A New Method on Detection of Microcalcifications Based on Principle Components Analysis and Neural Network Classifier
Kong Ying · Computer and Modernization · 2008
At the present a regular mammographic microcalcification detection system is based on a three-step procedure: preprocessing and segmentation;feature extraction and classification;computer-aided detection and analysis.Typically,a neural network is usually used in feature extraction and classification procedure.In order to improve the classification ability of a neural network,we need accept the most representative features as input part.And the number of features must be helpful to the most effective feature extraction,or else the efficiency of classification will be greatly depressed.So an important task of the classification module is to train the input data set of a neural network and optimize features.In order to optimize features,a PCA is applied to reduce the dimensionality of the input vector.The experiment results show that the method has extensively high assessments of their effectiveness in terms of sensitivity and reduction of false positive rate.