Segmentation Line Detection in Dental Model Based on Target Region Constraint

doi:10.16182/j.issn1004731x.joss.20-0758

Abstract

Abstract:

It is an important pretreatment of a virtual orthodontic system to accurately segment teeth from a dental model. In the present methods, all patches are computed directly. To solve this problem, this paper proposes a segmentation line detection method based on target region constraint, which narrows down the detection range to the area around the actual segmentation line. In this method, the cutting plane and the cutting line are automatically formed according to the positions of seed points. The detection range is determined by the search for the position with the greatest negative curvature on the cutting line. The segmentation line is detected depending on curvature and angle information. The experimental results show that this method has strong adaptability to various malformed tooth models and can greatly improve the segmentation efficiency while ensuring the accuracy of teeth segmentation.

Key words: virtual orthodontics, teeth segmentation, curvature features, segmentation line detection

CLC Number:

TP391.9

Tian Ma, Yun Li, Jiaojiao Li, Yuancheng Li. Segmentation Line Detection in Dental Model Based on Target Region Constraint[J]. Journal of System Simulation, 2022, 34(2): 376-384.

Figures/Tables 15

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References 16

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网格模型（顶点数）	切割面	切割线	分割线初始点	分割线探测	总计
1（15~20万）	3.062	0.155	5.262	2.976	12.455
2（10~15万）	2.206	0.083	4.370	2.354	9.013
2（5~10万）	1.328	0.031	3.985	1.831	7.175
3（3~5万）	0.875	0.047	2.609	1.808	5.339

算法	一阶邻域	二阶邻域	三阶邻域
本文算法	75	86	91
文献[1]算法	80	87	93

网格模型(顶点数)	耗时/s		提升率/%
网格模型(顶点数)	本文算法	文献[1]	提升率/%
1(3~5万)	7.17	12.84	44
2(5~10万)	9.21	20.35	54
3(10~15万)	10.37	26.74	61
4(15~20万)	16.69	45.20	63

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