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International Journal of Biomedical ImagingVolume 2011 2011, Article ID 870252, 10 pages

Research Article

Department of Informatics, Graduate School of Informatics and Engineering, University of Electro-Communications, Chofugaoka 1-5-1, Chofu, Tokyo 182-8585, Japan

Division of Transdisciplinary Science, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8561, Japan

Okanoya Emotional Information Project, RIKEN Brain Science Institute BSI, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan

Received 24 February 2011; Revised 3 August 2011; Accepted 18 August 2011

Academic Editor: Dinggang Shen

Copyright © 2011 Hayaru Shouno et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

We develop a hyperparameter inference method for image reconstruction from Radon transform which often appears in the computed tomography, in the manner of Bayesian inference. Hyperparameters are often introduced in Bayesian inference to control the strength ratio between prior information and the fidelity to the observation. Since the quality of the reconstructed image is controlled by the estimation accuracy of these hyperparameters, we apply Bayesian inference into the filtered back-projection FBP reconstruction method with hyperparameters inference and demonstrate that the estimated hyperparameters can adapt to the noise level in the observation automatically. In the computer simulation, at first, we show that our algorithm works well in the model framework environment, that is, observation noise is an additive white Gaussian noise case. Then, we also show that our algorithm works well in the more realistic environment, that is, observation noise is Poissonian noise case. After that, we demonstrate an application for the real chest CT image reconstruction under the Gaussian and Poissonian observation noises.





Autor: Hayaru Shouno, Madomi Yamasaki, and Masato Okada

Fuente: https://www.hindawi.com/



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