Three-Dimensional Tooth Model Reconstruction Using Statistical Randomization-Based Particle Swarm Optimization
The registration between images is a crucial part of the 3-D tooth reconstruction model. In this paper, we introduce a registration method using our proposed statistical randomization-based particle swarm optimization (SR-PSO) algorithm with the iterative closet point (ICP) method to find the optima...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/5/2363 |
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| author | Ritipong Wongkhuenkaew Sansanee Auephanwiriyakul Marasri Chaiworawitkul Nipon Theera-Umpon |
| author_facet | Ritipong Wongkhuenkaew Sansanee Auephanwiriyakul Marasri Chaiworawitkul Nipon Theera-Umpon |
| author_sort | Ritipong Wongkhuenkaew |
| collection | DOAJ |
| description | The registration between images is a crucial part of the 3-D tooth reconstruction model. In this paper, we introduce a registration method using our proposed statistical randomization-based particle swarm optimization (SR-PSO) algorithm with the iterative closet point (ICP) method to find the optimal affine transform between images. The hierarchical registration is also utilized in this paper since there are several consecutive images involving in the registration. We implemented this algorithm in the scanned commercial regular-tooth and orthodontic-tooth models. The results demonstrated that the final 3-D images provided good visualization to human eyes with the mean-squared error of 7.37 micrometer<sup>2</sup> and 7.41 micrometer<sup>2</sup> for both models, respectively. From the results compared with the particle swarm optimization (PSO) algorithm with the ICP method, it can be seen that the results from the proposed algorithm are much better than those from the PSO algorithm with the ICP method. |
| first_indexed | 2024-03-09T05:06:46Z |
| format | Article |
| id | doaj.art-31ffda01754a4e6a990bbbfaea6a2340 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T05:06:46Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-31ffda01754a4e6a990bbbfaea6a23402023-12-03T12:53:55ZengMDPI AGApplied Sciences2076-34172021-03-01115236310.3390/app11052363Three-Dimensional Tooth Model Reconstruction Using Statistical Randomization-Based Particle Swarm OptimizationRitipong Wongkhuenkaew0Sansanee Auephanwiriyakul1Marasri Chaiworawitkul2Nipon Theera-Umpon3Department of Computer Engineering, Faculty of Engineering, Graduate School, Chiang Mai University, Chiang Mai 50200, ThailandExcellence Center in Infrastructure Technology and Transportation Engineering, Department of Computer Engineering, Faculty of Engineering, Biomedical Engineering Institute, Chiang Mai University, Chiang Mai 50200, ThailandDepartment of Orthodontics and Pediatric Dentistry, Faculty of Dentistry, Chiang Mai University, Chiang Mai 50200, ThailandDepartment of Electrical Engineering, Faculty of Engineering, Biomedical Engineering Institute, Chiang Mai University, Chiang Mai 50200, ThailandThe registration between images is a crucial part of the 3-D tooth reconstruction model. In this paper, we introduce a registration method using our proposed statistical randomization-based particle swarm optimization (SR-PSO) algorithm with the iterative closet point (ICP) method to find the optimal affine transform between images. The hierarchical registration is also utilized in this paper since there are several consecutive images involving in the registration. We implemented this algorithm in the scanned commercial regular-tooth and orthodontic-tooth models. The results demonstrated that the final 3-D images provided good visualization to human eyes with the mean-squared error of 7.37 micrometer<sup>2</sup> and 7.41 micrometer<sup>2</sup> for both models, respectively. From the results compared with the particle swarm optimization (PSO) algorithm with the ICP method, it can be seen that the results from the proposed algorithm are much better than those from the PSO algorithm with the ICP method.https://www.mdpi.com/2076-3417/11/5/2363particle swarm optimization (PSO)iterative closest point (ICP)hierarchical registration3-D image registration3-D tooth model reconstructionoral healthcare |
| spellingShingle | Ritipong Wongkhuenkaew Sansanee Auephanwiriyakul Marasri Chaiworawitkul Nipon Theera-Umpon Three-Dimensional Tooth Model Reconstruction Using Statistical Randomization-Based Particle Swarm Optimization Applied Sciences particle swarm optimization (PSO) iterative closest point (ICP) hierarchical registration 3-D image registration 3-D tooth model reconstruction oral healthcare |
| title | Three-Dimensional Tooth Model Reconstruction Using Statistical Randomization-Based Particle Swarm Optimization |
| title_full | Three-Dimensional Tooth Model Reconstruction Using Statistical Randomization-Based Particle Swarm Optimization |
| title_fullStr | Three-Dimensional Tooth Model Reconstruction Using Statistical Randomization-Based Particle Swarm Optimization |
| title_full_unstemmed | Three-Dimensional Tooth Model Reconstruction Using Statistical Randomization-Based Particle Swarm Optimization |
| title_short | Three-Dimensional Tooth Model Reconstruction Using Statistical Randomization-Based Particle Swarm Optimization |
| title_sort | three dimensional tooth model reconstruction using statistical randomization based particle swarm optimization |
| topic | particle swarm optimization (PSO) iterative closest point (ICP) hierarchical registration 3-D image registration 3-D tooth model reconstruction oral healthcare |
| url | https://www.mdpi.com/2076-3417/11/5/2363 |
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