Knowledge Augmented Medical Image Retrieval System
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.
|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.|
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