A comparison of a similarity-based and a feature-based 2-D-3-D registration method for neurointerventional use
Abstract
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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Intensity-based approaches have demonstrated higher accuracy and reliability than feature-based and gradient-based approaches.
“Intensity-based approaches utilize all the intensity information contained in the 2D and 3D images, as opposed to the feature-based approach where, image features extracted from 2D and 3D images are used for the registration, and the gradient-based approach which utilizes the fact that rays connecting the x-ray source and edges in the projection image are tangent to surfaces of distinct anatomical”
From Robust 3D–2D image registration: application to spine interventions and vertebral labeling in the presence of anatomical deformation (2013) · cites this paper as McLaughlin et al 2005
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References 37 · Cited by 63
- Authors
- McLaughlin, R. A., Hipwell, J., Hawkes, D. J., Noble, J. A., Byrne, J. V., & Cox, T. C.
- Affiliations
- University of Oxford (GB)
- University College London (GB)
- John Radcliffe Hospital (GB)
- National Hospital for Neurology and Neurosurgery (GB)
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2005-08
- Volume
- 24
- Issue
- 8
- Pages
- 1058-1066
- ISSN
- 0278-0062 (ISSN-L)
- OpenAlex
- W2131035649
- Cited by
- 63
- References
- 37
- Citations by year
- 2025: 1 · 2023: 1 · 2022: 2 · 2021: 3 · 2019: 2 · 2018: 1 · 2016: 4 · 2015: 3 · 2014: 1 · 2013: 8 · 2012: 7
- FWCI
- 6.35
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Medical Imaging (publishedVersion)
- PubMed (publishedVersion)
- UWA Profiles and Research Repository (UWA) (submittedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Physics and Astronomy › Radiation › Advanced Radiotherapy Techniques
- Physical Sciences › Engineering › Biomedical Engineering › Anatomy and Medical Technology
- Keywords
- rigid registration, iterative closest point, intensity-based registration, feature-based registration, registration accuracy, computational time
- MeSH terms
- Algorithms, Arteriovenous Malformations, Artificial Intelligence, Intracranial Aneurysm, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Pattern Recognition, Automated, Sensitivity and Specificity, Subtraction Technique, Reproducibility of Results, Cluster Analysis, Imaging, Three-Dimensional, Neuronavigation
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