Bayesian segmentation of MR images using 3D Gibbsian priors

Michael M. Chang; A. Murat Tekalp; M. Ibrahim Sezan

doi:10.1117/12.143137

8 April 1993 Bayesian segmentation of MR images using 3D Gibbsian priors

Michael M. Chang, A. Murat Tekalp, M. Ibrahim Sezan

Proceedings Volume 1903, Image and Video Processing; (1993) https://doi.org/10.1117/12.143137
Event: IS&T/SPIE's Symposium on Electronic Imaging: Science and Technology, 1993, San Jose, CA, United States

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.

Citation Download Citation

Michael M. Chang, A. Murat Tekalp, and M. Ibrahim Sezan "Bayesian segmentation of MR images using 3D Gibbsian priors", Proc. SPIE 1903, Image and Video Processing, (8 April 1993); https://doi.org/10.1117/12.143137

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