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Causal Markov random field for brain MR image segmentation

Razlighi, Q. R.; Orekhov, A.; Laine, A.; Stern, Yaakov

We propose a new Bayesian classifier, based on the recently introduced causal Markov random field (MRF) model, Quadrilateral MRF (QMRF). We use a second order inhomogeneous anisotropic QMRF to model the prior and likelihood probabilities in the maximum a posteriori (MAP) classifier, named here as MAP-QMRF. The joint distribution of QMRF is given in terms of the product of two dimensional clique distributions existing in its neighboring structure. 20 manually labeled human brain MR images are used to train and assess the MAP-QMRF classifier using the jackknife validation method. Comparing the results of the proposed classifier and FreeSurfer on the Dice overlap measure shows an average gain of 1.8%. We have performed a power analysis to demonstrate that this increase in segmentation accuracy substantially reduces the number of samples required to detect a 5% change in volume of a brain region.

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Also Published In

Title
2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society
DOI
https://doi.org/10.1109/embc.2012.6346646

More About This Work

Academic Units
Neurology
Published Here
February 11, 2022