Unsupervised feature selection in digital mammogram image using rough set theory.
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.
Department of Computer Science, Periyar University, Salem, Tamil Nadu 636011, India. drktvelu@yahoo.com
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Thangavel
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Velayutham
D000465:Algorithms; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D057225:Data Mining / Q000379:methods; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D008327:Mammography / Q000379:methods*