SPECT IMAGE RECONSTRUCTION USING COMPOUND PRIOR MODELS-Reference-Cited by-同舟云学术

SPECT IMAGE RECONSTRUCTION USING COMPOUND PRIOR MODELS

Published:2002-05 Issue:03 Volume:16 Page:317-330
ISSN:0218-0014
Container-title:International Journal of Pattern Recognition and Artificial Intelligence
language:en
Short-container-title:Int. J. Patt. Recogn. Artif. Intell.

Author:

LÓPEZ ANTONIO¹,MOLINA RAFAEL²,MATEOS JAVIER²,KATSAGGELOS AGGELOS K.³

Affiliation:

1. Departamento de Lenguajes y Sistemas Informáticos, Universidad de Granada, 18071 Granada, Spain

2. Departamento de Ciencias de la Computación e I. A., Universidad de Granada, 18701 Granada, Spain

3. Department of Electrical and Computer Engineering, Northwestern University, Evaston, Illinois 60208-3118, USA

Abstract

We propose a new iterative method for Maximum a Posteriori (MAP) reconstruction of SPECT (Single Photon Emission Computed Tomography) images. The method uses Compound Gauss Markov Random Fields (CGMRF) as prior model and is stochastic for the line process and deterministic for the reconstruction. Synthetic and real images are used to compare the new method with existing ones.

Publisher

World Scientific Pub Co Pte Lt

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Software

Link

https://www.worldscientific.com/doi/pdf/10.1142/S0218001402001708

Reference22 articles.

1. A generalized Gaussian image model for edge-preserving MAP estimation

2. Iterative image reconstruction algorithms based on cross-entropy minimization

3. Conjugate Gradient Methods for Toeplitz Systems

4. Deterministic edge-preserving regularization in computed imaging

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Super-Resolution of Multispectral Images;The Computer Journal;2008-02-19

2. Transmission Tomography Reconstruction Using Compound Gauss-Markov Random Fields and Ordered Subsets;Lecture Notes in Computer Science;2006

3. Parameter estimation in Bayesian reconstruction of SPECT images: An aid in nuclear medicine diagnosis;International Journal of Imaging Systems and Technology;2004

4. Bayesian multichannel image restoration using compound gauss-markov random fields;IEEE Transactions on Image Processing;2003-12