Pattern classification of dermoscopy images: A perceptually uniform model

Authors:
Qaisar Abbas;M. E. Celebi;Carmen Serrano;Irene FondóN GarcíA;Guangzhi Ma
Affiliations:
Department of Computer Science and Technology, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan 430074, China and Center for Biomedical Imaging and Bioinformatics, Key Laborat ...;Department of Computer Science, Louisiana State University, Shreveport, Louisiana, LA, USA;Escuela Superior de Ingenieros, Universidad de Sevilla, Camino de los Descubrimientos, s/n, 41092 Sevilla, Spain;Escuela Superior de Ingenieros, Universidad de Sevilla, Camino de los Descubrimientos, s/n, 41092 Sevilla, Spain;Department of Computer Science and Technology, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan 430074, China and Center for Biomedical Imaging and Bioinformatics, Key Laborat ...
Venue:
Pattern Recognition
Year:
2013

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Abstract

Pattern classification of dermoscopy images is a challenging task of differentiating between benign melanocytic lesions and melanomas. In this paper, a novel pattern classification method based on color symmetry and multiscale texture analysis is developed to assist dermatologists' diagnosis. Our method aims to classify various tumor patterns using color-texture properties extracted in a perceptually uniform color space. In order to design an optimal classifier and to address the problem of multicomponent patterns, an adaptive boosting multi-label learning algorithm (AdaBoost.MC) is developed. Finally, the class label set of the test pattern is determined by fusing the results produced by boosting based on the maximum a posteriori (MAP) and robust ranking principles. The proposed discrimination model for multi-label learning algorithm is fully automatic and obtains higher accuracy compared to existing multi-label classification methods. Our classification model obtains a sensitivity (SE) of 89.28%, specificity (SP) of 93.75% and an area under the curve (AUC) of 0.986. The results demonstrate that our pattern classifier based on color-texture features agrees with dermatologists' perception.