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Learning-Based 2D/3D Rigid Registration Using Jensen-Shannon Divergence for Image-Guided Surgery

  • Rui Liao
  • Christoph Guetter
  • Chenyang Xu
  • Yiyong Sun
  • Ali Khamene
  • Frank Sauer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4091)

Abstract

Registration of 3D volumetric data to 2D X-ray images has many applications in image-guided surgery, varying from verification of patient position to working projection searching. In this work, we propose a learning-based method that incorporates the prior information on the expected joint intensity histogram for robust real-time 2D/3D registration. Jensen-Shannon divergence (JSD) is used to quantify the statistical (dis)similarity between the observed and expected joint histograms, and is shown to be superior to Kullback-Leibler divergence (KLD) in its symmetry, being theoretically upper-bounded, and well-defined with histogram non-continuity. A nonlinear histogram mapping technique is proposed to handle the intensity difference between the observed data and the training data so that the learned prior can be used for registration of a wide range of data subject to intensity variations. We applied the proposed method on synthetic, phantom and clinical data. Experimental results demonstrated that a combination of the prior knowledge and the low-level similarity measure between the images being registered led to a more robust and accurate registration in comparison with the cases where either of the two factors was used alone as the driving force for registration.

Keywords

Mutual Information Training Image Pattern Intensity Rigid Registration Training Pair 
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

  • Rui Liao
    • 1
  • Christoph Guetter
    • 1
  • Chenyang Xu
    • 1
  • Yiyong Sun
    • 1
  • Ali Khamene
    • 1
  • Frank Sauer
    • 1
  1. 1.Imaging and Visualization DepartmentSiemens Corporate ResearchPrincetonUSA

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