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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Zhigang Peng Wee, W. Jing-Huei Lee |
| Copyright Year | 2005 |
| Description | Author affiliation: Electr. adn Comput. Eng. & Comput. Sci., Cincinnati Univ., OH, USA (Zhigang Peng; Wee, W.) |
| Abstract | We present a novel method to effectively segment the three dimensional MR brain images (volumes) with severe intensity nonuniformity. The segmentation problem was formulated using maximum a posterior probability and Markov random filed (MAP-MRF) framework. A novel spatial Gaussian mixture model (SGMM) is used to represent the intensity probability distribution of each of the three brain tissues (WM, GM and CSF), and MRF is used to compute the prior probability. This method consists of a learning process based on expectation maximization algorithm (EM) to estimate the parameters of SGMM, and a classification algorithm based on iterated conditional modes (ICM) to perform the segmentation of the sequential brain images using the parameters obtained from the learning process. The results on the simulated and twenty in vivo MR brain volumes demonstrate the efficiency of this method. We also present the comparison results with other published methods. |
| File Size | 188408 |
| File Format | |
| ISBN | 0780391349 |
| DOI | 10.1109/ICIP.2005.1529750 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2005-09-14 |
| Publisher Place | Italy |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Image segmentation Brain modeling Markov random fields Image analysis Pixel Biomedical imaging Biomedical engineering Radio frequency Coils Biomedical computing Gaussian mixture model medical image analysi image segmentation Markov random filed maximum a posterior probability |
| Content Type | Text |
| Resource Type | Article |
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