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Kernelized fuzzy c-means method in fast segmentation of demyelination plaques in multiple sclerosis

  • IEEE

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

21 Citations (Scopus)

Abstract

Fuzzy c-means method (FCM) is a popular tool for a fuzzy data processing. In the current study, a FCMbased method of fuzzy clustering in a kernel space has been implemented. First, a "kernel trick" is applied to the fuzzy cmeans algorithm. Then, the new method is employed for a fast automated segmentation of demyelination plaques in Multiple Sclerosis (MS). The clusters in a Gaussian kernel space are analysed in the histogram context and used during the initial classification of the brain tissue. Received classification masks are then used to detect the region of interest, eliminate false positives and label MS lesions.

Original languageEnglish
Title of host publication29th Annual International Conference of IEEE-EMBS, Engineering in Medicine and Biology Society, EMBC'07
Pages5616-5619
Number of pages4
DOIs
Publication statusPublished - 2007
Event29th Annual International Conference of IEEE-EMBS, Engineering in Medicine and Biology Society, EMBC'07 - Lyon, France
Duration: 23 Aug 2007 → 26 Aug 2007

Publication series

NameAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
ISSN (Print)0589-1019

Conference

Conference29th Annual International Conference of IEEE-EMBS, Engineering in Medicine and Biology Society, EMBC'07
Country/TerritoryFrance
CityLyon
Period23/08/07 → 26/08/07

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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