Automatic segmentation of dermoscopic images by iterative classification

Authors:
Maciel Zortea;Stein Olav Skrøvseth;Thomas R. Schopf;Herbert M. Kirchesch;Fred Godtliebsen
Affiliations:
Department of Mathematics and Statistics, University of Tromsø, Tromsø, Norway;Norwegian Centre for Integrated Care and Telemedicine, University Hospital of North Norway, Tromsø, Norway;Norwegian Centre for Integrated Care and Telemedicine, University Hospital of North Norway, Tromsø, Norway;-;Department of Mathematics and Statistics, University of Tromsø, Tromsø, Norway
Venue:
Journal of Biomedical Imaging - Special issue on Machine Learning in Medical Imaging
Year:
2011

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Abstract

Accurate detection of the borders of skin lesions is a vital first step for computer aided diagnostic systems. This paper presents a novel automatic approach to segmentation of skin lesions that is particularly suitable for analysis of dermoscopic images. Assumptions about the image acquisition, in particular, the approximate location and color, are used to derive an automatic rule to select small seed regions, likely to correspond to samples of skin and the lesion of interest. The seed regions are used as initial training samples, and the lesion segmentation problem is treated as binary classification problem. An iterative hybrid classification strategy, based on a weighted combination of estimated posteriors of a linear and quadratic classifier, is used to update both the automatically selected training samples and the segmentation, increasing reliability and final accuracy, especially for those challenging images, where the contrast between the background skin and lesion is low.