Automatic detection of calcification in mammograms

The authors propose a system for the detection of mammographic calcifications. Their method first segments the image into suspected calcification regions and then classifies each detected region as calcification or normal background. The segmentation method exploits new local thresholding and region growing techniques suitable for the detection of small blobs in a textured background. The next step of processing is to decrease the number of falsely detected blobs obtained in the first step using pattern recognition techniques. Seven features of the detected regions are used for classification of the segmented region. A quadratic classifier was used to classify mammographic calcification using the region´s features. The results of the experimental study using a set of 20 mammographic images shows that the proposed system has a good capability to detect calcifications in mammographic images