Share Email Print

Proceedings Paper

Classification of mammographic breast density by the histogram approach using neural networks
Author(s): Sachiko Goto; Yoshiharu Azuma; Tetsuhiro Sumimoto; Shigeru Eiho
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Our aim was to improve the accuracy of classifying x-ray mammographic breast densities. The histogram approach using the neural network was used for the purpose of constructing a flexible system. In this study the phantom of the synthetic breast-equivalent resin material for the process of the A/D conversion of mammograms was employed. The digital values can offset the difference in characteristics between the mammography system, the unit, etc. Furthermore the features of our system use the neural network, and then tune the neural network by the histogram of the digital values and by the radiologists' and expert mammographers' assessment ability. Although there was an observer's bias, our system was able to classify the breast density automatically according to that observer. This is only possible if the observer has been trained to some extent and is capable of maintaining an objective assessment according to the assessment criteria.

Paper Details

Date Published: 2 September 2003
PDF: 4 pages
Proc. SPIE 5253, Fifth International Symposium on Instrumentation and Control Technology, (2 September 2003); doi: 10.1117/12.521828
Show Author Affiliations
Sachiko Goto, Okayama Univ. Medical School (Japan)
Yoshiharu Azuma, Okayama Univ. Medical School (Japan)
Tetsuhiro Sumimoto, Okayama Univ. Medical School (Japan)
Shigeru Eiho, Kyoto Univ. (Japan)

Published in SPIE Proceedings Vol. 5253:
Fifth International Symposium on Instrumentation and Control Technology
Guangjun Zhang; Huijie Zhao; Zhongyu Wang, Editor(s)

© SPIE. Terms of Use
Back to Top