Novel features for microcalcification detection in digital mammogram images based on wavelet and statistical analysis

Computer Aided Diagnosis (CAD) systems play an important role in early detection of breast cancer. In this study, we propose a CAD system based on a novel feature set for detection of microcalcifications. The new features are inspired from several statistical observations for some classical features such as higher order statistical (HOS) features, Discrete Wavelet Transform (DWT) and Wavelet Packet Decomposition (WPD) based features. Our study employs DWT for preprocessing and Student's t-test for evaluation and reduction of the features. Support vector machines (SVM) with linear and RBF kernels was used. The proposed system achieved 98.43%, 96.74% sensitivity, 93.34%, 94.87% specificity and 95.80%, 95.78% accuracy using RBF kernel for MIAS and DDSM databases respectively.