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

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

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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on medical imaging 1989-06, Vol.8 (2), p.194-202
Hauptverfasser: Hebert, T., Leahy, R.
Format: Artikel
Sprache:eng
Schlagworte:
Bayesian methods
Biological and medical sciences
Cameras
Data models
Image processing
Image reconstruction
Investigative techniques, diagnostic techniques (general aspects)
Markov random fields
Maximum likelihood estimation
Medical sciences
Miscellaneous. Technology
Pixel
Radionuclide investigations
Signal processing
Single photon emission computed tomography
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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.< >
ISSN:0278-0062
1558-254X
DOI:10.1109/42.24868