain unknown. Early detection is the key to improving breast cancer prognosis. Mammography is one of the reliable methods for early detection of breast carcinomas. There are some limitations of human observers, and it is difficult for radiologists to provide both accurate and uniform evaluation for the enormous number of mammograms generated in widespread screening. The presence of microcalcification clusters (MCCs) is an important sign for the detection of early breast carcinoma. An early sign of 30–50% of breast cancer detected mammographically is the appearance of clusters of fine, granular microcalcification, and 60–80% of breast carcinomas reveal MCCs upon histological examinations. The high correlation between the appearance of the microcalcification clusters and the diseases show that the CAD (computer aided diagnosis) systems for automated detection/classification of MCCs will be very useful and helpful for breast cancer control. In this survey paper, we summarize and compare the methods used in various stages of the computer-aided detection systems (CAD). In particular, the enhancement and segmentation algorithms, mammographic features, classifiers and their performances are studied and compared. Remaining challenges and future research directions are also discussed.
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paper_title: Comparison of Ultrasound Elastography, Mammography, and Sonography in the Diagnosis of Solid Breast Lesions

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Objective ::: The purpose of this study was to evaluate the value of ultrasound elastography (UE) in differentiating benign versus malignant lesions in the breast and compare it with conventional sonography and mammography. ::: ::: Methods ::: From September 2004 to May 2005, 296 solid lesions from 232 consecutive patients were diagnosed as benign or malignant by mammography and sonography and further analyzed with UE. The diagnostic results were compared with histopathologic findings. The sensitivity, specificity, accuracy, positive and negative predictive values, and false-positive and -negative rates were calculated for each modality and the combination of UE and sonography. ::: ::: Results ::: Of 296 lesions, 87 were histologically malignant, and 209 were benign. Ultrasound elastography was the most specific (95.7%) and had the lowest false-positive rate (4.3%) of the 3 modalities. The accuracy (88.2%) and positive predictive value (87.1%) of UE were higher than those of sonography (72.6% and 52.5%, respectively). The sensitivity values, negative predictive values, and false negative rates of the 3 modalities had no differences. A combination of UE and sonography had the best sensitivity (89.7%) and accuracy (93.9%) and the lowest false-negative rate (9.2%). The specificity (95.7%) and positive predictive value (89.7%) of the combination were better, and the false-positive rate (4.3%) of the combination was lower than those of mammography and sonography. ::: ::: Conclusions ::: In a clinical trial with Chinese women, UE was superior to sonography and equal or superior to mammography in differentiating benign and malignant lesions in the breast. A combination of UE and sonography had the best results in detecting cancer and potentially could reduce unnecessary biopsy. Ultrasound elastography is a promising technique for evaluating breast lesions.
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paper_title: Real-time speckle reduction and coherence enhancement in ultrasound imaging via nonlinear anisotropic diffusion

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This paper presents a novel approach for speckle reduction and coherence enhancement of ultrasound images based on nonlinear coherent diffusion (NCD) model. The proposed NCD model combines three different models. According to speckle extent and image anisotropy, the NCD model changes progressively from isotropic diffusion through anisotropic coherent diffusion to, finally, mean curvature motion. This structure maximally low-pass filters those parts of the image that correspond to fully developed speckle, while substantially preserving information associated with resolved-object structures. The proposed implementation algorithm utilizes an efficient discretization scheme that allows for real-time implementation on commercial systems. The theory and implementation of the new technique are presented and verified using phantom and clinical ultrasound images. In addition, the results from previous techniques are compared with the new method to demonstrate its performance.
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paper_title: Effective Density Estimators Based on the K Distribution: Interest of Low and Fractional Order Moments

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The K distribution is an efficient model to the nonRayleigh statistics of the envelope of backscattered signals in random media. This modeling leads to estimate a parameter of effective density by means of the calculation of statistical moments of the envelope signal. In this study, we propose a mathematical analysis of an effective density estimator previously used and based on superior order moments. In order to improve the effective density estimate, we propose several estimators based on low and fractional order moments. The performances of these estimators are evaluated both with simulated signals and in an experimental context with synthetic foams. Estimators based on low and fractional moments appear to be more robust than superior moment-based estimator and an improvement of the spatial resolution of the estimate can be obtained. Results also confirm the capability of the effective density parameter to characterize the spatial distribution of scattering structures.
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paper_title: Generalized K distribution: a statistical model for weak scattering

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The generalized K distribution, introduced recently by Barakat [ J. Opt. Soc. Am. A3, 401 ( 1986)] as a model for the intensity of radiation scattered with a nonuniform distribution of phase by a fluctuating population of objects, is derived on the bases of simpler and more general arguments. In particular, Barakat’s analysis is based on a random walk in two dimensions and a specific model for the phase distribution; our analysis is presented in terms of an n-dimensional walk with a directional bias that, apart from its scaling property as a function of the mean number of scatters, is arbitrary. The relationship of the generalized K model with possible underlying scattering mechanisms is also discussed.
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paper_title: Ultrasound speckle analysis based on the K distribution.

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The departure of speckle magnitude from Rayleigh statistics was applied to examine insonated phantoms with both low and high concentrations of scatterers. A mathematical model, the K distribution of Jakeman, was used to characterize non-Rayleigh statistics. This model contains a parameter, alpha, which characterizes the clustering of the scattering sites in a medium. It is shown from phantom experiments that alpha is linearly proportional to the log-scaled scatterer concentration in a range from about 1 to 30 scatterers per sample volume.
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paper_title: Comparative evaluation of despeckle filtering in ultrasound imaging of the carotid artery

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It is well-known that speckle is a multiplicative noise that degrades the visual evaluation in ultrasound imaging. The recent advancements in ultrasound instrumentation and portable ultrasound devices necessitate the need of more robust despeckling techniques for enhanced ultrasound medical imaging for both routine clinical practice and teleconsultation. The objective of this work was to carry out a comparative evaluation of despeckle filtering based on texture analysis, image quality evaluation metrics, and visual evaluation by medical experts in the assessment of 440 (220 asymptomatic and 220 symptomatic) ultrasound images of the carotid artery bifurcation. In this paper a total of 10 despeckle filters were evaluated based on local statistics, median filtering, pixel homogeneity, geometric filtering, homomorphic filtering, anisotropic diffusion, nonlinear coherence diffusion, and wavelet filtering. The results of this study suggest that the first order statistics filter lsmv, gave the best performance, followed by the geometric filter gf4d, and the homogeneous mask area filter lsminsc. These filters improved the class separation between the asymptomatic and the symptomatic classes based on the statistics of the extracted texture features, gave only a marginal improvement in the classification success rate, and improved the visual assessment carried out by the two experts. More specifically, filters lsmv or gf4d can be used for despeckling asymptomatic images in which the expert is interested mainly in the plaque composition and texture analysis; and filters lsmv, gf4d, or lsminsc can be used for the despeckling of symptomatic images in which the expert is interested in identifying the degree of stenosis and the plaque borders. The proper selection of a despeckle filter is very important in the enhancement of ultrasonic imaging of the carotid artery. Further work is needed to evaluate at a larger scale and in clinical practice the performance of the proposed despeckle filters in the automated segmentation, texture analysis, and classification of carotid ultrasound imaging.
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paper_title: Ultrasound Echo Envelope Analysis Using a Homodyned K Distribution Signal Model

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The statistics of ultrasound echo envelope signals can be used to characterize scattering media. The Rayleigh distribution and its generalized forms, the K and Rice distributions, have been previously used to model the echo signal. A more generalized statistical model, the homodyned K distribution, combines the K and Rice distribution features to better account for the statistics of the echo signal. We show that this model can give two parameters that are useful for media characterization: k, the ratio of coherent to diffuse signals, and, β, which characterizes the clustering of scatterers in the medium.
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paper_title: A SPECKLE SUPPRESSION METHOD BASED ON NONLINEAR THRESHOLD WAVELET PACKET IN ULTRASOUND IMAGES

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A novel method based on nonlinear threshold wavelet packet was explored in order to suppress the speckles in B scan 3 D ultrasound imaging. Firstly, an original image was separated into two parts by adaptive pre filtering. These two parts were then transformed into a multiscale wavelet packet (WP) domain and the WP coefficients were processed by a soft thresholding filter method. The processed coefficients for each part were then transformed back into the space domain. Finally, the de noised image was obtained as the sum of the two processed parts. Performance of this new method has been tested by a computer simulated image. Ultrasound images set of a plastic bowl in a water tank has been utilized for 3 D ultrasound reconstruction. It was found that this technique reduces the speckle noise effectively, and preserves the resolvable details.
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paper_title: Novel Bayesian multiscale method for speckle removal in medical ultrasound images

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A novel speckle suppression method for medical ultrasound images is presented. First, the logarithmic transform of the original image is analyzed into the multiscale wavelet domain. We show that the subband decompositions of ultrasound images have significantly non-Gaussian statistics that are best described by families of heavy-tailed distributions such as the alpha-stable. Then, we design a Bayesian estimator that exploits these statistics. We use the alpha-stable model to develop a blind noise-removal processor that performs a nonlinear operation on the data. Finally, we compare our technique with current state-of-the-art soft and hard thresholding methods applied on actual ultrasound medical images and we quantify the achieved performance improvement.
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paper_title: Nonlinear multiscale wavelet diffusion for speckle suppression and edge enhancement in ultrasound images

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This paper introduces a novel nonlinear multiscale wavelet diffusion method for ultrasound speckle suppression and edge enhancement. This method is designed to utilize the favorable denoising properties of two frequently used techniques: the sparsity and multiresolution properties of the wavelet, and the iterative edge enhancement feature of nonlinear diffusion. With fully exploited knowledge of speckle image models, the edges of images are detected using normalized wavelet modulus. Relying on this feature, both the envelope-detected speckle image and the log-compressed ultrasonic image can be directly processed by the algorithm without need for additional preprocessing. Speckle is suppressed by employing the iterative multiscale diffusion on the wavelet coefficients. With a tuning diffusion threshold strategy, the proposed method can improve the image quality for both visualization and auto-segmentation applications. We validate our method using synthetic speckle images and real ultrasonic images. Performance improvement over other despeckling filters is quantified in terms of noise suppression and edge preservation indices.
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paper_title: A new algorithm for shot noise removal in medical ultrasound images based on alpha-stable model

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Ultrasonic images are generally affected by multiplicative shot noise. Shot noise filtering is thus a critical pre-processing step in medical ultrasound imagery. This paper analyses and models the coefficients of 2-D multi-resolution wavelet decomposition of logarithmically transformed images using alpha-stable distribution model. Consequently, we propose a new function that performs a non-linear operation on the data of classifying the coefficients, thus achieving a novel form of noise removal based on multi-resolution wavelet decomposition and the alpha-stable model. We compare our new technique with current shot noise reduction methods applied on actual ultrasound medical images and simulations results show that the proposed new method is more robust than the methods based on Gaussian assumption. Copyright © 2006 John Wiley & Sons, Ltd.
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paper_title: Spatially adaptive wavelet thresholding with context modeling for image denoising

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The method of wavelet thresholding for removing noise, or denoising, has been researched extensively due to its effectiveness and simplicity. Much of the work has been concentrated on finding the best uniform threshold or best basis. However, not much has been done to make this method adaptive to spatially changing statistics which is typical of a large class of images. This work proposes a spatially adaptive wavelet thresholding method based on context modeling, a common technique used in image compression to adapt the coder to the non-stationarity of images. We model each coefficient as a random variable with the generalized Gaussian prior with unknown parameters. Context modeling is used to estimate the parameters for each coefficient, which are then used to adapt the thresholding strategy. Experimental results show that spatially adaptive wavelet thresholding yields significantly superior image quality and lower MSE than optimal uniform thresholding.
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paper_title: SOFT-THRESHOLDING FOR DENOISING OF MEDICAL IMAGES — A MULTIRESOLUTION APPROACH

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Medical images generally have low contrast and they get complex type of noise due to the use of various devices and applications of various algorithms. However, most of the denoising methods consider only additive noise or some special noise model dependent on their systems and conditions only. Such methods when applied to real medical images yield poor results. The present work proposes a method for denoising of medical images using soft-thresholding in wavelet domain on multiple levels. We have developed a method to compute the threshold values for denoising of medical images, which depend on the median as well as the contrast ratio of the wavelet coefficients and also on the level number. We have performed experiments by adding various proportions of Gaussian, Salt-and-Pepper and Speckle noise, and found that the proposed method performs better for these cases. The method is efficient because the threshold values can be calculated directly and it is adaptive as these values depend on mean, median and standard deviation of wavelet coefficients of the particular image. The proposed method also gives a criterion for level-dependent thresholding. Application of the proposed method to Ultrasound, X-ray and MRI images is demonstrated in experiments. In the present work, we have also done studies on how to select the mother wavelet for a particular problem.
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paper_title: The combined effect of spatial compounding and nonlinear filtering on the speckle reduction in ultrasound images.

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Recently, a spatial compounding ultrasound imaging method was presented that utilizes a conventional 64-element phased array transducer with two unfocused pistons, each placed at one of the sides of the phased array transducer. This method is augmented here by inclusion of nonlinear filtering of the compounded images. The combined effects of the specific spatial compounding and nonlinear filtering on speckle reduction in the generated ultrasound images are studied and evaluated in two stages: First, the image quality is studied when nonlinear filtering is used as part of the spatial compounding. The study is performed by simulations using the Field II program, by processing several B-mode images of a kidney. The second stage compares the results obtained by the simulations to those obtained by in vitro laboratory experiments. Five different compounding strategies and two nonlinear filters, Gaussian and anisotropic diffusion, are investigated and evaluated in terms of image quality parameters-contrast and signal-to-noise ratio. It is shown that the combination of "averaging+nonlinear Gaussian filtering" produces the greatest improvement of image quality. When compared to a conventional phased array imaging system, the spatial compounding method that includes the conventional 64-element phased array transducer with two unfocused pistons, and employs the "averaging+nonlinear Gaussian filtering" strategy, obtains improvement in SNR that has reached 334%. Thus, though this method necessitates a somewhat wider probe, it produces significantly improved images.
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paper_title: Lesion contrast enhancement in medical ultrasound imaging

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Methods for improving the contrast-to-noise ratio (CNR) of low-contrast lesions in medical ultrasound imaging are described. Differences in the frequency spectra and amplitude distributions of the lesion and its surroundings can be used to increase the CNR of the lesion relative to the background. Automated graylevel mapping is used in combination with a contrast-weighted form of frequency-diversity speckle reduction. In clinical studies, the techniques have yielded mean CNR improvements of 3.2 dB above ordinary frequency-diversity imaging and 5.6 dB over sharper conventional images, with no post-processing graylevel mapping.
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paper_title: Speckle reduction and enhancement of SAR images in the wavelet domain

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Three methods are investigated for speckle reduction and enhancement of synthetic aperture radar (SAR) images in the orthogonal wavelet domain. The first method is a nonlinear method based on soft thresholding the wavelet coefficients for logarithmically transformed SAR image data (Guo et al. 1994). The second method uses an enhanced adaptive Lee filter (Lopes et al., 1990) of the wavelet coefficients of SAR images. Finally, the third method introduces nonlinear speckle reduction based on adaptive sigmoid thresholding of the wavelet coefficients for logarithmically transformed SAR images. The wavelet methods show great promise for speckle removal and hence provide better detection performance for SAR based recognition.
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paper_title: De-Noising By Soft-Thresholding

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Donoho and Johnstone (1994) proposed a method for reconstructing an unknown function f on [0,1] from noisy data d/sub i/=f(t/sub i/)+/spl sigma/z/sub i/, i=0, ..., n-1,t/sub i/=i/n, where the z/sub i/ are independent and identically distributed standard Gaussian random variables. The reconstruction f/spl circ/*/sub n/ is defined in the wavelet domain by translating all the empirical wavelet coefficients of d toward 0 by an amount /spl sigma//spl middot//spl radic/(2log (n)/n). The authors prove two results about this type of estimator. [Smooth]: with high probability f/spl circ/*/sub n/ is at least as smooth as f, in any of a wide variety of smoothness measures. [Adapt]: the estimator comes nearly as close in mean square to f as any measurable estimator can come, uniformly over balls in each of two broad scales of smoothness classes. These two properties are unprecedented in several ways. The present proof of these results develops new facts about abstract statistical inference and its connection with an optimal recovery model. >
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paper_title: SAR speckle reduction using wavelet denoising and Markov random field modeling

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The granular appearance of speckle noise in synthetic aperture radar (SAR) imagery makes it very difficult to visually and automatically interpret SAR data. Therefore, speckle reduction is a prerequisite for many SAR image processing tasks. In this paper, we develop a speckle reduction algorithm by fusing the wavelet Bayesian denoising technique with Markov-random-field-based image regularization. Wavelet coefficients are modeled independently and identically by a two-state Gaussian mixture model, while their spatial dependence is characterized by a Markov random field imposed on the hidden state of Gaussian mixtures. The Expectation-Maximization algorithm is used to estimate hyperparameters and specify the mixture model, and the iterated-conditional-modes method is implemented to optimize the state configuration. The noise-free wavelet coefficients are finally estimated by a shrinkage function based on local weighted averaging of the Bayesian estimator. Experimental results show that the proposed method outperforms standard wavelet denoising techniques in terms of the signal-to-noise ratio and the equivalent-number-of-looks measures in most cases. It also achieves better performance than the refined Lee filter.
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paper_title: Speckle Reduction in Medical Ultrasound Images using a New Multiscale Bivariate Bayesian MMSE-Based Method

paper_content:

This paper introduces a new wavelet-based speckle reduction method for medical ultrasound images, which extends a recently emerged homomorphic Bayesian technique. The new method exploits interscale dependency of wavelet coefficients. For this purpose, bivariate alpha-stable distributions are proposed, which are able to better capture the heavy-tailed nature of the data. Using this new statistical model, we design a bivariate Bayesian estimator to effectively remove speckle from wavelet coefficients. Better results are obtained using the oriented two-dimensional dual-tree complex wavelet transform (2D DTCWT) which offers improved directional selectivity and near shift invariance property. To assess the performance of the proposed method, results are compared with some related earlier techniques. Both numerical and visual comparisons indicate improved speckle reduction while preserving structural features, as desired for better diagnosis in medical images.
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paper_title: Locally adaptive wavelet domain Bayesian processor for denoising medical ultrasound images using Speckle modelling based on Rayleigh distribution

paper_content:

The authors present a statistical approach to speckle reduction in medical ultrasound B-scan images based on maximum a posteriori (MAP) estimation in the wavelet domain. In this framework, a new class of statistical model for speckle noise is proposed to obtain a simple and tractable solution in a closed analytical form. The proposed method uses the Rayleigh distribution for speckle noise and a Gaussian distribution for modelling the statistics of wavelet coefficients in a logarithmically transformed ultrasound image. The method combines the MAP estimation with the assumption that speckle is spatially correlated within a small window and designs a locally adaptive Bayesian processor whose parameters are computed from the neighboring coefficients. Further, the locally adaptive estimator is extended to the redundant wavelet representation, which yields better results than the decimated wavelet transform. The experimental results show that the proposed method clearly outperforms the state-of-the-art medical image denoising algorithm of Pizurica et al., spatially adaptive single-resolution methods and band-adaptive multi-scale soft-thresholding techniques in terms of quantitative performance as well as in terms of visual quality of the images. The main advantage of the new method over the existing techniques is that it suppresses speckle noise well, while retaining the structure of the image, particularly the thin bright streaks, which tend to occur along boundaries between tissue layers.
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paper_title: A versatile wavelet domain noise filtration technique for medical imaging

paper_content:

We propose a robust wavelet domain method for noise filtering in medical images. The proposed method adapts itself to various types of image noise as well as to the preference of the medical expert; a single parameter can be used to balance the preservation of (expert-dependent) relevant details against the degree of noise reduction. The algorithm exploits generally valid knowledge about the correlation of significant image features across the resolution scales to perform a preliminary coefficient classification. This preliminary coefficient classification is used to empirically estimate the statistical distributions of the coefficients that represent useful image features on the one hand and mainly noise on the other. The adaptation to the spatial context in the image is achieved by using a wavelet domain indicator of the local spatial activity. The proposed method is of low complexity, both in its implementation and execution time. The results demonstrate its usefulness for noise suppression in medical ultrasound and magnetic resonance imaging. In these applications, the proposed method clearly outperforms single-resolution spatially adaptive algorithms, in terms of quantitative performance measures as well as in terms of visual quality of the images.
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paper_title: Three-dimensional spatial compounding of ultrasound images

paper_content:

One of the most promising applications of 3-D ultrasound lies in the visualization and volume estimation of internal 3-D structures. Unfortunately, the quality of the ultrasound data can be severely degraded by artefacts and speckle, making automatic analysis of the 3-D data sets very difficult. In this paper we investigate the use of 3-D spatial compounding to reduce speckle. We develop a new statistical theory to predict the improvement in signal-to-noise ratio with increased levels of compounding, and verify the predictions empirically. We also investigate how registration errors can affect automatic volume estimation of structures within the compounded 3-D data set. Having established the need to correct these errors, we present a novel reconstruction algorithm which uses landmarks to register each B-scan accurately as it is inserted into the voxel array. In a series of in vitro and in vivo trials, we demonstrate that 3-D spatial compounding is very effective for improving the signal-to-noise ratio, but correction of registration errors is essential.
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paper_title: A novel multiscale nonlinear thresholding method for ultrasonic speckle suppressing

paper_content:

This paper presents a novel speckle suppression method for medical B-scan ultrasonic images. An original image is first separated into two parts with an adaptive filter. These two parts are then transformed into a multiscale wavelet domain and the wavelet coefficients are processed by a soft thresholding method, which is a variation of Donoho's (1995) soft thresholding method. The processed coefficients for each part are then transformed back into the space domain. Finally, the denoised image is obtained as the sum of the two processed parts. A computer-simulated image and an in vitro B-scan image of a pig heart have been used to test the performance of this new method. This technique effectively reduces the speckle noise, while preserving the resolvable details. It performs well in comparison to the multiscale thresholding technique without adaptive preprocessing and two other speckle-suppression methods.
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paper_title: Robust non-homomorphic approach for speckle reduction in medical ultrasound images

paper_content:

