Document Citation

Title:
Content-based Image Retrieval For Scientific Literature Access.

Author(s):
Deserno TM, Antani S, Long LR.

Institution(s):
1) Department of Medical Informatics, Aachen University of Technology (RWTH), Aachen, Germany
2) U. S. National Library of Medicine, U. S. National Institutes of Health, Bethesda, Maryland, USA

Source:
Methods of Information in Medicine. April 2009;48(4):371-80.

Abstract:
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 contentbased 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 ex - tracted 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 Re - trieval 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.

Publication Type: JOURNAL

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