Analysis method for the adaptability between a complete edentulous denture and a titanium alloy abutment

Abstract

This application relates to the technical field of point cloud matching, and specifically relates to a method for analyzing the adaptability between a complete edentulous maxillofacial prosthesis and a titanium alloy abutment. The method includes: obtaining the point clouds of the complete edentulous maxillofacial prosthesis and the inner surface of the oral cavity; marking the points in the point cloud; determining the point cloud space of each point cloud; dividing the point cloud space to obtain a number of voxels; obtaining the Gaussian curvature of each point; analyzing the distribution differences of the points in different point clouds in the point cloud space, and combining with the Gaussian curvature to obtain the curvature space similarity between the points in different point clouds, and on the basis of the distance between points, obtaining the inter-point difference degree between the points in different point clouds; matching the points in the two point clouds to obtain the objective function of the ICP algorithm, and obtaining the adaptability between the complete edentulous maxillofacial prosthesis and the titanium alloy abutment. The purpose of this application is to improve the accuracy of the analysis of the adaptability between the complete edentulous maxillofacial prosthesis and the titanium alloy abutment.

Description

Technical Field

[0001] This application relates to the technical field of point cloud matching, and specifically relates to a method for analyzing the adaptability between a complete edentulous denture and a titanium alloy abutment. Background Art

[0002] The abutment is a key part connecting the implant and the prosthesis. The abutment not only bears the retention and stability of the prosthesis, but also affects the aesthetics and function of the implant. The titanium alloy abutment has good biocompatibility and mechanical properties, can reduce the stress concentration of bone tissue, and assist users in restoring masticatory function. It is the most widely used type of abutment at present. However, due to the differences in the oral conditions and tissue structures of users, during the customization process, in order to ensure a good experience for users, it is necessary to meet certain standards for the adaptability between the titanium alloy abutment and the edentulous denture.

[0003] At present, the evaluation of the adaptability between the titanium alloy abutment and the edentulous denture is divided into methods such as parameter precision measurement, software detection, and visual analysis. Among them, the ICP (Iterative Closest Point) matching technology evaluates the adaptability between the complete edentulous denture and the titanium alloy abutment by analyzing the matching degree of the point cloud data between the complete edentulous denture and the titanium alloy abutment, which belongs to a non-destructive and non-contact adaptability evaluation method. However, when analyzing the point cloud data between the complete edentulous denture and the titanium alloy abutment, the ICP matching technology is prone to ignoring the structural characteristics of the denture and the titanium alloy abutment, and only selects the matching point cloud data points by the size of the Euclidean distance. This method not only has a slow processing speed but also loses some structural detail features, affecting the analysis result of the adaptability between the complete edentulous denture and the titanium alloy abutment. Summary of the Invention

[0004] In view of the above, it is necessary to provide a method for analyzing the adaptability between a complete edentulous denture and a titanium alloy abutment to solve the above problems.

[0005] An embodiment of this application provides a method for analyzing the adaptability between a complete edentulous denture and a titanium alloy abutment, and the method includes:

[0006] Denote the set composed of the point cloud data of the complete edentulous denture and the set composed of the point cloud data of the inner surface of the oral cavity as point cloud set P and point cloud set Q respectively;

[0007] Cluster the points in each point cloud set, and mark the points in the point cloud set according to the density of each point in the point cloud set;

[0008] Based on the coordinates of all points in each point cloud set, determine the point cloud space of each point cloud set; divide the point cloud space to obtain a number of voxels; based on the surface distribution of points in each voxel, obtain the Gaussian curvature of each point; analyze the distribution differences of points in different point cloud sets in the point cloud space, and combine with the Gaussian curvature to obtain the curvature space similarity between points in different point cloud sets, and on the basis of the distance between points, obtain the inter-point difference degree between points in different point cloud sets;

[0009] Select matching points from the point cloud set Q according to the marking results of each point in the point cloud set P to obtain the objective function of the ICP algorithm, and obtain the adaptability of the complete edentulous denture and the titanium alloy abutment.

