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Proceedings Paper

Uncertainties in tomographic reconstructions based on deformable models
Author(s): Kenneth M. Hanson; Gregory S. Cunningham; Robert J. McKee
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Paper Abstract

Deformable geometric models fit very naturally into the context of Bayesian analysis. The prior probability of boundary shapes is taken to proportional to the negative exponential of the deformation energy used to control the boundary. This probabilistic interpretation is demonstrated using a Markov-Chain Monte-Carlo (MCMC) technique, which permits one to generate configurations that populate the prior. One of may uses for deformable models is to solve ill-posed tomographic reconstruction problems, which we demonstrate by reconstructing a two-dimensional object from two orthogonal noisy projections. We show how MCMC samples drawn from the posterior can be used to estimate uncertainties in the location of the edge of the reconstructed object.

Paper Details

Date Published: 25 April 1997
PDF: 11 pages
Proc. SPIE 3034, Medical Imaging 1997: Image Processing, (25 April 1997); doi: 10.1117/12.274095
Show Author Affiliations
Kenneth M. Hanson, Los Alamos National Lab. (United States)
Gregory S. Cunningham, Los Alamos National Lab. (United States)
Robert J. McKee, Los Alamos National Lab. (United States)


Published in SPIE Proceedings Vol. 3034:
Medical Imaging 1997: Image Processing
Kenneth M. Hanson, Editor(s)

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