SIFT algorithm-based 3D pose estimation of femur
Open accessAbstract
To address the lack of 3D space information in the digital radiography of a patient femur, a pose estimation method based on 2D–3D rigid registration is proposed in this study. The method uses two digital radiography images to realize the preoperative 3D visualization of a fractured femur. Compared with the pure Digital Radiography or Computed Tomography imaging diagnostic methods, the proposed method has the advantages of low cost, high precision, and minimal harmful radiation. First, stable matching point pairs in the frontal and lateral images of the patient femur and the universal femur are obtained by using the Scale Invariant Feature Transform method. Then, the 3D pose estimation registration parameters of the femur are calculated by using the Iterative Closest Point (ICP) algorithm. Finally, based on the deviation between the six degrees freedom parameter calculated by the proposed method, preset posture parameters are calculated to evaluate registration accuracy. After registration, the rotation error is less than l.5°, and the translation error is less than 1.2 mm, which indicate that the proposed method has high precision and robustness. The proposed method provides 3D image information for effective preoperative orthopedic diagnosis and surgery planning.
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Feature-based 2D/3D registration methods require features or anatomical landmarks that are specific to every object and must be selected with domain knowledge and be clearly visible in target images.
“However, these features and anatomical landmarks are specific to every object and must be selected with domain knowledge and such that they are clearly visible in the target images.”
From A Fully Differentiable Framework for 2D/3D Registration and the Projective Spatial Transformers (2024) · cites this paper as [9]
Section 2.1 describes 2D/3D femur registration using matching feature points, supporting the claim that this method uses image features. But Sections 2.1 and 3.2 say SIFT detects and matches those points; the paper does not say they must be selected with domain knowledge, are specific to every object, or must be clearly visible in target images. Page numbers are unavailable in our copy. (LLM, conf=0.83)
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Cites
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Cited by
Sorted by citations
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- Authors
- Zhang, X., Zhu, Y., Li, C., Zhao, J., & Li, G.
- Affiliations
- Harbin Institute of Technology (CN)
- Corresponding author
- Li, Ge
- Venue
- Bio-Medical Materials and Engineering
- Publisher
- SAGE Publications
- Published
- 2014-11
- Volume
- 24
- Issue
- 6
- Pages
- 2847-2855
- ISSN
- 0959-2989 (ISSN-L)
- 1878-3619
- OpenAlex
- W1547389837
- Cited by
- 8
- References
- 14
- Citations by year
- 2026: 1 · 2025: 1 · 2023: 1 · 2021: 1 · 2020: 1 · 2019: 1 · 2016: 1 · 2015: 1
- FWCI
- 0.60
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Bio-Medical Materials and Engineering (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Funders
- State Key Laboratory of Robotics
- State Key Laboratory of Robotics and System
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Engineering › Biomedical Engineering › Medical Imaging and Analysis
- Health Sciences › Medicine › Epidemiology › Bone fractures and treatments
- Keywords
- femur, 3D pose estimation, digital radiography, SIFT feature matching, iterative closest point, femoral fracture, preoperative surgical planning, 3D visualization, orthopedic diagnosis
- MeSH terms
- Algorithms, Femur, Humans, Radiographic Image Enhancement, Radiographic Image Interpretation, Computer-Assisted, Sensitivity and Specificity, Signal Processing, Computer-Assisted, Subtraction Technique, Tomography, X-Ray Computed, Reproducibility of Results, Imaging, Three-Dimensional, Patient Positioning
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