Overview of Bayesian methods in image reconstruction

Publisher Website
Google Scholar
Abstract
Bayesian methods of image reconstruction promise better, more appealing images. With the introduction of the EM algorithms for emission and transmission tomography, it is possible to incorporate Bayesian priors in a natural way. Priors tend to accelerate convergence of the algorithms and turn underdetermined systems of likelihood equations into overdetermined systems. Two types of priors have been suggested. One penalizes abrupt changes in estimated values for neighboring pixels. These smoothing priors are based on Gibbsian interaction terms. The second type of prior actually steers each pixel estimate toward a predetermined value. Because the smoothing priors require fewer assumptions, they probably are more defensible at the present time. Integration of emission tomography with other imaging modalities may eventually make the second type of prior the method of choice in emission tomography. Combination of the two types of priors is also possible and requires only minor adjustment of the EM algorithms.

This publication has 0 references indexed in Scilit: