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

Jacek Kawa; Ewa Pietka

doi:10.1109/IEMBS.2007.4353620

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

Annu Int Conf IEEE Eng Med Biol Soc. 2007:2007:5616-9. doi: 10.1109/IEMBS.2007.4353620.

Authors

Jacek Kawa¹, Ewa Pietka

Affiliation

¹ Faculty of Automatic Control, Electronics and Computer Science (Department of Biomedical Engineering, Gliwice), Silesian University of Technology, ul. Akademicka 16, Gliwice, Poland. jkawa@polsl.pl

PMID: 18003286
DOI: 10.1109/IEMBS.2007.4353620

Abstract

Fuzzy c-means method (FCM) is a popular tool for a fuzzy data processing. In the current study, a FCM-based method of fuzzy clustering in a kernel space has been implemented. First, a "kernel trick" is applied to the fuzzy c-means 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.

Publication types

Evaluation Study

MeSH terms

Algorithms
Expert Systems
Fuzzy Logic*
Humans
Image Enhancement / methods
Image Interpretation, Computer-Assisted / methods*
Magnetic Resonance Imaging / methods*
Multiple Sclerosis / diagnosis*
Nerve Fibers, Myelinated / pathology*
Pattern Recognition, Automated / methods*
Plaque, Amyloid / pathology*
Reproducibility of Results
Sensitivity and Specificity