Cart (Loading....) | Create Account
Close category search window
 

Online learning of relevance feedback from expert readers for mammogram retrieval

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Jung Hun Oh ; Dept. of Radiat. Oncology, Washington Univ. Sch. of Med., St. Louis, MO, USA ; El Naqa, I. ; Yongyi Yang

In content-based image retrieval (CBIR) relevance feedback schemes have been studied as a means to boost the retrieval performance in recent years. Despite the efforts in development of efficient algorithms for retrieving desired images from image databases, there often remains a gap between low-level image features and high-level semantic understanding in CBIR systems. In this paper, we investigate a technique based on online learning by relevance feedback for retrieval of mammogram images that contain perceptually similar lesions with clustered microcalcifications. Our approach applies support vector machine (SVM) regression for supervised learning and employs the concept of incremental learning to incorporate relevance feedback online. The proposed approach is demonstrated using a database of 200 mammogram images with clustered microcalcifications scored by experienced radiologists.

Published in:

Signals, Systems and Computers, 2009 Conference Record of the Forty-Third Asilomar Conference on

Date of Conference:

1-4 Nov. 2009

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.