Brain Tissues Volumes Assessment by Fuzzy Genetic Optimization Based Possibilistic Clustering: Application to Alzheimer Patients Images

Cerebral images include several artifacts, such as partial volume effect which limit the diagnostic potential of brain imaging. So, the main objective of this paper is to reduce the effect of partial volume averaging on the boundaries of the ventricles. We thus proposed 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 and from Alzheimer's Disease Neuroimaging Initiative (ADNI) database. This clustering process based on Possibilistic 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 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 conventional algorithms.