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Teeth

Teeth

The formation of teeth, also known as odontogenesis, is a complex process that begins in utero, around the sixth week of embryonic development. There are three stages to this process: the bud stage, the cap stage, and the bell stage.
In the bud stage, the tooth germ (an aggregation of cells) starts to form in the developing jawbone. During the cap stage, the tooth germ differentiates into enamel organ, dental papilla, and dental sac, which will later develop into the tooth's enamel, dentin...

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Grey Wolf Optimizer with Behavior Considerations and Dimensional Learning in Three-Dimensional Tooth Model

Ritipong Wongkhuenkaew¹, Sansanee Auephanwiriyakul², Marasri Chaiworawitkul³

¹Department of Computer Engineering, Faculty of Engineering, Biomedical Engineering Institute, Biomedical Engineering and Innovation Research Center, Chiang Mai University, Chiang Mai 50200, Thailand.

Bioengineering (Basel, Switzerland)

|March 27, 2024

Summary

A new modified grey wolf optimizer with behavior considerations and dimensional learning (BCDL-GWO) algorithm, combined with iterative closest point (ICP), improves 3D registration accuracy for dental models. This BCDL-GWO with ICP method outperforms statistical randomization-based particle swarm optimization (SR-PSO).

Keywords:

3D image registration 3D tooth model reconstruction grey wolf optimizer (GWO)hierarchical registration iterative closest point (ICP)oral healthcare

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Area of Science:

Computer Vision
Medical Imaging
Optimization Algorithms

Background:

Accurate 3D reconstruction relies heavily on precise 3D registration.
Existing registration methods may require further refinement for complex models like teeth.

Purpose of the Study:

To introduce and evaluate a novel 3D registration algorithm for enhanced 3D reconstruction.
To improve the accuracy and quality of 3D visualization for dental models.

Main Methods:

Development of a modified grey wolf optimizer with behavior considerations and dimensional learning (BCDL-GWO).
Integration of the BCDL-GWO algorithm with the iterative closest point (ICP) algorithm for refined registration.
Implementation of a hierarchical structure for multi-view optical image registration.
Application to scanned commercial orthodontic and regular tooth models.

Main Results:

The BCDL-GWO with ICP algorithm achieved high-quality 3D visualization.
The method produced minimal mean squared errors of approximately 7.2186 and 7.3999 μm² for the tested models.
Comparative analysis showed the proposed algorithm outperformed the statistical randomization-based particle swarm optimization (SR-PSO).

Conclusions:

The BCDL-GWO with ICP algorithm offers a superior approach to 3D registration for dental applications.
The proposed method demonstrates enhanced accuracy and visualization quality compared to SR-PSO.
Computational complexity remains comparable between the BCDL-GWO with ICP and SR-PSO methods.