ABSTRACT
Biomedical informatics is the study of the application of computational and statistical algorithms, data structures, and methods to improve communication, understanding, and management of biomedical information. Our objective in this chapter is to describe and demonstrate our research in the use of biomedical image databases, in both preclinical and clinical settings, to classify, predict, research, diagnose, and otherwise learn from the informational content encapsulated in historical image repositories. We detail our approach of describing image content in a Bayesian probabilistic framework to achieve learning from retrieved populations of similar images.
We use specific examples from two biomedical applications to describe anatomic segmentation, statistical feature generation and indexing, efficient retrieval architectures, and predictive results.
