Computer aided detection of microcalcifications in digital mammograms using a synthetic technique

Clustered microcalcifications, (MCCs) on mammograms are important hints of breast cancer. Nevertheless, it is a complex and difficult task for radiologists to detect the clustered MCCs from the tissue background of mammograms only by naked eyes. This paper presents a method for computer-aided detection of MCCs in digital mammograms. The detection algorithm mainly consists of two different methods. The first one, based on the difference-image technique, recognizes high-frequency signals and very high-frequency noise. The second one is able to extract high-frequency signal by exploiting a wavelet based noise suppression and neural network (NN) classification. In the false-positive reduction step, false signals are separated from MCCs by means of an AND operation on signals from two methods. The algorithm is tested with a series of clinical mammograms. A sensitivity of more than 90% is obtained at a relatively low false-positive (FP) detection of 2.18 per image. The results are compared with the judgement of radiological experts, and they are very encouraging.