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Early melanoma is often difficult to differentiate from benign pigmented skin lesions (PSLs). Digital epiluminescence microscopy (DELM) and automated image analysis could represent possible aids for inexperienced clinicians. We designed an automated computerized image analysis system that has the potential for use as an additional tool for the differentiation of melanoma from dysplastic naevi and common naevi. The PC-based pilot system was attached to a common DELM system as the image source. Digital images of PSLs were automatically segmented and a panel of 107 morphological parameters were measured. Additionally, seven clinical parameters were evaluated and used as an additional source of information. Neural networks were then trained to distinguish melanoma from benign PSLs. One class of networks was trained solely based on the morphometric features, whereas the second class of networks was trained on the combination of morphometric and clinical features. The automatic segmentation algorithm was correct in 96% of cases. Using three-way receiver operating characteristic (ROC) analysis, for networks trained solely on morphometric features the volume under surface (VUS) was 0.617 (SD 0.036). The performance was significantly better for networks trained on the combination of both morphometric and clinical features (VUS = 0.682, SD 0.035). In a dichotomous model, distinguishing benign lesion (common naevi + dysplastic naevi) from melanoma, the area under the curve (AUC) from two-way ROC analysis was 0.942 (SD 0.018) for networks trained solely on morphometric features and 0.968 (SD 0.012) for those trained on the combination of clinical and morphometric data (P= NS). Automated feature extraction from PSLs and the training of neural networks as classifiers has thus shown satisfactory performance in a large scale experiment. The addition of clinical data significantly increases the diagnostic performance for distinguishing three classes of lesions (i.e. common naevi, dysplastic naevi and melanoma). Such integrated systems hold promise as a decision aid for the diagnosis of PSLs.

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