IDEAS home Printed from https://ideas.repec.org/h/tkp/mklp13/1253-1258.html
   My bibliography  Save this book chapter

Knowledge Augmented Medical Image Retrieval System

Author

Listed:
  • 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)

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.

Suggested Citation

  • Yao Shieh & Mengkai Shieh & Chien-Hung Chang & Tsong-Hai Lee & Scott Goodwin, 2013. "Knowledge Augmented Medical Image Retrieval System," Active Citizenship by Knowledge Management & Innovation: Proceedings of the Management, Knowledge and Learning International Conference 2013,, ToKnowPress.
    • Handle: RePEc:tkp:mklp13:1253-1258
    as

    Download full text from publisher

    File URL: http://www.toknowpress.net/ISBN/978-961-6914-02-4/papers/ML13-397.pdf
    File Function: full text
    Download Restriction: no
    File URL: http://www.toknowpress.net/ISBN/978-961-6914-02-4/MakeLearn2013.pdf
    File Function: Conference Programme
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:tkp:mklp13:1253-1258. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Maks Jezovnik (email available below). General contact details of provider: http://www.toknowpress.net/proceedings/978-961-6914-02-4/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.