Bayesian reconstruction for transmission tomography with scale hyperparameter estimation

Antonio López*, Rafael Molina, Aggelos K. Katsaggelos

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

2 Scopus citations


In this work we propose a new method to estimate the scale hyperparameter for transmission tomography in Nuclear Medicine image reconstruction problems. Within the Bayesian paradigm, Evidence Analysis and circulant preconditioners are used to obtain the scale hyperparameter. For the prior distribution, we use Generalized Gaussian Markov Random Fields (GGMRF), a nonquadratic function that preserves the edges in the reconstructed image. The experimental results indicate that the proposed method produces satisfactory reconstructions.

Original languageEnglish (US)
Pages (from-to)455-462
Number of pages8
JournalLecture Notes in Computer Science
Issue numberII
StatePublished - 2005
EventSecond Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2005 - Estoril, Portugal
Duration: Jun 7 2005Jun 9 2005

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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