*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*
### Abstract

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

Get full paper (pdf, 217 KB)

Return to publication list

Send e-mail to author at kmh@hansonhub.com