Combination of features from skin pattern and ABCD analysis for lesion classification

BACKGROUND/PURPOSE: It is known that the standard features for lesion classification are ABCD features, that is, asymmetry, border irregularity, colour variegation and diameter of lesion. However, the observation that skin patterning tends to be disrupted by malignant but not by benign skin lesions suggests that measurements of skin pattern disruption on simply captured white light optical skin images could be a useful contribution to a diagnostic feature set. Previous work using both skin line direction and intensity for lesion classification was encouraging. But these features have not been combined with the ABCD features. This paper explores the possibility of combing features from skin pattern and ABCD analysis to enhance classification performance. METHODS: The skin line direction and intensity were extracted from a local tensor matrix of skin pattern. Meanwhile, ABCD analysis was conducted to generate six features. They were asymmetry, border irregularity, colour (red, green and blue) variegations and diameter of lesion. The eight features of each case were combined using a principal component analysis (PCA) to produce two dominant features for lesion classification. RESULTS: A larger set of images containing malignant melanoma (MM) and benign naevi were processed as above and the scatter plot in a two-dimensional dominant feature space showed excellent separation of benign and malignant lesions. An ROC (receiver operating characteristic) plot enclosed an area of 0.94. CONCLUSIONS: The classification results showed that the individual features have a limited discrimination capability and the combined features were promising to distinguish MM from benign lesion.     

Skin pattern analysis for lesion classification using local isotropy

Background/purpose: The observation that skin pattern tends to be disrupted by malignant skin lesions, but not by benign ones suggests that measurements of skin pattern disruption on simply captured white light optical clinical (WLC) skin images could be a useful contribution to a diagnostic feature set. Previous work, which generated a flow field of skin pattern using a measurement of local line direction and intensity, was encouraging. The aim of this paper is to investigate the possibility of extracting new features using local isotropy metrics to quantify the skin pattern disruption. Methods: The skin pattern was extracted from WLC skin images by high‐pass filtering. A local tensor matrix was computed. The local isotropy was measured by the condition number of the local tensor matrix. The difference of this measure over the lesion and normal skin areas, combined with the local line direction and the ABCD features, was used as a lesion classifier. Results: A set of images of malignant melanoma and benign naevi was analysed. A one‐dimensional scatter plot showed the potential of a local isotropy metric, showing an area of 0.70 under the receiver operating characteristic (ROC) curve. A two‐dimensional scatter plot, combined with the local line direction, indicated enhancement of the classification performance, showing an area of 0.89 under the ROC curve. A three‐dimensional scatter plot combined with the local line direction and the ABCD features, using principal component analysis, demonstrated excellent separation of benign and malignant lesions. An ROC plot for this case enclosed an area of 0.96. Conclusion: The experimental results show that the local isotropy metric has a potential to increase lesion classifier accuracy. Combined with the local line direction and the ABCD features, it is very promising as a method to distinguish malignant melanoma from benign lesions.     

Analysis of skin line pattern for lesion classification

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

Laypersons' perceptual discrimination of pigmented skin lesions

BACKGROUND: Most cutaneous malignant melanomas of the skin are visible and should, at least in theory, be possible to detect with the naked eye. OBJECTIVE: This study was conducted to learn more about laypersons' ability to discriminate between benign pigmented lesions and malignant ones. METHODS: Four groups of laypersons (n = 120) were asked to evaluate pictures of different types of pigmented skin lesions, before and after they received information about the ABCD (asymmetry, border irregularity, color variegation, and diameter greater than 6 mm) criteria, with respect to the necessity of action. RESULTS: The respondents made adequate assessments of melanomas but overestimated the danger of benign pigmented skin lesions. Information about the ABCD criteria enhanced their ability to make adequate assessments. CONCLUSION: People seem to make adequate decisions concerning how to act if they have a melanoma. On the other hand, common moles and dysplastic nevi were harder to discriminate. Providing information to the public about the features of melanomas, in accordance with the ABCD criteria, might help laypersons in their perceptual discrimination of skin lesions.     

