title: Automatic learning of spatial patterns for diagnosis of skin lesions.
abstract: We present a technique for automatic diagnosis of malignant melanoma based exclusively on local pattern analysis. The technique relies on local binary patterns in small sections in the image, and automatically selects the relevant texture features from those that discriminate best between benign and malignant skin lesions. The classification is performed using support vector machines, and the feature vectors are clustered using K-means clustering. The effects of K and window size are investigated. Reported average specificity and sensitivity are 73% for optimal parameter choice, indicating that the procedure is a useful part of a diagnostic system.
authors.affiliation: Department of Mathematics and Statistics, Faculty of Science and Technology, University of Tromsø, NO-9037, Norway.
authors.forename: Maciel
authors.initials: M
authors.lastname: Zortea
authors.forename: Stein Olav
authors.initials: SO
authors.lastname: Skrovseth
authors.forename: Fred
authors.initials: F
authors.lastname: Godtliebsen
mesh_terms: D000465:Algorithms; D006801:Humans; D007858:Learning; D008545:Melanoma / Q000175:diagnosis; D010363:Pattern Recognition, Automated / Q000379:methods*; D012680:Sensitivity and Specificity; D012878:Skin Neoplasms / Q000175:diagnosis*