Magnetic resonance voxel labeling based on Bayesian Decision Theory

Rudi Verbeeck; Dirk Vandermeulen; Paul Suetens; Guy Marchal

doi:10.1117/12.154527

14 September 1993 Magnetic resonance voxel labeling based on Bayesian Decision Theory

Rudi Verbeeck, Dirk Vandermeulen, Paul Suetens, Guy Marchal

Proceedings Volume 1898, Medical Imaging 1993: Image Processing; (1993) https://doi.org/10.1117/12.154527
Event: Medical Imaging 1993, 1993, Newport Beach, CA, United States

Abstract

In this paper, Bayesian decision theory is applied to the labelling of voxels in Magnetic Resonance (MR) images of the brain. The Bayes optimal decision rule defines a cost function that consists of a loss function weighted by the a posteriori probability of the labelling. Two options for the loss function are presented in this paper. A zero-one loss function gives rise to the maximum a posteriori (MAP) estimate, which requires a simulated annealing optimization process. The probability term of the cost function is the product of the a priori probability of the labelling (or an a priori model of the underlying scene) and the conditional probability of the data, given the labelling (or the model for the imaging modality). By modelling the label image as a Markov random field, the model for the underlying scene can be described by a Gibbs distribution. In the application discussed, here, they reflect the compatibility of anatomical structures. The imaging method represents the expected voxel intensities and possible noise or image distortions.

Citation Download Citation

Rudi Verbeeck, Dirk Vandermeulen, Paul Suetens, and Guy Marchal "Magnetic resonance voxel labeling based on Bayesian Decision Theory", Proc. SPIE 1898, Medical Imaging 1993: Image Processing, (14 September 1993); https://doi.org/10.1117/12.154527

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