Methods Inf Med 2009; 48(04): 361-370
DOI: 10.3414/ME0581
Original Articles
Schattauer GmbH

Design of a Web Portal for Interdisciplinary Image Retrieval from Multiple Online Image Resources

F. J. Kammerer
1   Lehrstuhl für Medizinische Informatik, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
,
T. Frankewitsch
2   IT-Zentrum – Forschung und Lehre, Universitätsklinikum Münster, Münster, Germany
,
H.-U. Prokosch
1   Lehrstuhl für Medizinische Informatik, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
› Author Affiliations
Further Information

Publication History

Received: 24 June 2008

Accepted: 10 January 2009

Publication Date:
17 January 2018 (online)

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Summary

Objectives: Images play an important role in medicine. Finding the desired images within the multitude of online image databases is a time-consuming and frustrating process. Existing websites do not meet all the requirements for an ideal learning environment for medical students. This work intends to establish a new web portal providing a centralized access point to a selected number of online image databases.

Methods: A back-end system locates images on given websites and extracts relevant meta-data. The images are indexed using UMLS and the MetaMap system provided by the US National Library of Medicine. Specially developed functions allow to create individual navigation structures. The front-end system suits the specific needs of medical students. A navigation structure consisting of several medical fields, university curricula and the ICD-10 was created. The images may be accessed via the given navigation structure or using different search functions. Cross-references are provided by the semantic relations of the UMLS.

Results: Over 25,000 images were identified and indexed. A pilot evaluation among medical students showed good first results concerning the acceptance of the developed navigation structures and search features.

Conclusion: The integration of the images from different sources into the UMLS semantic network offers a quick and an easy-to-use learning environment.