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A Method of Analyzing a Shape with Potential Symmetry and Its Application to Detecting Spinal Deformity

  • Conference paper
Computer Vision, Virtual Reality and Robotics in Medicine (CVRMed 1995)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 905))

Abstract

This paper describes a technique for analyzing a shape with potential symmetry which includes approximate symmetry and original symmetry. A technique is proposed for identifying a symmetry axis of a shape with potential axial symmetry by searching for the largest symmetric subset of the shape. It is applied to the axial detection and asymmetry evaluation of Moire topographic images of human backs to automate spinal deformity inspection. Some experimental results are shown and discussion is given.

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References

  1. Eades, P.: “Symmetry finding algorithms”, Computational Morphology, 41–51, North-Holland, Amsterdam, 1988.

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Author information

Authors and Affiliations

  1. Department of Civil, Mechanical and Control Engineering, Kyushu Institute of Technology, Tobata, Kitakyushu 804, Japan

    Seiji Ishikawa, Hiroyuki Kosaka & Kiyoshi Kato

  2. Department of Orthopedic Surgery, National Sanatorium Chiba Higashi Hospital, Nitonacho, Chiba 280, Japan

    Yoshinori Otsuka

Authors
  1. Seiji Ishikawa
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  2. Hiroyuki Kosaka
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  3. Kiyoshi Kato
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  4. Yoshinori Otsuka
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Editor information

Editors and Affiliations

  1. INRIA, B. P. 93, F-06902, Sophia-Antipolis, France

    Nicholas Ayache

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© 1995 Springer-Verlag Berlin Heidelberg

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Cite this paper

Ishikawa, S., Kosaka, H., Kato, K., Otsuka, Y. (1995). A Method of Analyzing a Shape with Potential Symmetry and Its Application to Detecting Spinal Deformity. In: Ayache, N. (eds) Computer Vision, Virtual Reality and Robotics in Medicine. CVRMed 1995. Lecture Notes in Computer Science, vol 905. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-49197-2_61

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  • DOI: https://doi.org/10.1007/978-3-540-49197-2_61

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-59120-7

  • Online ISBN: 978-3-540-49197-2

  • eBook Packages: Springer Book Archive

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