A Collaborative System for Pigmented Skin Lesions Malignancy Tracking

This paper presents a new collaborative computer-aided diagnosis system for skin lesions malignancy tracking composed of two modules for lesion description and decision. The decision module incorporates classification and content-based image retrieval schemes (CBIR). The final decision of lesion malignancy will be obtained by merging the two decisions while using the Dempster-Shafer theory in order to improve the accuracy of the final produced decisions. Indeed, after the preprocessing of the studied image and the extraction of the skin lesions by the segmentation process, the lesion description stage defines a set of descriptive features reflecting the clinical signs of the considered lesions malignancy. In fact, 21 features representing shape and radiometric properties are calculated. The quality of these features is evaluated by applying principal components analysis (PCA) and ROC assessment criteria. The results show that the feature set can be reduced to dimension 16. Then, the proposed system estimates the preliminary lesions class with the classification scheme, while using a perceptron neural network technique preceded by a training step. Moreover, given a database of skin lesions, CBIR of the images belonging to this database which gather with the studied image and whose lesions malignancy states are known, permits to have another preliminary idea on the type of the eventual skin lesion. Finally, the results of classification and retrieval schemes are combined while using the Dempster-Shafer theory. This consists to consider the results produced separately by each technique as being dubious sources of information on the lesions malignity with an aim of combining their respective opinions. The proposed architecture allows the production of a viable cost-effective set of opinions on skin lesions malignancy. Besides, since the decision can never be perfect, the CBIR subsystem displays also visually similar images with known pathologies, to provide an intuitive aid to the dermatologist to improve the diagnosis accuracy.