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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Wentao Wang Cong Chen |
| Copyright Year | 2010 |
| Abstract | Accurate segmentation of magnetic resonance images according to tissue type is widely studded by many researcher, Recently Markov Random Field (MRF) has been used in this area. However the original MRF is supervised. So we introduce a novel approach called Dirichlet Markov Random Field for Magnetic Resonance Image (MRI) brain tissue classification. The approach uses Dirchilet Process Mixture (DPM) to get local information of MRF's energy function. Instead of finite component, DPM use infinite component, in which the prior distribution is defined on the space of all possible distribution. But efficient implementations of the DP mixture model can be slowly to converge and their convergence can be difficult to diagnose with the Markov Chain Monte Carlo (MCMC) methods for sampling from the posterior distribution of the parameters. So this algorithm uses variational Bayesian (VB) approximations to the DP mixture model. Experiment result proved this algorithm can segment the MRI smoothly and accurately. |
| Starting Page | 1 |
| Ending Page | 4 |
| File Size | 274869 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424447121 |
| ISSN | 21517622 |
| DOI | 10.1109/ICBBE.2010.5516041 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-06-18 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Computer science Image segmentation Brain Magnetic resonance imaging Bayesian methods Magnetic resonance Clustering algorithms Educational institutions Nuclear magnetic resonance Markov random fields |
| Content Type | Text |
| Resource Type | Article |
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