Two-Level Algorithm for MCs Detection in Mammograms Using Diverse-Adaboost-SVM

Clustered micro calcifications (MCs) are one of the early signs of breast cancer. In this paper, we propose a new computer aided diagnosis (CAD) system for automatic detection of MCs in two steps. First, pixels corresponding to potential micro calcifications are found using a multilayer feed-forward neural network. The input of this network consists of 4 wavelet and 2 gray-level features. The output of the network is then transformed to potential micro calcification objects using spatial 4-point connectivity. Second, we extract 25 features from the potential MC objects and use Diverse Adaboost SVM (DA-SVM) and 3 other classifiers to detect individual MCs. A free-response operating characteristics (FROC) curve issued to evaluate the performance of the CAD system. The 90.44% mean TP detection rate is achieved at the cost of 1.043 FP per image by using DA-SVM shows a quite satisfactory detection performance of CAD system.