Variational posterior distribution approximation in Bayesian emission tomography reconstruction using a gamma mixture prior

Rafael Molina*, Antonio López, José Manuel Martin, Aggelos K Katsaggelos

*Corresponding author for this work

Research output: Contribution to conferencePaperpeer-review

4 Scopus citations

Abstract

Following the Bayesian framework we propose a method to reconstruct emission tomography images which uses gamma mixture prior and variational methods to approximate the posterior distribution of the unknown parameters and image instead of estimating them by using the Evidence Analysis or alternating between the estimation of parameters and image (Iterated Conditional Mode (ICM)) approach. By analyzing the posterior distribution approximation we can examine the quality of the proposed estimates. The method is tested on real Single Positron Emission Tomography (SPECT) images.

Original languageEnglish (US)
Pages165-173
Number of pages9
StatePublished - Dec 1 2007
Event2nd International Conference on Computer Vision Theory and Applications, VISAPP 2007 - Barcelona, Spain
Duration: Mar 8 2007Mar 11 2007

Other

Other2nd International Conference on Computer Vision Theory and Applications, VISAPP 2007
CountrySpain
CityBarcelona
Period3/8/073/11/07

Keywords

  • Bayesian framework
  • Image reconstruction
  • Parameter estimation
  • Tomography images
  • Variational methods

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Software

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