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Purpose: Algorithms employed for pigmented lesion segmentation perform poorly on dermoscopy images of basal cell carcinoma (BCC), the most common skin cancer. The main objective was to develop better methods for BCC segmentation., Methods: Fifteen thresholding methods were implemented for BCC lesion segmentation. We propose two error metrics that better measure the type II error: Relative XOR Error and Lesion Capture Ratio., Results: On training/test sets of 305 and 34 BCC images, respectively, five new techniques outperform two state-of-the-art methods used in segmentation of melanomas, based on the new error metrics., Conclusion: The proposed algorithms, which include solutions for image vignetting correction and border expansion to achieve dermatologist-like borders, provide more inclusive and feature-preserving border detection, favoring better BCC classification accuracy, in future work., (© 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

Background: Computer-aided pattern classification of melanoma and other pigmented skin lesions is one of the most important tasks for clinical diagnosis. To differentiate between benign and malignant lesions, the extraction of color, architectural order, symmetry of pattern and homogeneity (CASH) is a challenging task., Methods: In this article, a novel pattern classification system (PCS) based on the clinical CASH rule is presented to classify among six classes of patterns. The PCS system consists of the following five steps: transformation to the CIE L*a*b* color space, pre-processing to enhance the tumor region and removal of hairs, tumor-area segmentation, color and texture feature extraction, and finally, classification based on a multiclass support vector machine., Results: The PCS system is tested on a total of 180 dermoscopic images. To test the performance of the PCS diagnostic classifier, the area under the receiver operating characteristics curve (AUC) is utilized. The proposed classifier achieved a sensitivity of 91.64%, specificity of 94.14%, and AUC of 0.948., Conclusion: The experimental results demonstrate that the proposed pattern classifier is highly accurate and classify between benign and malignant lesions into some extend. The PCS method is fully automatic and can accurately detect different patterns from dermoscopy images using color and texture properties. Additional pattern features can be included to investigate the impact of pattern classification based on the CASH rule., (© 2011 John Wiley & Sons A/S.)

Background: Computer-aided diagnosis of dermoscopy images has shown great promise in developing a quantitative, objective way of classifying skin lesions. An important step in the classification process is lesion segmentation. Many studies have been successful in segmenting melanocytic skin lesions (MSLs), but few have focused on non-melanocytic skin lesions (NoMSLs), as the wide variety of lesions makes accurate segmentation difficult., Methods: We developed an automatic segmentation program for detecting borders of skin lesions in dermoscopy images. The method consists of a pre-processing phase, general lesion segmentation phase, including illumination correction, and bright region segmentation phase., Results: We tested our method on a set of 107 NoMSLs and a set of 319 MSLs. Our method achieved precision/recall scores of 84.5% and 88.5% for NoMSLs, and 93.9% and 93.8% for MSLs, in comparison with manual extractions from four or five dermatologists., Conclusion: The accuracy of our method was competitive or better than five recently published methods. Our new method is the first method for detecting borders of both non-melanocytic and melanocytic skin lesions., (© 2011 John Wiley & Sons A/S.)

Background/purpose: Border (B) description of melanoma and other pigmented skin lesions is one of the most important tasks for the clinical diagnosis of dermoscopy images using the ABCD rule. For an accurate description of the border, there must be an effective skin tumor area extraction (STAE) method. However, this task is complicated due to uneven illumination, artifacts present in the lesions and smooth areas or fuzzy borders of the desired regions., Methods: In this paper, a novel STAE algorithm based on improved dynamic programming (IDP) is presented. The STAE technique consists of the following four steps: color space transform, pre-processing, rough tumor area detection and refinement of the segmented area. The procedure is performed in the CIE L(*) a(*) b(*) color space, which is approximately uniform and is therefore related to dermatologist's perception. After pre-processing the skin lesions to reduce artifacts, the DP algorithm is improved by introducing a local cost function, which is based on color and texture weights., Results: The STAE method is tested on a total of 100 dermoscopic images. In order to compare the performance of STAE with other state-of-the-art algorithms, various statistical measures based on dermatologist-drawn borders are utilized as a ground truth. The proposed method outperforms the others with a sensitivity of 96.64%, a specificity of 98.14% and an error probability of 5.23%., Conclusion: The results demonstrate that this STAE method by IDP is an effective solution when compared with other state-of-the-art segmentation techniques. The proposed method can accurately extract tumor borders in dermoscopy images., (© 2011 John Wiley & Sons A/S.)

Background/purpose: Automated border detection is an important and challenging task in the computerized analysis of dermoscopy images. However, dermoscopic images often contain artifacts such as illumination, dermoscopic gel, and outline (hair, skin lines, ruler markings, and blood vessels). As a result, there is a need for robust methods to remove artifacts and detect lesion borders in dermoscopy images., Methods: This automated method consists of three main steps: (1) preprocessing, (2) edge candidate point detection, and (3) tumor outline delineation. First, algorithms to reduce artifacts were used. Second, a least-squares method (LSM) was performed to acquire edge points. Third, dynamic programming (DP) technique was used to find the optimal boundary of the lesion. Statistical measures based on dermatologist-drawn borders were utilized as ground-truth to evaluate the performance of the proposed method., Results: The method is tested on a total of 240 dermoscopic images: 30 benign melanocytic, 50 malignant melanomas, 50 basal cell carcinomas, 20 Merkel cell carcinomas, 60 seborrheic keratosis, and 30 atypical naevi. We obtained mean border detection error of 8.6%, 5.04%, 9.0%, 7.02%, 2.01%, and 3.24%, respectively., Conclusions: The results demonstrate that border detection combined with artifact removal increases sensitivity and specificity for segmentation of lesions in dermoscopy images., (© 2011 John Wiley & Sons A/S.)