Predictive power of irregular border shapes for malignant melanomas

BACKGROUND/PURPOSE: The Irregularity Index is a measure of border irregularity from pigmented skin lesion images. The measure attempts to quantify the degree of irregularity of the structural indentations and protrusions along a lesion border. A carefully designed study has shown that the parameters derived from the Irregularity Index were highly correlated with expert dermatologists' notion of border shape. This paper investigates the predictive power of these parameters on a set of data with known histological diagnosis. METHODS: A set of 188 pigmented skin lesions (30 malignant melanomas and 158 benign lesions) was selected for the study. Their images were segmented and their border shapes were analysed by the Irregularity Index, producing four border irregularity parameters. The predictive power of these four parameters was estimated by a series of statistical tests. RESULTS: The mean values of the four border irregularity parameters were significantly different between the melanoma group and the benign lesion group. When using the four parameters to predict its disease status, the leave-one-out classification rate was 82.4%, and the area under the receiver operating characteristic curve was 0.77. A malignant melanoma was 8.9 times more likely to have an irregular border than a benign lesion. CONCLUSION: This study confirmed that border irregularity is an important clinical feature for the diagnosis of malignant melanoma. It also indicates that the computer-derived measures based on the Irregularity Index capture to certain extent the kind of irregularity which is exhibited by melanomas.     

Benign dermoscopic network patterns in dysplastic melanocytic nevi

Background/aims: Epiluminescence microscopy (ELM) is a non-invasive clinical technique, which by employing the optical phenomenon of oil immersion makes surface structures of the skin accessible for in vivo examination and provides additional criteria for the diagnosis of pigment skin lesions (PSLs). Many ELM criteria have been described. One of the most important ELM criteria is the pigment network (PN).
Objective: The aim of this study is to identify benign ELM (dermoscopic) network patterns of dysplastic melanocytic nevi (DMN).
Methods: This study included 907 dysplastic melanocytic nevi in 178 patients. Prior to biopsy, each lesion was photographed with oil immersion, and the images were viewed on a high-resolution compact slide projector. For each PSL, the ELM Network Features and ABCD-score were evaluated.
Results and discussion: The benign dermoscopic network features in DMN are the presents of a regular PN with delicate lines and margins, which predominantly thins out at the border of the lesion. For DMN, with these features, the mean ABCD score is smaller than ABCD-score for DMNs with irregular, prominent PN and network patches, ending abruptly at the periphery. In DMN with a network predominantly thinning out at the border of the lesion several uniform network patterns were found—diffuse network pattern, patchy network pattern, structureless center pattern, globular center pattern, and pigmented-blotch center pattern.
Conclusions: Benign features of pigment network are regularity, delicacy and thinning out at the border of the lesion. Benign dermoscopic network patterns are diffuse network pattern, patchy network pattern, structureless center pattern, globular center pattern, and pigmented-blotch center pattern. They can be found in DMN with a network predominantly thinning out at the border of the lesion.     

Colour analysis of skin lesion regions for melanoma discrimination in clinical images

Background: Skin lesion colour is an important feature for diagnosing malignant melanoma. Colour histogram analysis over a training set of images has been used to identify colours characteristic of melanoma, i.e., melanoma colours. A percent melanoma colour feature defined as the percentage of the lesion pixels that are melanoma colours has been used as a feature to discriminate melanomas from benign lesions.
Methods: In this research, the colour histogram analysis technique is extended to evaluate skin lesion discrimination based on colour feature calculations in different regions of the skin lesion. The colour features examined include percent melanoma colour and a novel colour clustering ratio. Experiments are performed using clinical images of 129 malignant melanomas and 129 benign lesions consisting of 40 seborrheic keratoses and 89 nevocellular nevi.
Results: Experimental results show improved discrimination capability for feature calculations focused in the lesion boundary region. Specifically, correct melanoma and benign recognition rates are observed as high as 89 and 83%, respectively, for the percent melanoma colour feature computed using only the outermost, uniformly distributed 10% of the lesion's area.
Conclusions: The experimental results show for the features investigated that the region closest to the skin lesion boundary contains the greatest colour discrimination information for lesion screening. Furthermore, the percent melanoma colour feature consistently outperformed the colour clustering ratio for the different skin lesion regions examined. The clinical application of this result is that clustered colours appear to be no more significant than colours of arbitrary distribution within a lesion.     

