Hyperparameter estimation for emission computed tomography data

A. Lopez*, R. Molina, A. K. Katsaggelos

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations


Although many statistical methods have been proposed for the restoration of tomographic images, their use in medical environments has been limited due to two important factors. These factors are the need for greater computational time than deterministic methods and the selection of the hyperparameters in the image models. Consequently, deterministic methods, like the classical filtered back-projection (FBP) and algebraic reconstruction (AR), are commonly used. In this work, we propose a method to estimate, from observed image data in emission tomography, the hyperparameters in a Generalized Gaussian Markov Random Field (GGMRF). We use the hierarchical Bayesian approach and evidence analysis to reconstruct the image and estimate the unknown hyperparameters. The method is tested on synthetic images.

Original languageEnglish (US)
Title of host publicationIEEE International Conference on Image Processing
Number of pages4
StatePublished - Dec 1 1999
EventInternational Conference on Image Processing (ICIP'99) - Kobe, Jpn
Duration: Oct 24 1999Oct 28 1999


OtherInternational Conference on Image Processing (ICIP'99)
CityKobe, Jpn

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Electrical and Electronic Engineering

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    Lopez, A., Molina, R., & Katsaggelos, A. K. (1999). Hyperparameter estimation for emission computed tomography data. In IEEE International Conference on Image Processing (Vol. 2, pp. 677-680). IEEE.