Zhang, Zhengyan;
Min, Zhe;
Zhang, Ang;
Wang, Jiaole;
Song, Shuang;
Meng, Max Q-H;
(2021)
Reliable Hybrid Mixture Model for Generalized Point Set Registration.
IEEE Transactions on Instrumentation and Measurement
, 70
, Article 2516110. 10.1109/TIM.2021.3120377.
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Abstract
Point set registration (PSR) is an essential problem in the field of surgical navigation and augmented reality (AR). In surgical navigation, the aim of registration is mapping the pre-operative space to the intra-operative space. This article introduces a reliable hybrid mixture model, in which the reliability of the normal vectors in the generalized point set (GPS) is examined and exploited. The motivation of considering the reliability of orientation information is that normal vectors cannot be estimated or measured accurately in the clinic. The point set (PS) is divided into two subsets according to the reliability of normal vectors. PSR is cast into the maximum likelihood estimation (MLE) problem. The expectation maximization (EM) framework is used to solve the MLE problem. In the E-step, the posterior probabilities between points in two PSs are computed. In the M-step, the transformation matrix and model components are updated by optimizing the objective function. We have demonstrated through extensive experiments on the human femur bone PS that the proposed algorithm outperforms the state-of-the-art ones in terms of accuracy, robustness, and convergence speed.
| Type: | Article |
|---|---|
| Title: | Reliable Hybrid Mixture Model for Generalized Point Set Registration |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/TIM.2021.3120377 |
| Publisher version: | http://doi.org/10.1109/TIM.2021.3120377 |
| 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: | Science & Technology, Technology, Engineering, Electrical & Electronic, Instruments & Instrumentation, Engineering, Expectation maximization (EM), maximum likelihood estimation (MLE), partial hybrid mixture model, point set registration (PSR), surgical navigation, AUGMENTED REALITY |
| 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/10150654 |
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