Bayesian approach to the brain image matching problem

James C. Gee; Lionel Le Briquer; Christian Barillot; David R. Haynor; Ruzena K. Bajcsy

doi:10.1117/12.208686

12 May 1995 Bayesian approach to the brain image matching problem

James C. Gee, Lionel Le Briquer, Christian Barillot, David R. Haynor, Ruzena K. Bajcsy

Proceedings Volume 2434, Medical Imaging 1995: Image Processing; (1995) https://doi.org/10.1117/12.208686
Event: Medical Imaging 1995, 1995, San Diego, CA, United States

Abstract

The application of image matching to the problem of localizing structural anatomy in images of the human brain forms the specific aim of our work. The interpretation of such images is a difficult task for human observers because of the many ways in which the identity of a given structure can be obscured. Our approach is based on the assumption that a common topology underlies the anatomy of normal individuals. To the degree that this assumption holds, the localization problem can be solved by determining the mapping from the anatomy of a given individual to some reverential atlas of cerebral anatomy. Previous such approaches have in many cases relied on a physical interpretation of this mapping. In this paper, we examine a more general Bayesian formulation of the image matching problem and demonstrate the approach on two dimensional magnetic resonance images.

Citation Download Citation

James C. Gee, Lionel Le Briquer, Christian Barillot, David R. Haynor, and Ruzena K. Bajcsy "Bayesian approach to the brain image matching problem", Proc. SPIE 2434, Medical Imaging 1995: Image Processing, (12 May 1995); https://doi.org/10.1117/12.208686

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