Extraction of White Matter

observe details and to match morphological changes in the physical structure of the brain to changes in neurological and neuropsychiatric function such as cognitive performance over time. Following a vascular model, long-term changes in the vascular structure of the brain may appear as white matter lesions (WMLs) in cortical and sub cortical regions, which may directly or indirectly impact on brain functionality. White matter changes (lesions) are often seen in elderly people. 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. 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 takes place in the people. This paper presents new clustering methods to separate the white matter from the brain image by using clustering techniques. First the MRI brain image is segmented, and the computational models of fuzzy c-means clustering, the effect geostatistics and the combined models of both the clustering techniques are obtained by fusion. There by, increasing the accuracy and time processing is decreased. Keywords—Fuzzy clustering, geostatistics, image egmentation, information combination, magnetic resonance imaging (MRI), white matter changes. I