Rough set approach for classification of breast cancer mammogram images

Authors:
Aboul Ella Hassanien;Jafar M. Ali
Affiliations:
Collegue of Business Administration, Quantitative Methods and Information Systems Department, Kuwait University, Safat, Kuwait;Collegue of Business Administration, Quantitative Methods and Information Systems Department, Kuwait University, Safat, Kuwait
Venue:
WILF'03 Proceedings of the 5th international conference on Fuzzy Logic and Applications
Year:
2003

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Abstract

This paper presents a study on classification of breast cancers in digital mammography images, using rough set theory in conjunction with statistical feature extraction techniques. First, we improve the contrast of the digitized mammograms by applying computer image processing techniques to enhance x-ray images and then subsequently extract features from suspicious regions characterizing the underlying texture of the breast regions. Feature extractions are derived from the gray-level co-occurrence matrix, then the features were normalized and the rough set dependency rules are generated directly from the real value attribute vector. These rules can then be passed to a classifier for discrimination for different regions of interest to test whether they are normal or abnormal. The experimental results show that the proposed algorithm performs well reaching over 98 % in accuracy.