Papers

Peer-reviewed
Oct, 2004

Medical image classification using genetic-algorithm based fuzzy-logic approach

JOURNAL OF ELECTRONIC IMAGING
  • DY Tsai
  • ,
  • Y Lee
  • ,
  • M Sekiya
  • ,
  • M Ohkubo

Volume
13
Number
4
First page
780
Last page
788
Language
English
Publishing type
Research paper (scientific journal)
DOI
10.1117/1.1786607
Publisher
I S & T - SOC IMAGING SCIENCE TECHNOLOGY

In this paper we present a genetic-algorithm-based fuzzy-logic approach for computer-aided diagnosis scheme in medical imaging. The scheme is applied to discriminate myocardial heart disease from echocardiographic images and to detect and classify clustered microcalcifications from mammograms. Unlike the conventional types of membership functions such as trapezoid, triangle, S curve, and singleton used in fuzzy reasoning, Gaussian-distributed fuzzy membership functions (GDMFs) are employed in the present study. The GDMFs are initially generated using various texture-based features obtained from reference images. Subsequently the shapes of GDMFs are optimized by a genetic-algorithm learning process. After optimization, the classifier is used for disease discrimination. The results of our experiments are very promising. We achieve an average accuracy of 96% for myocardial heart disease and accuracy of 88.5% at 100% sensitivity level for micro-calcification on mammograms. The results demonstrated that our proposed genetic-algorithm-based fuzzy-logic approach is an effective method for computer-aided diagnosis in disease classification. 2004, SPIE and IST.

Link information
DOI
https://doi.org/10.1117/1.1786607
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000224678400012&DestApp=WOS_CPL
URL
http://electronicimaging.spiedigitallibrary.org/article.aspx?articleid=1098363&resultClick=1
ID information
  • DOI : 10.1117/1.1786607
  • ISSN : 1017-9909
  • Web of Science ID : WOS:000224678400012

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