Knowledge-based radiologic image retrieval using axes of clinical relevance.
This paper describes an approach to computer-based intelligent retrieval of feature-coded radiographic images relevant to a specific case being evaluated. The approach involves partitioning the search space along clinically natural groups of attributes which we call "axes of clinical relevance." By embedding knowledge about the domain to help direct the search process, a clinician's needs may be met more comprehensively. Domain knowledge, supplied to the system as "axis heuristics," may make search more robust. These heuristics provide a graded, progressive relaxation of the search constraints. This approach helps show the user groups of images in order of probable relevance to a current case. AXON is a prototype knowledge-based system constructed to illustrate this approach in the domain of chest imaging. This paper describes the AXON system, demonstrates some searches which illustrate the potential utility of this approach, and discusses preliminary tests of the search strategies used by AXON.
Cohn
A I
AI
Department of Anesthesiology, Yale University School of Medicine, New Haven, Connecticut 06510.
Miller
P L
PL
Fisher
P R
PR
Mutalik
P G
PG
Swett
H A
HA
D005103:Expert Systems; D006751:Hospital Information Systems; D011873:Radiology Information Systems; D012985:Software Design; D013358:Subject Headings; D014584:User-Computer Interface