Mammography Feature Selection using Rough set Theory

Abstract

Microcalcification on X-ray mammogram is a significant mark for early detection of breast cancer. Texture analysis methods can be applied to detect clustered microcalcification in digitized mammograms. In order to improve the predictive accuracy of the classifier, the original number of feature set is reduced into smaller set using feature reduction techniques. In this paper rough set based reduction algorithms such as , Quickreduct (QR) and proposes Modified Quickreduct (MQR) are used to reduce the extracted features. The performance of both algorithms is compared. The Gray Level Co-occurrence Matrix (GLCM) is generated for each mammogram to extract the Haralick features as feature set. The reduction algorithms are tested on 161 pairs of digitized mammograms from Mammography Image Analysis Society (MIAS) database.