Wavelet and curvelet analysis for the classification of microcalcifiaction using mammogram images
B. K. Bala, Audithan Sivaraman · 2014
Breast cancer is the second of the deadliest cancers causing women mortality around the world. The early prediction of breast cancer is the key to reduce women mortality. The major sign of breast cancer is the occurrence of microcalcification clusters in the breast. To efficiently diagnose the breast cancer, an efficient classification system for microcalcification in digital mammogram image is proposed in this study. The classification of microcalcification system is presented based on discrete curvelet transform (DCT) and discrete wavelet transforms (DWT). The energy features are extracted from the mammogram images by using aforementioned transformations at various level of decomposition and k nearest neighbor (KNN) classifier is used for classification task. Experimental results show that the DCT based classification system provides satisfactory result over DWT.