Microcalcification detection in digital mammograms based on wavelet analysis and neural networks

Mass detection is one of the main computer-aided mammographic breast cancer detection techniques. Early detection of primary tumor is an essential and effective method to reduce mortality. Computer-aided diagnosis system can be very helpful for radiologist in detecting and diagnosing abnormalities earlier and faster than traditional screening methods. This paper presents a new approach for detecting microcalcification in digital mammograms employing the combination of wavelet analysis of the image by applying artificial neural networks (ANN) for building the classifiers. The microcalcification corresponds to high frequency components and the detection of microcalcification is achieved by extracting the microcalcifiaction features from the wavelet analysis of the image and we use these results as an input of neural network for classification. The neural network contains one input, two hidden and one output .The system is classified normal from abnormal, mass for microcalcification and abnormal severity(benign or malignant).The experiments demonstrate that our approach can provide true detection rate approximately 87% and 0 false detection per image which is significant. The evaluation of the system is carried on Mammography Image analysis Society (MIAS) dataset.