Bayesian regularization of diffusion tensor images using hierarchical MCMC and loopy belief propagation
Based on the theory of Markov Random Fields, a Bayesian regularization model for diffusion tensor images (DTI) is proposed in this paper. The low-degree parameterization of diffusion tensors in our model makes it less computationally intensive to obtain a maximum a posteriori (MAP) estimation. An ap...
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| creator | Siming Wei Jing Hua Jiajun Bu Chun Chen Yizhou Yu |
| description | Based on the theory of Markov Random Fields, a Bayesian regularization model for diffusion tensor images (DTI) is proposed in this paper. The low-degree parameterization of diffusion tensors in our model makes it less computationally intensive to obtain a maximum a posteriori (MAP) estimation. An approximate solution to the problem is achieved efficiently using hierarchical Markov Chain Monte Carlo (HMCMC), and a loopy belief propagation algorithm is applied to a coarse grid to obtain a good initial solution for hierarchical MCMC. Experiments on synthetic and real data demonstrate the effectiveness of our methods. |
| doi_str_mv | 10.1109/ICIP.2010.5651519 |
| format | Conference Proceeding |
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| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 1522-4880 |
| ispartof | 2010 IEEE International Conference on Image Processing, 2010, p.65-68 |
| issn | 1522-4880 2381-8549 |
| language | eng |
| recordid | cdi_ieee_primary_5651519 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Bayesian methods Bayesian Models Belief propagation Diffusion Tensor Images Diffusion tensor imaging Estimation Image Restoration Markov Chain Monte Carlo Markov processes Noise measurement Tensile stress |
| title | Bayesian regularization of diffusion tensor images using hierarchical MCMC and loopy belief propagation |
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