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.     

Using 3D differential forms to characterize a pigmented lesion in vivo

Background/purpose: After the formulation of ABCD rules, many new feature extraction methods are emerging to describe the asymmetry, border irregularity, color variation and diameter of malignant melanoma. In this paper, a new research direction orthogonal to ABCD rules that characterizes 3D local disruption of skin surfaces to realize automatic recognition of melanoma is described.
Methods: This paper examines 3D differential forms of skin surfaces to characterize the local geometrical properties of melanoma. Firstly, 3D data of skin surfaces are obtained using a photometric stereo device. Then differential forms of lesion surfaces are determined to describe the geometrical texture patterns involved. Using only these geometrical features, a simple least-squared error-based linear classifier can be constructed to realize the classification of malignant melanomas and benign lesions.
Results: As with the 3D data of 35 melanoma and 66 benign lesion samples collected from local pigmented lesion clinics, the optimal sensitivity and specificity of the constructed linear classifier are 71.4% and 86.4%, respectively. The total area enclosed by the corresponding receiver operating characteristics curve is 0.823.
Conclusion: This study indicates that differential forms obtained from 3D data are very promising in characterizing melanoma. Combining these features with other skin features such as border irregularity and color variation might further improve the accuracy and reliability of the automatic diagnosis of melanoma.     

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.     

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.     

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.     

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.     

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.     

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.     

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.     

Simulation and analysis of optical skin lesion images

BACKGROUND/PURPOSE: In order to properly analyse the effectiveness of methods for optically differentiating malignant from benign skin lesions, it is necessary to have a set of images for which the ground truth is known. However, aspects of the ground truth of clinical images such as true lesion boundary position are unknown or not known precisely. Therefore, a skin/lesion image simulation with known features including boundary location, skin pattern and lesion colour is needed to enable accurate assessment of feature estimation algorithms for lesion classification. METHODS: In this paper, monochrome and colour skin/lesion images are synthesised with known characteristics including boundary, colour and skin pattern. Skin pattern is simulated with segmented lines with variations in length, orientation and intensity. Skin and lesion textures are modelled by an auto-regressive (AR) process. Monochrome skin lesion images are obtained by combining monochrome skin and lesion textures under the control of a known lesion shape with the addition of skin pattern. Colour skin lesion images are generated by mixing coloured skin and lesion textures. Finally, an inflammation area and image artefacts such as hair and specular reflection are added. RESULTS: The synthesised images provide the image set for evaluating image pre-processing, segmentation and skin pattern analysis. The pre-processing includes hair removal and specular reflection reduction. An AR model interpolation is suggested for hair removal, and multiple illumination processing is developed to decrease specular reflection. A fast snake algorithm is extended to detect the boundaries of skin lesion and inflammation areas. Skin line direction is detected as a feature to measure the disruption of skin pattern caused by lesion. CONCLUSIONS: Simulation of monochrome and colour skin/lesion image has been investigated, which is an alternative way to provide image set with known characteristics to validate image processing algorithms for image pre-processing, lesion/inflammation boundary detection and skin pattern analysis.     

The clinical diagnosis of early malignant melanoma: expansion of the ABCD criteria to improve diagnostic sensitivity

With the steady increase in incidence of malignant melanomas (MM) in the United States, early diagnosis and complete removal are critical for the containment of the malignancy. [1] The "ABCD" method of identification, originally described by Friedman et al., has been a useful tool in facilitating the diagnosis of MM. [2,3,4] This method analyzes four clinical characteristics to identify a malignant melanoma: Asymmetry, Border irregularity, Color variegation, and a Diameter of 6 mm or more.[4] Clinicians recognize that some melanomas lack all or most of the features defined in the "ABCD" rules. [5] This may be especially true of some early invasive and in situ melanomas. [6,7] In these instances, clinical history documenting morphologic change over time can be an important additional consideration. The following case reports underscore the need to expand the ABCD mnemonic to include an "E" for "Evolutionary change." An additional modification is also needed to emphasize the need for a low threshold for biopsy of unusual lesions which do not show typical benign features, even if they do not meet the ABCDE criteria. To this end we propose an "F" for "Funny looking lesions".     

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.     

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.     

