A multi-stage neural network aided system for detection of microcalcifications in digitized mammograms

We propose a multi-stage detection system for microcalcification. A connectionist online feature selection technique is used to identify a set of good features from a set of 87 features computed at a few randomly selected positive (calcified) and negative (normal) pixels. A neural network is then trained with the selected features. The network output is cleaned using connected component analysis and an algorithm for removing thin elongated structures. A measure of local density (called mountain potential) of the calcified points is then computed at every suspected pixel of these cleaned images and the peak of the mountain potential is used to classify mammograms as calcified or normal. The system is tested on a set of 17 mammograms comprising 10 abnormal and seven normal images which are not used in training and the system is found to perform very well. Moreover for each abnormal image, the system is able to locate the calcified regions quite accurately.