Classification of Color Images of Dermatological Ulcers

We present color image processing methods for the analysis of images of dermatological lesions. The focus of this study is on the application of feature extraction and selection methods for 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 into the classes mentioned previously. Indexing of the images was performed based on statistical texture features derived from cooccurrence matrices of the red, green, and blue (RGB), hue, saturation, and intensity (HSI), L*a*b*, and L*u*v* color components. Feature selection methods were applied using the Wrapper algorithm with different classifiers. The performance of classification was measured in terms of the percentage of correctly classified images and the area under the receiver operating characteristic curve, with values of up to 73.8% and 0.82, respectively.