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Segmentation of Medical Images Using Three-Dimensional Active Shape Models

  • Klas Josephson
  • Anders Ericsson
  • Johan Karlsson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3540)

Abstract

In this paper a fully automated segmentation system for the femur in the knee in Magnetic Resonance Images and the brain in Single Photon Emission Computed Tomography images is presented. To do this several data sets were first segmented manually. The resulting structures were represented by unorganised point clouds. With level set methods surfaces were fitted to these point clouds. The iterated closest point algorithm was then applied to establish correspondences between the different surfaces. Both surfaces and correspondences were used to build a three dimensional statistical shape model. The resulting model is then used to automatically segment structures in subsequent data sets through three dimensional Active Shape Models. The result of the segmentation is promising, but the quality of the segmentation is dependent on the initial guess.

Keywords

Point Cloud Shape Mode Initial Guess Shape Model Single Photon Emission Compute Tomography Image 
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 2005

Authors and Affiliations

  • Klas Josephson
    • 1
  • Anders Ericsson
    • 1
  • Johan Karlsson
    • 1
  1. 1.Centre for Mathematical SciencesLund UniversityLundSweden

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