Multigrid Nonlocal Gaussian Mixture Model for Segmentation of Brain Tissues in Magnetic Resonance Images
Joint Authors
Wang, Hong-Yuan
Chen, Yunjie
Zhan, Tianming
Zhang, Ji
Source
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-08-25
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Chen, Yunjie& Zhan, Tianming& Zhang, Ji& Wang, Hong-Yuan. 2016. Multigrid Nonlocal Gaussian Mixture Model for Segmentation of Brain Tissues in Magnetic Resonance Images. BioMed Research International،Vol. 2016, no. 2016, pp.1-10.
https://search.emarefa.net/detail/BIM-1098533
Modern Language Association (MLA)
Chen, Yunjie…[et al.]. Multigrid Nonlocal Gaussian Mixture Model for Segmentation of Brain Tissues in Magnetic Resonance Images. BioMed Research International No. 2016 (2016), pp.1-10.
https://search.emarefa.net/detail/BIM-1098533
American Medical Association (AMA)
Chen, Yunjie& Zhan, Tianming& Zhang, Ji& Wang, Hong-Yuan. Multigrid Nonlocal Gaussian Mixture Model for Segmentation of Brain Tissues in Magnetic Resonance Images. BioMed Research International. 2016. Vol. 2016, no. 2016, pp.1-10.
https://search.emarefa.net/detail/BIM-1098533
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1098533