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Proceedings Paper

Multi-agent method for masses classification in mammogram
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Paper Abstract

In this paper a new approach to mass classification based on multi-agent (MA) method is proposed for CAD in mammography. Multi-agent method is used here as a method that fuses the classification information from multiple classifiers in order to obtain a better decision result. Each agent receives the measurement value of individual classifier as initial value in classifying a sample and sends a message to a decision center. The decision center responds to this message with analysis of the correlation among these classifiers and their own decisions information. If the analysis result is conformable to a given standard, the center will provide a final result. Otherwise the message of agent had to be modified iteratively. 128 ROIs, including 64 benign masses and 64 malignant masses, from the DDSM, were used in the mass classification experiment. In comparison with the majority voting based fusion method, we evaluated the performance of proposed multi-agent fusion approach in distinguishing malignant and benign masses. The results demonstrated that the multi-agent method outperforms the majority voting method. Multi-agent fusion method yielded an accuracy of 95.47%, while the majority voting method had an accuracy of 92.23%. In addition, a preliminary study of MA method for mass classification under the bi-view model is reported. All of these experiments showed that the multi-agent method can play a significant role in multiple classifier fusion to improve mass classification in mammography.

Paper Details

Date Published: 9 March 2010
PDF: 8 pages
Proc. SPIE 7624, Medical Imaging 2010: Computer-Aided Diagnosis, 76242K (9 March 2010); doi: 10.1117/12.844305
Show Author Affiliations
Fangqing Peng, Hangzhou Dianzi Univ. (China)
Lihua Li, Hangzhou Dianzi Univ. (China)
Weidong Xu, Hangzhou Dianzi Univ. (China)
Wei Liu, Hangzhou Dianzi Univ. (China)


Published in SPIE Proceedings Vol. 7624:
Medical Imaging 2010: Computer-Aided Diagnosis
Nico Karssemeijer; Ronald M. Summers, Editor(s)

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