Automatic lesion boundary detection in dermoscopy images using gradient vector flow snakes

BACKGROUND: Malignant melanoma has a good prognosis if treated early. Dermoscopy images of pigmented lesions are most commonly taken at x 10 magnification under lighting at a low angle of incidence while the skin is immersed in oil under a glass plate. Accurate skin lesion segmentation from the background skin is important because some of the features anticipated to be used for diagnosis deal with shape of the lesion and others deal with the color of the lesion compared with the color of the surrounding skin. METHODS: In this research, gradient vector flow (GVF) snakes are investigated to find the border of skin lesions in dermoscopy images. An automatic initialization method is introduced to make the skin lesion border determination process fully automated. RESULTS: Skin lesion segmentation results are presented for 70 benign and 30 melanoma skin lesion images for the GVF-based method and a color histogram analysis technique. The average errors obtained by the GVF-based method are lower for both the benign and melanoma image sets than for the color histogram analysis technique based on comparison with manually segmented lesions determined by a dermatologist. CONCLUSIONS: The experimental results for the GVF-based method demonstrate promise as an automated technique for skin lesion segmentation in dermoscopy images.     

Current technologies for the in vivo diagnosis of cutaneous melanomas

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

Enhancement of lesion classification using divergence, curl and curvature of skin pattern

BACKGROUND/PURPOSE: The observation that skin pattern tends to be disrupted by malignant but not by benign skin lesions suggests that measurements of skin pattern disruption on simply captured white light optical clinical (WLC) skin images could be a useful contribution to a diagnostic feature set. Previous work which generated a flow field of skin pattern using a measurement of local line direction and variation determined by the minimum eigenvalue and its corresponding eigenvector of the local tensor matrix to measure skin pattern disruption was computationally low cost and encouraging. This paper explores the possibility of extracting new features from the first and second differentiations of this flow field to enhance classification performance. METHODS: The skin pattern was extracted from WLC skin images by high-pass filtering. The skin line direction was estimated using a local image gradient matrix to produce a flow field of skin pattern. Divergence, curl, mean and Gaussian curvatures of this flow field were computed from the first and second differentiations of this flow field. The difference of these measures combined with skin line direction across the lesion image boundary was used as a lesion classifier. RESULTS: A set of images of malignant melanoma and benign naevi were analysed as above and the scatter plot in a two-dimensional dominant feature space using principal component analysis showed excellent separation of benign and malignant lesions. A receiver operating characteristic plot enclosed an area of 0.96. CONCLUSIONS: The experimental results show that the divergence, curl, mean and Gaussian curvatures of the flow field can increase lesion classifier accuracy. Combined with skin line direction they are promising features for distinguishing malignant melanoma from benign lesions and the methods used are computationally efficient which is important if their use is to be considered in clinical practice.     

Obtaining malignant melanoma indicators through statistical analysis of 3D skin surface disruptions

Background/purpose: It has been observed that disruptions in skin patterns are larger for malignant melanoma (MM) than benign lesions. In order to extend the classification results achieved for 2D skin patterns, this work intends to investigate the feasibility of lesion classification using 3D skin surface texture, in the form of surface normals acquired from a previously built six-light photometric stereo device.
Material and methods: The proposed approach seeks to separate MM from benign lesions through analysis of the degree of surface disruptions in the tilt and slant direction of surface normals, so called skin tilt pattern and skin slant pattern. A 2D Gaussian function is used to simulate a normal region of skin for comparison with a lesion's observed tilt and slant patterns. The differences associated with the two patterns are estimated as the disruptions in the tilt and slant pattern respectively for lesion classification.
Results: Preliminary studies on 11 MMs and 28 benign lesions have given Receiver operating characteristic areas of 0.73 and 0.85 for tilt and slant pattern, respectively, which are better than 0.65 previously obtained for the skin line direction using the same samples.
Conclusions: This paper has demonstrated an important application of 3D skin texture for computer-assisted diagnosis of MM in vivo . By taking advantage of the extra dimensional information, preliminary studies suggest that some improvements over the existing 2D skin line pattern approach for the differentiation between MM and benign lesions.     

Colour histogram analysis for melanoma discrimination in clinical images

Background: Malignant melanoma, the most deadly form of skin cancer, has a good prognosis if treated in the curable early stages. Colour provides critical discriminating information for the diagnosis of malignant melanoma.
Methods: This research introduces a three-dimensional relative colour histogram analysis technique to identify colours characteristic of melanomas and then applies these 'melanoma colours' to differentiate benign skin lesions from melanomas. The relative colour of a skin lesion is determined based on subtracting a representative colour of the surrounding skin from each lesion pixel. A colour mapping for 'melanoma colours' is determined using a training set of images. A percent melanoma colour feature, defined as the percentage of the lesion pixels that are melanoma colours, is used for discriminating melanomas from benign lesions. The technique is evaluated using a clinical image data set of 129 malignant melanomas and 129 benign lesions consisting of 40 seborrheic keratoses and 89 nevocellular nevi.
Results: Using the percent melanoma colour feature for discrimination, experimental results yield correct melanoma and benign lesion discrimination rates of 84.3 and 83.0%, respectively.
Conclusions: The results presented in this work suggest that lesion colour in clinical images is strongly related to the presence of melanoma in that lesion. However, colour information should be combined with other information in order to further reduce the false negative and false positive rates.     

