SIFT and Shape Context for Feature-Based Nonlinear Registration of Thoracic CT Images

  • Martin Urschler
  • Joachim Bauer
  • Hendrik Ditt
  • Horst Bischof
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4241)


Nonlinear image registration is a prerequisite for various medical image analysis applications. Many data acquisition protocols suffer from problems due to breathing motion which has to be taken into account for further analysis. Intensity based nonlinear registration is often used to align differing images, however this requires a large computational effort, is sensitive to intensity variations and has problems with matching small structures. In this work a feature-based image registration method is proposed that combines runtime efficiency with good registration accuracy by making use of a fully automatic feature matching and registration approach. The algorithm stages are 3D corner detection, calculation of local (SIFT) and global (Shape Context) 3D descriptors, robust feature matching and calculation of a dense displacement field. An evaluation of the algorithm on seven synthetic and four clinical data sets is presented. The quantitative and qualitative evaluations show lower runtime and superior results when compared to the Demons algorithm.


Matching Cost Registration Approach Nonlinear Registration Shape Context Descriptor Dense Displacement 
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

  • Martin Urschler
    • 1
  • Joachim Bauer
    • 2
  • Hendrik Ditt
    • 3
  • Horst Bischof
    • 1
  1. 1.Institute for Computer Graphics & VisionGraz University of TechnologyAustria
  2. 2.VRVis Research CentreGrazAustria
  3. 3.Siemens Medical Solutions, CTE PAForchheimGermany

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