Detecting microcalcifications in digitised mammograms by a computer aided diagnostic system

Breast cancer is now one of the most common forms of cancer and the leading cause of mortality in women in the developed countries. Early detection of breast cancer is currently the way to reduce breast cancer mortality and enhance the cure rate. Mammogram screening is widely recognized as the most reliable method for early detection of lesions and clustered microcalcifications, which are the two prominent symptoms of breast cancer. This thesis presents an image processing procedure for the automatic detection of clustered microcalcifications in digitized mammograms. This method consists of two main steps. First, possible microcalcification pixels in the mammograms are segmented out using wavelet features, and grouped into potential individual microcalcification objects by their spatial connectivity. Second, individual microcalcifications are detected by using the structure features extracted from the potential microcalcification objects. The classifiers used in the two steps are feedforward neural networks. The method is applied to 40 regions of interest extracted from mammograms in the Nijimegen database containing 144 clusters of microcalcifications. Results show that the proposed procedure gives satisfactory detection performance. In particular, a 97 percent mean true positive detection rate is achieved at the cost of 1.67 false positive in the whole dataset