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International Journal of Biomedical Imaging
Volume 2009, Article ID 680508, 12 pages
Research Article

Hybrid Mammogram Classification Using Rough Set and Fuzzy Classifier

Department of Electrical and Computer Engineering, Western Michigan University, MI 49008, USA

Received 29 December 2008; Revised 18 June 2009; Accepted 25 July 2009

Academic Editor: Jayanta Mukherjee

Copyright © 2009 Fadi Abu-Amara and Ikhlas Abdel-Qader. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


We propose a computer aided detection (CAD) system for the detection and classification of suspicious regions in mammographic images. This system combines a dimensionality reduction module (using principal component analysis), a feature extraction module (using independent component analysis), and a feature subset selection module (using rough set model). Rough set model is used to reduce the effect of data inconsistency while a fuzzy classifier is integrated into the system to label subimages into normal or abnormal regions. The experimental results show that this system has an accuracy of 84.03% and a recall percentage of 87.28%.