A Fuzzy Region-Based Hidden Markov Model for Partial-Volume Classification in Brain MRI

Authors:
Albert Huang;Rafeef Abugharbieh;Roger Tam
Affiliations:
Department of Electrical and Computer Engineering,;Department of Electrical and Computer Engineering,;Department of Radiology, The University of British Columbia, Vancouver, Canada
Venue:
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
Year:
2009

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Abstract

We present a novel fuzzy region-based hidden Markov model (f rbHMM) for unsupervised partial-volume classification in brain magnetic resonance images (MRIs). The primary contribution is an efficient graphical representation of 3D image data in which irregularly-shaped image regions have memberships to a number of classes rather than one discrete class. Our model groups voxels into regions for efficient processing, but also refines the region boundaries to the voxel level for optimal accuracy. This strategy is most effective in data where partial-volume effects due to resolution-limited image acquisition result in intensity ambiguities. Our f rbHMM employs a forward-backward scheme for parameter estimation through iterative computation of region class likelihoods. We validate our proposed method on simulated and clinical brain MRIs of both normal and multiple sclerosis subjects. Quantitative results demonstrate the advantages of our fuzzy model over the discrete approach with significant improvements in classification accuracy (30% reduction in mean square error).