Intelligent segmentation and classification of pigmented skin lesions in dermatological images

Authors:
Ilias Maglogiannis;Elias Zafiropoulos;Christos Kyranoudis
Affiliations:
Department of Information and Communication Systems Engineering, University of the Aegean, Karlovasi, Samos, Greece;Department of Information and Communication Systems Engineering, University of the Aegean, Karlovasi, Samos, Greece;School of Chemical Engineering, National Technical University of Athens, Athens, Greece
Venue:
SETN'06 Proceedings of the 4th Helenic conference on Advances in Artificial Intelligence
Year:
2006

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Abstract

During the last years, computer vision-based diagnostic systems have been used in several hospitals and dermatology clinics, aiming mostly at the early detection of malignant melanoma tumor, which is among the most frequent types of skin cancer, versus other types of non-malignant cutaneous diseases. In this paper we discuss intelligent techniques for the segmentation and classification of pigmented skin lesions in such dermatological images. A local thresholding algorithm is proposed for skin lesion separation and border, texture and color based features, are then extracted from the digital images. Extracted features are used to construct a classification module based on Support Vector Machines (SVM) for the recognition of malignant melanoma versus dysplastic nevus.