Digital computer analysis of dermatoscopical images of 260 melanocytic skin lesions; perimeter/area ratio for the differentiation between malignant melanomas and melanocytic nevi

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

Morphologic changes of pigmented skin lesions: a useful extension of the ABCD rule for dermatoscopy

BACKGROUND: Epiluminescence microscopy (ELM) significantly increases the early diagnosis of pigmented skin lesions (PSL) using established criteria and pattern analysis. The ABCD rule for dermatoscopy (ie, ELM) provides a simplified approach to the interpretation of ELM images on the basis of asymmetry (A), border (B), color (C), and dermatoscopic structure (D). OBJECTIVE: We set out to determine whether the diagnostic accuracy of the ABCD scoring algorithm can be significantly improved by incorporating information about morphologic changes of the lesion observed and provided by the patient. METHODS: We prospectively collected 356 small pigmented skin lesions (< 1 cm) including 73 (20.5%) melanomas. Before excision all patients were asked whether the lesion had changed in size, color, or shape within the last year or whether they experienced any sign of ulceration or spontaneous bleeding. ELM images of the lesions were evaluated according to the ABCD rule for dermatoscopy to yield a semiquantitative score. Accuracy of diagnosis was evaluated in terms of sensitivity, specificity, and area under receiver operating characteristic curves (AUC). RESULTS: The frequency of reported changes was significantly higher for melanomas than benign PSL (65.8% vs 29.7%, P < .001). In a multivariate model morphologic change was a significant independent predictor of malignancy (odds ratio = 3.17, 95% confidence interval [CI]: 1.96 to 5.14, P < .001). The mean final score achieved when using the enhanced ABCD-E criteria including morphologic change (E) was significantly higher for melanomas (5.7, 95% CI: 5.3 to 6.0) than benign PSL (2.9, 95% CI: 2.8 to 3.1, P < .001). Diagnostic accuracy was significantly higher when the lesions were evaluated by the enhanced ABCD-E criteria as compared with the standard ABCD score (AUC(ABCD) = 0.87 vs AUC(ABCD-E) = 0.90; P = .006). CONCLUSION: Information about morphologic changes of PSL as reported by the patient is a useful extension of the ABCD rule for dermatoscopy.     

Digital image analysis for diagnosis of cutaneous melanoma. Development of a highly effective computer algorithm based on analysis of 837 melanocytic lesions

BACKGROUND: Digital image analysis has been introduced into the diagnosis of skin lesions based on dermoscopic pictures. OBJECTIVES: To develop a computer algorithm for the diagnosis of melanocytic lesions and to compare its diagnostic accuracy with the results of established dermoscopic classification rules. METHODS: In the Department of Dermatology, University of Tuebingen, Germany, 837 melanocytic skin lesions were prospectively imaged by a dermoscopy video system in consecutive patients. Of these lesions, 269 were excised and examined by histopathology: 84 were classified as cutaneous melanomas and 185 as benign melanocytic naevi. The remaining 568 lesions were diagnosed by dermoscopy as benign. Digital image analysis was performed in all 837 benign and malignant melanocytic lesions using 64 different analytical parameters. RESULTS: For lesions imaged completely (diameter < or = 12 mm), three analytical parameters were found to distinguish clearly between benign and malignant lesions, while in incompletely imaged lesions six parameters enabled differentiation. Based on the respective parameters and logistic regression analysis, a diagnostic computer algorithm for melanocytic lesions was developed. Its diagnostic accuracy was 82% for completely imaged and 84% for partially imaged lesions. All 837 melanocytic lesions were classified by established dermoscopic algorithms and the diagnostic accuracy was found to be in the same range (ABCD rule 78%, Menzies' score 83%, seven-point checklist 88%, and seven features for melanoma 81%). CONCLUSIONS: A diagnostic algorithm for digital image analysis of melanocytic lesions can achieve the same range of diagnostic accuracy as the application of dermoscopic classification rules by experts. The present diagnostic algorithm, however, still requires a medical expert who is qualified to recognize cutaneous lesions as being of melanocytic origin.     

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.     

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.     

Computer description of colours in dermoscopic melanocytic lesion images reproducing clinical assessment

BACKGROUND: The assessment of colours is essential for the diagnosis of malignant melanoma (MM), both for pattern analysis on dermoscopic images, and when employing semiquantitative methods. OBJECTIVES: To develop a computer program for colour assessment in MM images mimicking the human perception of lesion colours, and to compare the automatic colour evaluation with one performed by human observers. METHODS: A colour palette comprising six colour groups (black, dark brown, light brown, blue-grey, red and white) was created by selecting single colour components inside melanocytic lesion images acquired by means of a digital videomicroscope, and was implemented in the image analysis program. Subsequently, colours were assessed by the computer program on 331 melanocytic lesion images composing our image database, and the results were compared with the evaluation of lesion colours performed by the clinician. RESULTS: The black, white and blue-grey colours were more frequently found in MMs than in naevi, both by the clinicians and by the computer. In MM images we observed 4.27 +/- 1.14 colours (mean + or - SD) per lesion, as opposed to 3.22 +/- 0.68 in naevi. The correlation between clinical and computer evaluation of the colours was very good, with a value of 0.781 for overall assessment. CONCLUSIONS: This innovative method for automatic colour evaluation, reproducing clinical assessment of melanocytic lesion colours, may provide numerical parameters to be employed for computer-aided diagnosis of MM.