ArticleIEEE transactions on image processing : a publication of the IEEE Signal Processing Society2017
Volumetric Image Registration From Invariant Keypoints.
Article in IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
19 citing papers in PubMed.
- VFMStitch: A Vision-Foundation-Model Empowered Framework for 3D Ultrasound Stitching via Geometric-Semantic Feature Fusion.Proceedings of machine learning research · 2026Article
- A 3-D Cross-Modal Keypoint Descriptor for MR-US Matching and Registration.IEEE transactions on medical imaging · 2026Article
- Co-Registration of UAV and Handheld LiDAR Data for Fine Phenotyping of Rubber Plantations with Complex Canopies.Plants (Basel, Switzerland) · 2026Article
- Representative scale-invariant characteristics of male and female brains in magnetic resonance images.Neuroimage. Reports · 2025Article
- A Registration Method Based on Ordered Point Clouds for Key Components of Trains.Sensors (Basel, Switzerland) · 2024Article
- Analysis of the Relationship Between Scale Invariant Feature Transform Keypoint Properties and Their Invariance to Geometrical Transformation Applied to Cone-Beam Computed Tomography Images.Bioengineering (Basel, Switzerland) · 2024Article
- Anatomy-aware and acquisition-agnostic joint registration with SynthMorph.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Optical Coherence Tomography Angiography Demonstrates Strain and Volume Effects on Optic Disk and Peripapillary Vasculature Caused by Horizontal Duction.Current eye research · 2023Article
- Point cloud registration of arrester based on scale-invariant points feature histogram.Scientific reports · 2022Article
- One shot PACS: Patient specific Anatomic Context and Shape prior aware recurrent registration-segmentation of longitudinal thoracic cone beam CTs.IEEE transactions on medical imaging · 2022Article
- Automatic landmark detection and mapping for 2D/3D registration with BoneNet.Frontiers in veterinary science · 2022Article
- Estimation of three-dimensional chromatin morphology for nuclear classification and characterisation.Scientific reports · 2021Article
- A coarse-to-fine registration method for three-dimensional MR images.Medical & biological engineering & computing · 2021Article
- The Patterns of Morphological Change During Intracerebral Hemorrhage Expansion: A Multicenter Retrospective Cohort Study.Frontiers in medicine · 2021Article
- Combined transmission, dark field and fluorescence microscopy for intact, 3D tissue analysis of biopsies.Journal of biomedical optics · 2020Article
- Quantitative imaging feature pipeline: a web-based tool for utilizing, sharing, and building image-processing pipelines.Journal of medical imaging (Bellingham, Wash.) · 2020Article
- M3VR-A multi-stage, multi-resolution, and multi-volumes-of-interest volume registration method applied to 3D endovaginal ultrasound.PloS one · 2019Article
- Generation of Individual Whole-Brain Atlases With Resting-State fMRI Data Using Simultaneous Graph Computation and Parcellation.Frontiers in human neuroscience · 2018Article
- EyeLiner: A Deep Learning Pipeline for Longitudinal Image Registration Using Fundus Landmarks.Ophthalmology scienceArticle
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
We present a method for image registration based on 3D scale- and rotation-invariant keypoints. The method extends the scale invariant feature transform (SIFT) to arbitrary dimensions by making key modifications to orientation assignment and gradient histograms. Rotation invariance is proven mathematically. Additional modifications are made to extrema detection and keypoint matching based on the demands of image registration. Our experiments suggest that the choice of neighborhood in discrete extrema detection has a strong impact on image registration accuracy. In head MR images, the brain is registered to a labeled atlas with an average Dice coefficient of 92%, outperforming registration from mutual information as well as an existing 3D SIFT implementation. In abdominal CT images, the spine is registered with an average error of 4.82 mm. Furthermore, keypoints are matched with high precision in simulated head MR images exhibiting lesions from multiple sclerosis. These results were achieved using only affine transforms, and with no change in parameters across a wide variety of medical images. This paper is freely available as a cross-platform software library.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.