A generalized EM algorithm for 3-D Bayesian reconstruction from Poisson data using Gibbs priors

Author

Hebert, Tom ; Leahy, Richard

Author_Institution

Signal & Image Process. Inst., Univ. of Southern California, Los Angeles, CA, USA

Volume

8

Issue

2

fYear

1989

fDate

6/1/1989 12:00:00 AM

Firstpage

194

Lastpage

202

Abstract

A generalized expectation-maximization (GEM) algorithm is developed for Bayesian reconstruction, based on locally correlated Markov random-field priors in the form of Gibbs functions and on the Poisson data model. For the M-step of the algorithm, a form of coordinate gradient ascent is derived. The algorithm reduces to the EM maximum-likelihood algorithm as the Markov random-field prior tends towards a uniform distribution. Three different Gibbs function priors are examined. Reconstructions of 3-D images obtained from the Poisson model of single-photon-emission computed tomography are presented

Keywords

Bayes methods; computerised tomography; 3D Bayesian reconstruction; Gibbs functions; Poisson data model; coordinate gradient ascent; locally correlated Markov random-field priors; medical imaging; single-photon-emission computed tomography; Bayesian methods; Cameras; Data models; Image processing; Image reconstruction; Markov random fields; Maximum likelihood estimation; Pixel; Signal processing; Single photon emission computed tomography;

fLanguage

English

Journal_Title

Medical Imaging, IEEE Transactions on

Publisher

ieee

ISSN

0278-0062

Type

jour

DOI

10.1109/42.24868

Filename

24868