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21 May 1999 Bayesian inference and Markov chain Monte Carlo in imaging
David M. Higdon, James E. Bowsher
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Proceedings Volume 3661, Medical Imaging 1999: Image Processing; (1999) https://doi.org/10.1117/12.348550
Event: Medical Imaging '99, 1999, San Diego, CA, United States
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.
© (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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David M. Higdon and James E. Bowsher "Bayesian inference and Markov chain Monte Carlo in imaging", Proc. SPIE 3661, Medical Imaging 1999: Image Processing, (21 May 1999); https://doi.org/10.1117/12.348550
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KEYWORDS
Positron emission tomography
Monte Carlo methods
Magnetic resonance imaging
Single photon emission computed tomography
Bayesian inference
Tumors
Magnetorheological finishing
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