@inproceedings{Rafael2007c,
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.},
author = {Molina, Rafael and L{\'{o}}pez, Antonio and Martin, Jos{\'{e}} Manuel and Katsaggelos, Aggelos K.},
booktitle = {Proceedings of the Second International Conference on Computer Vision Theory and Applications},
doi = {10.5220/0002066001650173},
isbn = {978-972-8865-75-7},
keywords = {Bayesian framework,Image reconstruction,Parameter estimation,Tomography images,Variational methods},
pages = {165--173},
publisher = {SciTePress - Science and and Technology Publications},
title = {{VARIATIONAL POSTERIOR DISTRIBUTION APPROXIMATION IN BAYESIAN EMISSION TOMOGRAPHY RECONSTRUCTION USING A GAMMA MIXTURE PRIOR}},
url = {http://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0002066001650173},
volume = {SP},
year = {2007}
}

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