Combining knowledge discovery from databases (KDD) and case-based reasoning (CBR) to support diagnosis of medical images

Andrew Stranieri; John Yearwood; Binh Pham

doi:10.1117/12.351621

8 July 1999 Combining knowledge discovery from databases (KDD) and case-based reasoning (CBR) to support diagnosis of medical images

Andrew Stranieri, John Yearwood, Binh Pham

Author Affiliations +

Proceedings Volume 3747, New Approaches in Medical Image Analysis; (1999) https://doi.org/10.1117/12.351621
Event: Research Workshop on Automated Medical Image Analysis, 1998, Ballarat, Australia

Abstract

The development of data warehouses for the storage and analysis of very large corpora of medical image data represents a significant trend in health care and research. Amongst other benefits, the trend toward warehousing enables the use of techniques for automatically discovering knowledge from large and distributed databases. In this paper, we present an application design for knowledge discovery from databases (KDD) techniques that enhance the performance of the problem solving strategy known as case- based reasoning (CBR) for the diagnosis of radiological images. The problem of diagnosing the abnormality of the cervical spine is used to illustrate the method. The design of a case-based medical image diagnostic support system has three essential characteristics. The first is a case representation that comprises textual descriptions of the image, visual features that are known to be useful for indexing images, and additional visual features to be discovered by data mining many existing images. The second characteristic of the approach presented here involves the development of a case base that comprises an optimal number and distribution of cases. The third characteristic involves the automatic discovery, using KDD techniques, of adaptation knowledge to enhance the performance of the case based reasoner. Together, the three characteristics of our approach can overcome real time efficiency obstacles that otherwise mitigate against the use of CBR to the domain of medical image analysis.

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Andrew Stranieri, John Yearwood, and Binh Pham "Combining knowledge discovery from databases (KDD) and case-based reasoning (CBR) to support diagnosis of medical images", Proc. SPIE 3747, New Approaches in Medical Image Analysis, (8 July 1999); https://doi.org/10.1117/12.351621

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