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BAYESIAN REGULARIZATION OF DIFFUSION TENSOR IMAGES USING HIERARCHICAL MCMC AND LOOPY BELIEF PROPAGATION

By Siming W Jing, Hua Jiajun, Bu Chun and Chen Yizhouyu

Abstract

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

Topics: Index Terms — Diffusion Tensor Images, Image Restoration, Bayesian Models, Markov Chain Monte Carlo
Year: 2013
OAI identifier: oai:CiteSeerX.psu:10.1.1.309.4604
Provided by: CiteSeerX
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