Int. J. Imag. Syst. and Technol. 8, pp. 506-512, 1997.

Uncertainty assessment for reconstructions based on deformable geometry

Kenneth M. Hanson, Gregory S. Cunningham, and Robert J. McKee
Los Alamos National Laboratory


Deformable geometric models can be used in the context of Bayesian analysis to solve ill-posed tomographic reconstruction problems. The uncertainties associated with a Bayesian analysis may be assessed by generating a set of random samples from the posterior, which may be accomplished using a Markov-Chain Monte-Carlo (MCMC) technique. We demonstrate the combination of these techniques for a reconstruction of a two-dimensional object from two orthogonal noisy projections. The reconstructed object is modeled in terms of a deformable geometrically-defined boundary with a uniform interior density yielding a nonlinear reconstruction problem. We show how an MCMC sequence can be used to estimate uncertainties in the location of the edge of the reconstructed object.

Keywords: Bayesian estimation, deformable geometric models, tomographic reconstruction, uncertainty estimation, Markov Chain Monte Carlo

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