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Proceedings Paper

Bayesian inference and Markov chain Monte Carlo in imaging
Author(s): David M. Higdon; James E. Bowsher
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Paper Abstract

Over the past 20 years, many problems in Bayesian inference that were previously intractable can now be fairly routinely dealt with using a computationally intensive technique for exploring the posterior distribution called Markov chain Monte Carlo (MCMC). Primarily because of insufficient computing capabilities, most MCMC applications have been limited to rather standard statistical models. However, with the computing power of modern workstations, a fully Bayesian approach with MCMC, is now possible for many imaging applications. Such an approach can be quite useful because it leads not only to `point' estimates of an underlying image or emission source, but it also gives a means for quantifying uncertainties regarding the image. This paper gives an overview of Bayesian image analysis and focuses on applications relevant to medical imaging. Particular focus is on prior image models and outlining MCMC methods for these models.

Paper Details

Date Published: 21 May 1999
PDF: 10 pages
Proc. SPIE 3661, Medical Imaging 1999: Image Processing, (21 May 1999); doi: 10.1117/12.348550
Show Author Affiliations
David M. Higdon, Duke Univ. (United States)
James E. Bowsher, Duke Univ. (United States)


Published in SPIE Proceedings Vol. 3661:
Medical Imaging 1999: Image Processing
Kenneth M. Hanson, Editor(s)

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