Brain tissue classification based on DTI using an improved Fuzzy C-means algorithm with spatial constraints

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 inhomo...

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Bibliographic Details
Published in:Magnetic resonance imaging Vol. 31; no. 9; pp. 1623 - 1630
Main Authors: Wen, Ying, He, Lianghua, von Deneen, Karen M., Lu, Yue
Format: Journal Article
Language:English
Published: Netherlands Elsevier Inc 01.11.2013
Subjects:
Algorithms
Anisotropy
Automation
Brain - pathology
Cluster Analysis
Diffusion Tensor Imaging
Fuzzy c-means with spatial constraints
Fuzzy Logic
Humans
Image Processing, Computer-Assisted
Image segmentation
Normal Distribution
Parametric map
Pattern Recognition, Automated
Principal Component Analysis
Radiology
Time Factors
Image segmentation
Fuzzy c-means with spatial constraints
Parametric map
Diffusion tensor imaging
ISSN:0730-725X, 1873-5894, 1873-5894
Online Access:Get full text
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Summary: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.
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ISSN:0730-725X
1873-5894
1873-5894
DOI:10.1016/j.mri.2013.05.007