A basis function feature‐based approach for skin lesion discrimination in dermatology dermoscopy images

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

Pre-diagnostic digital imaging prediction model to discriminate between malignant melanoma and benign pigmented skin lesion

Background: Malignant cutaneous melanoma is the most deadly form of skin cancer with an increasing incidence over the past decades. The final diagnosis provided is typically based on a biopsy of the skin lesion under consideration. To assist the naked-eye examination and decision on whether or not a biopsy is necessary, digital image processing techniques provide promising results.
Hypothesis and aims: The hypothesis of this study was that a computer-aided assessment tool could assist the evaluation of a pigmented skin lesion. Hence, the overall aim was to discriminate between malignant and benign pigmented skin lesions using digital image processing.
Methods: Discriminating algorithms utilizing novel well-established morphological operations and methods were constructed. The algorithms were implemented utilizing graphical programming (LabVIEW Vision). Verification was performed with reference to an image database consisting of 97 pigmented skin lesion pictures of various resolutions and light distributions. The outcome of the algorithms was analysed statistically with MATLAB and a prediction model was constructed.
Results/Conclusion: The prediction model evaluates pigmented skin lesions with regards to the overall shape, border and colour distribution with a total of nine different discriminating parameters. The prediction model outputs an index score, and by using the optimal threshold value, a diagnostic accuracy of 77% in discriminating between malignant and benign skin lesions was obtained. This is an improvement compared with the naked-eye analysis performed by professionals, rendering the system a significant assistance in detecting malignant cutaneous melanoma.     

Detection of melanoma from dermoscopic images of naevi acquired under uncontrolled conditions

Background and objective: Several systems for the diagnosis of melanoma from images of naevi obtained under controlled conditions have demonstrated comparable efficiency with dermatologists. However, their robustness to analyze daily routine images was sometimes questionable. The purpose of this work is to investigate to what extent the automatic melanoma diagnosis may be achieved from the analysis of uncontrolled images of pigmented skin lesions.
Materials and methods: Images were acquired during regular practice by two dermatologists using Reflex^® 24 × 36 cameras combined with Heine Delta 10 dermascopes. The images were then digitalized using a scanner. In addition, five senior dermatologists were asked to give the diagnosis and therapeutic decision (exeresis) for 227 images of naevi, together with an opinion about the existence of malignancy-predictive features. Meanwhile, a learning by sample classifier for the diagnosis of melanoma was constructed, which combines image-processing with machine-learning techniques. After an automatic segmentation, geometric and colorimetric parameters were extracted from images and selected according to their efficiency in predicting malignancy features. A diagnosis was subsequently provided based on selected parameters. An extensive comparison of dermatologists' and computer results was subsequently performed.
Results and conclusion: The KL–PLS-based classifier shows comparable performances with respect to dermatologists (sensitivity: 95% and specificity: 60%). The algorithm provides an original insight into the clinical knowledge of pigmented skin lesions.     

A systematic heuristic approach for feature selection for melanoma discrimination using clinical images

BACKGROUND: Numerous features are derived from the asymmetry, border irregularity, color variegation, and diameter of the skin lesion in dermatology for diagnosing malignant melanoma. Feature selection for the development of automated skin lesion discrimination systems is an important consideration. METHODS: In this research, a systematic heuristic approach is investigated for feature selection and lesion classification. The approach integrates statistical-, correlation-, histogram-, and expert system-based components. Using statistical and correlation measures, interrelationships among features are determined. Expert system analysis is performed to identify redundant features. The feature selection process is applied to 19 shape and color features for a clinical image data set containing 355 malignant melanomas, 125 basal cell carcinomas, 177 dysplastic nevi, 199 nevocellular nevi, 139 seborrheic keratoses, and 45 vascular lesions. RESULTS: Experimental results show reduced lesion classification error rates based on condensing the shape and color feature set from 19 features to 13 features using the feature selection process. Specifically, average test lesion classification error rates for discriminating malignant melanoma from non-melanoma lesions were reduced from 26.6% for 19 features to 23.2% for 13 features over five randomly generated training and test sets. CONCLUSIONS: The experimental results show that the systematic heuristic approach for feature reduction can be successfully applied to achieve improved lesion discrimination. The feature reduction technique facilitates the elimination of redundant information that may inhibit lesion classification performance. The clinical application of this result is that automated skin lesion classification algorithm development can be fostered with systematic feature selection techniques.     

