DCT based features for the detection of microcalcifications in digital mammograms

In this paper, a set of spectral domain features based on the discrete cosine transform DCT of mammograms are extracted from the X-ray image, the extracted features by the proposed methods are exploited to classify regions of interest ROIs into positive ROIs containing clustered microcalcifications and negative ROIs containing normal tissues. A three-layer back-propagation neural network is used as a classifier, the results of the neural network for the extracted features are evaluated by using a receiver operation characteristics ROC analysis, the proposed technique is shown to be superior to the conventional methods with respect to classification accuracy and computational complexity