In this paper, an algorithm for the classification of microcalcification in digital mammograms using Nonsubsampled Contourlet Transform (NSCT) and Support Vector Machine (SVM) is presented. The classification of microcalcification is achieved by extracting the microcalcification features using NSCT with different scales. SVM classifier is used to classify the mammogram images based on the extracted microcalcification features. The system classifies the mammogram images as normal or abnormal, and the abnormal severity as benign or malignant. The evaluation of the system is carried on using mammography image analysis society (MIAS) database. The experimental result shows that the proposed method provides improved classification rate of over 90% for all cases. © 2012 Published by Elsevier Ltd.
Jasmine, J. S. L., Baskaran, S., & Govardhan, A. (2012). Nonsubsampled contourlet transform based classification of microcalcification in digital mammograms. In Procedia Engineering (Vol. 38, pp. 622–631). Elsevier Ltd. https://doi.org/10.1016/j.proeng.2012.06.077