Three-Dimensional Tooth Model Reconstruction Using Statistical Randomization-Based Particle Swarm Optimization

Three-Dimensional Tooth Model Reconstruction Using Statistical Randomization-Based Particle Swarm Optimization

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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 read more 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 micrometer2 and 7.41 micrometer2 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 jeff rosenstock buffalo algorithm are much better than those from the PSO algorithm with the ICP method.