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Proceedings Paper

Knowledge Representation Of CT Scans Of The Head
Author(s): Laurens V Ackerman; M W. Burke; Roy Rada
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Paper Abstract

We have been investigating diagnostic knowledge models which assist in the automatic classification of medical images by combining information extracted from each image with knowledge specific to that class of images. In a more general sense we are trying to integrate verbal and pictorial descriptions of disease via representations of knowledge, study automatic hypothesis generation as related to clinical medicine, evolve new mathematical image measures while integrating them into the total diagnostic process, and investigate ways to augment the knowledge of the physician. Specifically, we have constructed an artificial intelligence knowledge model using the technique of a production system blending pictorial and verbal knowledge about the respective CT scan and patient history. It is an attempt to tie together different sources of knowledge representation, picture feature extraction and hypothesis generation. Our knowledge reasoning and representation system (KRRS) works with data at the conscious reasoning level of the practicing physician while at the visual perceptional level we are building another production system, the picture parameter extractor (PPE). This paper describes KRRS and its relationship to PPE.

Paper Details

Date Published: 15 June 1984
PDF: 7 pages
Proc. SPIE 0454, Application of Optical Instrumentation in Medicine XII, (15 June 1984); doi: 10.1117/12.939365
Show Author Affiliations
Laurens V Ackerman, Henry Ford Hospital (United States)
M W. Burke, Henry Ford Hospital (United States)
Roy Rada, Wayne State University (United States)


Published in SPIE Proceedings Vol. 0454:
Application of Optical Instrumentation in Medicine XII
Samuel J. Dwyer; Roger H. Schneider, Editor(s)

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