KEYWORDS: Image segmentation, Functional magnetic resonance imaging, Expectation maximization algorithms, Monte Carlo methods, Statistical modeling, Data modeling, Statistical analysis
In this paper we consider the problem of segmentation of three-dimensional fMRI images within the Bayesian framework with Markov Random Field (MRF) as the prior distribution and von Mises-Fisher distribution as the likelihood. Usually, the learning of such models is a complicated task and the exact inference is impossible in practice. To fit the proposed model, we apply the mean field approximation on the inference step in the EM algorithm. Some numerical examples are presented to illustrate the proposed method.
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