Rigid registration is a key component of all image-guided surgical applications, either as an end in itself or as a precursor to nonrigid registration. This chapter reviews common methods used for rigidly registering pairs of three-dimensional data sets (3D/3D registration), and three-dimensional data to two-dimensional data (2D/3D registration). The chapter defines five criteria that should be addressed when evaluating a registration algorithm. These include execution time, accuracy in the region of interest, breakdown point, automation, and reliability. On the basis of these criteria, one can assess whether an algorithm is applicable for a specific medical procedure, where acceptable bounds on algorithm performance are defined subjectively by physicians. Currently, the only registration algorithms that address these criteria analytically are the paired-point registration methods. All other algorithms have been evaluated empirically, usually using proprietary data sets whose transformations were estimated using paired-point registration. Future efforts should thus focus on addressing the evaluation criteria analytically, and on the establishment of publicly available data sets with known gold standard transformations, enabling objective empirical evaluations.


Iterative Close Point Registration Algorithm Unscented Kalman Filter Normalize Cross Correlation Target Registration Error 
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© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Ziv Yaniv
    • 1
  1. 1.Georgetown UniversityWashingtonUSA

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