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Proceedings Paper

Magnetic resonance voxel labeling based on Bayesian Decision Theory
Author(s): Rudi Verbeeck; Dirk Vandermeulen; Paul Suetens; Guy Marchal
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Paper 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.

Paper Details

Date Published: 14 September 1993
PDF: 12 pages
Proc. SPIE 1898, Medical Imaging 1993: Image Processing, (14 September 1993); doi: 10.1117/12.154527
Show Author Affiliations
Rudi Verbeeck, Katholieke Univ. Leuven (Belgium)
Dirk Vandermeulen, Katholieke Univ. Leuven (Belgium)
Paul Suetens, Katholieke Univ. Leuven (Belgium)
Guy Marchal, Katholieke Univ. Leuven (Belgium)

Published in SPIE Proceedings Vol. 1898:
Medical Imaging 1993: Image Processing
Murray H. Loew, Editor(s)

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