Abstract
The effect of partial volume related to anatomical MRI and functional images limit the diagnostic potential of brain imaging. To remedy for this problem, we propose a fuzzy-genetic brain segmentation scheme for the assessment of white matter, gray matter and cerebrospinal fluid volumes, from brain images of Alzheimer patients from a real database. This clustering process based on Possibilistic C-Means (PCM) algorithm, which allows modeling the degree of relationship between each voxels and a given tissue; and based on fuzzy genetic initialization for the centers of clusters by a Fuzzy C-Means (FCM) algorithm, and for which the result is optimized by genetic process. The visual results show a concordance between the ground truth segmentation and the hybrid algorithm results, which allows efficient tissue classification. The superiority was also proved with the quantitative results of the proposed method in comparison with the both conventional FCM and PCM algorithms.
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