4D Shape Priors for a Level Set Segmentation of the Left Myocardium in SPECT Sequences

  • Timo Kohlberger
  • Daniel Cremers
  • Mikaël Rousson
  • Ramamani Ramaraj
  • Gareth Funka-Lea
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4190)


We develop a 4D (3D plus time) statistical shape model for implicit level set based shape representations. To this end, we represent hand segmented training sequences of the left ventricle by respective 4-dimensional embedding functions and approximate these by a principal component analysis. In contrast to recent 4D models on explicit shape representations, the implicit shape model developed in this work does not require the computation of point correspondences which is known to be quite challenging, especially in higher dimensions. Experimental results on the segmentation of SPECT sequences of the left myocardium confirm that the 4D shape model outperforms respective 3D models, because it takes into account a statistical model of the temporal shape evolution.


SPECT Sequence Active Contour Shape Model Statistical Shape Point Correspondence 
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 2006

Authors and Affiliations

  • Timo Kohlberger
    • 1
  • Daniel Cremers
    • 2
  • Mikaël Rousson
    • 1
  • Ramamani Ramaraj
    • 1
  • Gareth Funka-Lea
    • 1
  1. 1.Imaging and Visualization DepartmentSiemens Corporate Research, Inc.PrincetonUSA
  2. 2.Department of Computer ScienceUniversity of BonnGermany

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