title: Classification of color images of dermatological ulcers.
abstract: We present color image processing methods for the analysis of images of dermatological lesions. The focus of the present work 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 above. Indexing of the images was performed based on statistical texture features derived from cooccurrence matrices of the RGB (Red, Green, and Blue), HSI (Hue, Saturation, and Intensity), 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.
authors.forename: S M
authors.initials: SM
authors.lastname: Pereira
authors.forename: M A C
authors.initials: MA
authors.lastname: Frade
authors.forename: R M
authors.initials: RM
authors.lastname: Rangayyan
authors.forename: P M
authors.initials: PM
authors.lastname: Azevedo Marques
mesh_terms: D000465:Algorithms; D019540:Area Under Curve; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D012372:ROC Curve; D012883:Skin Ulcer / Q000145:classification* / Q000175:diagnosis / Q000473:pathology*