Most existing wavelet-based image denoising techniques are developed for additive white Gaussian noise. In applications to speckle reduction in medical ultrasound (US) images, the traditional approach is first to perform the logarithmic transform (homomorphic processing) to convert the multiplicative speckle noise model to an additive one, and then the wavelet filtering is performed on the log-transformed image, followed by an exponential operation. However, this non-linear operation leads to biased estimation of the signal and increases the computational complexity of the filtering method. To overcome these drawbacks, an efficient, non-homomorphic technique for speckle reduction in medical US images is proposed. The method relies on the true characterisation of the marginal statistics of the signal and speckle wavelet coefficients. The speckle component was modelled using the generalised Nakagami distribution, which is versatile enough to model the speckle statistics under various scattering conditions of interest in medical US images. By combining this speckle model with the generalised Gaussian signal first, the Bayesian shrinkage functions were derived using the maximum a posteriori (MAP) criterion. The resulting Bayesian processor used the local image statistics to achieve soft-adaptation from homogeneous to highly heterogeneous areas. Finally, the results showed that the proposed method, named GNDShrink, yielded a signal-to-noise ratio (SNR) gain of 0.42 dB over the best state-of-the-art despeckling method reported in the literature, 1.73 dB over the Lee filter and 1.31 dB over the Kaun filter at an input SNR of 12.0 dB, when tested on a US image. Further, the visual comparison of despeckled US images indicated that the new method suppressed the speckle noise well, while preserving the texture and organ surfaces.
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paper_title: Wavelet-based statistical approach for speckle reduction in medical ultrasound images

paper_content:

A novel speckle-reduction method is introduced, based on soft thresholding of the wavelet coefficients of a logarithmically transformed medical ultrasound image. The method is based on the generalised Gaussian distributed(GGD) modelling of sub-band coefficients. The method used was a variant of the recently published BayesShrink method by Chang and Vetterli, derived in the Bayesian framework for denoising natural images. It was scale adaptive, because the parameters required for estimating the threshold depend on scale and sub-band data. The threshold was computed by Kσ 2 /σ x , where σ and σ x were the standard deviation of the noise and the sub-band data of the noise-free image, respectively, and K was a scale parameter. Experimental results showed that the proposed method outperformed the median filter and the homomorphic Wiener filter by 29% in terms of the coefficient of correlation and 4% in terms of the edge preservation parameter. The numerical values of these quantitative parameters indicated the good feature preservation performance of the algorithm, as desired for better diagnosis in medical image processing.
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paper_title: A versatile technique for visual enhancement of medical ultrasound images

paper_content:

The paper presents a versatile wavelet domain despeckling technique to visually enhance the medical ultrasound (US) images for improving the clinical diagnosis. The method uses the two-sided generalized Nakagami distribution (GND) for modeling the speckle wavelet coefficients and the signal wavelet coefficients are approximated using the generalized Gaussian distribution (GGD). Combining these statistical priors with the Bayesian maximum a posteriori (MAP) criterion, the thresholding/shrinkage estimators are derived for processing the wavelet coefficients of detail subbands. Consequently, two blind speckle suppressors named as GNDThresh and GNDShrink have been implemented and evaluated on both the artificial speckle simulated images and real US images. The experimental results demonstrate the superiority of the suggested technique both quantitatively and qualitatively as compared to other competitive schemes reported in the image denoising literature, e.g., the proposed method yields a gain of more than 0.36 dB over the best state-of-the-art despeckling method (GenLik), 0.93 dB over SRAD filter, 2.35 dB over Lee filter, and 1.34 dB over Kuan filter in terms of signal-to-noise ratio, when tested on the realistic US images. The visual comparison of despeckled US images and the higher values of quality metrics (coefficient of correlation, edge preservation index, quality index, and structural similarity index) indicate that the new method suppresses the speckle noise well while preserving the texture and organ surfaces. Further, the proposed method will be evaluated on other class of images as well as by employing multiple observer evaluation.
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paper_title: A Model for Radar Images and Its Application to Adaptive Digital Filtering of Multiplicative Noise

paper_content:

Standard image processing techniques which are used to enhance noncoherent optically produced images are not applicable to radar images due to the coherent nature of the radar imaging process. A model for the radar imaging process is derived in this paper and a method for smoothing noisy radar images is also presented. The imaging model shows that the radar image is corrupted by multiplicative noise. The model leads to the functional form of an optimum (minimum MSE) filter for smoothing radar images. By using locally estimated parameter values the filter is made adaptive so that it provides minimum MSE estimates inside homogeneous areas of an image while preserving the edge structure. It is shown that the filter can be easily implemented in the spatial domain and is computationally efficient. The performance of the adaptive filter is compared (qualitatively and quantitatively) with several standard filters using real and simulated radar images.
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paper_title: Digital Image Enhancement and Noise Filtering by Use of Local Statistics

paper_content:

Computational techniques involving contrast enhancement and noise filtering on two-dimensional image arrays are developed based on their local mean and variance. These algorithms are nonrecursive and do not require the use of any kind of transform. They share the same characteristics in that each pixel is processed independently. Consequently, this approach has an obvious advantage when used in real-time digital image processing applications and where a parallel processor can be used. For both the additive and multiplicative cases, the a priori mean and variance of each pixel is derived from its local mean and variance. Then, the minimum mean-square error estimator in its simplest form is applied to obtain the noise filtering algorithms. For multiplicative noise a statistical optimal linear approximation is made. Experimental results show that such an assumption yields a very effective filtering algorithm. Examples on images containing 256 × 256 pixels are given. Results show that in most cases the techniques developed in this paper are readily adaptable to real-time image processing.
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paper_title: Adaptive Noise Smoothing Filter for Images with Signal-Dependent Noise

paper_content:

In this paper, we consider the restoration of images with signal-dependent noise. The filter is noise smoothing and adapts to local changes in image statistics based on a nonstationary mean, nonstationary variance (NMNV) image model. For images degraded by a class of uncorrelated, signal-dependent noise without blur, the adaptive noise smoothing filter becomes a point processor and is similar to Lee's local statistics algorithm [16]. The filter is able to adapt itself to the nonstationary local image statistics in the presence of different types of signal-dependent noise. For multiplicative noise, the adaptive noise smoothing filter is a systematic derivation of Lee's algorithm with some extensions that allow different estimators for the local image variance. The advantage of the derivation is its easy extension to deal with various types of signal-dependent noise. Film-grain and Poisson signal-dependent restoration problems are also considered as examples. All the nonstationary image statistical parameters needed for the filter can be estimated from the noisy image and no a priori information about the original image is required.
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paper_title: Adaptive speckle filters and scene heterogeneity

paper_content:

The presence of speckle in radar images makes the radiometric and textural aspects less efficient for class discrimination. Many adaptive filters have been developed for speckle reduction, the most well known of which are analyzed. It is shown that they are based on a test related to the local coefficient of variation of the observed image, which describes the scene heterogeneity. Some practical criteria are introduced to modify the filters in order to make them more efficient. The filters are tested on a simulated synthetic aperture radar (SAR) image and an SAR-580 image. As was expected, the new filters perform better, i.e. they average the homogeneous areas better and preserve texture information, edges, linear features, and point target responses better at the same time. Moreover, they can be adapted to features other than the coefficient of variation to reduce the speckle while preserving the corresponding information. >
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paper_title: Toward edge sharpening: a SAR speckle filtering algorithm

paper_content:

This paper makes two contributions. It first introduces an algorithm for synthetic aperture radar (SAR) speckle reduction and edge sharpening. Existing speckle filtering algorithms can effectively reduce the speckle effect but unfortunately also, to some degree, smear edges and blur images. Even for unfiltered images, there is still a need for edge sharpening; since SAR sensors have limited bandwidths, leading to slow responses to sudden changes (smearing sharp edges). The proposed algorithm functions as an adaptive-mean filter. Edge crossing points are detected by using the second-order derivative of the Gaussian function as a wavelet transform function. A proper dilation scale factor enables the wavelet transform function to detect only edge crossings and ignore the local oscillations. Then in a moving window the mean filter is applied if there is no edge crossing point. Otherwise, averaging is only applied to the part of the window separated by edge crossing points. Consequently, the algorithm smooths uniform areas while it sharpens and enhances edges. Edges of the filtered images are generally sharper than the original. Similarities between the proposed filter and other popular speckle filters, such as the Lee, Kuan, and Frost filters, designated for SAR multiplicative noise, are analyzed. Another contribution of the paper is the evaluation of popular filters from the viewpoint of texture preservation. The evaluation is carried out using the first and second-order histograms. Possible distortions caused by filters are explained.
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paper_title: Adaptive speckle reduction filter for log-compressed B-scan images

paper_content:

A good statistical model of speckle formation is useful for designing a good adaptive filter for speckle reduction In ultrasound B-scan images. Previously, statistical models have been used, but they failed to account for the log compression of the echo envelope employed by clinical ultrasound systems. Log-compression helps in reducing the dynamic range of the B-scan Images for display on a monitor as well as enhancing weak backscatters. In this article, statistics of log-compressed echo images, using the K-distribution statistical model for the echo envelope, are used to derive a parameter that can be used to quantify the extent of speckle formation. This speckle quantification can be used with an unsharp masking filter to adaptively reduce speckle. The effectiveness of the filter is demonstrated on images of contrast detail phantoms and on in-vivo abdominal images obtained by a clinical ultrasound system with log-compression.
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paper_title: Analysis of speckle in ultrasound images using fractional order statistics and the homodyned k-distribution.

paper_content:

It is necessary to identify speckled regions in ultrasound images to control adaptive speckle suppression algorithms, for tissue characterisation, and to estimate the elevational separation of B-scans by speckle decorrelation. Previous authors have proposed classification techniques based on second order powers of the homodyned k-distribution, or lower order powers of the more limited k-distribution. In this paper we explore the speckle discrimination properties of statistics based on arbitrary powers of the ultrasound echo envelope signal using a combination of simulations and theoretical results from the homodyned k-distribution. We conclude that statistics based on powers less than one are surprisingly less effective than some higher powers. A simple discriminant function for speckle is evaluated quantitatively in simulation and qualitatively on sample B-scan images.
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paper_title: Adaptive Restoration Of Images With Speckle

paper_content:

Speckle is a granular noise that inherently exists in all types of coherent imaging systems. The presence of speckle in an image reduces the resolution of the image and the detectability of the target. Many speckle reduction algorithms assume speckle noise is multiplicative. We instead model the speckle according to the exact physical process of coherent image formation. Thus, the model includes signal-dependent effects and accurately represents the higher order statistical properties of speckle that are important to the restoration procedure. Various adaptive restoration filters for intensity speckle images are derived based on different speckle model assumptions and a nonstationary image model. These filters respond adaptively to the signal-dependent speckle noise and the nonstationary statistics of the original image.© (1983) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
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paper_title: Mode filtering to reduce ultrasound speckle for feature extraction

paper_content:

The authors investigate the use of filtering techniques to reduce speckle in ultrasound images, to improve their suitability for later feature extraction. The maximum likelihood estimator for a speckle corrupted image is shown to correspond to the statistical mode but this is difficult to determine for small populations, such as those contained by a filter mask. The truncated median filter approximates the mode by using the order of known image statistics and provides a fully automated image processing technique for speckle filtering. The filter's performance is established using a new quantitative evaluation scheme that closely considers the effect of filtering on edges, a key factor when applying features extraction in automated image interpretation. Application to in vivo and phantom test images shows that the truncated median filter provides clear images with strong edges, of quality exceeding that of other techniques. These benefits are confirmed by the application of feature extraction in arterial wall labelling
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paper_title: An adaptive weighted median filter for speckle suppression in medical ultrasonic images

paper_content:

A method for reducing speckle noise in medical ultrasonic images is presented. It is called the adaptive weighted median filter (AWMF) and is based on the weighted median, which originates from the well-known median filter through the introduction of weight coefficients. By adjusting the weight coefficients and consequently the smoothing characteristics of the filter according to the local statistics around each point of the image, it is possible to suppress noise while edges and other important features are preserved. Application of the filter to several ultrasonic scans has shown that processing improves the detectability of small structures and subtle gray-scale variations without affecting the sharpness or anatomical information of the original image. Comparison with the pure median filter demonstrates the superiority of adaptive techniques over their space-invariant counterparts. Examples of processed images show that the AWMF preserves small details better than other nonlinear space-varying filters which offer equal noise reduction in uniform areas. >
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paper_title: Comparative evaluation of despeckle filtering in ultrasound imaging of the carotid artery

paper_content:

It is well-known that speckle is a multiplicative noise that degrades the visual evaluation in ultrasound imaging. The recent advancements in ultrasound instrumentation and portable ultrasound devices necessitate the need of more robust despeckling techniques for enhanced ultrasound medical imaging for both routine clinical practice and teleconsultation. The objective of this work was to carry out a comparative evaluation of despeckle filtering based on texture analysis, image quality evaluation metrics, and visual evaluation by medical experts in the assessment of 440 (220 asymptomatic and 220 symptomatic) ultrasound images of the carotid artery bifurcation. In this paper a total of 10 despeckle filters were evaluated based on local statistics, median filtering, pixel homogeneity, geometric filtering, homomorphic filtering, anisotropic diffusion, nonlinear coherence diffusion, and wavelet filtering. The results of this study suggest that the first order statistics filter lsmv, gave the best performance, followed by the geometric filter gf4d, and the homogeneous mask area filter lsminsc. These filters improved the class separation between the asymptomatic and the symptomatic classes based on the statistics of the extracted texture features, gave only a marginal improvement in the classification success rate, and improved the visual assessment carried out by the two experts. More specifically, filters lsmv or gf4d can be used for despeckling asymptomatic images in which the expert is interested mainly in the plaque composition and texture analysis; and filters lsmv, gf4d, or lsminsc can be used for the despeckling of symptomatic images in which the expert is interested in identifying the degree of stenosis and the plaque borders. The proper selection of a despeckle filter is very important in the enhancement of ultrasonic imaging of the carotid artery. Further work is needed to evaluate at a larger scale and in clinical practice the performance of the proposed despeckle filters in the automated segmentation, texture analysis, and classification of carotid ultrasound imaging.
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paper_title: A novel approach to speckle reduction in ultrasound imaging.

paper_content:

Speckle noise is inherent in ultrasound images, and it generally tends to reduce the resolution and contrast, thereby degrading the diagnostic accuracy of this modality. Speckle reduction is very important and critical for ultrasound imaging. In this paper, we propose a novel approach for speckle reduction using 2-D homogeneity and directional average filters. We have conducted experiments on numerous artificial images, clinic breast ultrasound images and vascular images. The experimental results are compared with that of other methods and the performance is evaluated using several merits, and they demonstrate that the proposed approach can reduce the speckle noise effectively without blurring the edges and damaging the textual information. It will be very useful for computer-aided diagnosis systems using ultrasound images.
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paper_title: Evaluation of speckle noise MAP filtering algorithms applied to SAR images

paper_content:

This work proposes new speckle reduction filters for multi-look, amplitude-detected Synthetic Aperture Radar (SAR) images based on the maximum a posteriori (MAP) approach and compares their performance. The new filters use an adaptive approach based on the one-dimensional k-means clustering algorithm over the variance ratio and also a region-growing procedure. The trade-off between the loss of radiometric resolution and edge preservation is evaluated in the filtered images. In order to obtain quantitative measures of the speckle reduction and of the edge blurring, we used some parameters such as the classical equivalent number of looks and the Hough transform. Experiments have been carried out with natural images corrupted with synthetic speckle noise following the Rayleigh and square root of gamma distributions and with real SAR images.
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paper_title: Adaptive speckle filters and scene heterogeneity

paper_content:

The presence of speckle in radar images makes the radiometric and textural aspects less efficient for class discrimination. Many adaptive filters have been developed for speckle reduction, the most well known of which are analyzed. It is shown that they are based on a test related to the local coefficient of variation of the observed image, which describes the scene heterogeneity. Some practical criteria are introduced to modify the filters in order to make them more efficient. The filters are tested on a simulated synthetic aperture radar (SAR) image and an SAR-580 image. As was expected, the new filters perform better, i.e. they average the homogeneous areas better and preserve texture information, edges, linear features, and point target responses better at the same time. Moreover, they can be adapted to features other than the coefficient of variation to reduce the speckle while preserving the corresponding information. >
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paper_title: Ultrasound speckle reduction by directional median filtering

paper_content:

This paper presents a novel adaptation of the median filter to the problem of boundary-preserving speckle reduction in ultrasonic imaging. The technique involves applying a bank of oriented one-dimensional median filters to the image, and retaining at each point the largest value among all the filter bank outputs. The result is an operator which suppresses speckle noise while retaining the structure of the image, particularly the thin bright streaks, which tend to occur along boundaries between tissue layers. The technique is compared to a block median filter and an algorithm discussed by Loupas et al. (1989), and is shown to be far superior to the median filter, and noticeably better than the Loupas filter at enhancing thin lines.
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paper_title: Adaptive Restoration Of Images With Speckle

paper_content:

Speckle is a granular noise that inherently exists in all types of coherent imaging systems. The presence of speckle in an image reduces the resolution of the image and the detectability of the target. Many speckle reduction algorithms assume speckle noise is multiplicative. We instead model the speckle according to the exact physical process of coherent image formation. Thus, the model includes signal-dependent effects and accurately represents the higher order statistical properties of speckle that are important to the restoration procedure. Various adaptive restoration filters for intensity speckle images are derived based on different speckle model assumptions and a nonstationary image model. These filters respond adaptively to the signal-dependent speckle noise and the nonstationary statistics of the original image.© (1983) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
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paper_title: Real-time speckle reduction and coherence enhancement in ultrasound imaging via nonlinear anisotropic diffusion

paper_content:

This paper presents a novel approach for speckle reduction and coherence enhancement of ultrasound images based on nonlinear coherent diffusion (NCD) model. The proposed NCD model combines three different models. According to speckle extent and image anisotropy, the NCD model changes progressively from isotropic diffusion through anisotropic coherent diffusion to, finally, mean curvature motion. This structure maximally low-pass filters those parts of the image that correspond to fully developed speckle, while substantially preserving information associated with resolved-object structures. The proposed implementation algorithm utilizes an efficient discretization scheme that allows for real-time implementation on commercial systems. The theory and implementation of the new technique are presented and verified using phantom and clinical ultrasound images. In addition, the results from previous techniques are compared with the new method to demonstrate its performance.
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paper_title: Multiresolution nonhomogeneous anisotropic diffusion approach to enhance ultrasound breast tumor image legibility

paper_content:

One factor used to tell if a tumor is benign or malignant is by examining the underlying structures of the tumor. In ultrasound images, these underlying structures are often buried in speckle noise. The proposed technique utilizes a median-anisotropic diffusion interactive algorithm to “dissolve” speckle noise and enhance images. This technique has two important differences from conventional anisotropic diffusion techniques. First, there is a two resolution level process, which converts the speckle to quasi-impulsive noise, making it more easily removable. Second, a median related reaction term is applied to the anisotropic diffusion equation, thus making it well suited for removing impulse type noise. In addition, the reaction term will make speckle reduction and image enhancement more adaptive to the local features in the image. The experimental data showed the proposed technique improved the legibility of the breast tumor images as important structures were emphasized.
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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: A diffusion stick method for speckle suppression in ultrasonic images

paper_content:

This paper presents a diffusion stick method for speckle suppression in ultrasonic images. An asymmetric stick filter kernel is firstly defined by decomposing the rectangle neighborhood into line segments of variable orientations. Then, the weighted sum of averages along each stick is used to produce the filtered images. Implemented in an iteration scheme, our method works as a pseudo-diffusion process, where the diffusivity is controlled by a normalized variance function. Experiments of synthetic and real images show that the diffusion stick technique performs effectively in suppressing speckle noise, preserving resolvable structures and enhancing linear features.
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paper_title: Three-dimensional speckle reducing anisotropic diffusion

paper_content:

The two-dimensional speckle reducing anisotropic diffusion (SRAD) algorithm developed for despeckling ultrasound imagery to obtain a SRAD algorithm capable of enhancing volumetric ultrasound data is presented in this paper. First, an instantaneous coefficient of variation edge detector appropriate for 3D data set presented. Then, the 3D SRAD partial differential equation is formulated by replacing the gradient operator in the traditional diffusion mechanism with the derived 3D ICOV operator. We demonstrate the performance of the 3D SRAD using synthetic 3D ultrasound data acquired from an ellipsoid and real data acquired from the left ventricle (LV) of a murine heart.
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paper_title: Edge Enhancement and Filtering of Medical Ultrasonic Images Using a Hybrid Method

paper_content:

A hybrid method based on anisotropic diffusion (AD) is proposed for ultrasound speckle suppression and edge enhancement. This method is designed to utilize the different denoising properties of three techniques: median filtering, improved AD filtering and isotropic diffusion filtering. The gradient matrix is analyzed, and thresholds are chosen by experiments. The hybrid method is made by combining the three filtering methods for three different grayscale gradient ranges respectively. The filtering program is realized by iteration, with iteration time determined by iteration stopping criterion (ISC) accurately. In the experiment hundreds of images are processed by hybrid method, in contrast with median filtering and speckle reducing anisotropic diffusion (SRAD) filtering. The experimental results shows hybrid method can greatly improve processing speed, while successfully suppress speckle and enhance edge, therefore suitable for high-speed noise elimination of 3D images with huge data.
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paper_title: Aggressive region growing for speckle reduction in ultrasound images

paper_content:

Abstract Speckle appears in all conventional medical B-mode ultrasonic images and can be an undesirable property since it may mask small but diagnostically significant features. In this paper, an adaptive filtering algorithm is proposed for speckle reduction. It selects a filtering region size using an appropriately estimated homogeneity value for region growth. Homogeneous regions are processed with an arithmetic mean filter. Edge pixels are filtered using a nonlinear median filter. The performance of the proposed technique is compared to two other methods––the adaptive weighted median filter and the homogeneous region growing mean filter. Results of processed images show that the method proposed reduces speckle noise and preserves edge details effectively.
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paper_title: Detection of lines and boundaries in speckle images-application to medical ultrasound

paper_content:

