Automatic Construction of Statistical Shape Models for Vertebrae

  • Meike Becker
  • Matthias Kirschner
  • Simon Fuhrmann
  • Stefan Wesarg
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6892)

Abstract

For segmenting complex structures like vertebrae, a priori knowledge by means of statistical shape models (SSMs) is often incorporated. One of the main challenges using SSMs is the solution of the correspondence problem. In this work we present a generic automated approach for solving the correspondence problem for vertebrae. We determine two closed loops on a reference shape and propagate them consistently to the remaining shapes of the training set. Then every shape is cut along these loops and parameterized to a rectangle. There, we optimize a novel combined energy to establish the correspondences and to reduce the unavoidable area and angle distortion. Finally, we present an adaptive resampling method to achieve a good shape representation. A qualitative and quantitative evaluation shows that using our method we can generate SSMs of higher quality than the ICP approach.

Keywords

Minimum Description Length Correspondence Problem Statistical Shape Model Automatic Construction Reference Shape 
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

  • Meike Becker
    • 1
  • Matthias Kirschner
    • 1
  • Simon Fuhrmann
    • 1
  • Stefan Wesarg
    • 1
  1. 1.GRISTU DarmstadtDarmstadtGermany

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