Decision trees and integrated features for computer aided mammographic screening

Breast cancer is a serious problem, which in the United States causes 43,000 deaths a year, eventually striking 1 in 9 women. Early detection is the only effective countermeasure, and mass mammography screening is the only reliable means for early detection. Mass screening has many shortcomings which could be addressed by a computer-aided mammographic screening system. Accordingly, we have applied the pattern recognition methods developed in earlier investigations of speculated lesions in mammograms to the detection of microcalcifications and circumscribed masses, generating new, more rigorous and uniform methods for the detection of both those signs. We have also improved the pattern recognition methods themselves, through the development of a new approach to combinations of multiple classifiers.