Detecting microcalcification clusters in digital mammograms using combination of wavelet and neural network

This paper presents an approach for detecting microcalcification in digital mammograms employing combination of artificial neural networks (ANN) and wavelet-based subband image decomposition. The microcalcifications correspond to high-frequency components of the image spectrum, detection of microcalcifications is achieved by decomposing the mammograms into different frequency subbands, suppressing the low-frequency subband, a and finally, reconstructing the mammogram from the subbands containing only high frequencies. We use these results as an input of neural network for classification. The neural network contains one input, two hidden and one output layers. Layers have 30, 45, 20, and 1 neurons respectively. The proposed methodology is tested using the Nijmegen and the mammographic image analysis society (MIAS) mammographic databases. Results are presented as the receiver operating characteristic (ROC) performance and are quantified by the area under the ROC curve (Az).