Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/12009
Title: Fuzzy local Gaussian mixture model for brain MR image segmentation
Authors: Ji, Z
Xia, Y
Sun, Q
Chen, Q
Xia, D
Feng, DD
Issue Date: 2012
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on information technology in biomedicine, 2012, v. 16, no. 3, p. 339-347 How to cite?
Journal: IEEE transactions on information technology in biomedicine 
Abstract: Accurate brain tissue segmentation from magnetic resonance (MR) images is an essential step in quantitative brain image analysis. However, due to the existence of noise and intensity inhomogeneity in brain MR images, many segmentation algorithms suffer from limited accuracy. In this paper, we assume that the local image data within each voxel's neighborhood satisfy the Gaussian mixture model (GMM), and thus propose the fuzzy local GMM (FLGMM) algorithm for automated brain MR image segmentation. This algorithm estimates the segmentation result that maximizes the posterior probability by minimizing an objective energy function, in which a truncated Gaussian kernel function is used to impose the spatial constraint and fuzzy memberships are employed to balance the contribution of each GMM. We compared our algorithm to state-of-the-art segmentation approaches in both synthetic and clinical data. Our results show that the proposed algorithm can largely overcome the difficulties raised by noise, low contrast, and bias field, and substantially improve the accuracy of brain MR image segmentation.
URI: http://hdl.handle.net/10397/12009
ISSN: 1089-7771
DOI: 10.1109/TITB.2012.2185852
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