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Putting the Content Into Context: Features and Gaps in Image Retrieval

Putting the Content Into Context: Features and Gaps in Image Retrieval

Henning Müller, Jayashree Kalpathy-Cramer
Copyright: © 2009 |Volume: 4 |Issue: 1 |Pages: 11
ISSN: 1555-3396|EISSN: 1555-340X|ISSN: 1555-3396|EISBN13: 9781615202904|EISSN: 1555-340X|DOI: 10.4018/jhisi.2009010106
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MLA

Müller, Henning, and Jayashree Kalpathy-Cramer. "Putting the Content Into Context: Features and Gaps in Image Retrieval." IJHISI vol.4, no.1 2009: pp.88-98. http://doi.org/10.4018/jhisi.2009010106

APA

Müller, H. & Kalpathy-Cramer, J. (2009). Putting the Content Into Context: Features and Gaps in Image Retrieval. International Journal of Healthcare Information Systems and Informatics (IJHISI), 4(1), 88-98. http://doi.org/10.4018/jhisi.2009010106

Chicago

Müller, Henning, and Jayashree Kalpathy-Cramer. "Putting the Content Into Context: Features and Gaps in Image Retrieval," International Journal of Healthcare Information Systems and Informatics (IJHISI) 4, no.1: 88-98. http://doi.org/10.4018/jhisi.2009010106

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Abstract

Digital management of medical images is becoming increasingly important as the number of images being created in medical settings everyday is growing rapidly. Content-based image retrieval or techniques based on the query-by-example paradigm have been studied extensively in computer vision. However, the global, low level visual features automatically extracted by these algorithms do not always correspond to high level concepts that a user has in his mind for searching. The role of image retrieval in diagnostic medicine can be quite complex, making it difficult for the user to express his/her information needs appropriately. Image retrieval in medicine needs to evolve from purely visual retrieval to a more holistic, case-based approach that incorporates various multimedia data sources. These include multiple images, free text, structured data, as well as external knowledge sources and ontologies.

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