Combining Bayesian network (BN) and Markov random field (MRF) models, this paper presents an effective supervised image segmentation algorithm. Representing information from different features, a Bayesian network generates the probability map for each pixel via the conditional PDF (probability density function) learned from a limited training data set. Considering the spatial relation and a priori knowledge of the image, MRF theory is used to generate a reasonable segmentation by minimizing the proposed energy functional. Applying this algorithm to multi-contrast MR image in vivo plaque composition measurement shows comparable results with expert manual segmentation.
['Fei Liu', 'Dongxiang Xu', 'Chun Yuan', 'William S. Kerwin']
Image segmentation based on Bayesian network-Markov random field model and its application to in vivo plaque composition