Min, Z;
Zhu, D;
Liu, J;
Ren, H;
Meng, MQH;
(2021)
Aligning 3D Curve with Surface Using Tangent and Normal Vectors for Computer-Assisted Orthopedic Surgery.
IEEE Transactions on Medical Robotics and Bionics
, 3
(2)
pp. 372-383.
10.1109/TMRB.2021.3075784.
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Abstract
Registration that aligns different views of one interested organ together is an essential technique and outstanding problem in medical robotics and image-guided surgery (IGS). This work introduces a novel rigid point set registration (PSR) approach that aims to accurately map the pre-operative space with the intra-operative space to enable successful image guidance for computer-assisted orthopaedic surgery (CAOS). The normal vectors and tangent vectors are first extracted from the pre-operative and intra-operative point sets (PSs) respectively, and are further utilized to enhance the registration accuracy and robustness. The contributions of this article are three-folds. First, we propose and formulate a novel distribution that describes the error between one normal vector and the corresponding tangent vector based on the von-Mises Fisher (vMF) distribution. Second, by modelling the anisotropic position localization error with the multi-variate Gaussian distribution, we formulate the PSR considering anisotropic localization error as a maximum likelihood estimation (MLE) problem and then solve it under the expectation maximization (EM) framework. Third, to facilitate the optimization process, the gradients of the objective function with respect to the desired parameters are computed and presented. Extensive experimental results on the human femur and pelvis models verify that the proposed approach outperforms the state-of-the-art methods, and demonstrate potential clinical values for relevant surgical navigation applications.
| Type: | Article |
|---|---|
| Title: | Aligning 3D Curve with Surface Using Tangent and Normal Vectors for Computer-Assisted Orthopedic Surgery |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/TMRB.2021.3075784 |
| Publisher version: | http://doi.org/10.1109/TMRB.2021.3075784 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Surgery , Maximum likelihood estimation , Location awareness , Feature extraction , Data models , Data mining , Robots |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10150626 |
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