A hyper-parameter inference for radon transformed image reconstruction using Bayesian inference

Authors:
Hayaru Shouno;Masato Okada
Affiliations:
University of Electro-Communications, Chofu, Japan;University of Tokyo, Kashiwa, Japan and RIKEN Brain Science Institute, Wako, Japan
Venue:
MLMI'10 Proceedings of the First international conference on Machine learning in medical imaging
Year:
2010

Citing 1
Cited 1

Information Theory, Inference & Learning Algorithms

Information Theory, Inference & Learning Algorithms

A Bayesian hyperparameter inference for radon-transformed image reconstruction

Journal of Biomedical Imaging - Special issue on Machine Learning in Medical Imaging

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Abstract

We propose an hyper-parameter inference method in the manner of Bayesian inference for image reconstruction from Radon transformed observation which often appears in the computed tomography. Hyper-parameters 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 influenced by the estimation accuracy of these hyper-parameters, we apply Bayesian inference into the filtered back projection (FBP) reconstruction method with hyper-parameters inference, and demonstrate that estimated hyper-parameters can adapt to the noise level in the observation automatically.