This paper describes an approach to boundary detection in ultrasound speckle based on an image enhancement technique. The enhancement algorithm works by filtering the image with "sticks," short line segments which are varied in orientation to achieve the maximum projected value at each point. The statistical properties of this approach have been described in an earlier paper; in this work we present three significant extensions to improve the performance of the basic method. First, we investigate the effect of varying the size and shape of the sticks. We show that these variations affect the performance of the algorithm in very fundamental ways, for example by making it more or less sensitive to thinner or more tightly curving boundaries. Second, we present a means of improving the performance of this technique by estimating the distribution function of the orientation of the line passing through each point. Finally, we show that images can be "stained" for easier visual interpretation by applying to each pixel a false color whose hue is related to the orientation of the most prominent line segment at that point. Examples are given to illustrate the performance of the different settings on a single image.
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paper_title: Geometric filter for speckle reduction

paper_content:

An algorithm is described which reduces speckle noise in images. It is a nonlinear algorithm based on geometric concepts. Tests were performed on synthetic aperture radar images which show that it compares favorably with a 3 × 3 median filter.
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paper_title: Comparative evaluation of despeckle filtering in ultrasound imaging of the carotid artery

paper_content:

It is well-known that speckle is a multiplicative noise that degrades the visual evaluation in ultrasound imaging. The recent advancements in ultrasound instrumentation and portable ultrasound devices necessitate the need of more robust despeckling techniques for enhanced ultrasound medical imaging for both routine clinical practice and teleconsultation. The objective of this work was to carry out a comparative evaluation of despeckle filtering based on texture analysis, image quality evaluation metrics, and visual evaluation by medical experts in the assessment of 440 (220 asymptomatic and 220 symptomatic) ultrasound images of the carotid artery bifurcation. In this paper a total of 10 despeckle filters were evaluated based on local statistics, median filtering, pixel homogeneity, geometric filtering, homomorphic filtering, anisotropic diffusion, nonlinear coherence diffusion, and wavelet filtering. The results of this study suggest that the first order statistics filter lsmv, gave the best performance, followed by the geometric filter gf4d, and the homogeneous mask area filter lsminsc. These filters improved the class separation between the asymptomatic and the symptomatic classes based on the statistics of the extracted texture features, gave only a marginal improvement in the classification success rate, and improved the visual assessment carried out by the two experts. More specifically, filters lsmv or gf4d can be used for despeckling asymptomatic images in which the expert is interested mainly in the plaque composition and texture analysis; and filters lsmv, gf4d, or lsminsc can be used for the despeckling of symptomatic images in which the expert is interested in identifying the degree of stenosis and the plaque borders. The proper selection of a despeckle filter is very important in the enhancement of ultrasonic imaging of the carotid artery. Further work is needed to evaluate at a larger scale and in clinical practice the performance of the proposed despeckle filters in the automated segmentation, texture analysis, and classification of carotid ultrasound imaging.
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paper_title: A SPECKLE SUPPRESSION METHOD BASED ON NONLINEAR THRESHOLD WAVELET PACKET IN ULTRASOUND IMAGES

paper_content:

A novel method based on nonlinear threshold wavelet packet was explored in order to suppress the speckles in B scan 3 D ultrasound imaging. Firstly, an original image was separated into two parts by adaptive pre filtering. These two parts were then transformed into a multiscale wavelet packet (WP) domain and the WP coefficients were processed by a soft thresholding filter method. The processed coefficients for each part were then transformed back into the space domain. Finally, the de noised image was obtained as the sum of the two processed parts. Performance of this new method has been tested by a computer simulated image. Ultrasound images set of a plastic bowl in a water tank has been utilized for 3 D ultrasound reconstruction. It was found that this technique reduces the speckle noise effectively, and preserves the resolvable details.
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paper_title: Spatially adaptive wavelet thresholding with context modeling for image denoising

paper_content:

The method of wavelet thresholding for removing noise, or denoising, has been researched extensively due to its effectiveness and simplicity. Much of the work has been concentrated on finding the best uniform threshold or best basis. However, not much has been done to make this method adaptive to spatially changing statistics which is typical of a large class of images. This work proposes a spatially adaptive wavelet thresholding method based on context modeling, a common technique used in image compression to adapt the coder to the non-stationarity of images. We model each coefficient as a random variable with the generalized Gaussian prior with unknown parameters. Context modeling is used to estimate the parameters for each coefficient, which are then used to adapt the thresholding strategy. Experimental results show that spatially adaptive wavelet thresholding yields significantly superior image quality and lower MSE than optimal uniform thresholding.
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paper_title: SOFT-THRESHOLDING FOR DENOISING OF MEDICAL IMAGES — A MULTIRESOLUTION APPROACH

paper_content:

Medical images generally have low contrast and they get complex type of noise due to the use of various devices and applications of various algorithms. However, most of the denoising methods consider only additive noise or some special noise model dependent on their systems and conditions only. Such methods when applied to real medical images yield poor results. The present work proposes a method for denoising of medical images using soft-thresholding in wavelet domain on multiple levels. We have developed a method to compute the threshold values for denoising of medical images, which depend on the median as well as the contrast ratio of the wavelet coefficients and also on the level number. We have performed experiments by adding various proportions of Gaussian, Salt-and-Pepper and Speckle noise, and found that the proposed method performs better for these cases. The method is efficient because the threshold values can be calculated directly and it is adaptive as these values depend on mean, median and standard deviation of wavelet coefficients of the particular image. The proposed method also gives a criterion for level-dependent thresholding. Application of the proposed method to Ultrasound, X-ray and MRI images is demonstrated in experiments. In the present work, we have also done studies on how to select the mother wavelet for a particular problem.
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paper_title: Speckle reduction and enhancement of SAR images in the wavelet domain

paper_content:

Three methods are investigated for speckle reduction and enhancement of synthetic aperture radar (SAR) images in the orthogonal wavelet domain. The first method is a nonlinear method based on soft thresholding the wavelet coefficients for logarithmically transformed SAR image data (Guo et al. 1994). The second method uses an enhanced adaptive Lee filter (Lopes et al., 1990) of the wavelet coefficients of SAR images. Finally, the third method introduces nonlinear speckle reduction based on adaptive sigmoid thresholding of the wavelet coefficients for logarithmically transformed SAR images. The wavelet methods show great promise for speckle removal and hence provide better detection performance for SAR based recognition.
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paper_title: De-Noising By Soft-Thresholding

paper_content:

Donoho and Johnstone (1994) proposed a method for reconstructing an unknown function f on [0,1] from noisy data d/sub i/=f(t/sub i/)+/spl sigma/z/sub i/, i=0, ..., n-1,t/sub i/=i/n, where the z/sub i/ are independent and identically distributed standard Gaussian random variables. The reconstruction f/spl circ/*/sub n/ is defined in the wavelet domain by translating all the empirical wavelet coefficients of d toward 0 by an amount /spl sigma//spl middot//spl radic/(2log (n)/n). The authors prove two results about this type of estimator. [Smooth]: with high probability f/spl circ/*/sub n/ is at least as smooth as f, in any of a wide variety of smoothness measures. [Adapt]: the estimator comes nearly as close in mean square to f as any measurable estimator can come, uniformly over balls in each of two broad scales of smoothness classes. These two properties are unprecedented in several ways. The present proof of these results develops new facts about abstract statistical inference and its connection with an optimal recovery model. >
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paper_title: A novel multiscale nonlinear thresholding method for ultrasonic speckle suppressing

paper_content:

This paper presents a novel speckle suppression method for medical B-scan ultrasonic images. An original image is first separated into two parts with an adaptive filter. These two parts are then transformed into a multiscale wavelet domain and the wavelet coefficients are processed by a soft thresholding method, which is a variation of Donoho's (1995) soft thresholding method. The processed coefficients for each part are then transformed back into the space domain. Finally, the denoised image is obtained as the sum of the two processed parts. A computer-simulated image and an in vitro B-scan image of a pig heart have been used to test the performance of this new method. This technique effectively reduces the speckle noise, while preserving the resolvable details. It performs well in comparison to the multiscale thresholding technique without adaptive preprocessing and two other speckle-suppression methods.
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paper_title: Novel Bayesian multiscale method for speckle removal in medical ultrasound images

paper_content:

A novel speckle suppression method for medical ultrasound images is presented. First, the logarithmic transform of the original image is analyzed into the multiscale wavelet domain. We show that the subband decompositions of ultrasound images have significantly non-Gaussian statistics that are best described by families of heavy-tailed distributions such as the alpha-stable. Then, we design a Bayesian estimator that exploits these statistics. We use the alpha-stable model to develop a blind noise-removal processor that performs a nonlinear operation on the data. Finally, we compare our technique with current state-of-the-art soft and hard thresholding methods applied on actual ultrasound medical images and we quantify the achieved performance improvement.
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paper_title: Nonlinear multiscale wavelet diffusion for speckle suppression and edge enhancement in ultrasound images

paper_content:

This paper introduces a novel nonlinear multiscale wavelet diffusion method for ultrasound speckle suppression and edge enhancement. This method is designed to utilize the favorable denoising properties of two frequently used techniques: the sparsity and multiresolution properties of the wavelet, and the iterative edge enhancement feature of nonlinear diffusion. With fully exploited knowledge of speckle image models, the edges of images are detected using normalized wavelet modulus. Relying on this feature, both the envelope-detected speckle image and the log-compressed ultrasonic image can be directly processed by the algorithm without need for additional preprocessing. Speckle is suppressed by employing the iterative multiscale diffusion on the wavelet coefficients. With a tuning diffusion threshold strategy, the proposed method can improve the image quality for both visualization and auto-segmentation applications. We validate our method using synthetic speckle images and real ultrasonic images. Performance improvement over other despeckling filters is quantified in terms of noise suppression and edge preservation indices.
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paper_title: A new algorithm for shot noise removal in medical ultrasound images based on alpha-stable model

paper_content:

Ultrasonic images are generally affected by multiplicative shot noise. Shot noise filtering is thus a critical pre-processing step in medical ultrasound imagery. This paper analyses and models the coefficients of 2-D multi-resolution wavelet decomposition of logarithmically transformed images using alpha-stable distribution model. Consequently, we propose a new function that performs a non-linear operation on the data of classifying the coefficients, thus achieving a novel form of noise removal based on multi-resolution wavelet decomposition and the alpha-stable model. We compare our new technique with current shot noise reduction methods applied on actual ultrasound medical images and simulations results show that the proposed new method is more robust than the methods based on Gaussian assumption. Copyright © 2006 John Wiley & Sons, Ltd.
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paper_title: SAR speckle reduction using wavelet denoising and Markov random field modeling

paper_content:

The granular appearance of speckle noise in synthetic aperture radar (SAR) imagery makes it very difficult to visually and automatically interpret SAR data. Therefore, speckle reduction is a prerequisite for many SAR image processing tasks. In this paper, we develop a speckle reduction algorithm by fusing the wavelet Bayesian denoising technique with Markov-random-field-based image regularization. Wavelet coefficients are modeled independently and identically by a two-state Gaussian mixture model, while their spatial dependence is characterized by a Markov random field imposed on the hidden state of Gaussian mixtures. The Expectation-Maximization algorithm is used to estimate hyperparameters and specify the mixture model, and the iterated-conditional-modes method is implemented to optimize the state configuration. The noise-free wavelet coefficients are finally estimated by a shrinkage function based on local weighted averaging of the Bayesian estimator. Experimental results show that the proposed method outperforms standard wavelet denoising techniques in terms of the signal-to-noise ratio and the equivalent-number-of-looks measures in most cases. It also achieves better performance than the refined Lee filter.
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paper_title: Speckle Reduction in Medical Ultrasound Images using a New Multiscale Bivariate Bayesian MMSE-Based Method

paper_content:

This paper introduces a new wavelet-based speckle reduction method for medical ultrasound images, which extends a recently emerged homomorphic Bayesian technique. The new method exploits interscale dependency of wavelet coefficients. For this purpose, bivariate alpha-stable distributions are proposed, which are able to better capture the heavy-tailed nature of the data. Using this new statistical model, we design a bivariate Bayesian estimator to effectively remove speckle from wavelet coefficients. Better results are obtained using the oriented two-dimensional dual-tree complex wavelet transform (2D DTCWT) which offers improved directional selectivity and near shift invariance property. To assess the performance of the proposed method, results are compared with some related earlier techniques. Both numerical and visual comparisons indicate improved speckle reduction while preserving structural features, as desired for better diagnosis in medical images.
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paper_title: Speckle filtering of SAR images: a comparative study between complex-wavelet-based and standard filters

paper_content:

We present a comparative study between a complex Wavelet Coefficient Shrinkage (WCS) filter and several standard speckle filters that are widely used in the radar imaging community. The WCS filter is based on the use of Symmetric Daubechies wavelets which share the same properties as the real Daubechies wavelets but with an additional symmetry property. The filtering operation is an elliptical soft- thresholding procedure with respect to the principal axes of the 2D complex wavelet coefficient distributions. Both qualitative and quantitative results (signal to mean square error ratio, equivalent number of looks, edgemap figure of merit) are reported. Tests have been performed using simulated speckle noise as well as real radar images. It is found that the WCS filter performs equally well as the standard filters for low-level noise and slightly outperforms them for higher-level noise.© (1997) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
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paper_title: Locally adaptive wavelet domain Bayesian processor for denoising medical ultrasound images using Speckle modelling based on Rayleigh distribution

paper_content:

The authors present a statistical approach to speckle reduction in medical ultrasound B-scan images based on maximum a posteriori (MAP) estimation in the wavelet domain. In this framework, a new class of statistical model for speckle noise is proposed to obtain a simple and tractable solution in a closed analytical form. The proposed method uses the Rayleigh distribution for speckle noise and a Gaussian distribution for modelling the statistics of wavelet coefficients in a logarithmically transformed ultrasound image. The method combines the MAP estimation with the assumption that speckle is spatially correlated within a small window and designs a locally adaptive Bayesian processor whose parameters are computed from the neighboring coefficients. Further, the locally adaptive estimator is extended to the redundant wavelet representation, which yields better results than the decimated wavelet transform. The experimental results show that the proposed method clearly outperforms the state-of-the-art medical image denoising algorithm of Pizurica et al., spatially adaptive single-resolution methods and band-adaptive multi-scale soft-thresholding techniques in terms of quantitative performance as well as in terms of visual quality of the images. The main advantage of the new method over the existing techniques is that it suppresses speckle noise well, while retaining the structure of the image, particularly the thin bright streaks, which tend to occur along boundaries between tissue layers.
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paper_title: A versatile wavelet domain noise filtration technique for medical imaging

paper_content:

We propose a robust wavelet domain method for noise filtering in medical images. The proposed method adapts itself to various types of image noise as well as to the preference of the medical expert; a single parameter can be used to balance the preservation of (expert-dependent) relevant details against the degree of noise reduction. The algorithm exploits generally valid knowledge about the correlation of significant image features across the resolution scales to perform a preliminary coefficient classification. This preliminary coefficient classification is used to empirically estimate the statistical distributions of the coefficients that represent useful image features on the one hand and mainly noise on the other. The adaptation to the spatial context in the image is achieved by using a wavelet domain indicator of the local spatial activity. The proposed method is of low complexity, both in its implementation and execution time. The results demonstrate its usefulness for noise suppression in medical ultrasound and magnetic resonance imaging. In these applications, the proposed method clearly outperforms single-resolution spatially adaptive algorithms, in terms of quantitative performance measures as well as in terms of visual quality of the images.
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paper_title: Robust non-homomorphic approach for speckle reduction in medical ultrasound images

paper_content:

Most existing wavelet-based image denoising techniques are developed for additive white Gaussian noise. In applications to speckle reduction in medical ultrasound (US) images, the traditional approach is first to perform the logarithmic transform (homomorphic processing) to convert the multiplicative speckle noise model to an additive one, and then the wavelet filtering is performed on the log-transformed image, followed by an exponential operation. However, this non-linear operation leads to biased estimation of the signal and increases the computational complexity of the filtering method. To overcome these drawbacks, an efficient, non-homomorphic technique for speckle reduction in medical US images is proposed. The method relies on the true characterisation of the marginal statistics of the signal and speckle wavelet coefficients. The speckle component was modelled using the generalised Nakagami distribution, which is versatile enough to model the speckle statistics under various scattering conditions of interest in medical US images. By combining this speckle model with the generalised Gaussian signal first, the Bayesian shrinkage functions were derived using the maximum a posteriori (MAP) criterion. The resulting Bayesian processor used the local image statistics to achieve soft-adaptation from homogeneous to highly heterogeneous areas. Finally, the results showed that the proposed method, named GNDShrink, yielded a signal-to-noise ratio (SNR) gain of 0.42 dB over the best state-of-the-art despeckling method reported in the literature, 1.73 dB over the Lee filter and 1.31 dB over the Kaun filter at an input SNR of 12.0 dB, when tested on a US image. Further, the visual comparison of despeckled US images indicated that the new method suppressed the speckle noise well, while preserving the texture and organ surfaces.
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paper_title: Wavelet-based statistical approach for speckle reduction in medical ultrasound images

paper_content:

A novel speckle-reduction method is introduced, based on soft thresholding of the wavelet coefficients of a logarithmically transformed medical ultrasound image. The method is based on the generalised Gaussian distributed(GGD) modelling of sub-band coefficients. The method used was a variant of the recently published BayesShrink method by Chang and Vetterli, derived in the Bayesian framework for denoising natural images. It was scale adaptive, because the parameters required for estimating the threshold depend on scale and sub-band data. The threshold was computed by Kσ 2 /σ x , where σ and σ x were the standard deviation of the noise and the sub-band data of the noise-free image, respectively, and K was a scale parameter. Experimental results showed that the proposed method outperformed the median filter and the homomorphic Wiener filter by 29% in terms of the coefficient of correlation and 4% in terms of the edge preservation parameter. The numerical values of these quantitative parameters indicated the good feature preservation performance of the algorithm, as desired for better diagnosis in medical image processing.
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paper_title: A versatile technique for visual enhancement of medical ultrasound images

paper_content:

The paper presents a versatile wavelet domain despeckling technique to visually enhance the medical ultrasound (US) images for improving the clinical diagnosis. The method uses the two-sided generalized Nakagami distribution (GND) for modeling the speckle wavelet coefficients and the signal wavelet coefficients are approximated using the generalized Gaussian distribution (GGD). Combining these statistical priors with the Bayesian maximum a posteriori (MAP) criterion, the thresholding/shrinkage estimators are derived for processing the wavelet coefficients of detail subbands. Consequently, two blind speckle suppressors named as GNDThresh and GNDShrink have been implemented and evaluated on both the artificial speckle simulated images and real US images. The experimental results demonstrate the superiority of the suggested technique both quantitatively and qualitatively as compared to other competitive schemes reported in the image denoising literature, e.g., the proposed method yields a gain of more than 0.36 dB over the best state-of-the-art despeckling method (GenLik), 0.93 dB over SRAD filter, 2.35 dB over Lee filter, and 1.34 dB over Kuan filter in terms of signal-to-noise ratio, when tested on the realistic US images. The visual comparison of despeckled US images and the higher values of quality metrics (coefficient of correlation, edge preservation index, quality index, and structural similarity index) indicate that the new method suppresses the speckle noise well while preserving the texture and organ surfaces. Further, the proposed method will be evaluated on other class of images as well as by employing multiple observer evaluation.
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paper_title: The combined effect of spatial compounding and nonlinear filtering on the speckle reduction in ultrasound images.

paper_content:

Recently, a spatial compounding ultrasound imaging method was presented that utilizes a conventional 64-element phased array transducer with two unfocused pistons, each placed at one of the sides of the phased array transducer. This method is augmented here by inclusion of nonlinear filtering of the compounded images. The combined effects of the specific spatial compounding and nonlinear filtering on speckle reduction in the generated ultrasound images are studied and evaluated in two stages: First, the image quality is studied when nonlinear filtering is used as part of the spatial compounding. The study is performed by simulations using the Field II program, by processing several B-mode images of a kidney. The second stage compares the results obtained by the simulations to those obtained by in vitro laboratory experiments. Five different compounding strategies and two nonlinear filters, Gaussian and anisotropic diffusion, are investigated and evaluated in terms of image quality parameters-contrast and signal-to-noise ratio. It is shown that the combination of "averaging+nonlinear Gaussian filtering" produces the greatest improvement of image quality. When compared to a conventional phased array imaging system, the spatial compounding method that includes the conventional 64-element phased array transducer with two unfocused pistons, and employs the "averaging+nonlinear Gaussian filtering" strategy, obtains improvement in SNR that has reached 334%. Thus, though this method necessitates a somewhat wider probe, it produces significantly improved images.
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paper_title: Lesion contrast enhancement in medical ultrasound imaging

paper_content:

Methods for improving the contrast-to-noise ratio (CNR) of low-contrast lesions in medical ultrasound imaging are described. Differences in the frequency spectra and amplitude distributions of the lesion and its surroundings can be used to increase the CNR of the lesion relative to the background. Automated graylevel mapping is used in combination with a contrast-weighted form of frequency-diversity speckle reduction. In clinical studies, the techniques have yielded mean CNR improvements of 3.2 dB above ordinary frequency-diversity imaging and 5.6 dB over sharper conventional images, with no post-processing graylevel mapping.
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paper_title: Three-dimensional spatial compounding of ultrasound images

paper_content:

One of the most promising applications of 3-D ultrasound lies in the visualization and volume estimation of internal 3-D structures. Unfortunately, the quality of the ultrasound data can be severely degraded by artefacts and speckle, making automatic analysis of the 3-D data sets very difficult. In this paper we investigate the use of 3-D spatial compounding to reduce speckle. We develop a new statistical theory to predict the improvement in signal-to-noise ratio with increased levels of compounding, and verify the predictions empirically. We also investigate how registration errors can affect automatic volume estimation of structures within the compounded 3-D data set. Having established the need to correct these errors, we present a novel reconstruction algorithm which uses landmarks to register each B-scan accurately as it is inserted into the voxel array. In a series of in vitro and in vivo trials, we demonstrate that 3-D spatial compounding is very effective for improving the signal-to-noise ratio, but correction of registration errors is essential.
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paper_title: Prostate's boundary detection in transrectal ultrasound images using scanning technique

paper_content:

