A comparison of a similarity-based and a feature-based 2-D-3-D registration method for neurointerventional use.
IEEE T MED IMAGING
1058 - 1066.
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.
|Title:||A comparison of a similarity-based and a feature-based 2-D-3-D registration method for neurointerventional use|
|Keywords:||image guided intervention, iterative closest point (ICP), rigid registration, similarity measure, 2-D-3-D registration, X-RAY IMAGES, INTRACRANIAL ANEURYSMS, ENDOVASCULAR TREATMENT, MEDICAL IMAGES, ANGIOGRAPHY, CT, SEGMENTATION, ALGORITHM, MR|
|UCL classification:||UCL > School of BEAMS > Faculty of Engineering Science
UCL > School of BEAMS > Faculty of Engineering Science > Medical Physics and Bioengineering
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