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Automatic detection of articular surfaces in 3-D image through minimal subset random sampling

  • Segmentation and Deformable Models
  • Conference paper
  • First Online:
CVRMed-MRCAS'97 (CVRMed 1997, MRCAS 1997)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1205))

Abstract

In situ extraction of biomedical geometric primitives is addressed in this paper through consecutive applications of global and local optimization procedures, both making use of a minimal subset encoding scheme. Global search is based on a canonic genetic algorithm. Local search uses as input the global genetic search results; it performs an iterative approximation which considers the final ordered set of solutions issued from the global search. Optimization is reached by maximizing an objective function along with a fuzzy geometric model. As a prerequisite, a powerful segmentation algorithm is also described which uses mainly a 3-D watershed transform. Application is done to in situ extraction of spheroidal joint surfaces with the help of an ellipsoidal model. The whole algorithm is shown to be accurate and time efficient.

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Author information

Authors and Affiliations

  1. Département Image et Traitement de l'Information Telecom Bretagne, Technopôle de Brest-Iroise, Laboratoire de Traitement de l'Information Médicale, B.P 832, 29285, Brest Cédex, France

    Jean-José Jacq & Christian Roux

Authors
  1. Jean-José Jacq
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  2. Christian Roux
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Editor information

Jocelyne Troccaz Eric Grimson Ralph Mösges

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© 1997 Springer-Verlag Berlin Heidelberg

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Jacq, JJ., Roux, C. (1997). Automatic detection of articular surfaces in 3-D image through minimal subset random sampling. In: Troccaz, J., Grimson, E., Mösges, R. (eds) CVRMed-MRCAS'97. CVRMed MRCAS 1997 1997. Lecture Notes in Computer Science, vol 1205. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0029226

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  • DOI: https://doi.org/10.1007/BFb0029226

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-62734-0

  • Online ISBN: 978-3-540-68499-2

  • eBook Packages: Springer Book Archive

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