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Content-Based Similarity Assessment in Multi-segmented Medical Image Data Bases

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Machine Learning and Data Mining in Pattern Recognition (MLDM 2001)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2123))

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Abstract

Image database systems and image management in general are extremely important in achieving both technical and functional integration of the various clinical functional units. In the emerging ‘film -less’ clinical environment it is possible to extend the capabilities of diagnostic medical image techniques and introduce intelligent content-based image retrieval operations, towards ‘evidence-based’ clinical decision support. In this paper we presented an integrated methodology for content-based retrieval of multi-segmented medical images. The system relies on the tight integration of clustering and pattern-(similarity) matching techniques and operations. Evaluation of the approach on a set of indicative medical images shows the reliability of our approach.

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Author information

Authors and Affiliations

  1. Institute of Computer Science, Foundation for Research and Technology - Hellas (FORTH), P.O. Box 1385, GR-711 10, Heraklion, Crete, Greece

    George Potamias

  2. Dept. of Computer Science, University of Crete, P.O. Pox 1470, GR-714 09, Heraklion, Crete, Greece

    George Potamias

Authors
  1. George Potamias
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Editor information

Editors and Affiliations

  1. Institute of Computer Vision and Applied Computer Sciences, Arno-Nitzsche-Str. 45, 04277, Leipzig, Germany

    Petra Perner

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© 2001 Springer-Verlag Berlin Heidelberg

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Potamias, G. (2001). Content-Based Similarity Assessment in Multi-segmented Medical Image Data Bases. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2001. Lecture Notes in Computer Science(), vol 2123. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44596-X_28

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  • DOI: https://doi.org/10.1007/3-540-44596-X_28

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42359-1

  • Online ISBN: 978-3-540-44596-8

  • eBook Packages: Springer Book Archive

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