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Knowledge Augmented Medical Image Retrieval System

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Author Info

  • 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)

Registered author(s):

    Abstract

    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.

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    Bibliographic Info

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    This chapter was published in: Yao Shieh & Mengkai Shieh & Chien-Hung Chang & Tsong-Hai Lee & Scott Goodwin , , pages 1253-1258, 2013.

    This item is provided by ToKnowPress in its series Active Citizenship by Knowledge Management & Innovation: Proceedings of the Management, Knowledge and Learning International Conference 2013 with number 1253-1258.

    Handle: RePEc:tkp:mklp13:1253-1258

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    Web page: http://www.toknowpress.net/proceedings/978-961-6914-02-4/

    Related research

    Keywords: knowledge; information technology; content based image retrieval; feature extraction; database; education;

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