Bayesian reconstruction for transmission tomography with scale hyperparameter estimation

Authors:
Antonio López;Rafael Molina;Aggelos K. Katsaggelos
Affiliations:
Universidad de Granada, Departamento de Lenguajes y Sistemas Informáticos, Granada, Spain;Departamento de Ciencias de la Computación e I.A, Universidad de Granada, Granada, Spain;Department of Electrical and Computer Engineering, Northwestern University, Evaston, Illinois
Venue:
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part II
Year:
2005

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Abstract

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.