A fuzzy membership based comparison of the grey matter (GM) in cognitively normal (CN), mild cognitive impairment (MCI), and Alzheimer’s disease (AD) using brain images

Abstract

Grey matter (GM) in human brain contains most of the important cells covering the regions involved in neurophysiological operations such as memory, emotions, decision making, etc. Alzheimer’s disease (AD) is a neurological disease that kills the brain cells in regions which are mostly involved in the neurophysiological operations. Mild Cognitive Impairment (MCI) is a stage between Cognitively Normal (CN) and AD, where a significant cognitive declination can be observed. The destruction of brain cells causes a reduction in the size of GM. Evaluation of changes in GM, may help in studying the overall brain transformations and accurate classification of different stages of AD. In this work, firstly skull of brain images is stripped for 5 different slices, then segmentation of GM is performed. Finally, the average number of pixels in grey region and the average atrophy in grey pixels per year is calculated and compared amongst CN, MCI, and AD patients of various ages and genders. It is observed that, for some subjects (in some particular ages) from different dementia stages, pattern of GM changes is almost identical. To solve this issue, we have used the concept of fuzzy membership functions to classify the dementia stages more accurately. It is observed from the comparison that average difference in the number of pixels between CN and MCI= 10.01%, CN and AD= 19.63%, MCI and AD= 10.72%. It can be also observed from the comparison that, the average atrophy in grey matter per year in CN= 1.92%, MCI= 3.13%, and AD= 4.33%.