Multi-modal image registration based on gradient orientations of minimal uncertainty
Abstract
In this paper, we propose a new multi-scale technique for multi-modal image registration based on the alignment of selected gradient orientations of reduced uncertainty. We show how the registration robustness and accuracy can be improved by restricting the evaluation of gradient orientation alignment to locations where the uncertainty of fixed image gradient orientations is minimal, which we formally demonstrate correspond to locations of high gradient magnitude. We also embed a computationally efficient technique for estimating the gradient orientations of the transformed moving image (rather than resampling pixel intensities and recomputing image gradients). We have applied our method to different rigid multi-modal registration contexts. Our approach outperforms mutual information and other competing metrics in the context of rigid multi-modal brain registration, where we show sub-millimeter accuracy with cases obtained from the retrospective image registration evaluation project. Furthermore, our approach shows significant improvements over standard methods in the highly challenging clinical context of image guided neurosurgery, where we demonstrate misregistration of less than 2 mm with relation to expert selected landmarks for the registration of pre-operative brain magnetic resonance images to intra-operative ultrasound images.
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Gradient orientation on its own has been widely used for feature matching, object detection, and image registration, and shown promise in robust matching under partial occlusion and view point changes.
“While all of the above metrics rely upon gradient magnitudes, gradient orientation on its own has been widely used for feature matching (Lowe 2004), object detection (Dalal and Triggs 2005), and image registration (De Nigris et al 2012), and shown promise in robust matching under partial occlusion and view point changes.”
From 3D–2D image registration for target localization in spine surgery: investigation of similarity metrics providing robustness to content mismatch (2016) · cites this paper as De Nigris et al 2012
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- Authors
- De Nigris, D., Collins, D. L., & Arbel, T.
- Affiliations
- McGill University (CA)
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2012-12
- Volume
- 31
- Issue
- 12
- Pages
- 2343-2354
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- OpenAlex
- W2003251747
- Cited by
- 73
- References
- 22
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 2 · 2023: 3 · 2022: 1 · 2020: 5 · 2019: 7 · 2018: 7 · 2017: 8 · 2016: 10 · 2015: 6 · 2014: 10 · 2013: 8 · 2012: 1
- FWCI
- 8.23
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Medical Imaging (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Canadian Institutes of Health Research (97820_1, 74725_1)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Keywords
- multimodal image registration, multi-scale registration, rigid registration, brain MRI registration, image-guided neurosurgery, ultrasound images, mutual information, registration accuracy
- MeSH terms
- Algorithms, Brain, Brain Neoplasms, Glioma, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Ultrasonography, Databases, Factual, Neurosurgical Procedures, Surgery, Computer-Assisted
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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