MRI tissue classification with neighborhood statistics: a nonparametric, entropy-minimizing approach

Authors:
Tolga Tasdizen;Suyash P. Awate;Ross T. Whitaker;Norman L. Foster
Affiliations:
School of Computing, University of Utah;School of Computing, University of Utah;School of Computing, University of Utah;Department of Neurology, University of Michigan
Venue:
MICCAI'05 Proceedings of the 8th international conference on Medical image computing and computer-assisted intervention - Volume Part II
Year:
2005

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Abstract

We introduce a novel approach for magnetic resonance image (MRI) brain tissue classification by learning image neighborhood statistics from noisy input data using nonparametric density estimation. The method models images as random fields and relies on minimizing an entropy-based metric defined on high dimensional probability density functions. Combined with an atlas-based initialization, it is completely automatic. Experiments on real and simulated data demonstrate the advantages of the method in comparison to other approaches.