Non-rigid Image Registration with Equally Weighted Assimilated Surface Constraint

  • Cheng Zhang
  • Gary E. Christensen
  • Martin J. Murphy
  • Elisabeth Weiss
  • Jeffrey F. Williamson
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8545)


An important research problem in image-guided radiation therapy is how to accurately register daily onboard Cone-beam CT (CBCT) images to higher quality pretreatment fan-beam CT (FBCT) images. Assuming the organ segmentations are both available on CBCT and FBCT images, methods have been proposed to use them to help the intensity-driven image registration. Due to the low contrast between soft-tissue structures exhibited in CBCT, the interobserver contouring variability (expressed as standard deviation) can be as large as 2-3 mm and varies systematically with organ, and relative location on each organ surface. Therefore the inclusion of the segmentations into registration may degrade registration accuracy. To address this issue we propose a surface assimilation method that estimates a new surface from the manual segmentation from a priori organ shape knowledge and the interobserver segmentation error. Our experiment results show the proposed method improves registration accuracy compared to previous methods.


Image Registration Manual Segmentation Registration Algorithm Target Registration Error Surface Registration 
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 International Publishing Switzerland 2014

Authors and Affiliations

  • Cheng Zhang
    • 1
  • Gary E. Christensen
    • 1
  • Martin J. Murphy
    • 2
  • Elisabeth Weiss
    • 2
  • Jeffrey F. Williamson
    • 2
  1. 1.Department of Electrical and Computer EngineeringUniversity of IowaIowa CityUSA
  2. 2.Department of Radiation OncologyVirginia Commonwealth UniversityRichmondUSA

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