Kernelized Fuzzy c-means Method in Fast Segmentation of Demyelination Plaques in Multiple Sclerosis

Author

Kawa, J. ; Pietka, E.

Author_Institution

Silesian Univ. of Technol., Gliwice

fYear

2007

fDate

22-26 Aug. 2007

Firstpage

5616

Lastpage

5619

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.

Keywords

biological tissues; biomedical MRI; brain; diseases; fuzzy set theory; image classification; image segmentation; medical image processing; neurophysiology; pattern clustering; statistical analysis; Gaussian kernel space clusters; brain tissue classification; fast demyelination plaques segmentation; fluid light attenuation inversion recovery MRI; fuzzy clustering; fuzzy data processing; kernelized fuzzy c-means method; magnetic resonance images; multiple sclerosis; Clustering algorithms; Data processing; Fuzzy sets; Image segmentation; Kernel; Lesions; Magnetic resonance imaging; Multiple sclerosis; Partitioning algorithms; Prototypes; Algorithms; Expert Systems; Fuzzy Logic; Humans; Image Enhancement; Image Interpretation, Computer-Assisted; Magnetic Resonance Imaging; Multiple Sclerosis; Nerve Fibers, Myelinated; Pattern Recognition, Automated; Plaque, Amyloid; Reproducibility of Results; Sensitivity and Specificity;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE

Conference_Location

Lyon

ISSN

1557-170X

Print_ISBN

978-1-4244-0787-3

Type

conf

DOI

10.1109/IEMBS.2007.4353620

Filename

4353620