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Anisotropic mean shift based fuzzy c-means segmentation of deroscopy images
journal contributionposted on 2014-06-04, 09:17 authored by Huiyu Zhou, Gerald SchaeferGerald Schaefer, A.H. Sadka, M.E. Celebi
Image segmentation is an important task in analysing dermoscopy images as the extraction of the borders of skin lesions provides important cues for accurate diagnosis. One family of segmentation algorithms is based on the idea of clustering pixels with similar characteristics. Fuzzy c-means has been shown to work well for clustering based segmentation, however due to its iterative nature this approach has excessive computational requirements. In this paper, we introduce a new mean shift based fuzzy c-means algorithm that requires less computational time than previous techniques while providing good segmentation results. The proposed segmentation method incorporates a mean field term within the standard fuzzy c-means objective function. Since mean shift can quickly and reliably find cluster centers, the entire strategy is capable of effectively detecting regions within an image. Experimental results on a large dataset of diverse dermoscopy images demonstrate that the presented method accurately and efficiently detects the borders of skin lesions.
- Computer Science
CitationZHOU, H. ... et al., 2009. Anisotropic mean shift based fuzzy c-means segmentation of deroscopy images. IEEE Journal of Selected Topics in Signal Processing, 3 (1), pp. 26 - 34.
- AM (Accepted Manuscript)
NotesThis article was published in the IEEE Journal of Selected Topics in Signal Processing [© IEEE] and the definitive version is available at: http://dx.doi.org/10.1109/JSTSP.2008.2010631 Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.