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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Hauptverfasser: Siming Wei, Jing Hua, Jiajun Bu, Chun Chen, Yizhou Yu
Format: Tagungsbericht
Sprache:eng
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Zusammenfassung: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.
ISSN:1522-4880
2381-8549
DOI:10.1109/ICIP.2010.5651519