Artificial-neural-network-based classification of mammographic microcalcifications using image structure features

Atam P. Dhawan; Yateen S. Chitre; Myron Moskowitz

doi:10.1117/12.148694

29 July 1993 Artificial-neural-network-based classification of mammographic microcalcifications using image structure features

Atam P. Dhawan, Yateen S. Chitre, Myron Moskowitz

Author Affiliations +

Proceedings Volume 1905, Biomedical Image Processing and Biomedical Visualization; (1993) https://doi.org/10.1117/12.148694
Event: IS&T/SPIE's Symposium on Electronic Imaging: Science and Technology, 1993, San Jose, CA, United States

Abstract

Mammography associated with clinical breast examination and self-breast examination is the only effective and viable method for mass breast screening. It is however, difficult to distinguish between benign and malignant microcalcifications associated with breast cancer. Most of the techniques used in the computerized analysis of mammographic microcalcifications segment the digitized gray-level image into regions representing microcalcifications. We present a second-order gray-level histogram based feature extraction approach to extract microcalcification features. These features, called image structure features, are computed from the second-order gray-level histogram statistics, and do not require segmentation of the original image into binary regions. Several image structure features were computed for 100 cases of `difficult to diagnose' microcalcification cases with known biopsy results. These features were analyzed in a correlation study which provided a set of five best image structure features. A feedforward backpropagation neural network was used to classify mammographic microcalcifications using the image structure features. The network was trained on 10 cases of mammographic microcalcifications and tested on additional 85 `difficult-to-diagnose' microcalcifications cases using the selected image structure features. The trained network yielded good results for classification of `difficult-to- diagnose' microcalcifications into benign and malignant categories.

Citation Download Citation

Atam P. Dhawan, Yateen S. Chitre, and Myron Moskowitz "Artificial-neural-network-based classification of mammographic microcalcifications using image structure features", Proc. SPIE 1905, Biomedical Image Processing and Biomedical Visualization, (29 July 1993); https://doi.org/10.1117/12.148694

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available