Diagnostic Screening of Digital Mammograms Using Wavelets and Neural Networks to Extract Structure

As the primary tool for detecting breast carcinoma, mammography provides visual images from which a trained radiologist can identify suspicious areas that suggest the presence of cancer. We describe an approach to image processing that reduces an image to a small number of values based on its structural characteristics using wavelets and neural networks. To illustrate its utility, we apply this methodology to the automatic screening of mammograms for mass lesions. Our results approach performance levels of trained human mammographers.