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Feature selection is an important pre-processing technique in the pattern recognition domain. This article proposes a hybridization between Genetic Algorithm (GA) and the Linear Discriminant Analysis (LDA) for solving the feature selection problem in Content-Based Image Retrieval (CBIR) applied to dermatological images. In the first step, we preprocess and segment the input image, then we derive color and texture features characterizing healthy skin and the segmented skin lesion. At this stage, a binary GA is used to evolve chromosome subsets whose fitness is evaluated by a Logistic Regression classifier. The optimal identified features are then used to feed LDA for a CBIR system, based on a K-Nearest Neighbor classification. To assess the proposed approach, the authors have opted for a K-fold cross validation method on a database of 1097 images of melanomas and other skin lesions. As a result, the authors obtained a reduced number of features and an improved CBDIR system compared to PCA, LDA and ICA methods.

The use of Computer-Aided Diagnosis in dermatology raises the necessity of integrating Content-Based Image Retrieval (CBIR) technologies. The latter could be helpful to untrained users as a decision support system for skin lesion diagnosis. However, classical CBIR systems perform poorly due to semantic gap. To alleviate this problem, we propose in this paper an intelligent Content-Based Dermoscopic Image Retrieval (CBDIR) system with Relevance Feedback (RF) for melanoma diagnosis that exhibits: efficient and accurate image retrieval as well as visual features extraction that is independent of any specific diagnostic method. After submitting a query image, the proposed system uses linear kernel-based active SVM, combined with histogram intersection-based similarity measure to retrieve the K most similar skin lesion images. The dominant (melanoma, benign) class in this set will be identified as the image query diagnosis. Extensive experiments conducted on our system using a 1097 image database show that the proposed scheme is more effective than CBDIR without the assistance of RF.