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Proceedings Paper

Automatic nevi segmentation using adaptive mean shift filters and feature analysis
Author(s): Michael A. King; Tim K. Lee; M. Stella Atkins; David I. McLean
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Paper Abstract

A novel automatic method of segmenting nevi is explained and analyzed in this paper. The first step in nevi segmentation is to iteratively apply an adaptive mean shift filter to form clusters in the image and to remove noise. The goal of this step is to remove differences in skin intensity and hairs from the image, while still preserving the shape of nevi present on the skin. Each iteration of the mean shift filter changes pixel values to be a weighted average of pixels in its neighborhood. Some new extensions to the mean shift filter are proposed to allow for better segmentation of nevi from the skin. The kernel, that describes how the pixels in its neighborhood will be averaged, is adaptive; the shape of the kernel is a function of the local histogram. After initial clustering, a simple merging of clusters is done. Finally, clusters that are local minima are found and analyzed to determine which clusters are nevi. When this algorithm was compared to an assessment by an expert dermatologist, it showed a sensitivity rate and diagnostic accuracy of over 95% on the test set, for nevi larger than 1.5mm.

Paper Details

Date Published: 12 May 2004
PDF: 8 pages
Proc. SPIE 5370, Medical Imaging 2004: Image Processing, (12 May 2004); doi: 10.1117/12.532549
Show Author Affiliations
Michael A. King, Simon Fraser Univ. (Canada)
Tim K. Lee, Simon Fraser Univ. (Canada)
BC Cancer Agency (Canada)
M. Stella Atkins, Simon Fraser Univ. (Canada)
David I. McLean, Vancouver Hospital and Health Sciences Ctr., Univ. of British Columbia (Canada)


Published in SPIE Proceedings Vol. 5370:
Medical Imaging 2004: Image Processing
J. Michael Fitzpatrick; Milan Sonka, Editor(s)

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