Bayesian SPECT image reconstruction with scale hyperparameter estimation for scalable prior

Antonio López; Rafael Molina; Aggelos K. Katsaggelos

doi:10.1007/978-3-540-44871-6_52

Bayesian SPECT image reconstruction with scale hyperparameter estimation for scalable prior

Antonio López^*, Rafael Molina, Aggelos K. Katsaggelos

^*Corresponding author for this work

MRC - Materials Research Center

Research output: Chapter in Book/Report/Conference proceeding › Chapter

Abstract

In this work we propose a now method to estimate the scale hyperparameter for convex priors with scalable energy functions in Single Photon Emission Computed Tomography (SPECT) image reconstruction problems. Within the Bayesian paradigm, Evidence Analysis and circulant preconditioners are used to obtain the scale hyperparameter. The proposed method is tested on synthetic SPECT images using Generalized Gaussian Markov Random Fields (GGMRF) as scalable prior distributions.

Original language	English (US)
Title of host publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Editors	Francisco Jose Perales, Aurelio J. C. Campilho, Nicolas Perez Perez, Nicolas Perez Perez
Publisher	Springer Verlag
Pages	445-452
Number of pages	8
ISBN (Print)	3540402179, 9783540402176
DOIs	https://doi.org/10.1007/978-3-540-44871-6_52
State	Published - 2003

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	2652
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-540-44871-6_52

Cite this

López, A., Molina, R., & Katsaggelos, A. K. (2003). Bayesian SPECT image reconstruction with scale hyperparameter estimation for scalable prior. In F. J. Perales, A. J. C. Campilho, N. P. Perez, & N. P. Perez (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 445-452). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2652). Springer Verlag. https://doi.org/10.1007/978-3-540-44871-6_52

López, Antonio ; Molina, Rafael ; Katsaggelos, Aggelos K. / Bayesian SPECT image reconstruction with scale hyperparameter estimation for scalable prior. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). editor / Francisco Jose Perales ; Aurelio J. C. Campilho ; Nicolas Perez Perez ; Nicolas Perez Perez. Springer Verlag, 2003. pp. 445-452 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Bayesian SPECT image reconstruction with scale hyperparameter estimation for scalable prior",

abstract = "In this work we propose a now method to estimate the scale hyperparameter for convex priors with scalable energy functions in Single Photon Emission Computed Tomography (SPECT) image reconstruction problems. Within the Bayesian paradigm, Evidence Analysis and circulant preconditioners are used to obtain the scale hyperparameter. The proposed method is tested on synthetic SPECT images using Generalized Gaussian Markov Random Fields (GGMRF) as scalable prior distributions.",

author = "Antonio L{\'o}pez and Rafael Molina and Katsaggelos, {Aggelos K.}",

year = "2003",

doi = "10.1007/978-3-540-44871-6_52",

language = "English (US)",

isbn = "3540402179",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "445--452",

editor = "Perales, {Francisco Jose} and Campilho, {Aurelio J. C.} and Perez, {Nicolas Perez} and Perez, {Nicolas Perez}",

booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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López, A, Molina, R & Katsaggelos, AK 2003, Bayesian SPECT image reconstruction with scale hyperparameter estimation for scalable prior. in FJ Perales, AJC Campilho, NP Perez & NP Perez (eds), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2652, Springer Verlag, pp. 445-452. https://doi.org/10.1007/978-3-540-44871-6_52

Bayesian SPECT image reconstruction with scale hyperparameter estimation for scalable prior. / López, Antonio; Molina, Rafael; Katsaggelos, Aggelos K.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). ed. / Francisco Jose Perales; Aurelio J. C. Campilho; Nicolas Perez Perez; Nicolas Perez Perez. Springer Verlag, 2003. p. 445-452 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2652).

Research output: Chapter in Book/Report/Conference proceeding › Chapter

TY - CHAP

T1 - Bayesian SPECT image reconstruction with scale hyperparameter estimation for scalable prior

AU - López, Antonio

AU - Molina, Rafael

AU - Katsaggelos, Aggelos K.

PY - 2003

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N2 - In this work we propose a now method to estimate the scale hyperparameter for convex priors with scalable energy functions in Single Photon Emission Computed Tomography (SPECT) image reconstruction problems. Within the Bayesian paradigm, Evidence Analysis and circulant preconditioners are used to obtain the scale hyperparameter. The proposed method is tested on synthetic SPECT images using Generalized Gaussian Markov Random Fields (GGMRF) as scalable prior distributions.

AB - In this work we propose a now method to estimate the scale hyperparameter for convex priors with scalable energy functions in Single Photon Emission Computed Tomography (SPECT) image reconstruction problems. Within the Bayesian paradigm, Evidence Analysis and circulant preconditioners are used to obtain the scale hyperparameter. The proposed method is tested on synthetic SPECT images using Generalized Gaussian Markov Random Fields (GGMRF) as scalable prior distributions.

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SN - 9783540402176

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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EP - 452

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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López A, Molina R, Katsaggelos AK. Bayesian SPECT image reconstruction with scale hyperparameter estimation for scalable prior. In Perales FJ, Campilho AJC, Perez NP, Perez NP, editors, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. 2003. p. 445-452. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-44871-6_52

Bayesian SPECT image reconstruction with scale hyperparameter estimation for scalable prior

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