title: Multigrid Nonlocal Gaussian Mixture Model for Segmentation of Brain Tissues in Magnetic Resonance Images.
abstract: We propose a novel segmentation method based on regional and nonlocal information to overcome the impact of image intensity inhomogeneities and noise in human brain magnetic resonance images. With the consideration of the spatial distribution of different tissues in brain images, our method does not need preestimation or precorrection procedures for intensity inhomogeneities and noise. A nonlocal information based Gaussian mixture model (NGMM) is proposed to reduce the effect of noise. To reduce the effect of intensity inhomogeneity, the multigrid nonlocal Gaussian mixture model (MNGMM) is proposed to segment brain MR images in each nonoverlapping multigrid generated by using a new multigrid generation method. Therefore the proposed model can simultaneously overcome the impact of noise and intensity inhomogeneity and automatically classify 2D and 3D MR data into tissues of white matter, gray matter, and cerebral spinal fluid. To maintain the statistical reliability and spatial continuity of the segmentation, a fusion strategy is adopted to integrate the clustering results from different grid. The experiments on synthetic and clinical brain MR images demonstrate the superior performance of the proposed model comparing with several state-of-the-art algorithms.
authors.affiliation: School of Math and Statistics, Nanjing University of Information Science and Technology, Nanjing 210044, China.
authors.forename: Yunjie
authors.initials: Y
authors.lastname: Chen
authors.affiliation: Jiangsu Key Laboratory of Meteorological Observation and Information Processing, Nanjing University of Information Science and Technology, Nanjing 210044, China.
authors.forename: Tianming
authors.initials: T
authors.lastname: Zhan
authors.affiliation: School of Information Science and Engineering, Changzhou University, Changzhou 213164, China.
authors.forename: Ji
authors.initials: J
authors.lastname: Zhang
authors.affiliation: School of Information Science and Engineering, Changzhou University, Changzhou 213164, China.
authors.forename: Hongyuan
authors.identifier: 0000-0003-1236-6141
authors.initials: H
authors.lastname: Wang
mesh_terms: D066128:Gray Matter / Q000000981:diagnostic imaging* / Q000473:pathology; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015233:Models, Statistical; D016011:Normal Distribution; D066127:White Matter / Q000000981:diagnostic imaging* / Q000473:pathology