Tooth occlusion analysis system based on point cloud spatial characteristics

Abstract

The invention discloses a tooth occlusion analysis system based on point cloud spatial characteristics. The system comprises a data acquisition unit, a preprocessing unit, an overlapping region positioning unit, a point cloud registration unit and an occlusion analysis unit; the data acquisition unit is used for acquiring tooth jaw point cloud data and tooth point cloud data of the same case in different periods; the preprocessing unit is used for extracting and obtaining auxiliary point cloud data based on the tooth jaw point cloud data and the tooth point cloud data; the overlapping region positioning unit determines an overlapping region according to the tooth jaw point cloud data, the tooth point cloud data and the auxiliary point cloud data; the point cloud registration unit completes registration of dental jaw point cloud data of the same case in different periods based on the overlapped area; and the occlusion analysis unit performs tooth occlusion analysis based on the registered tooth jaw point cloud data. According to the method, the influence of low overlapping rate and noise on the matching effect can be reduced to the maximum extent, and the registration precision is greatly improved.

Description

technical field [0001] The invention relates to the technical field of three-dimensional digitization of dental and jaw models, in particular to an occlusal analysis system based on point cloud spatial features. Background technique [0002] Dental model 3D digitization technology, that is, dental point cloud data processing, is a technology for the collection and analysis of dental 3D information that has been gradually developed with scanning technology and software processing technology. Through the point cloud, the orthodontist can better observe the internal structure of the teeth and the oral cavity, evaluate the occlusal relationship from different angles, and measure parameters according to different indicators. The introduction of point cloud also provides a more intuitive and accurate method for clinical research. The conversion of two-dimensional image analysis to three-dimensional graphic analysis extends the research dimension. Not only that, but also the resear...

AI Technical Summary

Problems solved by technology

[0006] Changes in growth and development: mainly refers to the patient's own growth and development will cause relatively large changes in the teeth and jaws before and after treatment. This change is mainly in the cases of adolescents. Even without orthodontic treatment during this period, their teeth and jaws will change regardless The teeth and gums will change greatly, generally there is a phenomenon of arch expansion, and even some patients' teeth have natural loss, which will bring noise effects to the registration

[0007] In addition, the plaster models of the same case but in different periods are quite different and cannot be completely overlapped together, which means that the dental point cloud data source has the characteristics of low overlap rate

However, the traditional point cloud registration method that uses features to estimate the correspondence relationship does not take into account the characteristics of high noise and low overlap in digital teeth. The traditional point cloud registration method relies on the correctness of the alignment relationship. Noise immunity and high robustness

However, this method can only find approximately correct correspondences; in addition, if the features are difficult to guarantee noise resistance and high robustness, then in the iterative selection of the correct transformation, the noise or the wrong correspondence that has not been discarded will be amplified. The influence of the relationship on the result may even cause the algorithm to fail to converge or to make mistakes

That is, the reliability and accuracy of traditional point cloud registration are low

Images