We present color image processing methods for the characterization of images of dermatological lesions for the purpose of content-based image retrieval (CBIR) and computer-aided diagnosis. The intended application is to segment the images and per form classification and analysis of the tissue composition of skin lesions or ulcers, in terms of granulation (red), fibrin (yellow), necrotic (black), callous (white), and mixed tissue composition. The images were analyzed and classified by an expert dermatologist following the red-yellow-black-white model. Automatic segmentation was performed by means of clustering using Gaussian mixture modeling, and its performance was evaluated by deriving the Jaccard coeficient between the automatically and manually segmented images. Statistical texture features were derived from cooccurrence matrices of RGB, HSI, L¤a¤b¤,, and L¤u¤v¤ color components. A retrieval engine was implemented using the knearest-neighbor classifier and the Euclidean, Manhattan, and Chebyshev distance metrics. Classification was performed by means of a metaclassifier using logistic regression. The average Jaccard coeficient after the segmentation step between the automatically and manually segmented images was 0.560, with a standard deviation of 0.220. The performance in CBIR was measured in terms of precision of retrieval, with average values of up to 0.617 obtained with the Chebyshev distance. The metaclassifier yielded an average area under the receiver operating characteristic curve of 0.772.