[0010] Among them, the marking of points in the point cloud set is specifically as follows:

[0011] Apply the clustering algorithm to all points in the point cloud set P to obtain the local density of each point; use the threshold segmentation algorithm to obtain the first segmentation threshold based on the local density of all points in the point cloud set P, mark the points with local density greater than the first segmentation threshold as resin gum points, and mark the points with local density less than the first segmentation threshold as resin perforation points;

[0012] Use the same method as the point cloud set P to obtain the local density and the second segmentation threshold of each point in the point cloud set Q, mark the points with local density greater than the second segmentation threshold as titanium alloy abutment points, and mark the points with local density less than the second segmentation threshold as oral tissue points.

[0013] Among them, the point cloud space of each point cloud set is specifically the space composed of the interval ranges of all points in each point cloud set on each coordinate axis.

[0014] Among them, the division of the point cloud space to obtain a number of voxels is specifically as follows:

[0015] Obtain the minimum interval range length of each point cloud set on all coordinate axes, take the integer value of the ratio between the minimum interval range length and a preset value greater than zero as the side length of the voxel, and divide the point cloud space.

[0016] Among them, the Gaussian curvature of each point is obtained by performing quadratic surface fitting on all points in the voxel where it is located.

[0017] Among them, the obtaining of the curvature space similarity between points in different point cloud sets is specifically as follows:

[0018] For each point cloud set, obtain the minimum distance of each point from the boundary in each coordinate axis direction in the point cloud space, and take the sum of all the minimum distances of each point as the minimum boundary distance of each point;

[0019] Calculate the difference in the minimum boundary distance between the points in the point cloud set P and the points in the point cloud set Q, and the difference in Gaussian curvature, and combine the two differences to obtain the curvature space similarity between the points in the point cloud set P and the points in the point cloud set Q.

[0020] Among them, the process of obtaining the minimum boundary distance of each point is as follows:

[0021] Denote the minimum boundary distance of the i-th point in the point cloud set P as , and its formula form is: ; where represents the absolute value of the difference between the X coordinate of the i-th point in the point cloud set P and the minimum value of the point cloud space on the X coordinate axis, represents the absolute value of the difference between the X coordinate of the i-th point in the point cloud set P and the maximum value of the point cloud space on the X coordinate axis, represents the absolute value of the difference between the Y coordinate of the i-th point in the point cloud set P and the minimum value of the point cloud space on the Y coordinate axis, represents the absolute value of the difference between the Y coordinate of the i-th point in the point cloud set P and the maximum value of the point cloud space on the Y coordinate axis, represents the absolute value of the difference between the Z coordinate of the i-th point in the point cloud set P and the minimum value of the point cloud space on the Z coordinate axis; represents the absolute value of the difference between the Z coordinate of the i-th point in the point cloud set P and the maximum value of the point cloud space on the Z coordinate axis, represents the minimum value function.

[0022] Among them, the specific steps for obtaining the point-to-point difference degree between the points in different point cloud sets are as follows:

[0023] Obtain the distance between each point in the point cloud set P and each point in the point cloud set Q, and fuse it with the curvature space similarity to obtain the point-to-point difference degree between each point in the point cloud set P and each point in the point cloud set Q.

[0024] Among them, the process of selecting matching points is specifically as follows:

[0025] If the i-th point in the point cloud set P is marked as a resin perforation point, find the point with the smallest point-to-point difference degree from all the points in the point cloud set Q that are marked as titanium alloy abutment points as the matching point of the i-th point in the point cloud set P;

[0026] If the i-th point is marked as a resin gum point, find the point with the smallest point-to-point difference degree from all the points in the point cloud set Q that are marked as oral tissue points as the matching point of the i-th point in the point cloud set P.

[0027] Among them, obtaining the objective function of the ICP algorithm and obtaining the adaptability of the complete edentulous denture and the titanium alloy abutment includes:

[0028] The objective function of the ICP algorithm is as follows: ; where is the matching objective function of point cloud set P and point cloud set Q; represents the parameter value at which a function attains its minimum value in its domain; represents point and point is the point - to - point difference degree; is the k - th point of point cloud set P after rotation and translation transformation; is point 's matching point in point cloud set Q; represents the k - th point of point cloud set P before rotation and translation transformation; M represents the total number of points in point cloud set P; R is the rotation matrix; t is the translation vector;

[0029] The negative correlation mapping result of the finally obtained objective function value is used as the adaptability between the complete edentulous maxillofacial prosthesis and the titanium alloy abutment.

[0030] This application has at least the following beneficial effects:

[0031] This application first marks the points in the two point cloud sets respectively. When subsequent point - to - point matching is carried out, search is performed according to the marking results, without having to search all points, which improves the calculation efficiency of the ICP algorithm; the curvature space similarity between points in the two point cloud sets is obtained. Its beneficial effect is to characterize the similarity features between the point cloud data of the complete edentulous maxillofacial prosthesis and the titanium alloy abutment according to the curvature characteristics and position characteristics, fully considering the detailed characteristics of the point cloud data distribution, which improves the accuracy of subsequent matching point pairs. Further combined with the distance, the point - to - point difference degree is obtained. Its beneficial effect is to enable the objective function of the subsequent ICP algorithm to better characterize the similarity features of the distribution between point cloud data, making the registration result more accurate, and thus improving the accuracy of adaptability analysis. Brief Description of the Drawings

[0032] Figure 1 is the flowchart of a method for analyzing the adaptability between a complete edentulous maxillofacial prosthesis and a titanium alloy abutment provided by this application;

[0033] Figure 2 is the marking result diagram of point cloud set P and point cloud set Q provided by this application. Detailed Embodiments

[0034] In the description of the embodiments of the present application, words such as "exemplary", "or", "for example", etc. are used to represent examples, illustrations or explanations. Any embodiment or design solution described as "exemplary" or "for example" in the embodiments of the present application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Rather, the use of words such as "exemplary", "or", "for example", etc. is intended to present relevant concepts in a specific manner.

[0035] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which this application belongs. The terms used in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application.

[0036] In addition, it should be noted that the terms "first", "second" in this application and its drawings are used to distinguish similar objects and are not used to describe a specific order or sequence. For the methods disclosed in the embodiments of this application or the methods shown in the flowcharts, including one or more steps for implementing the methods, without departing from the scope of protection of this application, the execution order of multiple steps can be interchanged with each other, and some steps can also be deleted.

[0037] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which this application belongs.

[0038] The present application proposes a method for analyzing the adaptability between a complete edentulous denture and a titanium alloy abutment, which is applied to the field of point cloud matching technology. Refer to the attached Figure 1 , and the method includes the following steps:

[0039] S1: Denote the set composed of the point cloud data of the complete edentulous denture and the set composed of the point cloud data of the inner surface of the oral cavity as point cloud set P and point cloud set Q respectively.

[0040] Generally, multiple abutments are required for full-mouth dental implants, and the specific number needs to be determined by a professional oral implant doctor after evaluation. Common full-mouth implant schemes may include All-on-4, All-on-6, etc., that is, using 4 or 6 abutments to support the prosthesis of the entire row of teeth. The area of the complete edentulous denture where the abutment is to be installed is concave, and the surfaces of each titanium alloy abutment in the oral cavity are convex. The concave areas of the complete edentulous denture and the convex surfaces of each titanium alloy abutment need to have a high degree of adaptability to avoid loosening and falling off of the denture.

[0041] Use an intraoral scanner to scan the concave surface of a complete edentulous denture and the inner surface of the oral cavity with a titanium alloy abutment implanted, to obtain the point cloud data of the concave surface of the complete edentulous denture and the inner surface of the oral cavity. Denote the point cloud set of the concave surface of the complete edentulous denture as point cloud set P; denote the point cloud set of the inner surface of the oral cavity as point cloud set Q. Use the statistical filtering method to denoise the point cloud data. Statistical filtering is a well-known technology, and the specific process will not be elaborated.

[0042] S2: Cluster the points in each point cloud set, and mark the points in the point cloud set according to the density of each point in the point cloud set.

[0043] In each iteration of the traditional ICP algorithm, it is necessary to calculate the Euclidean distance between all points in point cloud set P and point cloud set Q, and take the two points with the closest Euclidean distance in the two point cloud sets as the matching point pairs, which will generate a large amount of computational requirements and take too long.

[0044] In the concave surface of the complete edentulous denture, it is divided into a resin gum area composed of red resin material and a resin perforation area that appears to match the titanium alloy abutment. Among them, the resin gum surface is in direct contact with the user's oral tissue and the surface is relatively smooth. Therefore, the reflectivity of the resin gum area to laser is relatively high, and the point cloud in the resin gum area in the point cloud data is relatively dense; while the resin perforation area usually undergoes surface roughening treatment in order to make the complete edentulous denture contact the titanium alloy abutment more firmly, so the reflectivity of the resin perforation area to laser is relatively low, and the point cloud in the resin perforation area in the point cloud data will be relatively sparse.

[0045] Based on the above analysis, take the three-dimensional coordinates of all points in point cloud set P as the input of the Density Peak Clustering (DCP) algorithm. In this embodiment, the truncation distance is set to a value such that the number of neighbor points of each point in the point cloud is 2% of all points, to obtain the local density of each point. Take the local density of all points in point cloud set P as the input of the Otsu threshold segmentation algorithm to obtain the first segmentation threshold. Mark the points with local density greater than the first segmentation threshold as resin gum points, and mark the points with local density less than the first segmentation threshold as resin perforation points.

[0046] When scanning the inner surface of the oral cavity, the titanium alloy abutment has usually been implanted into the user's oral cavity. Therefore, in the point cloud data of the inner surface of the oral cavity, the point cloud data of the titanium alloy abutment area and the point cloud data of the real oral tissue are included. The surface of the real oral tissue is relatively soft, and most of the laser will be absorbed or scattered when it reaches the tissue surface. Therefore, the point cloud at the real oral tissue will be relatively sparse. Compared with the real oral tissue, the reflectivity of the titanium alloy abutment surface to the laser is greater than that of the real oral tissue to the laser. Therefore, in the point cloud data of the inner surface of the oral cavity, the point cloud at the titanium alloy abutment will be denser than the point cloud at the real oral tissue.

[0047] Based on the above analysis, the same clustering method as that of the point cloud set P is used to cluster the point cloud set Q to obtain the local density of each point. The local densities of all points in the point cloud set Q are used as the input of the Otsu threshold segmentation algorithm to obtain the second segmentation threshold. The points with local density greater than the second segmentation threshold are marked as titanium alloy abutment points, and the points with local density less than the second segmentation threshold are marked as oral tissue points.

[0048] Among them, the labeled result diagrams of the point cloud set P and the point cloud set Q are as Figure 2 shown.

[0049] S3: Based on the coordinates of all points in each point cloud set, determine the point cloud space of each point cloud set; divide the point cloud space to obtain several voxels; based on the surface distribution of points in each voxel, obtain the Gaussian curvature of each point; analyze the distribution differences of points in different point cloud sets in the point cloud space, combine with the Gaussian curvature to obtain the curvature space similarity between points in different point cloud sets, and based on the distance between points, obtain the inter-point difference degree between points in different point cloud sets.

[0050] When searching for matching point pairs, search for matching points for the resin gingiva points in the point cloud set P from the oral tissue points in the point cloud set Q, and search for matching points for the resin perforation points in the point cloud set P from the titanium alloy abutment points in the point cloud set Q. By using the above method of searching in different regions to match point pairs, the computational amount of the ICP algorithm is reduced.

[0051] When searching for matching point pairs, since the traditional ICP algorithm only searches according to the Euclidean distance, the accuracy and efficiency of the search are relatively low. When matching the points in the point cloud set P and the point cloud set Q, due to the similar shapes between the abutments, when matching feature points, it is easy to match the points in one hole with the points of another unmatched abutment.

[0052] Obtain the minimum and maximum values of all points in the point cloud set P in the coordinate axis directions to determine the point cloud space of the point cloud set P. It should be understood that the minimum and maximum values of all points in the point cloud set P in the coordinate axis directions form the interval ranges of the point cloud set P in each coordinate axis direction, and the space composed of all interval ranges is used as the point cloud space of the point cloud set P.

[0053] Then evenly divide the point cloud space into voxels of size s×s×s. In this embodiment, s takes , where A is the minimum side length of the point cloud space, is the rounding function, which is used to round the input data to an integer. 20 is a preset value, and the implementer can select a preset value greater than 0 according to the actual situation. Perform quadratic surface fitting on the points in each voxel respectively to obtain the Gaussian curvature of each point in the point cloud set P. Perform voxelization processing on the point cloud set Q in the same way and calculate the Gaussian curvature of each point in the point cloud set Q.

[0054] Since the surfaces at each position of the concave surface of the complete edentulous denture are not flat, the Gaussian curvatures in different positions are different. The higher the compatibility between the complete edentulous denture and the titanium alloy abutment, the closer the surface shapes of the two point cloud spaces are, and the closer the Gaussian curvatures of the matching point pairs are. When the Gaussian curvature of a certain point in the point cloud set P is close to the Gaussian curvature of a certain point in the point cloud set Q, it means that these two points are more likely to be a matching point pair. In addition, when two points are a matching point pair, the distances of these two points from the boundary of the point cloud space in the two point cloud spaces will be closer.

[0055] Based on this, analyze the distribution differences of points in different point clouds in the point cloud space, and combine with the Gaussian curvature to obtain the curvature space similarity between the points in different point clouds: Obtain the minimum distance of each point from the boundary in each coordinate axis direction in the point cloud space, and use the sum of all the minimum distances of each point as the minimum boundary distance of each point; calculate the difference in the minimum boundary distances between the points in the point cloud set P and the points in the point cloud set Q and the difference in the Gaussian curvatures to obtain the curvature space similarity between the points.

[0056] In this embodiment, denote the minimum boundary distance of the i-th point in the point cloud set P as , and its formula form is: ; where, represents the absolute value of the difference between the X coordinate of the i-th point in the point cloud set P and the minimum value of the point cloud space on the X coordinate axis, represents the absolute value of the difference between the X coordinate of the i-th point in the point cloud set P and the maximum value of the point cloud space on the X coordinate axis, represents the absolute value of the difference between the Y coordinate of the i-th point in the point cloud set P and the minimum value of the point cloud space on the Y coordinate axis, Denote the absolute value of the difference between the Y coordinate of the i-th point in the point cloud set P and the maximum value of the point cloud space on the Y coordinate axis. Denote the absolute value of the difference between the Z coordinate of the i-th point in the point cloud set P and the minimum value of the point cloud space on the Z coordinate axis; Denote the absolute value of the difference between the Z coordinate of the i-th point in the point cloud set P and the maximum value of the point cloud space on the Z coordinate axis. Denote the minimum value function.

[0057] It should be understood that 、 、 Denote the minimum distance of the i-th point in the point cloud set P from the boundary of the point cloud space in each coordinate axis direction.

[0058] Denote the minimum boundary distance and Gaussian curvature of the i-th point in the point cloud set P as 、 respectively, and denote the minimum boundary distance and Gaussian curvature of the j-th point in the point cloud set Q as 、 respectively. Denote the curvature space similarity between the i-th point in the point cloud set P and the j-th point in the point cloud set Q as , and its formula form is: .

[0059] It should be understood that The smaller the value of, the closer the Gaussian curvatures of the i-th point in the point cloud set P and the j-th point in the point cloud set Q are, and the more likely these two points are a matching point pair. The smaller the value of, the closer the minimum boundary distances of the i-th point in the point cloud set P and the j-th point in the point cloud set Q are, and the higher the probability of these two points being matched.

[0060] Furthermore, based on the distance between points, obtain the inter-point difference degree between points in different point cloud sets, which is used to characterize the non-matching degree between points in two point cloud sets; Denote the inter-point difference degree between the i-th point in the point cloud set P and the j-th point in the point cloud set Q as , and its formula form is: ; In the formula, Denote the inter-point difference degree between the i-th point in the point cloud set P and the j-th point in the point cloud set Q; Denote the Euclidean distance between the i-th point in the point cloud set P and the j-th point in the point cloud set Q.

[0061] It should be understood that The smaller the value of, the closer the distances between the i-th point in the point cloud set P and the j-th point in the point cloud set Q are, and the more these two points are matched. The smaller the value, the closer the curvature and spatial position of the $i$-th point in the point cloud set $P$ and the $j$-th point in the point cloud set $Q$, and the more matching these two points are. The smaller the value, the smaller the difference between the $i$-th point in the point cloud set $P$ and the $j$-th point in the point cloud set $Q$, and the more matching these two points are.

[0062] S4: Select matching points from the point cloud set $Q$ according to the marking results of each point in the point cloud set $P$, obtain the objective function of the ICP algorithm, and obtain the fitting degree between the complete edentulous denture and the titanium alloy abutment.

[0063] After all points in the point cloud set $P$ are subjected to rotation and translation transformation, the obtained points are denoted as , represents the $k$-th point in the point cloud set $P$, $R$ is the rotation matrix, and $t$ is the translation vector; in this embodiment, the two point cloud sets are used as the input of the Random Sample Consensus (RANSAC) algorithm to obtain the initial rotation matrix $R$ and the initial translation vector $t$. The rotation and translation transformation does not affect the spatial characteristics of each point in the point cloud set. In the rotated and translated point cloud set $P$, if the $i$-th point is a resin perforation point, find the point with the smallest inter-point difference from all the titanium alloy abutment points in the point cloud set $Q$ as the matching point of the $i$-th point in the point cloud set $P$; if the $i$-th point is a resin gingiva point, find the point with the smallest inter-point difference from all the oral tissue points in the point cloud set $Q$ as the matching point of the $i$-th point in the point cloud set $P$. And so on, the matching points of all points in the point cloud set $P$ in the point cloud set $Q$.

[0064] Based on the matching point pairs, construct the objective function, and further determine the fitting degree between the complete edentulous denture and the titanium alloy abutment. Construct the objective function: ; where is the matching objective function of the point cloud sets $P$ and $Q$; represents the parameter value at which a function obtains the minimum value in its domain; represents the point and the point the inter-point difference; is the $k$-th point of the point cloud set $P$ after rotation and translation transformation; is the point the matching point in the point cloud set $Q$; represents the $k$-th point of the point cloud set $P$ before rotation and translation transformation; $M$ represents the total number of points in the point cloud set $P$.

[0065] Use the method of singular value decomposition to solve the values of $R$ and $t$ to make the objective function the value is the smallest. Use the newly obtained $R$ and $t$ to continue to perform coordinate transformation on the point cloud set $P$ to obtain a new point cloud set The new point cloud set and the point cloud set Q are used to solve the values of R and t again according to the above steps, so that the objective function has the minimum value. Iterate in this way until the value of the objective function no longer decreases.

[0066] Finally, the reciprocal value of the obtained objective function can reflect the fitness of the two point cloud spaces, that is, the fitness of the complete edentulous denture and the titanium alloy abutment. The smaller the value of the objective function, the larger its reciprocal value, indicating the higher the fitness of the complete edentulous denture and the titanium alloy abutment.

[0067] The present application provides a method for analyzing the fitness between a complete edentulous denture and a titanium alloy abutment. The method includes: first, the points in the two point cloud sets are respectively marked, so that when matching points with each other later, searches can be performed according to the marking results, without having to search all points, which improves the calculation efficiency of the ICP algorithm; obtaining the curvature space similarity between the points in the two point cloud sets. The beneficial effect is that according to the curvature characteristics and position characteristics of the complete edentulous denture and the titanium alloy abutment, the similarity characteristics between the point cloud data of the two are characterized, fully considering the detailed characteristics of the point cloud data distribution, which improves the accuracy of the subsequent matching point pairs. Further combining with the distance, the inter-point difference degree is obtained. The beneficial effect is that it enables the objective function of the subsequent ICP algorithm to better characterize the similarity characteristics of the distribution between the point cloud data, making the registration result more accurate, and thus improving the accuracy of the fitness analysis.

[0068] The flowcharts and block diagrams in the accompanying drawings show the possible architectures, functions, and operations of systems, methods, and computer program products according to embodiments of the present application. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code, and the module, program segment, or part of code contains one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions marked in the block may occur in a different order than that marked in the accompanying drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in a different order than that disclosed in the description. Sometimes, there is no specific order between different operations or steps. For example, two consecutive operations or steps may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. Each block in the block diagram and / or flowchart, as well as combinations of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0069] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application, and should all be included within the protection scope of the present application.

Claims

1. A method for analyzing the adaptability between a complete edentulous denture and a titanium alloy abutment, characterized in that, The method includes the following steps: Denote the set composed of the point cloud data of the complete edentulous denture and the set composed of the point cloud data of the inner surface of the oral cavity as point cloud set P and point cloud set Q respectively; Cluster the points in each point cloud set, and mark the points in the point cloud set according to the density of each point in the point cloud set; Based on the coordinates of all points in each point cloud set, determine the point cloud space of each point cloud set; divide the point cloud space to obtain a number of voxels; based on the surface distribution of points in each voxel, obtain the Gaussian curvature of each point; analyze the distribution differences of points in different point cloud sets in the point cloud space, combine with the Gaussian curvature, obtain the curvature space similarity between points in different point cloud sets, and based on the distance between points, obtain the inter-point difference degree between points in different point cloud sets; According to the marking results of each point in point cloud set P, combined with the numerical values of the inter-point difference degree between each point in point cloud set P and each point in point cloud set Q, select matching points from point cloud set Q to obtain the objective function of the ICP algorithm, and obtain the adaptability between the complete edentulous denture and the titanium alloy abutment.

