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Bayesian regularization of diffusion tensor images using hierarchical MCMC and loopy belief propagation

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5 Author(s)
Siming Wei ; Coll. of Comput. Sci., Zhejiang Univ., Hangzhou, China ; Jing Hua ; Jiajun Bu ; Chun Chen
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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.

Published in:

Image Processing (ICIP), 2010 17th IEEE International Conference on

Date of Conference:

26-29 Sept. 2010

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