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Mixture Models with Adaptive Spatial Regularisation for Segmentation with an Application to FMRI Data

FMRIB Technical Report TR04MW1
(A related paper has been submitted to TMI IEEE)

Mark W. Woolrich, Timothy E.J. Behrens, Christian F. Beckmann and Stephen M. Smith

Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB),
Department of Clinical Neurology, University of Oxford, John Radcliffe Hospital,
Headley Way, Headington, Oxford, UK
Corresponding author is Mark Woolrich:


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 statistical parametric maps in functional imaging. Non-spatial 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 regularisation using Markov Random Fields. However, these techniques have control parameters, such as the strength of spatial regularisation, 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 regularisation using Markov Random Fields. This means that the amount of spatial regularisation does not have to be tuned heuristically but is adaptively determined from the data. We examine the behaviour of this model when applied to artificial data with different spatial characteristics, and to FMRI statistical parametric maps.

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Next: Introduction