Bayesian inference and Markov chain Monte Carlo in imaging

Published

Journal Article

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.

Duke Authors

Cited Authors

  • Higdon, DM; Bowsher, JE

Published Date

  • January 1, 1999

Published In

Volume / Issue

  • 3661 / I

Start / End Page

  • 2 - 11

International Standard Serial Number (ISSN)

  • 0277-786X

Citation Source

  • Scopus