Multigrid Nonlocal Gaussian Mixture Model for Segmentation of Brain Tissues in Magnetic Resonance Images
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...
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2016-01-01
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Series: | BioMed Research International |
Online Access: | http://dx.doi.org/10.1155/2016/6727290 |
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doaj-85c79e427a474d9a8f9d11535651d2272020-11-24T22:25:04ZengHindawi LimitedBioMed Research International2314-61332314-61412016-01-01201610.1155/2016/67272906727290Multigrid Nonlocal Gaussian Mixture Model for Segmentation of Brain Tissues in Magnetic Resonance ImagesYunjie Chen0Tianming Zhan1Ji Zhang2Hongyuan Wang3School of Math and Statistics, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaJiangsu Key Laboratory of Meteorological Observation and Information Processing, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaSchool of Information Science and Engineering, Changzhou University, Changzhou 213164, ChinaSchool of Information Science and Engineering, Changzhou University, Changzhou 213164, ChinaWe 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.http://dx.doi.org/10.1155/2016/6727290 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yunjie Chen Tianming Zhan Ji Zhang Hongyuan Wang |
spellingShingle |
Yunjie Chen Tianming Zhan Ji Zhang Hongyuan Wang Multigrid Nonlocal Gaussian Mixture Model for Segmentation of Brain Tissues in Magnetic Resonance Images BioMed Research International |
author_facet |
Yunjie Chen Tianming Zhan Ji Zhang Hongyuan Wang |
author_sort |
Yunjie Chen |
title |
Multigrid Nonlocal Gaussian Mixture Model for Segmentation of Brain Tissues in Magnetic Resonance Images |
title_short |
Multigrid Nonlocal Gaussian Mixture Model for Segmentation of Brain Tissues in Magnetic Resonance Images |
title_full |
Multigrid Nonlocal Gaussian Mixture Model for Segmentation of Brain Tissues in Magnetic Resonance Images |
title_fullStr |
Multigrid Nonlocal Gaussian Mixture Model for Segmentation of Brain Tissues in Magnetic Resonance Images |
title_full_unstemmed |
Multigrid Nonlocal Gaussian Mixture Model for Segmentation of Brain Tissues in Magnetic Resonance Images |
title_sort |
multigrid nonlocal gaussian mixture model for segmentation of brain tissues in magnetic resonance images |
publisher |
Hindawi Limited |
series |
BioMed Research International |
issn |
2314-6133 2314-6141 |
publishDate |
2016-01-01 |
description |
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. |
url |
http://dx.doi.org/10.1155/2016/6727290 |
work_keys_str_mv |
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