Purpose: This paper presents a novel approach for objective evaluation of border detection in dermoscopy images of melanoma., Background: In melanoma studies, border detection is a fundamental step toward the development of a computer-aided diagnosis system. Therefore, its accuracy is essential for accurate implementation of the subsequent parts of the diagnostic system., Method: An objective evaluation procedure of border detection methods is presented. The evaluation procedure uses the weighted performance index, which is composed of weighted metrics of sensitivity, specificity, accuracy, precision, border error and similarity. This index can also be used to optimize the parameters of a border detection method., Result and Conclusion: Experiments are performed on 55 high-resolution dermoscopy images. Using the union of four sets of dermatologist-drawn borders as the ground truth, weighted metrics of sensitivity, specificity, accuracy, precision, border error and similarity are evaluated. Then, the weighted performance index is constructed and used to optimize the parameters of the hybrid border detection method. The outcome of the optimization process, verified through statistical analysis, yields a higher degree of agreement between automatic borders and the ground truth, compared with using standard metrics only. Finally, the weighted performance index is used to evaluate five recently reported border detection methods., (© 2010 John Wiley & Sons A/S.)

Background/purpose: Automatic lesion segmentation is an important part of computer-based image analysis of pigmented skin lesions. In this research, a watershed algorithm is developed and investigated for adequacy of skin lesion segmentation in dermoscopy images., Methods: Hair, black border and vignette removal methods are introduced as preprocessing steps. The flooding variant of the watershed segmentation algorithm was implemented with novel features adapted to this domain. An outer bounding box, determined by a difference function derived from horizontal and vertical projection functions, is added to estimate the lesion area, and the lesion area error is reduced by a linear estimation function. As a post-processing step, a second-order B-Spline smoothing method is introduced to smooth the watershed border., Results: Using the average of three sets of dermatologist-drawn borders as the ground truth, an overall error of 15.98% was obtained using the watershed technique., Conclusion: The implementation of the flooding variant of the watershed algorithm presented here allows satisfactory automatic segmentation of pigmented skin lesions.

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

Background: Dermoscopy is one of the major imaging modalities used in the diagnosis of melanoma and other pigmented skin lesions. Owing to the difficulty and subjectivity of human interpretation, dermoscopy image analysis has become an important research area. One of the most important steps in dermoscopy image analysis is the automated detection of lesion borders. Although numerous methods have been developed for the detection of lesion borders, very few studies were comprehensive in the evaluation of their results., Methods: In this paper, we evaluate five recent border detection methods on a set of 90 dermoscopy images using three sets of dermatologist-drawn borders as the ground truth. In contrast to previous work, we utilize an objective measure, the normalized probabilistic rand index, which takes into account the variations in the ground-truth images., Conclusion: The results demonstrate that the differences between four of the evaluated border detection methods are in fact smaller than those predicted by the commonly used exclusive-OR measure.

Background: Semitranslucency, defined as a smooth, jelly-like area with varied, near-skin-tone color, can indicate a diagnosis of basal cell carcinoma (BCC) with high specificity. This study sought to analyze potential areas of semitranslucency with histogram-derived texture and color measures to discriminate BCC from non-semitranslucent areas in non-BCC skin lesions., Methods: For 210 dermoscopy images, the areas of semitranslucency in 42 BCCs and comparable areas of smoothness and color in 168 non-BCCs were selected manually. Six color measures and six texture measures were applied to the semitranslucent areas of the BCC and the comparable areas in the non-BCC images., Results: Receiver operating characteristic (ROC) curve analysis showed that the texture measures alone provided greater separation of BCC from non-BCC than the color measures alone. Statistical analysis showed that the four most important measures of semitranslucency are three histogram measures: contrast, smoothness, and entropy, and one color measure: blue chromaticity. Smoothness is the single most important measure. The combined 12 measures achieved a diagnostic accuracy of 95.05% based on area under the ROC curve., Conclusion: Texture and color analysis measures, especially smoothness, may afford automatic detection of BCC images with semitranslucency.

Background: Dermoscopy is one of the major imaging modalities used in the diagnosis of melanoma and other pigmented skin lesions. Because of the difficulty and subjectivity of human interpretation, automated analysis of dermoscopy images has become an important research area. Border detection is often the first step in this analysis., Methods: In this article, we present an approximate lesion localization method that serves as a preprocessing step for detecting borders in dermoscopy images. In this method, first the black frame around the image is removed using an iterative algorithm. The approximate location of the lesion is then determined using an ensemble of thresholding algorithms., Results: The method is tested on a set of 428 dermoscopy images. The localization error is quantified by a metric that uses dermatologist-determined borders as the ground truth., Conclusion: The results demonstrate that the method presented here achieves both fast and accurate localization of lesions in dermoscopy images.

Background: As a result of advances in skin imaging technology and the development of suitable image processing techniques, during the last decade, there has been a significant increase of interest in the computer-aided diagnosis of melanoma. Automated border detection is one of the most important steps in this procedure, because the accuracy of the subsequent steps crucially depends on it., Methods: In this article, we present a fast and unsupervised approach to border detection in dermoscopy images of pigmented skin lesions based on the statistical region merging algorithm., Results: The method is tested on a set of 90 dermoscopy images. The border detection error is quantified by a metric in which three sets of dermatologist-determined borders are used as the ground-truth. The proposed method is compared with four state-of-the-art automated methods (orientation-sensitive fuzzy c-means, dermatologist-like tumor extraction algorithm, meanshift clustering, and the modified JSEG method)., Conclusion: The results demonstrate that the method presented here achieves both fast and accurate border detection in dermoscopy images.