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 approximate solution to the problem is achieved efficiently… (More)
DOI: 10.1109/ICIP.2010.5651519


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