Prostate cancer is one of the leading causes of cancer death in men. Early detection of prostate cancer is very essential for the success of the treatment. Ultrasound B-mode images is the standard mean for imaging the prostate. The manual analysis of the ultrasound images consumes much time and effort. It is necessary to develop an automated algorithm to analyze the ultrasound images. The first important step in detecting the cancer is to detect the boundary of the prostate itself and to extract it from the image for further analysis. In this paper a multistage algorithm for prostate boundary detection is proposed. In the first stage, the proposed algorithm starts with enhancing the contrast of the image by sticks technique followed by smoothing the image by gauss kernel. In the second stage, scanning the image and applying knowledge base rules to find a seed point inside the prostate is implemented. This seed point is used to remove the false edges. Then by using a morphological opening algorithm, the remaining false edges can be removed. The final step is to use the seed point to scan the image in radial directions to find the prostate' s boundary.
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paper_title: Breast cancer diagnosis using image retrieval for different ultrasonic systems

paper_content:

This paper employs the image retrieval technique to classify breast tumors as benign or malignant lesions. We evaluated 600 ultrasound (US) images of pathologically proven solid breast nodules including 230 malignant and 370 benign tumors. The US images were acquired from four different ultrasound systems. Firstly, the physician located regions-of-interest (ROI) of ultrasound images. The textual features from ROI sub-image are utilized to classify breast tumors. The principal component analysis (PCA) is used to reduce the dimension of textual feature vector and then the image retrieval technique was utilized to differentiate between benign and malignant tumors. Historical cases can be directly added into the database and training of the diagnosis system again is not needed. The accuracy of the proposed computer-aided diagnosis (CAD) system was 91.2%, the sensitivity was 97.0% and the specificity was 87.6%. This system differentiates solid breast nodules with a relatively high accuracy in the different US systems and helps inexperienced operators avoid misdiagnosis.
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paper_title: Breast ultrasound image enhancement using fuzzy logic

paper_content:

Abstract Breast cancer is still a serious disease in the world. Early detection is very essential for breast cancer prevention and diagnosis. Breast ultrasound (US) imaging has been proven to be a valuable adjunct to mammography in the detection and classification of breast lesions. Because of the fuzzy and noisy nature of the US images and the low contrast between the breast cancer and tissue, it is difficult to provide an accurate and effective diagnosis. This paper presents a novel algorithm based on fuzzy logic that uses both the global and local information and has the ability to enhance the fine details of the US images while avoiding noise amplification and overenhancement. We normalize the images and then fuzzify the normalized images based on the maximum entropy principle. Edge and textural information are extracted to describe the lesion features and the scattering phenomenon of US images and the contrast ratio measuring the degree of enhancement is computed and modified. The defuzzification process is used to obtain the enhanced US images. To demonstrate the performance of the proposed approach, the algorithm was tested on 86 breast US images. Experimental results confirm that the proposed method can effectively enhance the details of the breast lesions without overenhancement or underenhancement. (E-mail: hengda.cheng@usu.edu )
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paper_title: Color image segmentation: Advances and prospects

paper_content:

Abstract Image segmentation is very essential and critical to image processing and pattern recognition. This survey provides a summary of color image segmentation techniques available now. Basically, color segmentation approaches are based on monochrome segmentation approaches operating in different color spaces. Therefore, we first discuss the major segmentation approaches for segmenting monochrome images: histogram thresholding, characteristic feature clustering, edge detection, region-based methods, fuzzy techniques, neural networks, etc.; then review some major color representation methods and their advantages/disadvantages; finally summarize the color image segmentation techniques using different color representations. The usage of color models for image segmentation is also discussed. Some novel approaches such as fuzzy method and physics-based method are investigated as well.
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paper_title: Segmentation of breast tumor in three-dimensional ultrasound images using three-dimensional discrete active contour model.

paper_content:

In this paper, we apply the three-dimensional (3-D) active contour model to a 3-D ultrasonic data file for segmenting of the breast tumor. The 3-D ultrasonic file is composed of a series of two-dimensional (2-D) images. Most of traditional techniques of 2-D image segmentation will not use the information between adjacent images. To suit the property of the 3-D data, we introduce the concept of the 3-D stick, the 3-D morphologic process and the 3-D active contour model. The 3-D stick can get over the problem that the ultrasonic image is full of speckle noise and highlight the edge information in images. The 3-D morphologic process helps to determine the contour of the tumor and the resulting contour can be regarded as the initial contour of the active contour model. Finally, the 3-D active contour model will make the initial contour approach to the real contour of the tumor. However, there is emphasis on these 3-D techniques that they do not consist of a series of 2-D techniques. When they work, they will consider the horizontal, vertical and depth directions at the same time. The use of these 3-D techniques not only segments the 3-D shape but also obtains the volume of the tumor. The volume of the tumor calculated by the proposed method will be compared with the volume calculated by the VOCAL software with the physician's manually drawn shape and it shows that the performance of our method is satisfactory.
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paper_title: Adaptive color segmentation-a comparison of neural and statistical methods

paper_content:

With the availability of more powerful computers it is nowadays possible to perform pixel based operations on real camera images even in the full color space. New adaptive classification tools like neural networks make it possible to develop special-purpose object detectors that can segment arbitrary objects in real images with a complex distribution in the feature space after training with one or several previously labeled image(s). The paper focuses on a detailed comparison of a neural approach based on local linear maps (LLMs) to a classifier based on normal distributions. The proposed adaptive segmentation method uses local color information to estimate the membership probability in the object, respectively, background class. The method is applied to the recognition and localization of human hands in color camera images of complex laboratory scenes.
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paper_title: Automatic segmentation of breast lesions on ultrasound

paper_content:

In this paper we present a computationally efficient segmentation algorithm for breast masses on sonography that is based on maximizing a utility function over partition margins defined through gray-value thresholding of a preprocessed image. The performance of the segmentation algorithm is evaluated on a database of 400 cases in two ways. Of the 400 cases, 124 were complex cysts, 182 were benign solid lesions, and 94 were malignant lesions. In the first evaluation, the computer-delineated margins were compared to manually delineated margins. At an overlap threshold of 0.40, the segmentation algorithm correctly delineated 94% of the lesions. In the second evaluation, the performance of our computer-aided diagnosis method on the computer-delineated margins was compared to the performance of our method on the manually delineated margins. Round robin evaluation yielded Az values of 0.90 and 0.87 on the manually delineated margins and the computer-delineated margins, respectively, in the task of distinguishing between malignant and nonmalignant lesions.
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paper_title: Computerized diagnosis of breast lesions on ultrasound

paper_content:

We present a computer-aided diagnosis(CAD) method for breast lesions on ultrasound that is based on the automatic segmentation of lesions and the automatic extraction of four features related to the lesion shape, margin, texture, and posterior acoustic behavior. Using a database of 400 cases (94 malignant lesions, 124 complex cysts, and 182 benign solid lesions), we investigate the marginal benefit of each feature in our CAD method and the performance of our CAD method in distinguishing malignant lesions from various classes of benign lesions. Finally, independent validation is performed on our CAD method. Eleven independent trials yielded an average A z value of 0.87 in the task of distinguishing malignant from benign lesions.
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paper_title: Color image segmentation: Advances and prospects

paper_content:

Abstract Image segmentation is very essential and critical to image processing and pattern recognition. This survey provides a summary of color image segmentation techniques available now. Basically, color segmentation approaches are based on monochrome segmentation approaches operating in different color spaces. Therefore, we first discuss the major segmentation approaches for segmenting monochrome images: histogram thresholding, characteristic feature clustering, edge detection, region-based methods, fuzzy techniques, neural networks, etc.; then review some major color representation methods and their advantages/disadvantages; finally summarize the color image segmentation techniques using different color representations. The usage of color models for image segmentation is also discussed. Some novel approaches such as fuzzy method and physics-based method are investigated as well.
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paper_title: Computer-aided diagnosis applied to US of solid breast nodules by using neural networks.

paper_content:

PURPOSE ::: To increase the capabilities of ultrasonographic (US) technology for the differential diagnosis of solid breast tumors by using a neural network. ::: ::: ::: MATERIALS AND METHODS ::: One hundred forty US images of solid breast nodules were evaluated. When a sonogram was obtained, an analog video signal from the VCR output of the scanner was transmitted to a notebook computer. A frame grabber connected to the printer port of the computer was then used to digitize the data. The suspicious tumor region on the digitized US image was manually selected. The texture information of the subimage was extracted, and a neural network classifier with autocorrelation features was used to classify the tumor as benign or malignant. In this experiment, 140 pathologically proved tumors (52 malignant and 88 benign tumors) were sampled with k-fold cross-validation (k = 10) to evaluate the performance with receiver operating characteristic curves. ::: ::: ::: RESULTS ::: The accuracy of neural networks for classifying malignancies was 95.0% (133 of 140 tumors), the sensitivity was 98% (51 of 52), the specificity was 93% (82 of 88), the positive predictive value was 89% (51 of 57), and the negative predictive value was 99% (82 of 83). ::: ::: ::: CONCLUSION ::: This system differentiated solid breast nodules with relatively high accuracy and helped inexperienced operators to avoid misdiagnoses. Because the neural network is trainable, it could be optimized if a larger set of tumor images is supplied.
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paper_title: 3-D breast ultrasound segmentation using active contour model.

paper_content:

In this study, we made use of the discrete active contour model to overcome the natural properties of ultrasound (US) images, speckle, noise and tissue-related textures, to segment the breast tumors precisely. Determination of the real tumor boundary with the snake-deformation process requires an initial contour estimate. However, the manual way to sketch an initial contour is very time-consuming. Thus, we propose an automatic initial contour-finding method that not only maintains the tumor shape, but also is close to the tumor boundary and inside the tumor. During the deformation process, to prevent the snake trapping into the false position caused by tissue-related texture or speckle, we added the edge information as an image feature to define the external force. In addition, because the 3-D volume of a tumor is essentially constructed by a sequence of 2-D images, our method for finding boundaries of a tumor can be extended to 3-D cases. By precisely counting the volume of the 3-D images, we can get the volume of tumor. Finally, we will show that the proposed techniques have rather good performance and lead to a satisfactory result in comparison with the estimated volume and physician's estimate.
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paper_title: Segmentation of breast tumor in three-dimensional ultrasound images using three-dimensional discrete active contour model.

paper_content:

In this paper, we apply the three-dimensional (3-D) active contour model to a 3-D ultrasonic data file for segmenting of the breast tumor. The 3-D ultrasonic file is composed of a series of two-dimensional (2-D) images. Most of traditional techniques of 2-D image segmentation will not use the information between adjacent images. To suit the property of the 3-D data, we introduce the concept of the 3-D stick, the 3-D morphologic process and the 3-D active contour model. The 3-D stick can get over the problem that the ultrasonic image is full of speckle noise and highlight the edge information in images. The 3-D morphologic process helps to determine the contour of the tumor and the resulting contour can be regarded as the initial contour of the active contour model. Finally, the 3-D active contour model will make the initial contour approach to the real contour of the tumor. However, there is emphasis on these 3-D techniques that they do not consist of a series of 2-D techniques. When they work, they will consider the horizontal, vertical and depth directions at the same time. The use of these 3-D techniques not only segments the 3-D shape but also obtains the volume of the tumor. The volume of the tumor calculated by the proposed method will be compared with the volume calculated by the VOCAL software with the physician's manually drawn shape and it shows that the performance of our method is satisfactory.
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paper_title: Maximum likelihood segmentation of ultrasound images with Rayleigh distribution

paper_content:

This study presents a geometric model and a computational algorithm for segmentation of ultrasound images. A partial differential equation (PDE)-based flow is designed in order to achieve a maximum likelihood segmentation of the target in the scene. The flow is derived as the steepest descent of an energy functional taking into account the density probability distribution of the gray levels of the image as well as smoothness constraints. To model gray level behavior of ultrasound images, the classic Rayleigh probability distribution is considered. The steady state of the flow presents a maximum likelihood segmentation of the target. A finite difference approximation of the flow is derived, and numerical experiments are provided. Results are presented on ultrasound medical images as fetal echography and echocardiography.
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paper_title: Combining low-, high-level and empirical domain knowledge for automated segmentation of ultrasonic breast lesions

paper_content:

Breast cancer is the most frequently diagnosed malignancy and the second leading cause of mortality in women . In the last decade, ultrasound along with digital mammography has come to be regarded as the gold standard for breast cancer diagnosis. Automatically detecting tumors and extracting lesion boundaries in ultrasound images is difficult due to their specular nature and the variance in shape and appearance of sonographic lesions. Past work on automated ultrasonic breast lesion segmentation has not addressed important issues such as shadowing artifacts or dealing with similar tumor like structures in the sonogram. Algorithms that claim to automatically classify ultrasonic breast lesions, rely on manual delineation of the tumor boundaries. In this paper, we present a novel technique to automatically find lesion margins in ultrasound images, by combining intensity and texture with empirical domain specific knowledge along with directional gradient and a deformable shape-based model. The images are first filtered to remove speckle noise and then contrast enhanced to emphasize the tumor regions. For the first time, a mathematical formulation of the empirical rules used by radiologists in detecting ultrasonic breast lesions, popularly known as the "Stavros Criteria" is presented in this paper. We have applied this formulation to automatically determine a seed point within the image. Probabilistic classification of image pixels based on intensity and texture is followed by region growing using the automatically determined seed point to obtain an initial segmentation of the lesion. Boundary points are found on the directional gradient of the image. Outliers are removed by a process of recursive refinement. These boundary points are then supplied as an initial estimate to a deformable model. Incorporating empirical domain specific knowledge along with low and high-level knowledge makes it possible to avoid shadowing artifacts and lowers the chance of confusing similar tumor like structures for the lesion. The system was validated on a database of breast sonograms for 42 patients. The average mean boundary error between manual and automated segmentation was 6.6 pixels and the normalized true positive area overlap was 75.1%. The algorithm was found to be robust to 1) variations in system parameters, 2) number of training samples used, and 3) the position of the seed point within the tumor. Running time for segmenting a single sonogram was 18 s on a 1.8-GHz Pentium machine.
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paper_title: An early vision-based snake model for ultrasound image segmentation.

paper_content:

Due to the speckles and the ill-defined edges of the object of interest, the classic image-segmentation techniques are usually ineffective in segmenting ultrasound (US) images. In this paper, we present a new algorithm for segmenting general US images that is composed of two major techniques; namely, the early-vision model and the discrete-snake model. By simulating human early vision, the early-vision model can capture both grey-scale and textural edges while the speckle noise is suppressed. By performing deformation only on the peaks of the distance map, the discrete-snake model promises better noise immunity and more accurate convergence. Moreover, the constraint for most conventional snake models that the initial contour needs to be located very close to the actual boundary has been relaxed substantially. The performance of the proposed snake model has been shown to be comparable to manual delineation and superior to that of the gradient vector flow (GVF) snake model.
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paper_title: 3-D snake for US in margin evaluation for malignant breast tumor excision using mammotome

paper_content:

The goal of this paper is to use the three-dimensional (3-D) snake technique in 3-D ultrasound to obtain the tumor contour for the pre- and the post-operative malignant breast excision by the vacuum assisted biopsy instrument Mammotome. This technique of assessing the margin of two can help the physician to evaluate the effect of the surgery. By using the anisotropic diffusion filter, the noise and speckles can be reduced. Then the stick detection is adopted for enhancing the edge. Finally, the gradient vector flow (GVF) snake is used to obtain the tumor contour. These techniques are extended to the 3-D techniques to increase the accuracy and robust of segmentation results. We hope that this study can help physicians to improve the minimal invasive operation for a breast tumor.
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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: Ultrasound image segmentation: a survey

paper_content:

This paper reviews ultrasound segmentation methods, in a broad sense, focusing on techniques developed for medical B-mode ultrasound images. First, we present a review of articles by clinical application to highlight the approaches that have been investigated and degree of validation that has been done in different clinical domains. Then, we present a classification of methodology in terms of use of prior information. We conclude by selecting ten papers which have presented original ideas that have demonstrated particular clinical usefulness or potential specific to the ultrasound segmentation problem
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paper_title: 3-D breast ultrasound segmentation using active contour model.

paper_content:

In this study, we made use of the discrete active contour model to overcome the natural properties of ultrasound (US) images, speckle, noise and tissue-related textures, to segment the breast tumors precisely. Determination of the real tumor boundary with the snake-deformation process requires an initial contour estimate. However, the manual way to sketch an initial contour is very time-consuming. Thus, we propose an automatic initial contour-finding method that not only maintains the tumor shape, but also is close to the tumor boundary and inside the tumor. During the deformation process, to prevent the snake trapping into the false position caused by tissue-related texture or speckle, we added the edge information as an image feature to define the external force. In addition, because the 3-D volume of a tumor is essentially constructed by a sequence of 2-D images, our method for finding boundaries of a tumor can be extended to 3-D cases. By precisely counting the volume of the 3-D images, we can get the volume of tumor. Finally, we will show that the proposed techniques have rather good performance and lead to a satisfactory result in comparison with the estimated volume and physician's estimate.
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paper_title: Segmentation of ultrasound images––multiresolution 2D and 3D algorithm based on global and local statistics

paper_content:

In this paper, we propose a robust adaptive region segmentation algorithm for noisy images, within a Bayesian framework. A multiresolution implementation of the algorithm is performed using a wavelets basis and can be used to process both 2D and 3D data. In this work we focus on the adaptive character of the algorithm and we discuss how global and local statistics can be utilised in the segmentation process. We propose an improvement on the adaptivity by introducing an enhancement to control the adaptive properties of the segmentation process. This takes the form of a weighting function accounting for both local and global statistics, and is introduced in the minimisation. A new formulation of the segmentation problem allows us to control the effective contribution of each statistical component. The segmentation algorithm is demonstrated on synthetic data, 2D breast ultrasound data and on echocardiographic sequences ð2D þ TÞ. An evaluation of the performance of the proposed algorithm is also presented. � 2002 Elsevier Science B.V. All rights reserved.
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paper_title: Multiresolution texture based adaptive clustering algorithm for breast lesion segmentation.

paper_content:

OBJECTIVE ::: A specific algorithm is presented for the automatic extraction of breast tumors in ultrasonic imaging. ::: ::: ::: METHOD ::: The algorithm involves two-dimensional adaptive K-means clustering of the gray scale and textural feature images. The segmentation problem is formulated as a maximum a posteriori (MAP) estimation problem. The MAP estimation is achieved using Besag's iterated conditional modes algorithm for the minimization of an energy function. This function has three components: the first constrains the region to be close to the data; the second imposes spatial continuity; and the third takes into consideration the texture of the various regions. A multiresolution implementation of the algorithm is performed using a waveless basis. ::: ::: ::: RESULTS ::: Experiments were carried out on synthetic images and on in vivo breast ultrasound images. Various parameters involved in the algorithm are discussed to evaluate the robustness and accuracy of the segmentation method. ::: ::: ::: CONCLUSION ::: Including textural features in the segmentation of ultrasonic data improves the robustness of the algorithm and makes the segmentation result less parameter dependent.
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paper_title: Multiple resolution Bayesian segmentation of ultrasound images

paper_content:

We propose a novel method for obtaining the maximum a posteriori (MAP) probabilistic segmentation of speckle-laden ultrasound images. Our technique is multiple-resolution based, and relies on the conversion of speckle images with Rayleigh statistics to subsampled images with Gaussian statistics. This conversion reduces computation time, as well as allowing accurate parameter estimation. Results appear to provide improvements over previous techniques, in terms of both low-resolution detail and accuracy.© (1994) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
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paper_title: Segmentation of ultrasound B-mode images with intensity inhomogeneity correction

paper_content:

Displayed ultrasound (US) B-mode images often exhibit tissue intensity inhomogeneities dominated by nonuniform beam attenuation within the body. This is a major problem for intensity-based, automatic segmentation of video-intensity images because conventional threshold-based or intensity-statistic-based approaches do not work well in the presence of such image distortions. Time gain compensation (TGC) is typically used in standard US machines in an attempt to overcome this. However this compensation method is position-dependent which means that different tissues in the same TGC time-range (or corresponding depth range) will be, incorrectly, compensated by the same amount. Compensation should really be tissue-type dependent but automating this step is difficult. The main contribution of this paper is to develop a method for simultaneous estimation of video-intensity inhomogeities and segmentation of US image tissue regions. The method uses a combination of the maximum a posteriori (MAP) and Markov random field (MRF) methods to estimate the US image distortion field assuming it follows a multiplicative model while at the same time labeling image regions based on the corrected intensity statistics. The MAP step is used to estimate the intensity model parameters while the MRF step provides a novel way of incorporating the distributions of image tissue classes as a spatial smoothness constraint. We explain how this multiplicative model can be related to the ultrasonic physics of image formation to justify our approach. Experiments are presented on synthetic images and a gelatin phantom to evaluate quantitatively the accuracy of the method. We also discuss qualitatively the application of the method to clinical breast and cardiac US images. Limitations of the method and potential clinical applications are outlined in the conclusion.
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paper_title: 3-D Bayesian ultrasound breast image segmentation using the EM/MPM algorithm

paper_content:

In this paper, ultrasound breast image segmentation is improved by using the volumetric data available in neighboring slices. The new algorithm extends the EM/MPM framework to 3D by including pixels from neighboring frames in the Markov Random Field (MRF) clique. In addition, this paper describes a unique linear cost factor introduced in the optimization loop to compensate for the attenuation common to ultrasound images.
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paper_title: Computerized lesion detection on breast ultrasound

paper_content:

We investigated the use of a radial gradient index (RGI) filtering technique to automatically detect lesions on breast ultrasound. After initial RGI filtering, a sensitivity of 87% at 0.76 false-positive detections per image was obtained on a database of 400 patients (757 images). Next, lesion candidates were segmented from the background by maximizing an average radial gradient (ARD) index for regions grown from the detected points. At an overlap of 0.4 with a radiologist lesion outline, 75% of the lesions were correctly detected. Subsequently, round robin analysis was used to assess the quality of the classification of lesion candidates into actual lesions and false-positives by a Bayesian neural network. The round robin analysis yielded an Az value of 0.84, and an overall performance by case of 94% sensitivity at 0.48 false-positives per image. Use of computerized analysis of breast sonograms may ultimately facilitate the use of sonography in breast cancer screening programs.
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paper_title: Diagnosis of breast tumors with sonographic texture analysis using wavelet transform and neural networks.

paper_content:

