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Mixture models are often used in the statistical segmentation of medical images. For example, they can be used for the segmentation of structural images into different matter types or of functional statistical parametric maps (SPMs) into activations and nonactivations. Nonspatial mixture models segment using models of just the histogram of intensity values. Spatial mixture models have also been developed which augment this histogram information with spatial regularization using Markov random fields. However, these techniques have control parameters, such as the strength of spatial regularization, which need to be tuned heuristically to particular datasets. We present a novel spatial mixture model within a fully Bayesian framework with the ability to perform fully adaptive spatial regularization using Markov random fields. This means that the amount of spatial regularization does not have to be tuned heuristically but is adaptively determined from the data. We examine the behavior of this model when applied to artificial data with different spatial characteristics, and to functional magnetic resonance imaging SPMs.

Original publication




Journal article


IEEE transactions on medical imaging

Publication Date





1 - 11


Oxford Centre for Functional Magnetic Resonance Imaging of the Brain, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UK.


Brain, Humans, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Image Enhancement, Cluster Analysis, Models, Statistical, Sensitivity and Specificity, Reproducibility of Results, Algorithms, Models, Biological, Artificial Intelligence, Computer Simulation, Signal Processing, Computer-Assisted, Information Storage and Retrieval, Pattern Recognition, Automated