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International Journal of Biomedical Imaging
Volume 2010, Article ID 974957, 14 pages
http://dx.doi.org/10.1155/2010/974957
Research Article

A Bayesian Generative Model for Surface Template Estimation

1Center for Imaging Science, Department of Biomedical Engineering, The Johns Hopkins University, 320 Clark Hall, Baltimore, MD 21218, USA
2Center for Imaging Science, Department of Biomedical Engineering, The Johns Hopkins University, 301 Clark Hall, Baltimore, MD 21218, USA
3Center for Imaging Science, Department of Applied Math and Statistics, The Johns Hopkins University, 3245 Clark Hall, Baltimore, MD 21218, USA

Received 6 November 2009; Revised 21 June 2010; Accepted 27 June 2010

Academic Editor: M. Jiang

Copyright © 2010 Jun Ma et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

3D surfaces are important geometric models for many objects of interest in image analysis and Computational Anatomy. In this paper, we describe a Bayesian inference scheme for estimating a template surface from a set of observed surface data. In order to achieve this, we use the geodesic shooting approach to construct a statistical model for the generation and the observations of random surfaces. We develop a mode approximation EM algorithm to infer the maximum a posteriori estimation of initial momentum , which determines the template surface. Experimental results of caudate, thalamus, and hippocampus data are presented.