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A comparison of a similarity-based and a feature-based 2-D-3-D registration method for neurointerventional use

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6 Author(s)
McLaughlin, R.A. ; Dept. of Eng. Sci., Univ. of Oxford, UK ; Hipwell, J. ; Hawkes, D.J. ; Noble, J.A.
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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.

Published in:

Medical Imaging, IEEE Transactions on  (Volume:24 ,  Issue: 8 )

Date of Publication:

Aug. 2005

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