Content-based Image Retrieval for Scientific Literature Access

2009 ◽  
Vol 48 (04) ◽  
pp. 371-380 ◽  
Author(s):  
S. Antani ◽  
Rodney Long ◽  
T. M. Deserno
Keyword(s):  
Image Retrieval ◽  
Quantitative Estimate ◽  
Literature Search ◽  
Medical Literature ◽  
Scientific Literature ◽  
High Impact ◽  
Content Based Image Retrieval ◽  
Medical Applications ◽  
Cross Language ◽  
Search And Retrieval

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.

Critical Evaluation of a Computer-Based Medical Literature Search and Retrieval System

1972 ◽  
Vol 51 (5) ◽  
pp. 47-50
Author(s):  
William E. Chapman ◽  
Richard L. Rapport ◽  
F. W. Lancaster ◽  
J. Kiffin Penry
Keyword(s):  
Literature Search ◽  
Retrieval System ◽  
Medical Literature ◽  
Critical Evaluation ◽  
Computer Based ◽  
Search And Retrieval

Content-based image retrieval in medical applications: a novel multistep approach

1999 ◽  
Author(s):  
Thomas M. Lehmann ◽  
Berthold B. Wein ◽  
Joerg Dahmen ◽  
Joerg Bredno ◽  
Frank Vogelsang ◽  
...  
Keyword(s):  
Image Retrieval ◽  
Content Based Image Retrieval ◽  
Medical Applications

scholarly journals Survey on content based image retrieval

2018 ◽  
Vol 7 (4.5) ◽  
pp. 471 ◽  
Author(s):  
Ankitha Varma ◽  
Dr. Kamalpreet Kaur
Keyword(s):  
Image Retrieval ◽  
Digital Technology ◽  
Digital Data ◽  
Geographical Information ◽  
Content Based Image Retrieval ◽  
Huge Amount ◽  
Query Image ◽  
Use Of Internet ◽  
Evaluation Parameters ◽  
Search And Retrieval

Now-a-days, because of the advancement in the digital technology and the use of internet, a huge amount of digital data is available in the form of medical images, remote sensing, digital museums, geographical information, etc. This has lead to the need of accurate and efficient techniques for the search and retrieval of relevant images from such voluminous datasets. Content based image retrieval (CBIR) is one such approach which is increasingly being used to search and retrieve query image from the databases. CBIR combines features of color, texture as well as shape which ease out the process of extracting desired information from the retrieved images. This paper pre- sents a systematic and a detailed review of the CBIR method along with the different databases and evaluation parameters used for the analysis. An attempt has been made to include an exhaustive literature survey of the various CBIR approaches. 

Your Personal, Virtual Librarian

2010 ◽  
pp. 199-234
Author(s):  
Alexander Krumpholz ◽  
David Hawking ◽  
Tom Gedeon
Keyword(s):  
Literature Search ◽  
Medical Literature ◽  
Scientific Literature ◽  
Knowledge Worker ◽  
Special Focus ◽  
Retrieval Process ◽  
Management Tools ◽  
Search Tasks ◽  
Automatic Retrieval ◽  
Critical Activity

Searching scientific literature is a common and critical activity for research scientists, students, and professionals such as medical clinicians. These search tasks can be time consuming and repetitive, but literature search and management tools are already making the job much easier. This chapter analyses the literature retrieval process, reviews some currently available tools and elaborates on potential future support for the knowledge worker by an intelligent automated assistant. A special focus of this chapter is the automatic retrieval of medical literature and the exploration of the answer space.

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