Journal of Theoretical Medicine
Volume 1 (1997), Issue 1, Pages 63-77

Bayesian Image Analysis

Department of Statistics, University of Leeds, Leeds LS2 9JT, UK

Copyright © 1997 Hindawi Publishing Corporation. 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.


Bayes' theorem is a vehicle for incorporating prior knowledge in updating the degree of belief in light of data. For example, the state of tomorrow's weather can be predicted using belief or likelihood of tomorrow's weather given today's weather data. We give a brief review of the recent advances in the area with emphasis on high-level Bayesian image analysis. It has been gradually recognised that knowledge-based algorithms based on Bayesian analysis are more widely applicable and reliable than ad hoc algorithms. Advantages include the use of explicit and realistic statistic models making it easier to understand the working behind such algorithms and allowing confidence statements to be made about conclusions. These systems are not necessarily as time consuming as might be expected. However, more care is required in using the knowledge effectively for a given specific problem; this is very much an art rather than a science.