Kernelized fuzzy c-means method in fast segmentation of demyelination plaques in multiple sclerosis.
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.
Kawa
Jacek
J
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
Pietka
Ewa
E
D000465:Algorithms; D005103:Expert Systems; D017143:Fuzzy Logic; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009103:Multiple Sclerosis / Q000175:diagnosis*; D009413:Nerve Fibers, Myelinated / Q000473:pathology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D058225:Plaque, Amyloid / Q000473:pathology*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity