A method and system for constructing occlusion based on tooth surface feature points

Abstract

This invention discloses a method and system for constructing upper and lower jaw occlusion based on tooth surface feature points. This invention belongs to the field of digital three-dimensional image processing. The method includes the following steps: 1. Extracting feature points of tooth crowns at different locations; 2. Performing ICP registration on these feature points to determine the optimal rotation and translation matrix; and 3. Performing collision detection to determine the optimal occlusal depth. The innovation of this invention lies in its speed and accuracy. The resulting tooth occlusion achieved by this method can meet the speed and accuracy requirements of oral-assisted diagnosis and treatment systems.

Description

Technical Field

[0001] The present invention belongs to the field of digital three-dimensional graphic image processing, and is a method and system for constructing upper and lower jaw occlusion based on tooth surface feature points. Background Art

[0002] With the rapid adoption of digital dental technology, more and more patients are choosing medical orthodontics. During orthodontic procedures, two key considerations are: 1. How to determine a good occlusal relationship; 2. How to distinguish a good occlusal depth. To allow clinicians to more intuitively visualize the effects of dental occlusion, a fast and precise occlusal method is required. Summary of the Invention

[0003] The present invention addresses the shortcomings of existing technologies by providing a method for constructing upper and lower jaw occlusion based on tooth surface feature points. The core innovation of this method lies in: first, determining feature points at different locations based on the shape of different teeth; then, roughly determining the position of the upper and lower jaw surfaces by matching these feature points; and finally, accurately determining the occlusal depth of the upper and lower teeth through collision detection.

[0004] The technical solutions adopted by the present invention to solve the technical problems are as follows:

[0005] Step 1: Extract the characteristic points of the crown at different positions;

[0006] Step 2: Perform ICP registration on the feature points to find the optimal rotation and translation matrix;

[0007] Step 3: Collision detection to find the optimal bite depth.

[0008] Experimental results show that the tooth occlusion effect of this method can meet the speed and accuracy requirements of oral auxiliary diagnosis and treatment systems.

[0009] Furthermore, the extraction of characteristic points of the crown at different positions described in step 1 of the present invention is specifically implemented as follows:

[0010] 1.1 Initialize the postures of the maxillary and mandibular models respectively, so that their occlusal surfaces are parallel to the XOY plane and the teeth face the positive direction of the Z axis;

[0011] 1.2 Take the center points of all maxillary teeth and use the least squares method to perform curve fitting on the obtained center points to finally obtain the dental arch line of the maxillary teeth; perform the same operation on the mandibular teeth to obtain the dental arch line of the mandibular teeth;

[0012] 1.3 Draw a plane perpendicular to the maxillary arch line and intersect it with the maxillary model to obtain the cross-sectional curve of the maxillary teeth, as shown in Figure 4 As shown; the same operation is performed on the mandible to obtain the cross-sectional curve of the mandibular teeth;

[0013] 1.4 For the posterior teeth of the maxillary teeth: take the lowest point of the middle section of each cross-sectional curve as the feature point (e.g. Figure 5 For the anterior teeth of the maxillary teeth, the average value of the Z-axis height of the posterior teeth feature points is taken as the height value of the anterior teeth feature points, and then the point on the lingual side of the anterior teeth cross-section curve below this height value is taken as the anterior teeth feature point;

[0014] 1.5 For mandibular posterior molars:

[0015] Take the buccal tip of its cross-sectional curve as the feature point (such as Figure 6 Gray point); for the mandibular canines and incisors, the highest point of their cross-sectional curve is taken as the feature point;

[0016] Furthermore, the ICP registration of the feature points in step 2 of the present invention is performed to find the optimal rotation and translation transformation, which is specifically implemented as follows:

[0017] 2.1 Perform ICP registration on the upper and lower teeth feature points;

[0018] 2.1.1 The ICP algorithm is an optimal registration method based on the least squares method. The algorithm repeatedly selects corresponding point pairs and calculates the optimal rigid body transformation until the convergence accuracy requirements for correct registration are met. ICP is a widely used registration algorithm. Its main purpose is to find the rotation and translation parameters, and take one of the point cloud coordinate systems as the global coordinate system, and rotate and translate the other point cloud so that the overlapping parts of the two sets of point clouds are completely overlapped. Let the feature points of the upper teeth group be the target point cloud; the feature points of the lower teeth group be the reference point cloud; then the basic process of ICP registration of the two sets of feature points of the upper and lower teeth is as follows:

[0019] 1. For each point in the target point cloud, match the nearest point in the reference point cloud (or selected set);

[0020] 2. Obtain the rigid body transformation that minimizes the root mean square (RMS) of the corresponding point pairs, and obtain the translation and rotation parameters;

[0021] 3. Use the obtained transformation matrix to transform the target point cloud;

[0022] 4. Iterate (re-associate points) until the termination condition (number of iterations or error less than a threshold) is met. The minimum error here can be the absolute value of the root mean square difference between two consecutive times less than a certain limit.

[0023] Furthermore, the collision detection described in step 3 of the present invention finds the optimal bite depth, which is specifically implemented as follows:

[0024] 3.1 Apply the optimal rotation and translation transformation obtained after ICP registration to the maxillary and mandibular models to establish an OBB hierarchical bounding box tree;

[0025] 3.2 Move the maxillary model up and down, and perform real-time collision detection;

[0026] 3.3 Determine the number of intersections where the maxillary model and the mandibular model collide. When the difference in the number of intersections of three consecutive adjacent collisions is within the set threshold, it indicates that the optimal occlusal depth has been found ( Figure 8 The left side has too deep a bite depth, the right side is just right).

[0027] A system for constructing upper and lower jaw occlusion based on tooth surface feature points, including a feature point extraction module, a feature point registration module, and a collision detection module;

[0028] The feature point extraction module is used to extract feature points of the crown at different positions;

[0029] The feature point registration module performs ICP registration on the feature points to find the optimal rotation and translation matrix;

[0030] The collision detection module finds the optimal bite depth through collision detection.

[0031] The present invention provides an occlusion algorithm based on tooth surface feature points, which has the following beneficial effects:

[0032] The present invention determines feature points at different positions in advance according to the shapes of different teeth, then roughly determines the positions of the upper and lower jaw surfaces by matching the feature points, and finally accurately determines the occlusal depth of the upper and lower teeth through collision detection.

[0033] The innovation of the present invention lies in its speed and accuracy, and the tooth occlusion effect finally achieved by the method of the present invention can meet the speed and accuracy requirements of the oral auxiliary diagnosis and treatment system. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a schematic diagram of the process of the present invention;

[0035] Figure 2 Schematic diagram of the upper and lower jaw initialization models of the present invention;

[0036] Figure 3 The maxillary model and cross-sectional curve diagram of the present invention;

[0037] Figure 4 Schematic diagram of the distribution of upper teeth feature points of the present invention;

[0038] Figure 5 Schematic diagram of the distribution of characteristic points of lower teeth according to the present invention;

[0039] Figure 6 This is a schematic diagram of the present invention in a state where point cloud matching is not performed;

[0040] Figure 7 This is a schematic diagram of the state of point cloud matching performed in the present invention;

[0041] Figure 8 It is a schematic diagram of the upper and lower jaw occlusal state of the present invention. DETAILED DESCRIPTION

[0042] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0043] Implementation Case 1

[0044] like Figure 1 As shown, an occlusion algorithm based on tooth surface feature points includes the following steps:

[0045] S1, extract the characteristic points of the crown at different positions;

[0046] like Figure 2 As shown, step S11, pre-initialize the postures of the upper and lower jaw models, and make their occlusal surfaces parallel to the XOY plane, and the teeth face the positive direction of the Z axis;

[0047] like Figure 3 As shown, step S12, taking the center point of the maxillary teeth and performing curve fitting using the least square method to finally obtain the dental arch line of the maxillary teeth, and performing the same operation on the mandibular teeth;

[0048] like Figure 3 As shown, step S13, draw a plane perpendicular to the maxillary dental arch line and intersect it with the maxillary model to obtain the cross-sectional curve of the maxillary teeth, and perform the same operation on the mandibular;

[0049] like Figure 4 As shown, S14, for the posterior teeth of the maxillary teeth, take the lowest point of the middle section on the cross section as the feature point; for the anterior teeth of the maxillary teeth, take the average value of the Z-axis height of the posterior teeth feature points as the height value of the anterior teeth feature points, and then take the point at this height on the lingual side of the anterior teeth cross section curve as its feature point;

[0050] like Figure 5 As shown, S15, for the mandibular posterior molars, the buccal tip of the cross section is taken as the feature point; for the mandibular canines and incisors, the highest point of the cross section is taken as the feature point.

[0051] like Figure 6 and Figure 7 As shown, S2, perform ICP registration on the feature points to find the optimal rotation and translation transformation;

[0052] Among them, the ICP algorithm is an optimal registration method based on the least squares method. The algorithm repeatedly selects corresponding point pairs and calculates the optimal rigid body transformation until the convergence accuracy requirement for correct registration is met. ICP is a widely used registration algorithm. Its main purpose is to find the rotation and translation parameters. The point clouds in two different coordinate systems are rotated and translated with one point cloud coordinate system as the global coordinate system. The overlapping parts of the two sets of point clouds are completely overlapped after the rotation and translation of the other point cloud. The specific steps include:

[0053] S21. For each point in the target point cloud, match the nearest point in the reference point cloud (or the selected set);

[0054] S22, obtaining a rigid body transformation that minimizes the calculated root mean square of the corresponding point pairs, and obtaining translation parameters and rotation parameters;

[0055] S23, using the obtained transformation matrix to transform the target point cloud;

[0056] S24, iterate (re-associate points) until the condition for terminating the iteration is met (the number of iterations or the error is less than a threshold), wherein the error here is minimized, which can be that the absolute value of the root mean square difference between two adjacent times is less than a certain limit.

[0057] S3, collision detection, finding the best bite depth;

[0058] S31, establishing an OBB hierarchical bounding box tree for the upper teeth and the lower teeth;

[0059] S32, moving the upper teeth up and down, and performing real-time collision detection;

[0060] S33, determine the number of collision intersections and find the optimal bite depth ( Figure 8 The left side has too deep a bite depth, the right side is just right).

[0061] Implementation Case 2

[0062] Several patch-type pressure sensors are applied to the upper and lower teeth, and the pressure sensors are connected to a host computer that can realize data modeling to realize data transmission. With the help of a CT scanner and an oral scanner connected to the host computer, the scanned upper and lower tooth model data can be transmitted to the host computer. Through tooth occlusion, the force application points of the upper and lower teeth are collected, and the data is fed back to the host computer to determine the position of the action point on the tooth, and the occlusion depth is determined in combination with the tooth model.

[0063] The above are only preferred specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with this technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solutions and inventive concepts of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A method for constructing upper and lower jaw occlusion based on tooth surface feature points, characterized in that The steps include: Step 1: Extract the characteristic points of the crown at different positions; Step 2: Perform ICP registration on the feature points to find the optimal rotation and translation matrix; Step 3: Collision detection to find the optimal bite depth; The extraction of characteristic points of the crown at different positions described in step 1 is specifically implemented as follows: 1.1 Initialize the postures of the maxillary and mandibular models respectively, so that their occlusal surfaces are parallel to the XOY plane and the teeth face the positive direction of the Z axis; 1.2 Take the center points of all maxillary teeth and use the least squares method to perform curve fitting on the obtained center points to finally obtain the dental arch line of the maxillary teeth; perform the same operation on the mandibular teeth to obtain the dental arch line of the mandibular teeth; 1.3 Draw a plane perpendicular to the maxillary dental arch line and intersect it with the maxillary model to obtain the cross-sectional curve of the maxillary teeth; perform the same operation on the mandibular model to obtain the cross-sectional curve of the mandibular teeth; 1.4 For the posterior teeth of the maxillary teeth: the lowest point in the middle section of each cross-sectional curve is used as the feature point; for the anterior teeth of the maxillary teeth, the average Z-axis height of the posterior teeth feature points is used as the height value of the anterior teeth feature point, and the point on the lingual side of the anterior teeth cross-sectional curve below this height value is used as the anterior teeth feature point; 1.5 For the mandibular posterior molars: take the buccal tip of their cross-sectional curve as the feature point; for the mandibular canines and incisors, take the highest point of their cross-sectional curve as the feature point.

2. The method for constructing upper and lower jaw occlusion based on tooth surface feature points according to claim 1, characterized in that Perform ICP registration on the feature points as described in step 2 to find the optimal rotation and translation transformation. The specific implementation is as follows: 2.1 Perform ICP registration on the upper and lower teeth feature points. Let the upper teeth feature points be the target point cloud; the lower teeth feature points be the reference point cloud. The basic process of ICP registration of the upper and lower teeth feature points is as follows:

1. For each point in the target point cloud, match the nearest point in the reference point cloud; 2. Obtain the rigid body transformation that minimizes the root mean square of the corresponding point pairs, and obtain the translation and rotation parameters; 3. Use the obtained transformation matrix to transform the target point cloud; 4. Iterate until the conditions for termination of iteration are met, that is, the number of iterations or the error is less than the threshold.

3. The method for constructing upper and lower jaw occlusion based on tooth surface feature points according to claim 1, characterized in that The collision detection described in step 3 finds the optimal bite depth, which is specifically implemented as follows: 3.1 Apply the optimal rotation and translation transformation obtained after ICP registration to the maxillary and mandibular models to establish an OBB hierarchical bounding box tree; 3.2 Move the maxillary model up and down, and perform real-time collision detection; 3.3 Determine the number of intersection points where the maxillary model and the mandibular model collide. When the difference in the number of intersection points of three consecutive adjacent collisions is within the set threshold, it indicates that the optimal occlusal depth has been found.

4. A system for constructing upper and lower jaw occlusion based on tooth surface feature points, characterized in that Including feature point extraction module, feature point registration module, collision detection module; The feature point extraction module is used to extract feature points of the crown at different positions; The feature point registration module performs ICP registration on the feature points to find the optimal rotation and translation matrix; The collision detection module finds the optimal bite depth through collision detection; The feature point extraction module extracts the feature points of the crown at different positions, and is specifically implemented as follows: 1.1 Initialize the postures of the maxillary and mandibular models respectively, so that their occlusal surfaces are parallel to the XOY plane and the teeth face the positive direction of the Z axis; 1.2 Take the center points of all maxillary teeth and use the least squares method to perform curve fitting on the obtained center points to finally obtain the dental arch line of the maxillary teeth; perform the same operation on the mandibular teeth to obtain the dental arch line of the mandibular teeth; 1.3 Draw a plane perpendicular to the maxillary dental arch line and intersect it with the maxillary model to obtain the cross-sectional curve of the maxillary teeth; perform the same operation on the mandibular model to obtain the cross-sectional curve of the mandibular teeth; 1.4 For the posterior teeth of the maxillary teeth: the lowest point in the middle section of each cross-sectional curve is used as the feature point; for the anterior teeth of the maxillary teeth, the average Z-axis height of the posterior teeth feature points is used as the height value of the anterior teeth feature point, and the point on the lingual side of the anterior teeth cross-sectional curve below this height value is used as the anterior teeth feature point; 1.5 For the mandibular posterior molars: take the buccal tip of their cross-sectional curve as the feature point; for the mandibular canines and incisors, take the highest point of their cross-sectional curve as the feature point.

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