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Link to original content: https://pubmed.ncbi.nlm.nih.gov/20885934/
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. 2010:2010:974957.
doi: 10.1155/2010/974957. Epub 2010 Sep 20.

A bayesian generative model for surface template estimation

Jun Ma  1 , Michael I Miller, Laurent Younes
Affiliations

A bayesian generative model for surface template estimation

Jun Ma et al. Int J Biomed Imaging. 2010.

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 μ, which determines the template surface. Experimental results of caudate, thalamus, and hippocampus data are presented.

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Figures

Figure 1
Figure 1
Random deformation of a template caudate surface (left). The six surfaces following the template are independent realization of the model described in (9).
Figure 2
Figure 2
Estimating the surface template from 9 caudate data. (a)–(i) observed surfaces. (j) is the hypertemplate. (k) is the result.
Figure 3
Figure 3
Estimating the surface template from 9 thalamus data. (a)–(i) observed surfaces. (j) is the hypertemplate. (k) is the result.
Figure 4
Figure 4
Estimating the surface template from 101 data. (a)–(h) are 8 examples out of 101 observed surfaces. (i) is the hypertemplate. (j) is the result.
Figure 5
Figure 5
Energy change with the iteration.
Figure 6
Figure 6
For the same observed population, we choose different surfaces as hypertemplate. The results only have minor differences.
See this image and copyright information in PMC

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