To increase the ability of ultrasonographic technology for the differential diagnosis of solid breast tumors, we describe a novel computer-aided diagnosis (CADx) system using neural networks for classification of breast tumors. Tumor regions and surrounding tissues are segmented from the physician-located region-of-interest (ROI) images by applying our proposed segmentation algorithm. Cooperating with the segmentation algorithm, three feasible features, including variance contrast, autocorrelation contrast and distribution distortion of wavelet coefficients, were extracted from the ROI images for further classification. A multilayered perceptron (MLP) neural network trained using error back-propagation algorithm with momentum was then used for the differential diagnosis of breast tumors on sonograms. In the experiment, 242 cases (including benign breast tumors from 161 patients and carcinomas from 82 patients) were sampled with k-fold cross-validation (k = 10) to evaluate the performance. The receiver operating characteristic (ROC) area index for the proposed CADx system is 0.9396 +/- 0.0183, the sensitivity is 98.77%, the specificity is 81.37%, the positive predictive value is 72.73% and the negative predictive value is 99.24%. Experimental results showed that our diagnosis model performed very well for breast tumor diagnosis.
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paper_title: Watershed segmentation for breast tumor in 2-D sonography.

paper_content:

Automatic contouring for breast tumors using medical ultrasound (US) imaging may assist physicians without relevant experience, in making correct diagnoses. This study integrates the advantages of neural network (NN) classification and morphological watershed segmentation to extract precise contours of breast tumors from US images. Textural analysis is employed to yield inputs to the NN to classify ultrasonic images. Autocovariance coefficients specify texture features to classify breasts imaged by US using a self-organizing map (SOM). After the texture features in sonography have been classified, an adaptive preprocessing procedure is selected by SOM output. Finally, watershed transformation automatically determines the contours of the tumor. In this study, the proposed method was trained and tested using images from 60 patients. The results of computer simulations reveal that the proposed method always identified similar contours and regions-of-interest (ROIs) to those obtained by manual contouring (by an experienced physician) of the breast tumor in ultrasonic images. As US imaging becomes more widespread, a functional automatic contouring method is essential and its clinical application is becoming urgent. Such a method provides robust and fast automatic contouring of US images. This study is not to emphasize that the automatic contouring technique is superior to the one undertaken manually. Both automatic and manual contours did not, after all, necessarily result in the same factual pathologic border. In computer-aided diagnosis (CAD) applications, automatic segmentation can save much of the time required to sketch a precise contour, with very high stability.
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paper_title: Computerized radiographic mass detection. I. Lesion site selection by morphological enhancement and contextual segmentation

paper_content:

This paper presents a statistical model supported approach for enhanced segmentation and extraction of suspicious mass areas from mammographic images. With an appropriate statistical description of various discriminate characteristics of both true and false candidates from the localized areas, an improved mass detection may be achieved in computer-assisted diagnosis (CAD). In this study, one type of morphological operation is derived to enhance disease patterns of suspected masses by cleaning up unrelated background clutters, and a model-based image segmentation is performed to localize the suspected mass areas using stochastic relaxation labeling scheme. We discuss the importance of model selection when a finite generalized Gaussian mixture is employed, and use the information theoretic criteria to determine the optimal model structure and parameters. Examples are presented to show the effectiveness of the proposed methods on mass lesion enhancement and segmentation when applied to mammographical images. Experimental results demonstrate that the proposed method achieves a very satisfactory performance as a preprocessing procedure for mass detection in CAD.
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paper_title: Learning from Data: Concepts, Theory, and Methods

paper_content:

From the Publisher: ::: This is an interdisciplinary book on neural networks, statistics and fuzzy systems. A unique feature is the establishment of a general framework for adaptive data modeling within which various methods from statistics, neural networks and fuzzy logic are presented. Chapter summaries, examples and case studies are also included.[Includes companion Web site with ... Software for use with the book.
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paper_title: Detection of breast lesion regions in ultrasound images using wavelets and order statistics.

paper_content:

Accurate detection and segmentation of suspicious regions within the complex and irregular tissues of the breast, as depicted with ultrasonic B scans, typically require human analysis and decision making. Tissue characterization methods for classifying suspicious regions often depend on identifying and then accurately segmenting these regions. Motivated by an ultimate goal to automate this critical identification and segmentation step for tissue characterization problems, this work examines ultrasonic signal characteristics between various regions of breast tissue broadly classified as normal tissue and breast lesions. This paper introduces a nonparametric model based on order statistics (OS) estimated from multiresolution (MR) decompositions of energy-normalized subregions. Experimental results demonstrate the classification performance of the OS-based features extracted from the tumor and normal tissue regions in multiple scans from 84 patients, which resulted in a total of 204 tumor regions (from 43 malignant and 161 benign) and 816 normal tissue regions. Performance results indicate that OS-based features achieved an area under the receiver-operator characteristic curve of 91% in the discrimination between breast lesions and surrounding normal tissues.
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paper_title: Diagnosis of breast tumors with ultrasonic texture analysis using support vector machines

paper_content:

This study presents a computer-aided diagnosis (CAD) system with textural features for classifying benign and malignant breast tumors on medical ultrasound systems. A series of pathologically proven breast tumors were evaluated using the support vector machine (SVM) in the differential diagnosis of breast tumors. The proposed CAD system utilized facile textural features, i.e., block difference of inverse probabilities, block variation of local correlation coefficients and auto-covariance matrix, to identify breast tumor. An SVM classifier using the textual features classified the tumor as benign or malignant. The proposed system identifies breast tumors with a comparatively high accuracy. This can help inexperienced physicians avoid misdiagnosis. The main advantage of the proposed system is that the training and diagnosis procedure of SVM are faster and more stable than that of multilayer perception neural networks. With the expansion of the database, new cases can easily be gathered and used as references. This study dramatically reduces the training and diagnosis time. The SVM is a reliable choice for the proposed CAD system because it is fast and excellent in ultrasound image classification.
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paper_title: C4.5: Programs for Machine Learning

paper_content:

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

paper_content:

We present a computer-aided diagnosis(CAD) method for breast lesions on ultrasound that is based on the automatic segmentation of lesions and the automatic extraction of four features related to the lesion shape, margin, texture, and posterior acoustic behavior. Using a database of 400 cases (94 malignant lesions, 124 complex cysts, and 182 benign solid lesions), we investigate the marginal benefit of each feature in our CAD method and the performance of our CAD method in distinguishing malignant lesions from various classes of benign lesions. Finally, independent validation is performed on our CAD method. Eleven independent trials yielded an average A z value of 0.87 in the task of distinguishing malignant from benign lesions.
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paper_title: Solid breast nodules: use of sonography to distinguish between benign and malignant lesions.

paper_content:

PURPOSE ::: To determine whether sonography could help accurately distinguish benign solid breast nodules from indeterminate or malignant nodules and whether this distinction could be definite enough to obviate biopsy. ::: ::: ::: MATERIALS AND METHODS ::: Seven hundred fifty sonographically solid breast nodules were prospectively classified as benign, indeterminate, or malignant. Benign nodules had no malignant characteristics and had either intense homogeneous hyperechogenicity or a thin echogenic pseudocapsule with an ellipsoid shape or fewer than four gentle lobulations. Sonographic classifications were compared with biopsy results. The sensitivity, specificity, and negative and positive predictive values of the classifications were calculated. ::: ::: ::: RESULTS ::: Benign histologic features were found in 625 (83%) lesions; malignant histologic features, in 125 (17%). Of benign lesions, 424 had been prospectively classified as benign. Two lesions classified as benign were found to be malignant at biopsy. Thus, the classification scheme had a negative predictive value of 99.5%. Of 125 malignant lesions, 123 were correctly classified as indeterminate or malignant (98.4% sensitivity). ::: ::: ::: CONCLUSION ::: Sonography can be used to accurately classify some solid lesions as benign, allowing imaging follow-up rather than biopsy.
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paper_title: Improving the distinction between benign and malignant breast lesions: the value of sonographic texture analysis.

paper_content:

To improve the ability of ultrasound to distinguish benign from malignant breast lesions, we used quantitative analysis of ultrasound image texture. Eight cancers, 22 cysts, 28 fibroadenomata, and 22 fibrocystic nodules were studied. The true nature of each lesion was determined by aspiration (for some cysts) or by open biopsy. Analysis of image texture was performed on digitized video output from the ultrasound scanner using fractal analysis and statistical texture analysis methods. The most useful features were those derived from co-occurrence matrices of the images. Using two features together (contrast of a co-occurrence matrix taken in an oblique direction, and correlation of a co-occurrence matrix taken in the horizontal direction), it was possible to exclude 78% of fibroadenomata, 73% of cysts, and 91% of fibrocystic nodules while maintaining 100% sensitivity for cancer. These findings suggest that ultrasonic image texture analysis is a simple way to markedly reduce the number of benign lesion biopsies without missing additional cancers.
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paper_title: MASS DETECTION AND CLASSIFICATION IN BREAST ULTRASOUND IMAGES USING FUZZY SVM

paper_content:

Breast cancer can be most effectively treated when detected during its early stages. Currently, the most effective method for early detection and screening of breast cancer is X-ray mammography. But the high number of false positives in mammography causes a lot of unnecessary biopsies. Sonography is an important adjunct to mammography in breast cancer detection and has been primarily useful for differentiating cysts from solid tumors. It has been shown that breast sonography is superior to mammography in its ability to detect focal abnormalities in the dense breasts of adolescent women. The accuracy rate of breast ultrasounds can reach a high level in the diagnosis of simple benign cysts and reduce the number of false positives. The lesions characterized as benign cysts do not require biopsies. ::: This research focuses on developing a novel computer-aided diagnosis system of mass detection and classification in breast ultrasound images using statistical methods. First, we propose a novel, automatic enhancement approach to preprocess the regions of interest in breast ultrasound images. Next, a Markov random field segmentation algorithm is developed to segment the suspicious areas from the regions of interest. Third, three kinds of features, including textural features, fractal dimensions, and histogram-based features, are analyzed and extracted based on the suspicious areas and regions of interest, and a stepwise regression method is used to select an optimal subset of features. Fourth, based on the optimal subset of features, we develop a novel classification method based on the fuzzy support vector machines to classify the regions of interest as benign or malignant masses. Finally, two objective evaluation methods, five objective indices, and the receiver operating characteristic curve are used to evaluate the performance of the proposed computer-aided diagnosis system. The experimental results show that the proposed computer-aided diagnosis system greatly improves the five objective indices and the value of Az (the area under the receiver operating characteristic curve), as opposed to other classification methods and radiologists' assessments. Our method's ability to improve the accuracy of breast cancer diagnosis while reducing the number of unnecessary biopsies is a valuable tool for patients and radiologists alike.
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paper_title: Fractal feature analysis and classification in medical imaging.

paper_content:

Following B.B. Mandelbrot's fractal theory (1982), it was found that the fractal dimension could be obtained in medical images by the concept of fractional Brownian motion. An estimation concept for determination of the fractal dimension based upon the concept of fractional Brownian motion is discussed. Two applications are found: (1) classification; (2) edge enhancement and detection. For the purpose of classification, a normalized fractional Brownian motion feature vector is defined from this estimation concept. It represented the normalized average absolute intensity difference of pixel pairs on a surface of different scales. The feature vector uses relatively few data items to represent the statistical characteristics of the medial image surface and is invariant to linear intensity transformation. For edge enhancement and detection application, a transformed image is obtained by calculating the fractal dimension of each pixel over the whole medical image. The fractal dimension value of each pixel is obtained by calculating the fractal dimension of 7x7 pixel block centered on this pixel.
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paper_title: Breast lesions on sonograms: computer-aided diagnosis with nearly setting-independent features and artificial neural networks.

paper_content:

PURPOSE ::: To develop a computer-aided diagnosis (CAD) algorithm with setting-independent features and artificial neural networks to differentiate benign from malignant breast lesions. ::: ::: ::: MATERIALS AND METHODS ::: Two sets of breast sonograms were evaluated. The first set contained 160 lesions and was stored directly on the magnetic optic disks from the ultrasonographic (US) system. Four different boundaries were delineated by four persons for each lesion in the first set. The second set comprised 111 lesions that were extracted from the hard-copy images. Seven morphologic features were used, five of which were newly developed. A multilayer feed-forward neural network was used as the classifier. Reliability, extendability, and robustness of the proposed CAD algorithm were evaluated. Results with the proposed algorithm were compared with those with two previous CAD algorithms. All performance comparisons were based on paired-samples t tests. ::: ::: ::: RESULTS ::: The area under the receiver operating characteristic curve (A(z)) was 0.952 +/- 0.014 for the first set, 0.982 +/- 0.004 for the first set as the training set and the second set as the prediction set, 0.954 +/- 0.016 for the second set as the training set and the first set as the prediction set, and 0.950 +/- 0.005 for all 271 lesions. At the 5% significance level, the performance of the proposed CAD algorithm was shown to be extendible from one set of US images to the other set and robust for both small and large sample sizes. Moreover, the proposed CAD algorithm was shown to outperform the two previous CAD algorithms in terms of the A(z) value. ::: ::: ::: CONCLUSION ::: The proposed CAD algorithm could effectively and reliably differentiate benign and malignant lesions. The proposed morphologic features were nearly setting independent and could tolerate reasonable variation in boundary delineation.
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paper_title: Computerized diagnosis of breast lesions on ultrasound

paper_content:

We present a computer-aided diagnosis(CAD) method for breast lesions on ultrasound that is based on the automatic segmentation of lesions and the automatic extraction of four features related to the lesion shape, margin, texture, and posterior acoustic behavior. Using a database of 400 cases (94 malignant lesions, 124 complex cysts, and 182 benign solid lesions), we investigate the marginal benefit of each feature in our CAD method and the performance of our CAD method in distinguishing malignant lesions from various classes of benign lesions. Finally, independent validation is performed on our CAD method. Eleven independent trials yielded an average A z value of 0.87 in the task of distinguishing malignant from benign lesions.
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paper_title: Breast ultrasound computer-aided diagnosis using BI-RADS features.

paper_content:

RATIONALE AND OBJECTIVES ::: Based on the definitions in mass category of Breast Imaging Reporting and Data System developed by American College of Radiology, eight computerized features including shape, orientation, margin, lesion boundary, echo pattern, and posterior acoustic feature classes are proposed. ::: ::: ::: MATERIALS AND METHODS ::: Our experimental database consists of 265 pathology-proven cases including 180 benign and 85 malignant masses. The capacity of each proposed feature in differentiating malignant from benign masses was validated by Student's t test and the correlation between each proposed feature and the pathological result was evaluated by point biserial coefficient. Binary logistic regression model was used to relate all proposed features and pathological result as a computer-aided diagnosis (CAD) system. The diagnostic value of each proposed feature in the CAD system was further evaluated by the feature selection methods. Additionally, the likelihood of malignancy for each individual feature was also estimated by binary logistic regression. ::: ::: ::: RESULTS ::: On each proposed feature, the malignant cases were significantly different from the benign ones. The correlation between the angular characteristic and pathological result was indicated as very high. Three substantial correlations appear in features irregular shape, undulation characteristic, and degree of abrupt interface, but the relationship for orientation feature is low. For the constructed CAD system, the performance indices accuracy, sensitivity, specificity, PPV, and NPV were 91.70% (243 of 265), 90.59% (77 of 85), 92.22% (166 of 180), 84.62% (77 of 91), and 95.40% (166 of 174), respectively, and the area index in the ROC analysis was 0.97. Compared with the significant contribution of angular characteristic, the diagnostic values of posterior acoustic feature and orientation feature were relatively low for the CAD system. When three or more angular characteristics are discovered or the degree of abrupt interface is lower than 18, the likelihood of malignancy could be predicted as greater than 40%. ::: ::: ::: CONCLUSION ::: The computerized BI-RADS sonographic features conform to the sign of malignancy in the clinical experience and efficiently help the CAD system to diagnose the mass.
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paper_title: Computer-Based Margin Analysis of Breast Sonography for Differentiating Malignant and Benign Masses

paper_content:

Objective. To evaluate the role of quantitative margin features in the computer-aided diagnosis of malignant and benign solid breast masses using sonographic imaging. Methods. Sonographic images from 56 patients with 58 biopsy-proven masses were analyzed quantitatively for the following features: margin sharpness, margin echogenicity, and angular variation in margin. Of the 58 masses, 38 were benign and 20 were malignant. Each feature was evaluated individually and in combination with the others to determine its association with malignancy. The combination of features yielding the highest association with malignancy was analyzed by logistic regression to determine the probability of malignancy. The performance of the probability measurements was evaluated by receiver operating characteristic analysis using a round-robin technique. Results. Margin sharpness, margin echogenicity, and angular variation in margin were significantly different for the malignant and benign masses (P < .03, 2-tailed Student t test). According to quantitative measures, tumor-tissue margins of the malignant masses were less distinct than for the benign masses. Although the mean size of the lesions for the two groups was the same, the mean age of the patients was statistically different (P = .000625). After logistic regression analysis, the individual features age, margin sharpness, margin echogenicity, and angular variation in margin were found to be associated with the probability of malignancy (P < .03). The area under the receiver operating characteristic curve ± SD for the 3-feature logistic regression model combining age, margin echogenicity, and angular variation of margin was 0.87 ± 0.05. Conclusions. The proposed quantitative margin features are robust and can reliably measure margin distinctiveness. These features combined with logistic regression analysis can be useful for
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paper_title: Classification of breast masses in ultrasonic B scans using Nakagami and K distributions

paper_content:

Classification of breast masses in greyscale ultrasound images is undertaken using a multiparameter approach. Five parameters reflecting the non-Rayleigh nature of the backscattered echo were used. These parameters, based mostly on the Nakagami and K distributions, were extracted from the envelope of the echoes at the site, boundary, spiculated region and shadow of the mass. They were combined to create a linear discriminant. The performance of this discriminant for the classification of breast masses was studied using a data set consisting of 70 benign and 29 malignant cases. The Az value for the discriminant was 0.96 ± 0.02, showing great promise in the classification of masses into benign and malignant ones. The discriminant was combined with the level of suspicion values of the radiologist leading to an Az value of 0.97 ± 0.014. The parameters used here can be calculated with minimal clinical intervention, so the method proposed here may therefore be easily implemented in an automated fashion. These results also support the recent reports suggesting that ultrasound may help as an adjunct to mammography in breast cancer diagnostics to enhance the classification of breast masses.
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paper_title: Breast ultrasound computer-aided diagnosis using BI-RADS features.

paper_content:

RATIONALE AND OBJECTIVES ::: Based on the definitions in mass category of Breast Imaging Reporting and Data System developed by American College of Radiology, eight computerized features including shape, orientation, margin, lesion boundary, echo pattern, and posterior acoustic feature classes are proposed. ::: ::: ::: MATERIALS AND METHODS ::: Our experimental database consists of 265 pathology-proven cases including 180 benign and 85 malignant masses. The capacity of each proposed feature in differentiating malignant from benign masses was validated by Student's t test and the correlation between each proposed feature and the pathological result was evaluated by point biserial coefficient. Binary logistic regression model was used to relate all proposed features and pathological result as a computer-aided diagnosis (CAD) system. The diagnostic value of each proposed feature in the CAD system was further evaluated by the feature selection methods. Additionally, the likelihood of malignancy for each individual feature was also estimated by binary logistic regression. ::: ::: ::: RESULTS ::: On each proposed feature, the malignant cases were significantly different from the benign ones. The correlation between the angular characteristic and pathological result was indicated as very high. Three substantial correlations appear in features irregular shape, undulation characteristic, and degree of abrupt interface, but the relationship for orientation feature is low. For the constructed CAD system, the performance indices accuracy, sensitivity, specificity, PPV, and NPV were 91.70% (243 of 265), 90.59% (77 of 85), 92.22% (166 of 180), 84.62% (77 of 91), and 95.40% (166 of 174), respectively, and the area index in the ROC analysis was 0.97. Compared with the significant contribution of angular characteristic, the diagnostic values of posterior acoustic feature and orientation feature were relatively low for the CAD system. When three or more angular characteristics are discovered or the degree of abrupt interface is lower than 18, the likelihood of malignancy could be predicted as greater than 40%. ::: ::: ::: CONCLUSION ::: The computerized BI-RADS sonographic features conform to the sign of malignancy in the clinical experience and efficiently help the CAD system to diagnose the mass.
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paper_title: Solid breast nodules: use of sonography to distinguish between benign and malignant lesions.

paper_content:

PURPOSE ::: To determine whether sonography could help accurately distinguish benign solid breast nodules from indeterminate or malignant nodules and whether this distinction could be definite enough to obviate biopsy. ::: ::: ::: MATERIALS AND METHODS ::: Seven hundred fifty sonographically solid breast nodules were prospectively classified as benign, indeterminate, or malignant. Benign nodules had no malignant characteristics and had either intense homogeneous hyperechogenicity or a thin echogenic pseudocapsule with an ellipsoid shape or fewer than four gentle lobulations. Sonographic classifications were compared with biopsy results. The sensitivity, specificity, and negative and positive predictive values of the classifications were calculated. ::: ::: ::: RESULTS ::: Benign histologic features were found in 625 (83%) lesions; malignant histologic features, in 125 (17%). Of benign lesions, 424 had been prospectively classified as benign. Two lesions classified as benign were found to be malignant at biopsy. Thus, the classification scheme had a negative predictive value of 99.5%. Of 125 malignant lesions, 123 were correctly classified as indeterminate or malignant (98.4% sensitivity). ::: ::: ::: CONCLUSION ::: Sonography can be used to accurately classify some solid lesions as benign, allowing imaging follow-up rather than biopsy.
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paper_title: Computer-Based Margin Analysis of Breast Sonography for Differentiating Malignant and Benign Masses

paper_content:

Objective. To evaluate the role of quantitative margin features in the computer-aided diagnosis of malignant and benign solid breast masses using sonographic imaging. Methods. Sonographic images from 56 patients with 58 biopsy-proven masses were analyzed quantitatively for the following features: margin sharpness, margin echogenicity, and angular variation in margin. Of the 58 masses, 38 were benign and 20 were malignant. Each feature was evaluated individually and in combination with the others to determine its association with malignancy. The combination of features yielding the highest association with malignancy was analyzed by logistic regression to determine the probability of malignancy. The performance of the probability measurements was evaluated by receiver operating characteristic analysis using a round-robin technique. Results. Margin sharpness, margin echogenicity, and angular variation in margin were significantly different for the malignant and benign masses (P < .03, 2-tailed Student t test). According to quantitative measures, tumor-tissue margins of the malignant masses were less distinct than for the benign masses. Although the mean size of the lesions for the two groups was the same, the mean age of the patients was statistically different (P = .000625). After logistic regression analysis, the individual features age, margin sharpness, margin echogenicity, and angular variation in margin were found to be associated with the probability of malignancy (P < .03). The area under the receiver operating characteristic curve ± SD for the 3-feature logistic regression model combining age, margin echogenicity, and angular variation of margin was 0.87 ± 0.05. Conclusions. The proposed quantitative margin features are robust and can reliably measure margin distinctiveness. These features combined with logistic regression analysis can be useful for
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paper_title: ROC analysis of ultrasound tissue characterization classifiers for breast cancer diagnosis

paper_content:

