Adaptive mixture estimation and unsupervised local Bayesian image segmentation
Graphical Models and Image Processing
Markov random field modeling in image analysis
Markov random field modeling in image analysis
The mean field theory in EM procedures for Markov random fields
IEEE Transactions on Signal Processing
Gaussian mixture model based segmentation methods for brain MRI images
Artificial Intelligence Review
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This work deals with global statistical unsupervised segmentation algorithms. In the context of Magnetic Resonance Image (MRI), an accurate and robust segmentation can be achieved by combining both the Hidden Markov Random Field (HMRF) model and the Expectation-Maximization (EM) algorithm. This EM-HMRF approach is accomplished by taking into account spatial information to improve the segmentation process which, in turn, slows the approach and consequently prevents its adoption for real-time applications such as three-dimensional medical image segmentation. We propose in this paper the use of the Bootstrap resampling to speed up the processing time of the EM-HMRF algorithm. This is accomplished by randomly selecting an optimal representative set of pixels according to some criteria originally defined for the blind segmentation. We will show how to adapt such criteria to the HMRF_EM algorithm context. We validated our proposition through a set of experiments and we proved that the use of the Bootstrap resampling yields the same accuracy and robustness as the basic algorithm, yet it amounts to a considerable processing speed up.