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:
woolrich@fmrib.ox.ac.uk
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.