Breast cancer diagnosis through ultrasound tissue characterization was studied using receiver operating characteristic (ROC) analysis of combinations of acoustic features, patient age, and radiological findings. A feature fusion method was devised that operates even if only partial diagnostic data are available. The ROC methodology uses ordinal dominance theory and bootstrap resampling to evaluate A/sub z/ and confidence intervals in simple as well as paired data analyses. The combined diagnostic feature had an A/sub z/ of 0.96 with a confidence interval of [0.93, 0.99] at a significance level of 0.05. The combined features show statistically significant improvement over prebiopsy radiological findings. These results indicate that ultrasound tissue characterization, in combination with patient record and clinical findings, may greatly reduce the need to perform biopsies of benign breast lesions.
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paper_title: A novel feature selection approach: Combining feature wrappers and filters

paper_content:

Abstract Feature selection is one of the most important issues in the research fields such as system modelling, data mining and pattern recognition. In this study, a new feature selection algorithm that combines feature wrapper and feature filter approaches is proposed in order to identify the significant input variables in systems with continuous domains. The proposed method utilizes functional dependency concept, correlation coefficients and K -nearest neighbourhood (KNN) method to implement the feature filter and feature wrappers. Four feature selection methods independently select the significant input variables and the input variable combination, which yields best result with respect to their corresponding evaluation function, is selected as the winner. This is similar to the basic information fusion notion of integrating the information collected from different sources. All of the four feature selection methods are performed in two stages: (i) pre-selection, (ii) selection. Two of the four feature selection methods utilize KNN method for evaluating the candidates. These two methods use sequential forward and sequential backward search mechanism, respectively, in pre-selection stage. Whereas, the third feature selection method uses correlation coefficients in the pre-selection stage. It is common to have outliers and noise in real-life data. In order to make the proposed feature selection algorithm noise and outlier resistant, approximate functional dependencies are used by utilizing membership values that inherently cope with uncertainty in the data. Thus, the fourth feature selection method makes use of approximate functional dependencies to evaluate candidates in pre-selection stage. All of these four methods apply KNN method with exhaustive search strategy in order to find the most suitable input variable combination with respect to a performance measure.
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paper_title: A novel feature selection approach: Combining feature wrappers and filters

paper_content:

Abstract Feature selection is one of the most important issues in the research fields such as system modelling, data mining and pattern recognition. In this study, a new feature selection algorithm that combines feature wrapper and feature filter approaches is proposed in order to identify the significant input variables in systems with continuous domains. The proposed method utilizes functional dependency concept, correlation coefficients and K -nearest neighbourhood (KNN) method to implement the feature filter and feature wrappers. Four feature selection methods independently select the significant input variables and the input variable combination, which yields best result with respect to their corresponding evaluation function, is selected as the winner. This is similar to the basic information fusion notion of integrating the information collected from different sources. All of the four feature selection methods are performed in two stages: (i) pre-selection, (ii) selection. Two of the four feature selection methods utilize KNN method for evaluating the candidates. These two methods use sequential forward and sequential backward search mechanism, respectively, in pre-selection stage. Whereas, the third feature selection method uses correlation coefficients in the pre-selection stage. It is common to have outliers and noise in real-life data. In order to make the proposed feature selection algorithm noise and outlier resistant, approximate functional dependencies are used by utilizing membership values that inherently cope with uncertainty in the data. Thus, the fourth feature selection method makes use of approximate functional dependencies to evaluate candidates in pre-selection stage. All of these four methods apply KNN method with exhaustive search strategy in order to find the most suitable input variable combination with respect to a performance measure.
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paper_title: Breast cancer diagnosis using image retrieval for different ultrasonic systems

paper_content:

This paper employs the image retrieval technique to classify breast tumors as benign or malignant lesions. We evaluated 600 ultrasound (US) images of pathologically proven solid breast nodules including 230 malignant and 370 benign tumors. The US images were acquired from four different ultrasound systems. Firstly, the physician located regions-of-interest (ROI) of ultrasound images. The textual features from ROI sub-image are utilized to classify breast tumors. The principal component analysis (PCA) is used to reduce the dimension of textual feature vector and then the image retrieval technique was utilized to differentiate between benign and malignant tumors. Historical cases can be directly added into the database and training of the diagnosis system again is not needed. The accuracy of the proposed computer-aided diagnosis (CAD) system was 91.2%, the sensitivity was 97.0% and the specificity was 87.6%. This system differentiates solid breast nodules with a relatively high accuracy in the different US systems and helps inexperienced operators avoid misdiagnosis.
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paper_title: Very Simple Classification Rules Perform Well on Most Commonly Used Datasets

paper_content:

This article reports an empirical investigation of the accuracy of rules that classify examples on the basis of a single attribute. On most datasets studied, the best of these very simple rules is as accurate as the rules induced by the majority of machine learning systems. The article explores the implications of this finding for machine learning research and applications.
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paper_title: Wrappers for feature subset selection

paper_content:

Abstract In the feature subset selection problem, a learning algorithm is faced with the problem of selecting a relevant subset of features upon which to focus its attention, while ignoring the rest. To achieve the best possible performance with a particular learning algorithm on a particular training set, a feature subset selection method should consider how the algorithm and the training set interact. We explore the relation between optimal feature subset selection and relevance. Our wrapper method searches for an optimal feature subset tailored to a particular algorithm and a domain. We study the strengths and weaknesses of the wrapper approach and show a series of improved designs. We compare the wrapper approach to induction without feature subset selection and to Relief, a filter approach to feature subset selection. Significant improvement in accuracy is achieved for some datasets for the two families of induction algorithms used: decision trees and Naive-Bayes.
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paper_title: Malignant and Benign Breast Masses on 3D US Volumetric Images: Effect of Computer-aided Diagnosis on Radiologist Accuracy

paper_content:

Purpose: To retrospectively investigate the effect of using a custom-designed computer classifier on radiologists' sensitivity and specificity for discriminating malignant masses from benign masses on three-dimensional (3D) volumetric ultrasonographic (US) images, with histologic analysis serving as the reference standard. Materials and Methods: Informed consent and institutional review board approval were obtained. Our data set contained 3D US volumetric images obtained in 101 women (average age, 51 years; age range, 25–86 years) with 101 biopsy-proved breast masses (45 benign, 56 malignant). A computer algorithm was designed to automatically delineate mass boundaries and extract features on the basis of segmented mass shapes and margins. A computer classifier was used to merge features into a malignancy score. Five experienced radiologists participated as readers. Each radiologist read cases first without computer-aided diagnosis (CAD) and immediately thereafter with CAD. Observers' malignancy rating da...
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paper_title: MASS DETECTION AND CLASSIFICATION IN BREAST ULTRASOUND IMAGES USING FUZZY SVM

paper_content:

Breast cancer can be most effectively treated when detected during its early stages. Currently, the most effective method for early detection and screening of breast cancer is X-ray mammography. But the high number of false positives in mammography causes a lot of unnecessary biopsies. Sonography is an important adjunct to mammography in breast cancer detection and has been primarily useful for differentiating cysts from solid tumors. It has been shown that breast sonography is superior to mammography in its ability to detect focal abnormalities in the dense breasts of adolescent women. The accuracy rate of breast ultrasounds can reach a high level in the diagnosis of simple benign cysts and reduce the number of false positives. The lesions characterized as benign cysts do not require biopsies. ::: This research focuses on developing a novel computer-aided diagnosis system of mass detection and classification in breast ultrasound images using statistical methods. First, we propose a novel, automatic enhancement approach to preprocess the regions of interest in breast ultrasound images. Next, a Markov random field segmentation algorithm is developed to segment the suspicious areas from the regions of interest. Third, three kinds of features, including textural features, fractal dimensions, and histogram-based features, are analyzed and extracted based on the suspicious areas and regions of interest, and a stepwise regression method is used to select an optimal subset of features. Fourth, based on the optimal subset of features, we develop a novel classification method based on the fuzzy support vector machines to classify the regions of interest as benign or malignant masses. Finally, two objective evaluation methods, five objective indices, and the receiver operating characteristic curve are used to evaluate the performance of the proposed computer-aided diagnosis system. The experimental results show that the proposed computer-aided diagnosis system greatly improves the five objective indices and the value of Az (the area under the receiver operating characteristic curve), as opposed to other classification methods and radiologists' assessments. Our method's ability to improve the accuracy of breast cancer diagnosis while reducing the number of unnecessary biopsies is a valuable tool for patients and radiologists alike.
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paper_title: The effect of image quality on the appearance of lesions on breast ultrasound: implications for CADx

paper_content:

With the emergence of recent technology in breast ultrasound, sonographic image ::: quality has changed profoundly. Most notably, the technique of real-time ::: spatial compounding impacts the appearance of lesions and parenchyma. During ::: image acquisition, spatial compounding can be turned on or off at the ::: discretion of the radiologist, but this information is not stored along with ::: the image data. ::: ::: The ability to distinguishing between lesions imaged with and without spatial ::: compounding, using either single image features or a Bayesian neural net (BNN), was assessed using ROC analysis. Our database consisted of consecutively ::: collected HDI5000 images of 129 lesions imaged without spatial compounding ::: (357 images, cancer prevalence of 18%) and 370 lesions imaged with spatial ::: compounding (965 images, cancer prevalence 15%). These were used in automated ::: feature selection and BNN training. An additional 33 lesions were imaged for ::: which identical views with and without spatial compounding were available (70 ::: images, cancer prevalence 15%). These served as an independent test dataset. ::: Lesions were outlined by a radiologist and image features, mathematically ::: describing lesion characteristics, were calculated. ::: In feature selection, the 4 best performing features were related to gradient ::: strength and entropy. The average gradient strength within a lesion obtained an ::: area under the ROC curve (AUC) of 0.78 in the task of distinguishing lesions ::: imaged with and without spatial compounding. The BNN, using 4 features, ::: achieved an AUC on the independent test dataset of 0.98 in this task. ::: ::: The sonographic appearance of breast lesions is affected by spatial compound ::: imaging and lesion features may be used to automatically separate images as ::: obtained with or without this technique. In computer-aided diagnosis (CADx), ::: it will likely be beneficial ::: to separate images as such before using separate classifiers for assessment of ::: malignancy.
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paper_title: Computerized diagnosis of breast lesions on ultrasound

paper_content:

We present a computer-aided diagnosis(CAD) method for breast lesions on ultrasound that is based on the automatic segmentation of lesions and the automatic extraction of four features related to the lesion shape, margin, texture, and posterior acoustic behavior. Using a database of 400 cases (94 malignant lesions, 124 complex cysts, and 182 benign solid lesions), we investigate the marginal benefit of each feature in our CAD method and the performance of our CAD method in distinguishing malignant lesions from various classes of benign lesions. Finally, independent validation is performed on our CAD method. Eleven independent trials yielded an average A z value of 0.87 in the task of distinguishing malignant from benign lesions.
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paper_title: Computer-Based Margin Analysis of Breast Sonography for Differentiating Malignant and Benign Masses

paper_content:

Objective. To evaluate the role of quantitative margin features in the computer-aided diagnosis of malignant and benign solid breast masses using sonographic imaging. Methods. Sonographic images from 56 patients with 58 biopsy-proven masses were analyzed quantitatively for the following features: margin sharpness, margin echogenicity, and angular variation in margin. Of the 58 masses, 38 were benign and 20 were malignant. Each feature was evaluated individually and in combination with the others to determine its association with malignancy. The combination of features yielding the highest association with malignancy was analyzed by logistic regression to determine the probability of malignancy. The performance of the probability measurements was evaluated by receiver operating characteristic analysis using a round-robin technique. Results. Margin sharpness, margin echogenicity, and angular variation in margin were significantly different for the malignant and benign masses (P < .03, 2-tailed Student t test). According to quantitative measures, tumor-tissue margins of the malignant masses were less distinct than for the benign masses. Although the mean size of the lesions for the two groups was the same, the mean age of the patients was statistically different (P = .000625). After logistic regression analysis, the individual features age, margin sharpness, margin echogenicity, and angular variation in margin were found to be associated with the probability of malignancy (P < .03). The area under the receiver operating characteristic curve ± SD for the 3-feature logistic regression model combining age, margin echogenicity, and angular variation of margin was 0.87 ± 0.05. Conclusions. The proposed quantitative margin features are robust and can reliably measure margin distinctiveness. These features combined with logistic regression analysis can be useful for
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paper_title: Breast lesions on sonograms: computer-aided diagnosis with nearly setting-independent features and artificial neural networks.

paper_content:

PURPOSE ::: To develop a computer-aided diagnosis (CAD) algorithm with setting-independent features and artificial neural networks to differentiate benign from malignant breast lesions. ::: ::: ::: MATERIALS AND METHODS ::: Two sets of breast sonograms were evaluated. The first set contained 160 lesions and was stored directly on the magnetic optic disks from the ultrasonographic (US) system. Four different boundaries were delineated by four persons for each lesion in the first set. The second set comprised 111 lesions that were extracted from the hard-copy images. Seven morphologic features were used, five of which were newly developed. A multilayer feed-forward neural network was used as the classifier. Reliability, extendability, and robustness of the proposed CAD algorithm were evaluated. Results with the proposed algorithm were compared with those with two previous CAD algorithms. All performance comparisons were based on paired-samples t tests. ::: ::: ::: RESULTS ::: The area under the receiver operating characteristic curve (A(z)) was 0.952 +/- 0.014 for the first set, 0.982 +/- 0.004 for the first set as the training set and the second set as the prediction set, 0.954 +/- 0.016 for the second set as the training set and the first set as the prediction set, and 0.950 +/- 0.005 for all 271 lesions. At the 5% significance level, the performance of the proposed CAD algorithm was shown to be extendible from one set of US images to the other set and robust for both small and large sample sizes. Moreover, the proposed CAD algorithm was shown to outperform the two previous CAD algorithms in terms of the A(z) value. ::: ::: ::: CONCLUSION ::: The proposed CAD algorithm could effectively and reliably differentiate benign and malignant lesions. The proposed morphologic features were nearly setting independent and could tolerate reasonable variation in boundary delineation.
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paper_title: Diagnosis of breast tumors with sonographic texture analysis using wavelet transform and neural networks.

paper_content:

To increase the ability of ultrasonographic technology for the differential diagnosis of solid breast tumors, we describe a novel computer-aided diagnosis (CADx) system using neural networks for classification of breast tumors. Tumor regions and surrounding tissues are segmented from the physician-located region-of-interest (ROI) images by applying our proposed segmentation algorithm. Cooperating with the segmentation algorithm, three feasible features, including variance contrast, autocorrelation contrast and distribution distortion of wavelet coefficients, were extracted from the ROI images for further classification. A multilayered perceptron (MLP) neural network trained using error back-propagation algorithm with momentum was then used for the differential diagnosis of breast tumors on sonograms. In the experiment, 242 cases (including benign breast tumors from 161 patients and carcinomas from 82 patients) were sampled with k-fold cross-validation (k = 10) to evaluate the performance. The receiver operating characteristic (ROC) area index for the proposed CADx system is 0.9396 +/- 0.0183, the sensitivity is 98.77%, the specificity is 81.37%, the positive predictive value is 72.73% and the negative predictive value is 99.24%. Experimental results showed that our diagnosis model performed very well for breast tumor diagnosis.
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paper_title: Texture analysis of breast tumors on sonograms.

paper_content:

We performed a feasibility study to determine if the texture features extracted from sonograms can be used to predict malignant or benign breast pathology by the proposed artificial neural network and to compare the diagnostic results with the radiologists' results. A total of 1,020 images (4 different rectangular regions from the 2 orthogonal imaging planes of each tumor) from 255 patients were used as samples. When a sonogram was performed, 1 physician identified the region of interest in the sonogram; then, a neural network model, using 24 autocorrelation texture features, classified the tumor as benign or malignant. Three radiologists who were unfamiliar with the samples also classified these images. The receiver operating characteristic (ROC) area index for the proposed neural network system is 0.9840 +/- 0.0072. The neural network identified 35 of 36 malignancies and 211 of 219 benign tumors using all 4 regions of interest. The radiologists, on average, identified 19 of 36 malignancies, with 12 tumors called indeterminate and 4 tumors called benign. We conclude that benign and malignant breast tumors can be distinguished using interpixel correlation in digital ultrasonic images.
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paper_title: Approaches For Automated Detection And Classification Of Masses In Mammograms

paper_content:

Breast cancer is one of the most common cancer among women around the world. Several techniques are available for detection of breast cancer. Mammography is one of the most effective tools for early detection. The goal of this research is to increase the diagnostic accuracy of image processing and machine learning techniques for optimum classification between normal and abnormalities in digital mammograms. GLCM texture feature extractions are known to be the most common and powerful techniques for texture analysis. This paper presents an evaluation and comparison of the performance of two different classification methods used to classify the normal and abnormal patterns. The experimental result suggest that Artificial Neural Network is outperformed the other method.
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paper_title: Computer-aided diagnosis applied to US of solid breast nodules by using neural networks.

paper_content:

PURPOSE ::: To increase the capabilities of ultrasonographic (US) technology for the differential diagnosis of solid breast tumors by using a neural network. ::: ::: ::: MATERIALS AND METHODS ::: One hundred forty US images of solid breast nodules were evaluated. When a sonogram was obtained, an analog video signal from the VCR output of the scanner was transmitted to a notebook computer. A frame grabber connected to the printer port of the computer was then used to digitize the data. The suspicious tumor region on the digitized US image was manually selected. The texture information of the subimage was extracted, and a neural network classifier with autocorrelation features was used to classify the tumor as benign or malignant. In this experiment, 140 pathologically proved tumors (52 malignant and 88 benign tumors) were sampled with k-fold cross-validation (k = 10) to evaluate the performance with receiver operating characteristic curves. ::: ::: ::: RESULTS ::: The accuracy of neural networks for classifying malignancies was 95.0% (133 of 140 tumors), the sensitivity was 98% (51 of 52), the specificity was 93% (82 of 88), the positive predictive value was 89% (51 of 57), and the negative predictive value was 99% (82 of 83). ::: ::: ::: CONCLUSION ::: This system differentiated solid breast nodules with relatively high accuracy and helped inexperienced operators to avoid misdiagnoses. Because the neural network is trainable, it could be optimized if a larger set of tumor images is supplied.
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paper_title: Use of the bootstrap technique with small training sets for computer-aided diagnosis in breast ultrasound.

paper_content:

The purpose of this study was to test the efficacy of using small training sets in computer-aided diagnostic systems (CAD) and to increase the capabilities of ultrasound (US) technology in the differential diagnosis of solid breast tumors. A total of 263 sonographic images of solid breast nodules, including 129 malignancies and 134 benign nodules, were evaluated by using a bootstrap technique with 10 original training samples. Texture parameters of a region-of-interest (ROI) were resampled with a bootstrap technique and a decision-tree model was used to classify the tumor as benign or malignant. The accuracy was 87.07% (229 of 263 tumors), the sensitivity was 95.35% (123 of 129), the specificity was 79.10% (106 of 134), the positive predictive value was 81.46% (123 of 151), and the negative predictive value was 94.64% (106 of 112). This analysis method provides a second opinion for physicians with high accuracy. The new method shows a potential to be useful in future application of CAD, especially when a large database cannot be obtained for training or a newly developed ultrasonic system has smaller sets of samples.
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paper_title: Breast lesions on sonograms: computer-aided diagnosis with nearly setting-independent features and artificial neural networks.

paper_content:

PURPOSE ::: To develop a computer-aided diagnosis (CAD) algorithm with setting-independent features and artificial neural networks to differentiate benign from malignant breast lesions. ::: ::: ::: MATERIALS AND METHODS ::: Two sets of breast sonograms were evaluated. The first set contained 160 lesions and was stored directly on the magnetic optic disks from the ultrasonographic (US) system. Four different boundaries were delineated by four persons for each lesion in the first set. The second set comprised 111 lesions that were extracted from the hard-copy images. Seven morphologic features were used, five of which were newly developed. A multilayer feed-forward neural network was used as the classifier. Reliability, extendability, and robustness of the proposed CAD algorithm were evaluated. Results with the proposed algorithm were compared with those with two previous CAD algorithms. All performance comparisons were based on paired-samples t tests. ::: ::: ::: RESULTS ::: The area under the receiver operating characteristic curve (A(z)) was 0.952 +/- 0.014 for the first set, 0.982 +/- 0.004 for the first set as the training set and the second set as the prediction set, 0.954 +/- 0.016 for the second set as the training set and the first set as the prediction set, and 0.950 +/- 0.005 for all 271 lesions. At the 5% significance level, the performance of the proposed CAD algorithm was shown to be extendible from one set of US images to the other set and robust for both small and large sample sizes. Moreover, the proposed CAD algorithm was shown to outperform the two previous CAD algorithms in terms of the A(z) value. ::: ::: ::: CONCLUSION ::: The proposed CAD algorithm could effectively and reliably differentiate benign and malignant lesions. The proposed morphologic features were nearly setting independent and could tolerate reasonable variation in boundary delineation.
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paper_title: MASS DETECTION AND CLASSIFICATION IN BREAST ULTRASOUND IMAGES USING FUZZY SVM

paper_content:

