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

Authors

Barry L. Kalman, Washington University in St Louis
Stan C. Kwasny, Washington University in St. Louis
William R. Reinus, Washington University in St. Louis

Document Type

Technical Report

Department

Computer Science and Engineering

Publication Date

1998-01-01

Filename

WUCS-98-20.PDF

Technical Report Number

WUCS-98-20

Abstract

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.

Comments

Permanent URL: http://dx.doi.org/10.7936/K7FQ9TTM

Recommended Citation

Kalman, Barry L.; Kwasny, Stan C.; and Reinus, William R., "Diagnostic Screening of Digital Mammograms Using Wavelets and Neural Networks to Extract Structure" Report Number: WUCS-98-20 (1998). All Computer Science and Engineering Research.
https://openscholarship.wustl.edu/cse_research/472