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A Locally Deformable Statistical Shape Model

  • Carsten Last
  • Simon Winkelbach
  • Friedrich M. Wahl
  • Klaus W. G. Eichhorn
  • Friedrich Bootz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7009)

Abstract

Statistical shape models are one of the most powerful methods in medical image segmentation problems. However, if the task is to segment complex structures, they are often too constrained to capture the full amount of anatomical variation. This is due to the fact that the number of training samples is limited in general, because generating hand-segmented reference data is a tedious and time-consuming task. To circumvent this problem, we present a Locally Deformable Statistical Shape Model that is able to segment complex structures with only a few training samples at hand. This is achieved by allowing a unique solution in each contour point. Unlike previous approaches, trying to tackle this problem by partitioning the statistical model, we do not need predefined segments. Smoothness constraints ensure that the local solution is restricted to the space of feasible shapes. Very promising results are obtained when we compare our new approach to a global fitting approach.

Keywords

Segmentation Result Target Function Shape Model Point Correspondence Contour Point 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Carsten Last
    • 1
  • Simon Winkelbach
    • 1
  • Friedrich M. Wahl
    • 1
  • Klaus W. G. Eichhorn
    • 2
  • Friedrich Bootz
    • 2
  1. 1.Institut fuer Robotik und ProzessinformatikTU BraunschweigGermany
  2. 2.Klinik und Poliklinik fuer HNO-Heilkunde/ChirurgieUKB BonnGermany

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