Title :
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
fDate :
6/1/1989 12:00:00 AM
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;
Journal_Title :
Medical Imaging, IEEE Transactions on
