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

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.

Content-based Image Retrieval in Medical Applications

2004 ◽  
Vol 43 (04) ◽  
pp. 354-361 ◽  
Author(s):  
M. O. Güld ◽  
C. Thies ◽  
B. Fischer ◽  
K. Spitzer ◽  
D. Keysers ◽  
...  
Keyword(s):  
Image Retrieval ◽  
Distributed System ◽  
Communication Systems ◽  
Expert Knowledge ◽  
Information Modeling ◽  
Content Based Image Retrieval ◽  
Reference Database ◽  
Medical Applications ◽  
Processing Scheme ◽  
Data Layer

Summary Objectives: To develop a general structure for semantic image analysis that is suitable for content-based image retrieval in medical applications and an architecture for its efficient implementation. Methods: Stepwise content analysis of medical images results in six layers of information modeling incorporating medical expert knowledge (raw data layer, registered data layer, feature layer, scheme layer, object layer, knowledge layer). A reference database with 10,000 images categorized according to the image modality, orientation, body region, and biological system is used. By means of prototypes in each category, identification of objects and their geometrical or temporal relationships are handled in the object and the knowledge layer, respectively. A distributed system designed with only three core elements is implemented: (i) the central database holds program sources, processing scheme descriptions, images, features, and administrative information about the workstation cluster; (ii) the scheduler balances distributed computing; and (iii) the web server provides graphical user interfaces for data entry and retrieval, which can be easily adapted to a variety of applications for content-based image retrieval in medicine. Results: Leaving-one-out experiments were distributed by the scheduler and controlled via corresponding job lists offering transparency regarding the viewpoints of a distributed system and the user. The proposed architecture is suitable for content-based image retrieval in medical applications. It improves current picture archiving and communication systems that still rely on alphanumerical descriptions, which are insufficient for image retrieval of high recall and precision.

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