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Proceedings Paper

Evidential reasoning based on Dempster-Shafer theory and its application to medical image analysis
Author(s): Shiuh-Yung James Chen; Wei-Chung Lin; Chin-Tu Chen
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Paper Abstract

In this paper, we propose the knowledge representation and evidence propagation schemes based on multivariate belief functions and present a medical image recognition system as an example to demonstrate their effectiveness in evidential reasoning. The multivariate belief functions, defined in a product space, are employed to represent domain specific knowledge such as rules or propositions. The product space and its sub-spaces (margins) are composed of a set of compatible frames. The logical relationships among these margins can be easily defined by using multivariate belief functions. Propagation of evidence is executed by extending or marginalizing the associated multivariate belief function to those margins characterized by their logical relationships. By using the blackboard-based architecture and the profound features of D-S theory, the proposed image recognition system is capable of mimicking the reasoning process of a human expert in recognizing anatomical entities efficiently in a set of correlated x-ray computed tomography, proton density weighted, and T2-weighted magnetic resonance images. Additionally, the proposed schemes can be also applied to other problem domains by employing the appropriate knowledge base. Several experiment results are given to illustrate the performance of the proposed system.

Paper Details

Date Published: 29 October 1993
PDF: 12 pages
Proc. SPIE 2032, Neural and Stochastic Methods in Image and Signal Processing II, (29 October 1993); doi: 10.1117/12.162048
Show Author Affiliations
Shiuh-Yung James Chen, Univ. of Chicago (United States)
Wei-Chung Lin, Northwestern Univ. (United States)
Chin-Tu Chen, Univ. of Chicago (United States)

Published in SPIE Proceedings Vol. 2032:
Neural and Stochastic Methods in Image and Signal Processing II
Su-Shing Chen, Editor(s)

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