Method and system for the detection of microcalcifications in digital mammograms

A method and system for the detection of microcalcifications in digital mammograms. Digital mammograms are obtained and regions-of-interest (ROIs) are selected therefrom which contain suspected microcalcifications, either individual or clustered microcalcifications. The suspect ROIs are background-trend corrected, followed by Fourier transformation and power spectrum calculation to perform detection in the frequency domain. Detection can also be carried out in the spatial domain by omitting the Fourier transformation and power spectrum calculation. The ROI is then scaled for input into a neural network trained to detect microcalcifications. The neural network outputs ROIs with detected microcalcifications. The method and system can also include normalizing the background-trend corrected ROIs and imputing the normalized ROI to a shift-invariant neural network trained to detect microcalcifications. The output ROI of the shift-invariant neural network is thresholded to remove additional false positive detections, and then the thresholded ROI undergoes a cluster detection to detect clustered microcalcifications. Feature extraction techniques can be applied to the remaining ROI to remove additional false positive detections.