title: A statistical approach to segmentation of diffusion tensor imaging.
abstract: The aim of this study is to design a statistical segmentation technique to allow extraction of grey matter, white matter and cerebral spinal fluid volumes from diffusion tensor imaging. Four channel maps of the DTI are used as the input features, which provide more information for brain tissue segmentation compared with single channel map. An Improved Bayesian decision in the subspace spanned by the eigenvectors which are associated with the smaller eigenvalues in each class is adopted as the brain tissue segmentation criterion. Our method performed well, giving an average segmentation accuracy of about 0.88, 0.85 and 0.76 for white matter, gray matter and cerebrospinal fluid respectively in terms of volume overlap.
authors.affiliation: Department of Computer Science and Technology, East China Normal University, Shanghai 200241, China.
authors.forename: Ying
authors.initials: Y
authors.lastname: Wen
authors.forename: Lianghua
authors.initials: L
authors.lastname: He
mesh_terms: D016880:Anisotropy; D001499:Bayes Theorem; D001921:Brain / Q000473:pathology*; D056324:Diffusion Tensor Imaging; D017143:Fuzzy Logic; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D015233:Models, Statistical; D015203:Reproducibility of Results
keywords: Diffusion tensor imaging; bayesian decision; image segmentation