Modified ABC-point list of dermoscopy: A simplified and highly accurate dermoscopic algorithm for the diagnosis of cutaneous melanocytic lesions

BACKGROUND: The use of dermoscopy (epiluminescence microscopy, surface microscopy, dermatoscopy) improves clinical diagnostic sensitivity by 10% to 27%, particularly achieved by different algorithms or scores. OBJECTIVE: We sought to develop a simplified and highly accurate dermoscopic-point list for cutaneous melanocytic lesions. METHOD: We studied consecutive patients with suspicious melanocytic lesions, which were excised and histopathologically examined at our institution. On the basis of the ABCD rule of Stolz, Menzies score, and the modified ABCD rule of Kittler, a simplified ABC-point list was developed. Simple points were given for the following: asymmetry of outer shape (A) or differential structures inside the lesion in at least 1 axis ((A)); the abrupt cutoff of network at the border in at least one quarter of circumference (B); 3 or more colors (C); 3 or more differential structures (D); or noticed change (evolution) in the last 3 months (E). Using 20-fold magnification of computer dermoscopy, the sensitivity, specificity, and diagnostic accuracy were examined in 269 cutaneous melanocytic lesions. Of these, 84 (31.2%) were cutaneous melanomas. Also, the sensitivity, specificity, and diagnostic accuracy were investigated with a 7-point checklist and the 7 features for melanoma. RESULTS: With the ABC-point list for the diagnosis of cutaneous melanoma, sensitivity was 90.5%, specificity was 87%, and diagnostic accuracy was 88.1%, confirmed by cross-validation. The ABCD rule resulted in 90.5%, 72.4%, and 78.1%; Menzies score in 95.2%, 77.8%, and 83.3%; 7-point checklist in 90.5%, 87%, and 88.1%; and 7 features for melanoma in 94%, 74.6%, and 80.7%, respectively, CONCLUSIONS: The ABC-point list is simpler than the already established algorithms. Despite its simplicity, a high sensitivity, specificity, and diagnostic accuracy was achieved. This simplified approach in dermoscopic diagnostics may contribute to further spread and enable to learn and use this method more easily.     

Automatic differentiation of melanoma from melanocytic nevi with multispectral digital dermoscopy: a feasibility study

BACKGROUND: Differentiation of melanoma from melanocytic nevi is difficult even for skin cancer specialists. This motivates interest in computer-assisted analysis of lesion images. OBJECTIVE: Our purpose was to offer fully automatic differentiation of melanoma from dysplastic and other melanocytic nevi through multispectral digital dermoscopy. METHOD: At 4 clinical centers, images were taken of pigmented lesions suspected of being melanoma before biopsy. Ten gray-level (MelaFind) images of each lesion were acquired, each in a different portion of the visible and near-infrared spectrum. The images of 63 melanomas (33 invasive, 30 in situ) and 183 melanocytic nevi (of which 111 were dysplastic) were processed automatically through a computer expert system to separate melanomas from nevi. The expert system used either a linear or a nonlinear classifier. The "gold standard" for training and testing these classifiers was concordant diagnosis by two dermatopathologists. RESULTS: On resubstitution, 100% sensitivity was achieved at 85% specificity with a 13-parameter linear classifier and 100%/73% with a 12-parameter nonlinear classifier. Under leave-one-out cross-validation, the linear classifier gave 100%/84% (sensitivity/specificity), whereas the nonlinear classifier gave 95%/68%. Infrared image features were significant, as were features based on wavelet analysis. CONCLUSION: Automatic differentiation of invasive and in situ melanomas from melanocytic nevi is feasible, through multispectral digital dermoscopy.     

Can early malignant melanoma be differentiated from atypical melanocytic nevus by in vivo techniques?

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