A statistical approach to segmentation of diffusion tensor imaging
Issue title: Frontiers in Biomedical Engineering and Biotechnology – Proceedings of the 2nd International Conference on Biomedical Engineering and Biotechnology, 11–13 October 2013, Wuhan, China
Affiliations: Department of Computer Science and Technology, East China Normal University, Shanghai 200241, China | Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, Shanghai 200092, China
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.
