Bayesian reconstruction and restoration methods require the choice of parameters related to variance in both stochastic image models and observed data. In practice these parameters, or their ratio, is often chosen heuristically. The authors present a method for joint maximum-likelihood (ML) estimation of these two parameters in transmission tomography, with emphasis on the X-ray//spl gamma/-ray dosage parameter. The estimation algorithm employs the expectation-maximization method, with the unobserved image as the complete data. The ML parameter estimator is shown to yield values which are practical for tomographic reconstruction, both with synthetic phantoms and real data.<>
Maximum likelihood dosage estimation for Bayesian transmission tomography
Proceedings of 1st International Conference on Image Processing ; 2 ; 844-848 vol.2
1994-01-01
359682 byte
Conference paper
Electronic Resource
English
Maximum Likelihood Dosage Estimation for Bayesian Transmission Tomography
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