Knowledge Augmented Medical Image Retrieval System
Tsong-Hai Lee and
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Yao Shieh: Chang Gung University, Taiwan
Mengkai Shieh: Russian Medical Academy of Postgraduate Education, Russia
Chien-Hung Chang: Chang Gung University, Taiwan
Tsong-Hai Lee: Chang Gung University, Taiwan
Scott Goodwin: University of California at Irvine, USA
We are living in an era of information explosion. Medical images are generated at an accelerating rate. A more effective information technology to deal with storage and retrieval of such huge amount of medical image data is needed. The purpose of this paper is to demonstrate by presenting a concrete example that a knowledge augmented medical image retrieval system by means of automated feature extraction is possible. It provides not only decision support in the clinical setting but an education/ research platform upon which issues regarding computer-aided diagnosis and inter-observer variations among radiologists can be addressed systematically and effectively. It inspires more productive man-computer collaboration by bringing computer intelligence to new heights through knowledge transfer to meet the challenge of information explosion.
Keywords: knowledge; information technology; content based image retrieval; feature extraction; database; education (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:tkp:mklp13:1253-1258
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