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Proceedings Paper

Bayesian segmentation of MR images using 3D Gibbsian priors
Author(s): Michael M. Chang; A. Murat Tekalp; M. Ibrahim Sezan
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Paper Abstract

A Bayesian approach for segmentation of three-dimensional (3-D) magnetic resonance imaging (MRI) data of the human brain is presented. Connectivity and smoothness constraints are imposed on the segmentation in 3 dimensions. The resulting segmentation is suitable for 3-D display and for volumetric analysis of structures. The algorithm is based on the maximum a posteriori probability (MAP) criterion, where a 3-D Gibbs random field (GRF) is used to model the a priori probability distribution of the segmentation. The proposed method can be applied to a spatial sequence of 2-D images (cross-sections through a volume), as well as 3-D sampled data. We discuss the optimization methods for obtaining the MAP estimate. Experimental results obtained using clinical data are included.

Paper Details

Date Published: 8 April 1993
PDF: 12 pages
Proc. SPIE 1903, Image and Video Processing, (8 April 1993); doi: 10.1117/12.143137
Show Author Affiliations
Michael M. Chang, Univ. of Rochester (United States)
A. Murat Tekalp, Univ. of Rochester (United States)
M. Ibrahim Sezan, Eastman Kodak Co. (United States)

Published in SPIE Proceedings Vol. 1903:
Image and Video Processing
Majid Rabbani; M. Ibrahim Sezan; A. Murat Tekalp, Editor(s)

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