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During the last years, computer vision-based diagnosis systems have beenwidely used in several hospitals and dermatology clinics, aiming at the earlydetection of malignant melanoma tumor, which is among the most frequent typesof skin cancer. In this work, we present an automated diagnosis system based onthe ABCD rule used in clinical diagnosis in order to discriminate benign frommalignant skin lesions. First, to reduce the influence of small structures, apreprocessing step based on morphological and fast marching schemes is used. Inthe second step, an unsupervised approach for lesion segmentation is proposed.Iterative thresholding is applied to initialize level set automatically. As thedetection of an automated border is an important step for the correctness ofsubsequent phases in the computerized melanoma recognition systems, we compareits accuracy with growcut and mean shift algorithms, and discuss how theseresults may influence in the following steps: the feature extraction and thefinal lesion classification. Relying on visual diagnosis four features:Asymmetry (A), Border (B), Color (C) and Diversity (D) are computed and used toconstruct a classification module based on artificial neural network for therecognition of malignant melanoma. This framework has been tested on adermoscopic database [16] of 320 images. The classification results show anincreasing true detection rate and a decreasing false positive rate.

Segmentation and ABCD rule extraction for skin tumors classification