Title: Unsupervised feature selection in digital mammogram image using rough set theory

Authors: K. Thangavel; C. Velayutham

Addresses: Department of Computer Science, Periyar University, Salem, Tamil Nadu 636011, India ' Department of Computer Science, Aditanar College of Arts and Science, Tiruchendur, Thoothukudi, Tamil Nadu 628216, India

Abstract: Feature Selection (FS) is a process which attempts to select features which are more informative. In this paper, a novel unsupervised FS in mammogram images, using rough set-based relative dependency measures, is proposed. A typical mammogram image processing system generally consists of mammogram image acquisition, pre-processing of image, segmentation and features extraction from the segmented mammogram image. The proposed unsupervised FS method is used to select features from data sets; the method is compared with existing rough set based supervised FS methods, and the classification performance of both methods are recorded and demonstrate the efficiency of this method.

Keywords: unsupervised feature selection; mammogram images; GLCM; grey level co-occurrence matrix; data mining; rough set theory; rough sets; digital mammograms; image processing; classification performance; breast cancer.

DOI: 10.1504/IJBRA.2012.049626

International Journal of Bioinformatics Research and Applications, 2012 Vol.8 No.5/6, pp.436 - 454

Published online: 10 Oct 2012 *

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