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Mammogram retrieval through machine learning within BI-RADS standards.
- Source :
-
Journal of biomedical informatics [J Biomed Inform] 2011 Aug; Vol. 44 (4), pp. 607-14. Date of Electronic Publication: 2011 Jan 28. - Publication Year :
- 2011
-
Abstract
- A content-based mammogram retrieval system can support usual comparisons made on images by physicians, answering similarity queries over images stored in the database. The importance of searching for similar mammograms lies in the fact that physicians usually try to recall similar cases by seeking images that are pathologically similar to a given image. This paper presents a content-based mammogram retrieval system, which employs a query example to search for similar mammograms in the database. In this system the mammographic lesions are interpreted based on their medical characteristics specified in the Breast Imaging Reporting and Data System (BI-RADS) standards. A hierarchical similarity measurement scheme based on a distance weighting function is proposed to model user's perception and maximizes the effectiveness of each feature in a mammographic descriptor. A machine learning approach based on support vector machines and user's relevance feedback is also proposed to analyze the user's information need in order to retrieve target images more accurately. Experimental results demonstrate that the proposed machine learning approach with Radial Basis Function (RBF) kernel function achieves the best performance among all tested ones. Furthermore, the results also show that the proposed learning approach can improve retrieval performance when applied to retrieve mammograms with similar mass and calcification lesions, respectively.<br /> (Copyright © 2011 Elsevier Inc. All rights reserved.)
- Subjects :
- Algorithms
Breast anatomy & histology
Breast pathology
Breast Neoplasms classification
Breast Neoplasms diagnostic imaging
Breast Neoplasms pathology
Calcinosis classification
Calcinosis pathology
Databases, Factual
Female
Humans
Logistic Models
Artificial Intelligence
Database Management Systems
Image Interpretation, Computer-Assisted methods
Mammography methods
Subjects
Details
- Language :
- English
- ISSN :
- 1532-0480
- Volume :
- 44
- Issue :
- 4
- Database :
- MEDLINE
- Journal :
- Journal of biomedical informatics
- Publication Type :
- Academic Journal
- Accession number :
- 21277387
- Full Text :
- https://doi.org/10.1016/j.jbi.2011.01.012