Content-based Image Retrieval for Scientific Literature Access

T. M. Deserno; S. Antani; Rodney L. Long

doi:10.3414/ME0561

Methods of Information in Medicine, Inhaltsverzeichnis

Methods Inf Med 2009; 48(04): 371-380
DOI: 10.3414/ME0561

Original Articles

Schattauer GmbH

Content-based Image Retrieval for Scientific Literature Access

T. M. Deserno

¹Department of Medical Informatics, Aachen University of Technology (RWTH), Aachen, Germany

²U. S. National Library of Medicine, U. S. National Institutes of Health, Bethesda, Maryland, USA

,

S. Antani

²U. S. National Library of Medicine, U. S. National Institutes of Health, Bethesda, Maryland, USA

,

Rodney L. Long

¹Department of Medical Informatics, Aachen University of Technology (RWTH), Aachen, Germany

› Institutsangaben

Abstract

Summary

Objectives: An increasing number of articles are published electronically in the scientific literature, but access is limited to alphanumerical search on title, author, or abstract, and may disregard numerous figures. In this paper, we estimate the benefits of using content-based image retrieval (CBIR) on article figures to augment traditional access to articles. Methods: We selected four high-impact journals from the Journal Citations Report (JCR) 2005. Figures were automatically extracted from the PDF article files, and manually classified on their content and number of sub-figure panels. We make a quantitative estimate by projecting from data from the Cross-Language Evaluation Forum (Image-CLEF) campaigns, and qualitatively validate it through experiments using the Image Retrieval in Medical Applications (IRMA) project.

Results: Based on 2077 articles with 11,753 pages, 4493 figures, and 11,238 individual images, the predicted accuracy for article retrieval may reach 97.08%.

Conclusions: Therefore, CBIR potentially has a high impact in medical literature search and retrieval.

Keywords

Content-based image retrieval (CBIR) - scientific literature - information system integration - radiology - data mining - information retrieval

Volltext

Referenzen

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