Automated detection of white matter changes in elderly people using fuzzy, geostatistical, and information combining models.
Detection of white matter changes of the brain using magnetic resonance imaging (MRI) has increasingly been an active and challenging research area in computational neuroscience. There have rarely been any single image analysis methods that can effectively address the issue of automated quantification of neuroimages, which are subject to different interests of various medical hypotheses. This paper presents new image segmentation models for automated detection of white matter changes of the brain in an elderly population. The methods are based on the computational models of fuzzy clustering, possibilistic clustering, geostatistics, and knowledge combination. Experimental results on MRI data have shown that the proposed image analysis methodology can be applied as a very useful computerized tool for the validation of our particular medical question, where white matter changes of the brain are thought to be the most important social medical evidence.
Pham
Tuan D
TD
School of Engineering and Information Technology, University of New South Wales, Canberra, ACT 2600, Australia. t.pham@adfa.edu.au
Berger
Klaus
K
D000368:Aged; D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology*; D016000:Cluster Analysis; D017143:Fuzzy Logic; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*