NDLI: Learning of Perceptual Similarity From Expert Readers for Mammogram Retrieval

Content Provider	IEEE Xplore Digital Library
Author	Liyang Wei Yongyi Yang Wernick, M.N. Nishikawa, R.M.
Copyright Year	2007
Abstract	Content-based image retrieval relies critically on the use of a computerized measure of the similarity (i.e., relevance) of a query image to other images in a database. In this work, we explore a superivised learning approach for retrieval of mammogram images, of which the goal is to serve as a diagnostic aid for breast cancer. We propose that the most meaningful measure is one that is designed specifically to match that perceived by the radiologists in their interpretation of mammogram lesions. In our approach, we model the notion of similarity as an unknown function of the image features characterizing the lesions, and use modern machine-learning algorithms to learn this function from similarity scores collected from radiologists in reader studies. This approach is evaluated using data collected from an observer study with a set of clinical mammograms. Our results demonstrate that the proposed machine learning approach can be used to model the notion of similarity as judged by expert readers in their interpretation of mammogram images and that it can outperform alternative similarity measures derived from unsupervised learning.
Sponsorship	IEEE Signal Processing Society
Starting Page	53
Ending Page	61
Page Count	9
File Size	2208036
File Format	PDF
ISSN	19324553
Volume Number	3
Issue Number	1
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-02-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image retrieval Lesions Information retrieval Image databases Breast cancer Biomedical imaging Medical diagnostic imaging Pathology Mammography Machine learning supervised learning Content-based image retrieval mammogram multidimensional scaling perceptual similarity similarity measure
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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