Breast cancer can be most effectively treated when detected during its early stages. Currently, the most effective method for early detection and screening of breast cancer is X-ray mammography. But the high number of false positives in mammography causes a lot of unnecessary biopsies. Sonography is an important adjunct to mammography in breast cancer detection and has been primarily useful for differentiating cysts from solid tumors. It has been shown that breast sonography is superior to mammography in its ability to detect focal abnormalities in the dense breasts of adolescent women. The accuracy rate of breast ultrasounds can reach a high level in the diagnosis of simple benign cysts and reduce the number of false positives. The lesions characterized as benign cysts do not require biopsies. ::: This research focuses on developing a novel computer-aided diagnosis system of mass detection and classification in breast ultrasound images using statistical methods. First, we propose a novel, automatic enhancement approach to preprocess the regions of interest in breast ultrasound images. Next, a Markov random field segmentation algorithm is developed to segment the suspicious areas from the regions of interest. Third, three kinds of features, including textural features, fractal dimensions, and histogram-based features, are analyzed and extracted based on the suspicious areas and regions of interest, and a stepwise regression method is used to select an optimal subset of features. Fourth, based on the optimal subset of features, we develop a novel classification method based on the fuzzy support vector machines to classify the regions of interest as benign or malignant masses. Finally, two objective evaluation methods, five objective indices, and the receiver operating characteristic curve are used to evaluate the performance of the proposed computer-aided diagnosis system. The experimental results show that the proposed computer-aided diagnosis system greatly improves the five objective indices and the value of Az (the area under the receiver operating characteristic curve), as opposed to other classification methods and radiologists' assessments. Our method's ability to improve the accuracy of breast cancer diagnosis while reducing the number of unnecessary biopsies is a valuable tool for patients and radiologists alike.
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paper_title: Recognition of Breast Ultrasound Images Using A Hybrid Method

paper_content:

Breast cancer is one of the most common cancer in women. A novel method is presented in this paper for classification of the breast tumors as benign or malignant. The method combines k-means classification and a multilayer perception network with error back-propagation (BP) algorithm. The k-means which is an unsupervised classification method is used to get the cluster centers and select the training samples. Only the samples within a specified distance from the cluster centers could be selected as our training samples. The BP neural network is used to train the samples and recognize the tumors. The fractal dimension of an ultrasound image of breast tumor is extracted as our feature, which is estimated with the difference between gray values of neighboring pixels by using the fractal Brownian motion. Experiments are done on 125 benign tumors and 110 malignant ones. The recognition rate of the malignant tumors is 94.5% while that of the benign ones is 93.6%. The result shows that the proposed method can classify the breast tumors effectively.
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paper_title: Diagnosis of breast tumors with ultrasonic texture analysis using support vector machines

paper_content:

This study presents a computer-aided diagnosis (CAD) system with textural features for classifying benign and malignant breast tumors on medical ultrasound systems. A series of pathologically proven breast tumors were evaluated using the support vector machine (SVM) in the differential diagnosis of breast tumors. The proposed CAD system utilized facile textural features, i.e., block difference of inverse probabilities, block variation of local correlation coefficients and auto-covariance matrix, to identify breast tumor. An SVM classifier using the textual features classified the tumor as benign or malignant. The proposed system identifies breast tumors with a comparatively high accuracy. This can help inexperienced physicians avoid misdiagnosis. The main advantage of the proposed system is that the training and diagnosis procedure of SVM are faster and more stable than that of multilayer perception neural networks. With the expansion of the database, new cases can easily be gathered and used as references. This study dramatically reduces the training and diagnosis time. The SVM is a reliable choice for the proposed CAD system because it is fast and excellent in ultrasound image classification.
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paper_title: Data mining with decision trees for diagnosis of breast tumor in medical ultrasonic images

paper_content:

To increase the ability of ultrasonographic (US) technology for the differential diagnosis of solid breast tumors, we describe a novel computer-aided diagnosis (CADx) system using data mining with decision tree for classification of breast tumor to increase the levels of diagnostic confidence and to provide the immediate second opinion for physicians. Cooperating with the texture information extracted from the region of interest (ROI) image, a decision tree model generated from the training data in a top-down, general-to-specific direction with 24 co-variance texture features is used to classify the tumors as benign or malignant. In the experiments, accuracy rates for a experienced physician and the proposed CADx are 86.67% (78/90) and 95.50% (86/90), respectively.
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paper_title: Computer-aided diagnosis applied to US of solid breast nodules by using neural networks.

paper_content:

PURPOSE ::: To increase the capabilities of ultrasonographic (US) technology for the differential diagnosis of solid breast tumors by using a neural network. ::: ::: ::: MATERIALS AND METHODS ::: One hundred forty US images of solid breast nodules were evaluated. When a sonogram was obtained, an analog video signal from the VCR output of the scanner was transmitted to a notebook computer. A frame grabber connected to the printer port of the computer was then used to digitize the data. The suspicious tumor region on the digitized US image was manually selected. The texture information of the subimage was extracted, and a neural network classifier with autocorrelation features was used to classify the tumor as benign or malignant. In this experiment, 140 pathologically proved tumors (52 malignant and 88 benign tumors) were sampled with k-fold cross-validation (k = 10) to evaluate the performance with receiver operating characteristic curves. ::: ::: ::: RESULTS ::: The accuracy of neural networks for classifying malignancies was 95.0% (133 of 140 tumors), the sensitivity was 98% (51 of 52), the specificity was 93% (82 of 88), the positive predictive value was 89% (51 of 57), and the negative predictive value was 99% (82 of 83). ::: ::: ::: CONCLUSION ::: This system differentiated solid breast nodules with relatively high accuracy and helped inexperienced operators to avoid misdiagnoses. Because the neural network is trainable, it could be optimized if a larger set of tumor images is supplied.
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paper_title: Texture analysis of breast tumors on sonograms.

paper_content:

We performed a feasibility study to determine if the texture features extracted from sonograms can be used to predict malignant or benign breast pathology by the proposed artificial neural network and to compare the diagnostic results with the radiologists' results. A total of 1,020 images (4 different rectangular regions from the 2 orthogonal imaging planes of each tumor) from 255 patients were used as samples. When a sonogram was performed, 1 physician identified the region of interest in the sonogram; then, a neural network model, using 24 autocorrelation texture features, classified the tumor as benign or malignant. Three radiologists who were unfamiliar with the samples also classified these images. The receiver operating characteristic (ROC) area index for the proposed neural network system is 0.9840 +/- 0.0072. The neural network identified 35 of 36 malignancies and 211 of 219 benign tumors using all 4 regions of interest. The radiologists, on average, identified 19 of 36 malignancies, with 12 tumors called indeterminate and 4 tumors called benign. We conclude that benign and malignant breast tumors can be distinguished using interpixel correlation in digital ultrasonic images.
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paper_title: Computerized detection and classification of cancer on breast ultrasound.

paper_content:

RATIONALE AND OBJECTIVES ::: To develop and evaluate a two-stage computerized method that first detects suspicious regions on ultrasound images, and subsequently distinguishes among different lesion types. ::: ::: ::: MATERIALS AND METHODS ::: The first stage of detecting potential lesions was based on expected lesion shape and margin characteristics. After the detection stage, all candidate lesions were classified by a Bayesian neural net based on computer-extracted lesion features. Two separate tasks were performed and evaluated at the classification stage: the first classification task was the distinction between all actual lesions and false-positive detections; the second classification task was the distinction between actual cancer and all other detected lesion candidates (including false-positive detections). The neural nets were trained on a database of 400 cases (757 images), consisting of complex cysts and benign and malignant lesions, and tested on an independent database of 458 cases (1,740 images including 578 normal images). ::: ::: ::: RESULTS ::: In the distinction between all actual lesions and false-positive detections, Az values of 0.94 and 0.91 were obtained with the training and testing data sets, respectively. Sensitivity by patient of 90% at 0.45 false-positive detections per image was achieved for this detection-plus-classification scheme for the testing data set. Distinguishing cancer from all other detections (false-positives plus all benign lesions) proved to be more challenging, and Az values of 0.87 and 0.81 were obtained during training and testing, respectively. Sensitivity by patient of 100% at 0.43 false-positive malignancies per image was achieved in the detection and classification of cancerous lesions for the testing dataset. ::: ::: ::: CONCLUSION ::: The results show promising performance of the computerized lesion detection and classification method, and indicate the potential of such a system for clinical breast ultrasound.
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paper_title: Computerized lesion detection on breast ultrasound

paper_content:

We investigated the use of a radial gradient index (RGI) filtering technique to automatically detect lesions on breast ultrasound. After initial RGI filtering, a sensitivity of 87% at 0.76 false-positive detections per image was obtained on a database of 400 patients (757 images). Next, lesion candidates were segmented from the background by maximizing an average radial gradient (ARD) index for regions grown from the detected points. At an overlap of 0.4 with a radiologist lesion outline, 75% of the lesions were correctly detected. Subsequently, round robin analysis was used to assess the quality of the classification of lesion candidates into actual lesions and false-positives by a Bayesian neural network. The round robin analysis yielded an Az value of 0.84, and an overall performance by case of 94% sensitivity at 0.48 false-positives per image. Use of computerized analysis of breast sonograms may ultimately facilitate the use of sonography in breast cancer screening programs.
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paper_title: Ideal observer approximation using Bayesian classification neural networks

paper_content:

It is well understood that the optimal classification decision variable is the likelihood ratio or any monotonic transformation of the likelihood ratio. An automated classifier which maps from an input space to one of the likelihood ratio family of decision variables is an optimal classifier or "ideal observer." Artificial neural networks (ANNs) are frequently used as classifiers for many problems. In the limit of large training sample sizes, an ANN approximates a mapping function which is a monotonic transformation of the likelihood ratio, i.e., it estimates an ideal observer decision variable. A principal disadvantage of conventional ANNs is the potential over-parameterization of the mapping function which results in a poor approximation of an optimal mapping function for smaller training samples. Recently, Bayesian methods have been applied to ANNs in order to regularize training to improve the robustness of the classifier. The goal of training a Bayesian ANN with finite sample sizes is, as with unlimited data, to approximate the ideal observer. The authors have evaluated the accuracy of Bayesian ANN models of ideal observer decision variables as a function of the number of hidden units used, the signal-to-noise ratio of the data and the number of features or dimensionality of the data. The authors show that when enough training data are present, excess hidden units do not substantially degrade the accuracy of Bayesian ANNs. However, the minimum number of hidden units required to best model the optimal mapping function varies with the complexity of the data.
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paper_title: Computerized detection and 3-way classification of breast lesions on ultrasound images

paper_content:

Computer-aided diagnosis has great potential to improve performance of the detection and classification of abnormalities on breast ultrasound. Our goal is to develop a computerized tool that detects suspicious areas and distinguishes among false-positive ::: detections, benign lesions, and malignancies. One-step classification into 3 categories is a largely unexplored territory with many possibilities. ::: ::: The computerized scheme first identifies potential lesions based on expected lesion shape and margin characteristics. Our main focus here, however, is the subsequent classification of the potential lesions into 3 categories. For this purpose, we use a 3-way Bayesian neural net (BNN) based on extracted image features of the lesion candidates. ::: ::: The method was tested on a database of 858 cases (1832 images) consisting of complex and simple cysts, benign solid lesions, and malignant lesions. In order to verify whether performance conforms to expectations, the output of a 3-way classifier ("A" vs. "B" vs. "C") can be projected onto that of two 2-way classifiers ("A" vs. "B or C", and "A or B" vs. "C"). We compared the projected performance of the 3-way classifier to two specifically trained 2-way classifiers. The first task was to distinguish cancer from all other lesion candidates, and the second was to distinguish actual lesions from false-positive detections. For these tasks, the performance of the 2-way classifiers and the projected performance of the 3-way classifier were indistinguishable based on calculated means and standard deviations of ROC area (Az). For example, in round robin analysis the average Az values obtained with both approaches were 0.92 and 0.83, for the two tasks, with standard deviations of 0.006 and 0.010, respectively. The potential of 3-way classification is illustrated graphically through the estimated probability density functions of the three truth categories. We have implemented a promising computerized scheme for detection and subsequent one-step 3-way classification of breast lesions on ultrasound images. The method was tested on an extensive database. The main challenge is the development of an objective evaluation method of performance such as the equivalent of ROC analysis for the 2-class problem.
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paper_title: Use of the bootstrap technique with small training sets for computer-aided diagnosis in breast ultrasound.

paper_content:

The purpose of this study was to test the efficacy of using small training sets in computer-aided diagnostic systems (CAD) and to increase the capabilities of ultrasound (US) technology in the differential diagnosis of solid breast tumors. A total of 263 sonographic images of solid breast nodules, including 129 malignancies and 134 benign nodules, were evaluated by using a bootstrap technique with 10 original training samples. Texture parameters of a region-of-interest (ROI) were resampled with a bootstrap technique and a decision-tree model was used to classify the tumor as benign or malignant. The accuracy was 87.07% (229 of 263 tumors), the sensitivity was 95.35% (123 of 129), the specificity was 79.10% (106 of 134), the positive predictive value was 81.46% (123 of 151), and the negative predictive value was 94.64% (106 of 112). This analysis method provides a second opinion for physicians with high accuracy. The new method shows a potential to be useful in future application of CAD, especially when a large database cannot be obtained for training or a newly developed ultrasonic system has smaller sets of samples.
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paper_title: C4.5: Programs for Machine Learning

paper_content:

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

paper_content:

Breast cancer is one of the most common cancer among women around the world. Several techniques are available for detection of breast cancer. Mammography is one of the most effective tools for early detection. The goal of this research is to increase the diagnostic accuracy of image processing and machine learning techniques for optimum classification between normal and abnormalities in digital mammograms. GLCM texture feature extractions are known to be the most common and powerful techniques for texture analysis. This paper presents an evaluation and comparison of the performance of two different classification methods used to classify the normal and abnormal patterns. The experimental result suggest that Artificial Neural Network is outperformed the other method.
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paper_title: Data mining with decision trees for diagnosis of breast tumor in medical ultrasonic images

paper_content:

To increase the ability of ultrasonographic (US) technology for the differential diagnosis of solid breast tumors, we describe a novel computer-aided diagnosis (CADx) system using data mining with decision tree for classification of breast tumor to increase the levels of diagnostic confidence and to provide the immediate second opinion for physicians. Cooperating with the texture information extracted from the region of interest (ROI) image, a decision tree model generated from the training data in a top-down, general-to-specific direction with 24 co-variance texture features is used to classify the tumors as benign or malignant. In the experiments, accuracy rates for a experienced physician and the proposed CADx are 86.67% (78/90) and 95.50% (86/90), respectively.
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paper_title: MASS DETECTION AND CLASSIFICATION IN BREAST ULTRASOUND IMAGES USING FUZZY SVM

paper_content:

Breast cancer can be most effectively treated when detected during its early stages. Currently, the most effective method for early detection and screening of breast cancer is X-ray mammography. But the high number of false positives in mammography causes a lot of unnecessary biopsies. Sonography is an important adjunct to mammography in breast cancer detection and has been primarily useful for differentiating cysts from solid tumors. It has been shown that breast sonography is superior to mammography in its ability to detect focal abnormalities in the dense breasts of adolescent women. The accuracy rate of breast ultrasounds can reach a high level in the diagnosis of simple benign cysts and reduce the number of false positives. The lesions characterized as benign cysts do not require biopsies. ::: This research focuses on developing a novel computer-aided diagnosis system of mass detection and classification in breast ultrasound images using statistical methods. First, we propose a novel, automatic enhancement approach to preprocess the regions of interest in breast ultrasound images. Next, a Markov random field segmentation algorithm is developed to segment the suspicious areas from the regions of interest. Third, three kinds of features, including textural features, fractal dimensions, and histogram-based features, are analyzed and extracted based on the suspicious areas and regions of interest, and a stepwise regression method is used to select an optimal subset of features. Fourth, based on the optimal subset of features, we develop a novel classification method based on the fuzzy support vector machines to classify the regions of interest as benign or malignant masses. Finally, two objective evaluation methods, five objective indices, and the receiver operating characteristic curve are used to evaluate the performance of the proposed computer-aided diagnosis system. The experimental results show that the proposed computer-aided diagnosis system greatly improves the five objective indices and the value of Az (the area under the receiver operating characteristic curve), as opposed to other classification methods and radiologists' assessments. Our method's ability to improve the accuracy of breast cancer diagnosis while reducing the number of unnecessary biopsies is a valuable tool for patients and radiologists alike.
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paper_title: Diagnosis of breast tumors with ultrasonic texture analysis using support vector machines

paper_content:

This study presents a computer-aided diagnosis (CAD) system with textural features for classifying benign and malignant breast tumors on medical ultrasound systems. A series of pathologically proven breast tumors were evaluated using the support vector machine (SVM) in the differential diagnosis of breast tumors. The proposed CAD system utilized facile textural features, i.e., block difference of inverse probabilities, block variation of local correlation coefficients and auto-covariance matrix, to identify breast tumor. An SVM classifier using the textual features classified the tumor as benign or malignant. The proposed system identifies breast tumors with a comparatively high accuracy. This can help inexperienced physicians avoid misdiagnosis. The main advantage of the proposed system is that the training and diagnosis procedure of SVM are faster and more stable than that of multilayer perception neural networks. With the expansion of the database, new cases can easily be gathered and used as references. This study dramatically reduces the training and diagnosis time. The SVM is a reliable choice for the proposed CAD system because it is fast and excellent in ultrasound image classification.
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paper_title: Breast cancer diagnosis using image retrieval for different ultrasonic systems

paper_content:

This paper employs the image retrieval technique to classify breast tumors as benign or malignant lesions. We evaluated 600 ultrasound (US) images of pathologically proven solid breast nodules including 230 malignant and 370 benign tumors. The US images were acquired from four different ultrasound systems. Firstly, the physician located regions-of-interest (ROI) of ultrasound images. The textual features from ROI sub-image are utilized to classify breast tumors. The principal component analysis (PCA) is used to reduce the dimension of textual feature vector and then the image retrieval technique was utilized to differentiate between benign and malignant tumors. Historical cases can be directly added into the database and training of the diagnosis system again is not needed. The accuracy of the proposed computer-aided diagnosis (CAD) system was 91.2%, the sensitivity was 97.0% and the specificity was 87.6%. This system differentiates solid breast nodules with a relatively high accuracy in the different US systems and helps inexperienced operators avoid misdiagnosis.
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paper_title: Malignant and Benign Breast Masses on 3D US Volumetric Images: Effect of Computer-aided Diagnosis on Radiologist Accuracy

paper_content:

Purpose: To retrospectively investigate the effect of using a custom-designed computer classifier on radiologists' sensitivity and specificity for discriminating malignant masses from benign masses on three-dimensional (3D) volumetric ultrasonographic (US) images, with histologic analysis serving as the reference standard. Materials and Methods: Informed consent and institutional review board approval were obtained. Our data set contained 3D US volumetric images obtained in 101 women (average age, 51 years; age range, 25–86 years) with 101 biopsy-proved breast masses (45 benign, 56 malignant). A computer algorithm was designed to automatically delineate mass boundaries and extract features on the basis of segmented mass shapes and margins. A computer classifier was used to merge features into a malignancy score. Five experienced radiologists participated as readers. Each radiologist read cases first without computer-aided diagnosis (CAD) and immediately thereafter with CAD. Observers' malignancy rating da...
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paper_title: Breast lesions on sonograms: computer-aided diagnosis with nearly setting-independent features and artificial neural networks.

paper_content:

PURPOSE ::: To develop a computer-aided diagnosis (CAD) algorithm with setting-independent features and artificial neural networks to differentiate benign from malignant breast lesions. ::: ::: ::: MATERIALS AND METHODS ::: Two sets of breast sonograms were evaluated. The first set contained 160 lesions and was stored directly on the magnetic optic disks from the ultrasonographic (US) system. Four different boundaries were delineated by four persons for each lesion in the first set. The second set comprised 111 lesions that were extracted from the hard-copy images. Seven morphologic features were used, five of which were newly developed. A multilayer feed-forward neural network was used as the classifier. Reliability, extendability, and robustness of the proposed CAD algorithm were evaluated. Results with the proposed algorithm were compared with those with two previous CAD algorithms. All performance comparisons were based on paired-samples t tests. ::: ::: ::: RESULTS ::: The area under the receiver operating characteristic curve (A(z)) was 0.952 +/- 0.014 for the first set, 0.982 +/- 0.004 for the first set as the training set and the second set as the prediction set, 0.954 +/- 0.016 for the second set as the training set and the first set as the prediction set, and 0.950 +/- 0.005 for all 271 lesions. At the 5% significance level, the performance of the proposed CAD algorithm was shown to be extendible from one set of US images to the other set and robust for both small and large sample sizes. Moreover, the proposed CAD algorithm was shown to outperform the two previous CAD algorithms in terms of the A(z) value. ::: ::: ::: CONCLUSION ::: The proposed CAD algorithm could effectively and reliably differentiate benign and malignant lesions. The proposed morphologic features were nearly setting independent and could tolerate reasonable variation in boundary delineation.
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paper_title: MASS DETECTION AND CLASSIFICATION IN BREAST ULTRASOUND IMAGES USING FUZZY SVM

paper_content:

Breast cancer can be most effectively treated when detected during its early stages. Currently, the most effective method for early detection and screening of breast cancer is X-ray mammography. But the high number of false positives in mammography causes a lot of unnecessary biopsies. Sonography is an important adjunct to mammography in breast cancer detection and has been primarily useful for differentiating cysts from solid tumors. It has been shown that breast sonography is superior to mammography in its ability to detect focal abnormalities in the dense breasts of adolescent women. The accuracy rate of breast ultrasounds can reach a high level in the diagnosis of simple benign cysts and reduce the number of false positives. The lesions characterized as benign cysts do not require biopsies. ::: This research focuses on developing a novel computer-aided diagnosis system of mass detection and classification in breast ultrasound images using statistical methods. First, we propose a novel, automatic enhancement approach to preprocess the regions of interest in breast ultrasound images. Next, a Markov random field segmentation algorithm is developed to segment the suspicious areas from the regions of interest. Third, three kinds of features, including textural features, fractal dimensions, and histogram-based features, are analyzed and extracted based on the suspicious areas and regions of interest, and a stepwise regression method is used to select an optimal subset of features. Fourth, based on the optimal subset of features, we develop a novel classification method based on the fuzzy support vector machines to classify the regions of interest as benign or malignant masses. Finally, two objective evaluation methods, five objective indices, and the receiver operating characteristic curve are used to evaluate the performance of the proposed computer-aided diagnosis system. The experimental results show that the proposed computer-aided diagnosis system greatly improves the five objective indices and the value of Az (the area under the receiver operating characteristic curve), as opposed to other classification methods and radiologists' assessments. Our method's ability to improve the accuracy of breast cancer diagnosis while reducing the number of unnecessary biopsies is a valuable tool for patients and radiologists alike.
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paper_title: Computerized-aid diagnosis of breast mass using ultrasound image

paper_content:

