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- Title
A Modified Brain MR Image Segmentation and Bias Field Estimation Model Based on Local and Global Information.
- Authors
Cong, Wang; Song, Jianhua; Luan, Kuan; Liang, Hong; Wang, Lei; Ma, Xingcheng; Li, Jin
- Abstract
Because of the poor radio frequency coil uniformity and gradient-driven eddy currents, there is much noise and intensity inhomogeneity (bias) in brain magnetic resonance (MR) image, and it severely affects the segmentation accuracy. Better segmentation results are difficult to achieve by traditional methods; therefore, in this paper, a modified brain MR image segmentation and bias field estimation model based on local and global information is proposed. We first construct local constraints including image neighborhood information in Gaussian kernel mapping space, and then the complete regularization is established by introducing nonlocal spatial information of MR image. The weighting between local and global information is automatically adjusted according to image local information. At the same time, bias field information is coupled with the model, and it makes the model reduce noise interference but also can effectively estimate the bias field information. Experimental results demonstrate that the proposed algorithm has strong robustness to noise and bias field is well corrected.
- Subjects
DIAGNOSIS of brain abnormalities; MAGNETIC resonance imaging; IMAGE segmentation; MATHEMATICAL models; ESTIMATION theory; MATHEMATICAL mappings
- Publication
Computational & Mathematical Methods in Medicine, 2016, p1
- ISSN
1748-670X
- Publication type
Article
- DOI
10.1155/2016/9871529