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Publication Detail
A comparison of a similarity-based and a feature-based 2-D-3-D registration method for neurointerventional use
  • Publication Type:
    Journal article
  • Publication Sub Type:
  • Authors:
    McLaughlin RA, Hipwell J, Hawkes DJ, Noble JA, Byrne JV, Cox TC
  • Publication date:
  • Pagination:
    1058, 1066
  • Journal:
  • Volume:
  • Issue:
    278-0062 (Print), 8
  • Keywords:
    Algorithms, Arteriovenous Malformations, Artificial Intelligence, Cluster Analysis, Comparative Study, diagnosis, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Imaging, Three-Dimensional, Intracranial Aneurysm, methods, Neuronavigation, Pattern Recognition, Automated, Reproducibility of Results, Research Support, Non-U.S.Gov't, Sensitivity and Specificity, Subtraction Technique
  • Addresses:
    Wolfson Medical Vision Laboratory, Department of Engineering Science, University of Oxford, Oxford OX2 0BU, UK. r.mclaughlin@ieee.org
  • Notes:
    DA - 20050811
Two-dimensional (2-D)-to-three-dimensional (3-D) registration can improve visualization which may aid minimally invasive neurointerventions. Using clinical and phantom studies, two state-of-the-art approaches to rigid registration are compared quantitatively: an intensity-based algorithm using the gradient difference similarity measure; and an iterative closest point (ICP)-based algorithm. The gradient difference approach was found to be more accurate, with an average registration accuracy of 1.7 mm for clinical data, compared to the ICP-based algorithm with an average accuracy of 2.8 mm. In phantom studies, the ICP-based algorithm proved more reliable, but with more complicated clinical data, the gradient difference algorithm was more robust. Average computation time for the ICP-based algorithm was 20 s per registration, compared with 14 min and 50 s for the gradient difference algorithm
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