This paper presents a comparative study of the diagnostic results of the ultrasologists with/without using a novel enhancement algorithm for breast ultrasonic images based on fuzzy entropy principle and textural information. Totally, 350 ultrasound images of 115 cases were analyzed including 59 benign and 56 malignant lesions. The original breast images were fuzzified, the edge and textural information were extracted, and the images were enhanced. The original and enhanced images were assessed and evaluated by ultrasologists using double blind method before and after enhancement. The diagnostic sensitivity and specificity were calculated by the areas (Az) under the receiver operating characteristic (ROC) curves. And the two diagnostic results before and after enhancement were compared by Chi-square test in a 2 x 2 table. The results demonstrated that the discrimination rate of breast masses had been highly improved after employing the novel enhancement algorithm. The result indicates the sensitivity could be raised from 74.3% to 89.3% with the false-positive rate 14.3%, and the area (Az) under the ROC curve of diagnosis also increased from 0.84 to 0.93. The novel enhancement algorithm can increase the classification accuracy and decrease the rate of missing and misdiagnosis, and it is useful for breast cancer control.
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paper_title: The effect of image quality on the appearance of lesions on breast ultrasound: implications for CADx

paper_content:

With the emergence of recent technology in breast ultrasound, sonographic image ::: quality has changed profoundly. Most notably, the technique of real-time ::: spatial compounding impacts the appearance of lesions and parenchyma. During ::: image acquisition, spatial compounding can be turned on or off at the ::: discretion of the radiologist, but this information is not stored along with ::: the image data. ::: ::: The ability to distinguishing between lesions imaged with and without spatial ::: compounding, using either single image features or a Bayesian neural net (BNN), was assessed using ROC analysis. Our database consisted of consecutively ::: collected HDI5000 images of 129 lesions imaged without spatial compounding ::: (357 images, cancer prevalence of 18%) and 370 lesions imaged with spatial ::: compounding (965 images, cancer prevalence 15%). These were used in automated ::: feature selection and BNN training. An additional 33 lesions were imaged for ::: which identical views with and without spatial compounding were available (70 ::: images, cancer prevalence 15%). These served as an independent test dataset. ::: Lesions were outlined by a radiologist and image features, mathematically ::: describing lesion characteristics, were calculated. ::: In feature selection, the 4 best performing features were related to gradient ::: strength and entropy. The average gradient strength within a lesion obtained an ::: area under the ROC curve (AUC) of 0.78 in the task of distinguishing lesions ::: imaged with and without spatial compounding. The BNN, using 4 features, ::: achieved an AUC on the independent test dataset of 0.98 in this task. ::: ::: The sonographic appearance of breast lesions is affected by spatial compound ::: imaging and lesion features may be used to automatically separate images as ::: obtained with or without this technique. In computer-aided diagnosis (CADx), ::: it will likely be beneficial ::: to separate images as such before using separate classifiers for assessment of ::: malignancy.
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paper_title: 3-D breast ultrasound segmentation using active contour model.

paper_content:

In this study, we made use of the discrete active contour model to overcome the natural properties of ultrasound (US) images, speckle, noise and tissue-related textures, to segment the breast tumors precisely. Determination of the real tumor boundary with the snake-deformation process requires an initial contour estimate. However, the manual way to sketch an initial contour is very time-consuming. Thus, we propose an automatic initial contour-finding method that not only maintains the tumor shape, but also is close to the tumor boundary and inside the tumor. During the deformation process, to prevent the snake trapping into the false position caused by tissue-related texture or speckle, we added the edge information as an image feature to define the external force. In addition, because the 3-D volume of a tumor is essentially constructed by a sequence of 2-D images, our method for finding boundaries of a tumor can be extended to 3-D cases. By precisely counting the volume of the 3-D images, we can get the volume of tumor. Finally, we will show that the proposed techniques have rather good performance and lead to a satisfactory result in comparison with the estimated volume and physician's estimate.
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paper_title: Segmentation of breast tumor in three-dimensional ultrasound images using three-dimensional discrete active contour model.

paper_content:

In this paper, we apply the three-dimensional (3-D) active contour model to a 3-D ultrasonic data file for segmenting of the breast tumor. The 3-D ultrasonic file is composed of a series of two-dimensional (2-D) images. Most of traditional techniques of 2-D image segmentation will not use the information between adjacent images. To suit the property of the 3-D data, we introduce the concept of the 3-D stick, the 3-D morphologic process and the 3-D active contour model. The 3-D stick can get over the problem that the ultrasonic image is full of speckle noise and highlight the edge information in images. The 3-D morphologic process helps to determine the contour of the tumor and the resulting contour can be regarded as the initial contour of the active contour model. Finally, the 3-D active contour model will make the initial contour approach to the real contour of the tumor. However, there is emphasis on these 3-D techniques that they do not consist of a series of 2-D techniques. When they work, they will consider the horizontal, vertical and depth directions at the same time. The use of these 3-D techniques not only segments the 3-D shape but also obtains the volume of the tumor. The volume of the tumor calculated by the proposed method will be compared with the volume calculated by the VOCAL software with the physician's manually drawn shape and it shows that the performance of our method is satisfactory.
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paper_title: In vivo demonstration of microcalcification in breast cancer using high resolution ultrasound

paper_content:

84 patients with 89 primary breast cancers underwent high resolution ultrasound, mammography and histological evaluation. Pathological specimens and hard copy images of both imaging modalities were reviewed in order to document the amount of microcalcification present in each tumour. A total of 44, 40 and 46 tumours had visible microcalcification using high resolution ultrasound, mammography and histology, respectively. Using mammography as the gold standard, ultrasound achieved a sensitivity of 95%, specificity of 87.8% and accuracy of 91% in the detection of microcalcification. Histology had a sensitivity of 80%, specificity of 71.4% and accuracy of 75.3%. We conclude that ultrasound is sensitive in the detection of microcalcification in breast cancer when they are within a mass lesion, and is a reliable diagnostic sign of carcinoma when present.
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paper_title: Computer-Aided Diagnosis for 3-Dimensional Breast Ultrasonography

paper_content:

HYPOTHESIS ::: Using 3-dimensional (3-D) over 2-dimensional (2-D) ultrasonographic (US) images of the breast represents a potentially significant advantage for computer-aided diagnosis (CAD). ::: ::: ::: BACKGROUND ::: Although conventional 2-D US images of the breast are increasingly used in surgical clinical practice, 3-D US imaging of the breast, a newly introduced technique, can offer more information than 2-D US images do. ::: ::: ::: DESIGN ::: This study deals with a CAD method for use with the proposed 3-D US images of the breast and compares its performance with conventional 2-D US versions. ::: ::: ::: METHODS ::: The test databases included 3-D US images of 107 benign and 54 malignant breast tumors for a total of 161 US images. All solid nodules at US belong to categories above C3 (ie, probably benign). The 3-D US imaging was performed using a scanner (Voluson 530; Kretz Technik, Zipf, Austria). New 3-D autocorrelation coefficients extended from the traditional 2-D autocorrelations were developed to extract the texture characteristics of the 3-D US images. The extracted texture features of the 3-D US images were used to classify the tumor as benign or malignant using the neural network. ::: ::: ::: RESULTS ::: At the receiver operating characteristic analysis, 3-D and 2-D autocorrelation calculating schemes yielded Az values (ie, area under the receiver operating characteristic curve) of 0.97 and 0.85 in distinguishing between benign and malignant lesions, respectively. Accuracy, sensitivity, specificity, positive predictive value, and negative predictive value are statistically significantly improved using 3-D instead of 2-D US images for CAD. ::: ::: ::: CONCLUSIONS ::: The proposed system (for 3-D and 2-D CAD) is expected to be a useful computer-aided diagnostic tool for classifying benign and malignant tumors on ultrasonograms and can provide a second reading to help reduce misdiagnosis. Findings from this study suggest that using 3-D over 2-D US images for CAD represents a potentially significant advantage.
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paper_title: Malignant and Benign Breast Masses on 3D US Volumetric Images: Effect of Computer-aided Diagnosis on Radiologist Accuracy

paper_content:

Purpose: To retrospectively investigate the effect of using a custom-designed computer classifier on radiologists' sensitivity and specificity for discriminating malignant masses from benign masses on three-dimensional (3D) volumetric ultrasonographic (US) images, with histologic analysis serving as the reference standard. Materials and Methods: Informed consent and institutional review board approval were obtained. Our data set contained 3D US volumetric images obtained in 101 women (average age, 51 years; age range, 25–86 years) with 101 biopsy-proved breast masses (45 benign, 56 malignant). A computer algorithm was designed to automatically delineate mass boundaries and extract features on the basis of segmented mass shapes and margins. A computer classifier was used to merge features into a malignancy score. Five experienced radiologists participated as readers. Each radiologist read cases first without computer-aided diagnosis (CAD) and immediately thereafter with CAD. Observers' malignancy rating da...
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paper_title: Lesion contrast enhancement in medical ultrasound imaging

paper_content:

Methods for improving the contrast-to-noise ratio (CNR) of low-contrast lesions in medical ultrasound imaging are described. Differences in the frequency spectra and amplitude distributions of the lesion and its surroundings can be used to increase the CNR of the lesion relative to the background. Automated graylevel mapping is used in combination with a contrast-weighted form of frequency-diversity speckle reduction. In clinical studies, the techniques have yielded mean CNR improvements of 3.2 dB above ordinary frequency-diversity imaging and 5.6 dB over sharper conventional images, with no post-processing graylevel mapping.
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paper_title: 11B-4 Quantitative Ultrasound Assessment of Breast Cancer Using a Multiparameter Approach

paper_content:

Early detection and diagnosis of breast cancer leads to improved prognosis. Quantitative ultrasound (QUS) techniques utilizing a multiparameter set have been developed for classifying rodent models of breast cancer. The improvement in detection and diagnosis of breast cancer using QUS will have significant medical impact. Two kinds of mammary tumors, carcinoma and sarcoma, were examined in mice using QUS imaging Ten tumors for each kind of cancer were scanned with a 20-MHz single-element transducer (f/3). The tumors contained microstructural differences in size, shape, and organizational patterns of the scatterers. Cells were identified as a prominent source of scattering in the tumors. The average scatterer diameter (ASD) and average acoustic concentration (AAC) were estimated by comparing the normalized backscattered power spectra from the tumors with newly developed models of cell scattering. The organizational structure of the tumors was also characterized by a clustering parameter (the beta parameter) and the randomness of the scatterer locations (the S parameter) by comparing the envelope statistics of the backscatter to a homodyned-K distribution. F-tests conducted on the backscattered power spectra from the two kinds of tumors revealed statistically significant differences for frequencies above 16 MHz. QUS images of the tumors utilizing the ASD, AAC, beta, and S parameter estimates from the new model and the envelope statistics were constructed. Statistically significant differences were observed between the carcinomas and sarcomas for all estimated parameters for ultrasonic frequencies above 16 MHz. Feature analysis plots incorporating all four parameters indicated cancer classification was improved compared with analysis using only two parameters. High-frequency QUS utilizing a multiparameter feature set improved the diagnostic potential of ultrasound for breast cancer detection.
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paper_title: Development of a fully automatic scheme for detection of masses in whole breast ultrasound images.

paper_content:

Ultrasonography has been used for breast cancer screening in Japan. Screening using a conventional hand-held probe is operator dependent and thus it is possible that some areas of the breast may not be scanned. To overcome such problems, a mechanical whole breast ultrasound (US) scanner has been proposed and developed for screening purposes. However, another issue is that radiologists might tire while interpreting all images in a large-volume screening; this increases the likelihood that masses may remain undetected. Therefore, the aim of this study is to develop a fully automatic scheme for the detection of masses in whole breast US images in order to assist the interpretations of radiologists and potentially improve the screening accuracy. The authors database comprised 109 whole breast US imagoes, which include 36 masses (16 malignant masses, 5 fibroadenomas, and 15 cysts). A whole breast US image with 84 slice images (interval between two slice images: 2 mm) was obtained by the ASU-1004 US scanner (ALOKA Co., Ltd., Japan). The feature based on the edge directions in each slice and a method for subtracting between the slice images were used for the detection of masses in the authors proposed scheme. The Canny edge detector was applied to detect edges in US images; these edges were classified as near-vertical edges or near-horizontal edges using a morphological method. The positions of mass candidates were located using the near-vertical edges as a cue. Then, the located positions were segmented by the watershed algorithm and mass candidate regions were detected using the segmented regions and the low-density regions extracted by the slice subtraction method. For the removal of false positives (FPs), rule-based schemes and a quadratic discriminant analysis were applied for the distribution between masses and FPs. As a result, the sensitivity of the authors scheme for the detection of masses was 80.6% (29/36) with 3.8 FPs per whole breast image. The authors scheme for a computer-aided detection may be useful in improving the screening performance and efficiency.
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paper_title: MASS DETECTION AND CLASSIFICATION IN BREAST ULTRASOUND IMAGES USING FUZZY SVM

paper_content:

Breast cancer can be most effectively treated when detected during its early stages. Currently, the most effective method for early detection and screening of breast cancer is X-ray mammography. But the high number of false positives in mammography causes a lot of unnecessary biopsies. Sonography is an important adjunct to mammography in breast cancer detection and has been primarily useful for differentiating cysts from solid tumors. It has been shown that breast sonography is superior to mammography in its ability to detect focal abnormalities in the dense breasts of adolescent women. The accuracy rate of breast ultrasounds can reach a high level in the diagnosis of simple benign cysts and reduce the number of false positives. The lesions characterized as benign cysts do not require biopsies. ::: This research focuses on developing a novel computer-aided diagnosis system of mass detection and classification in breast ultrasound images using statistical methods. First, we propose a novel, automatic enhancement approach to preprocess the regions of interest in breast ultrasound images. Next, a Markov random field segmentation algorithm is developed to segment the suspicious areas from the regions of interest. Third, three kinds of features, including textural features, fractal dimensions, and histogram-based features, are analyzed and extracted based on the suspicious areas and regions of interest, and a stepwise regression method is used to select an optimal subset of features. Fourth, based on the optimal subset of features, we develop a novel classification method based on the fuzzy support vector machines to classify the regions of interest as benign or malignant masses. Finally, two objective evaluation methods, five objective indices, and the receiver operating characteristic curve are used to evaluate the performance of the proposed computer-aided diagnosis system. The experimental results show that the proposed computer-aided diagnosis system greatly improves the five objective indices and the value of Az (the area under the receiver operating characteristic curve), as opposed to other classification methods and radiologists' assessments. Our method's ability to improve the accuracy of breast cancer diagnosis while reducing the number of unnecessary biopsies is a valuable tool for patients and radiologists alike.
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paper_title: A NEW NEUTROSOPHIC APPROACH TO IMAGE THRESHOLDING

paper_content:

A neutrosophic set (Ns), a part of neutrosophy theory, studies the origin, nature, and scope of neutralities, as well as their interactions with different ideational spectra. The neutrosophic set is a powerful general formal framework that has been recently proposed. However, the neutrosophic set needs to be specified from a technical point of view. We apply the neutrosophic set in image domain and define some concepts and operations for image thresholding.The image G is transformed into Ns domain, which is described using three subsets T, I and F. The entropy in neutrosophic set is defined and employed to evaluate the indetermination. A new λ-mean operation is proposed to reduce the set's indetermination. Finally, the proposed method is employed to perform image thresholding. We have conducted experiments on a variety of images. The experimental results demonstrate that the proposed approach can select the thresholds automatically and effectively. Especially, it can process the "clean" images, the images with different kinds of noise and the images with multiple kinds of noise well without knowing the type of the noise, which is the most difficult task for image thresholding.
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paper_title: Ultrasound demonstration of mammographically detected microcalcifications in patients with ductal carcinomain situ of the breast

paper_content:

Background ::: Breast microcalcifications are difficult to depict by ultrasound (US). However, recent advances in US equipment and the refinement of breast imaging techniques have improved the detection and characterization of small breast lesions. The present study attempts to determine whether US examination is able to demonstrate nonpalpable breast lesions associated with mammographically detected microcalcifications without mass density or distortion, and to evaluate the clinical reliability of US-guided procedures, especially in cases of ductal carcinomain situ (DCIS) of the breast.
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paper_title: Approaches For Automated Detection And Classification Of Masses In Mammograms

paper_content:

Breast cancer is one of the most common cancer among women around the world. Several techniques are available for detection of breast cancer. Mammography is one of the most effective tools for early detection. The goal of this research is to increase the diagnostic accuracy of image processing and machine learning techniques for optimum classification between normal and abnormalities in digital mammograms. GLCM texture feature extractions are known to be the most common and powerful techniques for texture analysis. This paper presents an evaluation and comparison of the performance of two different classification methods used to classify the normal and abnormal patterns. The experimental result suggest that Artificial Neural Network is outperformed the other method.
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paper_title: Diagnostic breast ultrasound: current status and future directions.

paper_content:

This summary of breast ultrasound reviews the current indications for its use, discusses the potential technical and human pitfalls in its performance, and briefly examines possible future applications that currently are works in progress. It also contains an in-depth discussion of the use and interpretation of color Doppler and power Doppler imaging, techniques that do not seem to be understood or used fully in daily practice.
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paper_title: Three-dimensional spatial compounding of ultrasound images

paper_content:

One of the most promising applications of 3-D ultrasound lies in the visualization and volume estimation of internal 3-D structures. Unfortunately, the quality of the ultrasound data can be severely degraded by artefacts and speckle, making automatic analysis of the 3-D data sets very difficult. In this paper we investigate the use of 3-D spatial compounding to reduce speckle. We develop a new statistical theory to predict the improvement in signal-to-noise ratio with increased levels of compounding, and verify the predictions empirically. We also investigate how registration errors can affect automatic volume estimation of structures within the compounded 3-D data set. Having established the need to correct these errors, we present a novel reconstruction algorithm which uses landmarks to register each B-scan accurately as it is inserted into the voxel array. In a series of in vitro and in vivo trials, we demonstrate that 3-D spatial compounding is very effective for improving the signal-to-noise ratio, but correction of registration errors is essential.
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paper_title: Computer-aided detection and classification of microcalcifications in mammograms: a survey

paper_content:

Abstract Breast cancer continues to be a significant public health problem in the world. Approximately, 182,000 new cases of breast cancer are diagnosed and 46,000 women die of breast cancer each year in the United States. Even more disturbing is the fact that one out of eight women in US will develop breast cancer at some point during her lifetime. Primary prevention seems impossible since the causes of this disease still remain unknown. Early detection is the key to improving breast cancer prognosis. Mammography is one of the reliable methods for early detection of breast carcinomas. There are some limitations of human observers, and it is difficult for radiologists to provide both accurate and uniform evaluation for the enormous number of mammograms generated in widespread screening. The presence of microcalcification clusters (MCCs) is an important sign for the detection of early breast carcinoma. An early sign of 30–50% of breast cancer detected mammographically is the appearance of clusters of fine, granular microcalcification, and 60–80% of breast carcinomas reveal MCCs upon histological examinations. The high correlation between the appearance of the microcalcification clusters and the diseases show that the CAD (computer aided diagnosis) systems for automated detection/classification of MCCs will be very useful and helpful for breast cancer control. In this survey paper, we summarize and compare the methods used in various stages of the computer-aided detection systems (CAD). In particular, the enhancement and segmentation algorithms, mammographic features, classifiers and their performances are studied and compared. Remaining challenges and future research directions are also discussed.
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paper_title: Breast ultrasound image enhancement using fuzzy logic

paper_content:

Abstract Breast cancer is still a serious disease in the world. Early detection is very essential for breast cancer prevention and diagnosis. Breast ultrasound (US) imaging has been proven to be a valuable adjunct to mammography in the detection and classification of breast lesions. Because of the fuzzy and noisy nature of the US images and the low contrast between the breast cancer and tissue, it is difficult to provide an accurate and effective diagnosis. This paper presents a novel algorithm based on fuzzy logic that uses both the global and local information and has the ability to enhance the fine details of the US images while avoiding noise amplification and overenhancement. We normalize the images and then fuzzify the normalized images based on the maximum entropy principle. Edge and textural information are extracted to describe the lesion features and the scattering phenomenon of US images and the contrast ratio measuring the degree of enhancement is computed and modified. The defuzzification process is used to obtain the enhanced US images. To demonstrate the performance of the proposed approach, the algorithm was tested on 86 breast US images. Experimental results confirm that the proposed method can effectively enhance the details of the breast lesions without overenhancement or underenhancement. (E-mail: hengda.cheng@usu.edu )
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Title: Automated breast cancer detection and classification using ultrasound images: A survey

Section 1: Introduction
Description 1: Introduce the significance of breast cancer detection, limitations of mammography, and the potential of ultrasound imaging, along with the necessity for computer-aided diagnosis (CAD).

Section 2: Preprocessing
Description 2: Discuss the preprocessing techniques applied to ultrasound images, including speckle reduction and image enhancement methods.

Section 3: Speckle reduction
Description 3: Detail various speckle reduction techniques used in ultrasound image processing, including filtering techniques, wavelet domain techniques, and compounding approaches.

Section 4: Image enhancement
Description 4: Outline methods specifically for enhancing the image quality, particularly focusing on contrast enhancement techniques.

Section 5: Segmentation
Description 5: Explain the techniques used for segmenting ultrasound images to locate suspicious areas, including histogram thresholding, active contour models, Markov random fields, and neural networks.

Section 6: Feature extraction and selection
Description 6: Describe the process of extracting and selecting significant features from ultrasound images for effective breast cancer detection, covering texture features, morphologic features, model-based features, descriptor features, and methods for reducing the feature space dimension.

Section 7: Classifiers
Description 7: Provide a detailed overview of different classifiers used for categorizing ultrasound images into lesion/non-lesion or benign/malignant, including linear classifiers, artificial neural networks, decision trees, support vector machines, template matching, and human classifiers.

Section 8: Evaluations
Description 8: Discuss the evaluation criteria and performance metrics used to assess CAD systems, and highlight the necessity for a benchmark ultrasound image database.

Section 9: Future directions
Description 9: Present future research directions and advancements in ultrasound imaging and CAD systems for breast cancer detection, including high-resolution imaging, 3D ultrasound imaging, fuzzy logic, quantitative ultrasound techniques, and the development of public benchmark databases.

Section 10: Conclusions
Description 10: Summarize the paper's review of CAD systems for breast cancer detection, highlighting key techniques, advantages, disadvantages, evaluation metrics, and future trends.