Our objective is to offer clinicians wider access to evolving medical image processing (MIP) techniques, crucial to improve assessment and quantification of physiological processes, but difficult to handle for non-specialists in MIP. Based on artificial intelligence techniques, our approach consists in the development of a knowledge-based program supervision system, automating the management of MIP libraries. It comprises a library of programs, a knowledge base capturing the expertise about programs and data and a supervision engine. It selects, organizes and executes the appropriate MIP programs given a goal to achieve and a data set, with dynamic feedback based on the results obtained. It also advises users in the development of new procedures chaining MIP programs.. We have experimented the approach for an application of factor analysis of medical image sequences as a means of predicting the response of osteosarcoma to chemotherapy, with both MRI and NM dynamic image sequences. As a result our program supervision system frees clinical end-users from performing tasks outside their competence, permitting them to concentrate on clinical issues. Therefore our approach enables a better exploitation of possibilities offered by MIP and higher quality results, both in terms of robustness and reliability.

Date Published: 22 May 1997
PDF: 12 pages
Proc. SPIE 3035, Medical Imaging 1997: PACS Design and Evaluation: Engineering and Clinical Issues, (22 May 1997); doi: 10.1117/12.274625

Published in SPIE Proceedings Vol. 3035:
Medical Imaging 1997: PACS Design and Evaluation: Engineering and Clinical Issues
Steven C. Horii M.D.; G. James Blaine, Editor(s)