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In this paper, the problem of medical image segmentation is addressed in an unsupervised framework. We propose a novel method considering the hidden Markov random field model (HMRF) to model the image class labels, which takes into account the mutual influences of neighboring sites formulated on the basis of fuzzy clustering principle. The model parameters, number of class labels and the image labels...
Medical imaging applications produce large sets of similar images. The huge amount of data makes the manual analysis and interpretation a fastidious task. Medical image segmentation is thus an important process in image processing used to partition the images into different regions (e.g. gray matter, white matter and cerebrospinal fluid). Hidden Markov Random Field (HMRF) Model and Gibbs distributions...
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