Paper Details: Downloads: 656
Serial Number: P1150545008
Title: Performance Analysis of Rough Reduct Algorithms in Mammogram
Authors: K. Thangavel and M. Karnan and A. Pethalakshmi
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 Decision Relative Discernibility based reduction, Heuristic approach, Hu’s algorithm, Quick Reduct (QR), and Variable Precision Rough Set (VPRS) are used to reduce the extracted features. The performance of all the 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.
Keywords: Feature reduction, Rough set, Mammogram-Breast cancer.
Journal/Conference: International Journal of Graphics, Vision and Image Processing
Volume: 5
Issue: 8
Submission Date: 4/1/2005 12:00:00 AM
Review Date: 6/1/2005 12:00:00 AM
Publishing Date: 8/1/2005 12:00:00 AM
Article Downloads: 656
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