Fast density-based lesion detection in dermoscopy images

Abstract

Dermoscopy is one of the major imaging modalities used in the diagnosis of melanoma and other pigmented skin lesions. Automated assessment tools for dermoscopy images have become an important research field mainly because of inter- and intra-observer variations in human interpretation. One of the most important steps in dermoscopy image analysis is automated detection of lesion borders. In this study, we introduce a border-driven density-based framework to identify skin lesion(s) in dermoscopy images. Unlike the conventional density-based clustering algorithms, proposed algorithm expands regions only at borders of a cluster that in turn speeds up the process without losing precision or recall. In our method, border regions are represented with one or more simple polygons at any time. We tested our algorithm on a dataset of 100 dermoscopy cases with multiple physicians' drawn ground truth borders. The results show that border error and f-measure of assessment averages out at 6.9% and 0.86 respectively.