Paper Info
Title
Learning Of Perceptual Similarity From Expert Readers For Mammogram Retrieval
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.
Year
DOI
Venue
2006
10.1109/JSTSP.2008.2011159
IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING
Keywords
Field
DocType
Content-based image retrieval, mammogram, multidimensional scaling, perceptual similarity, similarity measure, supervised learning
Computer vision,Mammography,Information retrieval,Similarity measure,Multidimensional scaling,Computer science,Feature (computer vision),Image retrieval,Supervised learning,Unsupervised learning,Artificial intelligence,Content-based image retrieval
Conference
Volume
Issue
ISSN
3
1
1932-4553
Citations 
PageRank 
References 
9
0.94
9
Authors
4
Name
Order
Citations
PageRank
Liyang Wei116712.04
Yongyi Yang21409140.74
Robert M Nishikawa359958.25
Miles N. Wernick459561.13