Overview of Bayesian methods in image reconstruction

Kenneth Lange

doi:10.1117/12.23640

1 November 1990 Overview of Bayesian methods in image reconstruction

Kenneth Lange

Proceedings Volume 1351, Digital Image Synthesis and Inverse Optics; (1990) https://doi.org/10.1117/12.23640
Event: 34th Annual International Technical Symposium on Optical and Optoelectronic Applied Science and Engineering, 1990, San Diego, CA, United States

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.

Citation Download Citation

Kenneth Lange "Overview of Bayesian methods in image reconstruction", Proc. SPIE 1351, Digital Image Synthesis and Inverse Optics, (1 November 1990); https://doi.org/10.1117/12.23640

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available