Blackboard architecture for medical image interpretation

Darryl N. Davis; Christopher J. Taylor

doi:10.1117/12.45239

1 June 1991 Blackboard architecture for medical image interpretation

Darryl N. Davis, Christopher J. Taylor

Proceedings Volume 1445, Medical Imaging V: Image Processing; (1991) https://doi.org/10.1117/12.45239
Event: Medical Imaging '91, 1991, San Jose, CA, United States

Abstract

There is a growing interest in using sophisticated knowledge-based systems for biomedical image interpretation. We present a principled attempt to use artificial intelligence methodologies in interpreting lateral skull x-ray images. Such radiographs are routinely used in cephalometric analysis to provide quantitative measurements useful to clinical orthodontists. Manual and interactive methods of analysis are known to be error prone and previous attempts to automate this analysis typically fail to capture the expertise and adaptability required to cope with the variability in biological structure and image quality. An integrated model-based system has been developed which makes use of a blackboard architecture and multiple knowledge sources. A model definition interface allows quantitative models, of feature appearance and location, to be built from examples as well as more qualitative modelling constructs. Visual task definition and blackboard control modules allow task-specific knowledge sources to act on information available to the blackboard in a hypothesise and test reasoning cycle. Further knowledge-based modules include object selection, location hypothesis, intelligent segmentation, and constraint propagation systems. Alternative solutions to given tasks are permitted.

Citation Download Citation

Darryl N. Davis and Christopher J. Taylor "Blackboard architecture for medical image interpretation", Proc. SPIE 1445, Medical Imaging V: Image Processing, (1 June 1991); https://doi.org/10.1117/12.45239

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