title: Brain tissue classification based on DTI using an improved fuzzy C-means algorithm with spatial constraints.
abstract: We present an effective method for brain tissue classification based on diffusion tensor imaging (DTI) data. The method accounts for two main DTI segmentation obstacles: random noise and magnetic field inhomogeneities. In the proposed method, DTI parametric maps were used to resolve intensity inhomogeneities of brain tissue segmentation because they could provide complementary information for tissues and define accurate tissue maps. An improved fuzzy c-means with spatial constraints proposal was used to enhance the noise and artifact robustness of DTI segmentation. Fuzzy c-means clustering with spatial constraints (FCM_S) could effectively segment images corrupted by noise, outliers, and other imaging artifacts. Its effectiveness contributes not only to the introduction of fuzziness for belongingness of each pixel but also to the exploitation of spatial contextual information. We proposed an improved FCM_S applied on DTI parametric maps, which explores the mean and covariance of the feature spatial information for automated segmentation of DTI. The experiments on synthetic images and real-world datasets showed that our proposed algorithms, especially with new spatial constraints, were more effective.
authors.affiliation: Department of Computer Science and Technology, East China Normal University, Shanghai 200241, China. Electronic address: ywen@cs.ecnu.edu.cn.
authors.forename: Ying
authors.initials: Y
authors.lastname: Wen
authors.forename: Lianghua
authors.initials: L
authors.lastname: He
authors.forename: Karen M
authors.initials: KM
authors.lastname: von Deneen
authors.forename: Yue
authors.initials: Y
authors.lastname: Lu
mesh_terms: D000465:Algorithms; D016880:Anisotropy; D016477:Artifacts; D001331:Automation; D001921:Brain / Q000473:pathology*; D016000:Cluster Analysis; D056324:Diffusion Tensor Imaging; D017143:Fuzzy Logic; D006801:Humans; D007091:Image Processing, Computer-Assisted; D016011:Normal Distribution; D010363:Pattern Recognition, Automated; D025341:Principal Component Analysis; D013997:Time Factors
keywords: Diffusion tensor imaging; Fuzzy c-means with spatial constraints; Image segmentation; Parametric map