You are here

Content-based image retrieval for scientific literature access.

Printer-friendly versionPrinter-friendly version
Deserno TM, Antani S, Long LR
Methods Inf Med. 2009;48(4):371-80. doi: 10.3414/ME0561. Epub 2009 Jul 20.
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 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 (ImageCLEF) 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.

Deserno TM, Antani S, Long LR. Content-based image retrieval for scientific literature access. Methods Inf Med. 2009;48(4):371-80. doi: 10.3414/ME0561. Epub 2009 Jul 20.