Uncertainties in tomographic reconstructions based on deformable models

Kenneth M. Hanson; Gregory S. Cunningham; Robert J. McKee

doi:10.1117/12.274095

25 April 1997 Uncertainties in tomographic reconstructions based on deformable models

Kenneth M. Hanson, Gregory S. Cunningham, Robert J. McKee

Proceedings Volume 3034, Medical Imaging 1997: Image Processing; (1997) https://doi.org/10.1117/12.274095
Event: Medical Imaging 1997, 1997, Newport Beach, CA, United States

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.

Citation Download Citation

Kenneth M. Hanson, Gregory S. Cunningham, and Robert J. McKee "Uncertainties in tomographic reconstructions based on deformable models", Proc. SPIE 3034, Medical Imaging 1997: Image Processing, (25 April 1997); https://doi.org/10.1117/12.274095

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