2. The method for analyzing the adaptability between a complete edentulous denture and a titanium alloy abutment according to claim 1, wherein, The marking of the points in the point cloud set is specifically as follows: Use the clustering algorithm for all points in point cloud set P to obtain the local density of each point; use the threshold segmentation algorithm to obtain the first segmentation threshold based on the local density of all points in point cloud set P, mark the points with local density greater than the first segmentation threshold as resin gum points, and mark the points with local density less than the first segmentation threshold as resin perforation points; Use the same method as point cloud set P to obtain the local density of each point in point cloud set Q and the second segmentation threshold, mark the points with local density greater than the second segmentation threshold as titanium alloy abutment points, and mark the points with local density less than the second segmentation threshold as oral tissue points.

3. The method for analyzing the adaptability between a complete edentulous denture and a titanium alloy abutment according to claim 1, wherein The point cloud space of each point cloud set is specifically the space composed of the interval ranges of all points in each point cloud set on each coordinate axis.

4. A method for analyzing the adaptability between a complete edentulous denture and a titanium alloy abutment as described in claim 1, characterized in that, The division of the point cloud space to obtain a number of voxels is specifically as follows: Obtain the minimum interval range length of each point cloud set on all coordinate axes, take the integer value of the ratio between the minimum interval range length and a preset value greater than zero as the side length of the voxel, and divide the point cloud space.

5. A method for analyzing the adaptability between a complete edentulous denture and a titanium alloy abutment as described in claim 1, characterized in that, The Gaussian curvature of each point is obtained by performing quadratic surface fitting on all points in the voxel where it is located.

6. The method for analyzing the adaptability between a complete edentulous denture and a titanium alloy abutment according to claim 1, wherein, The obtaining of the curvature space similarity between points in different point cloud sets is specifically as follows: For each point cloud set, obtain the minimum distance of each point from the boundary in each coordinate axis direction in the point cloud space, and take the sum of all the minimum distances of each point as the minimum boundary distance of each point; Calculate the difference between the minimum boundary distances between the points in point cloud set P and the points in point cloud set Q and the difference between the Gaussian curvatures, and combine the two differences to obtain the curvature space similarity between the points in point cloud set P and the points in point cloud set Q.

7. A method for analyzing the adaptability between a complete edentulous denture and a titanium alloy abutment as described in claim 6, characterized in that The process of obtaining the minimum boundary distance of each point is: Denote the minimum boundary distance of the \(i\)-th point in the point cloud set \(P\) as , and its formula form is: ; where represents the absolute value of the difference between the \(X\)-coordinate of the \(i\)-th point in the point cloud set \(P\) and the minimum value of the point cloud space on the \(X\)-axis, represents the absolute value of the difference between the \(X\)-coordinate of the \(i\)-th point in the point cloud set \(P\) and the maximum value of the point cloud space on the \(X\)-axis, represents the absolute value of the difference between the \(Y\)-coordinate of the \(i\)-th point in the point cloud set \(P\) and the minimum value of the point cloud space on the \(Y\)-axis, represents the absolute value of the difference between the \(Y\)-coordinate of the \(i\)-th point in the point cloud set \(P\) and the maximum value of the point cloud space on the \(Y\)-axis, represents the absolute value of the difference between the \(Z\)-coordinate of the \(i\)-th point in the point cloud set \(P\) and the minimum value of the point cloud space on the \(Z\)-axis; represents the absolute value of the difference between the \(Z\)-coordinate of the \(i\)-th point in the point cloud set \(P\) and the maximum value of the point cloud space on the \(Z\)-axis, represents the minimum value function.

8. A method for analyzing the adaptability between a complete edentulous denture and a titanium alloy abutment according to claim 1, characterized in that, The specific steps of obtaining the inter-point difference degree between points in different point cloud sets are: Obtain the distance between each point in point cloud set P and each point in point cloud set Q, and fuse it with the curvature space similarity to obtain the inter-point difference degree between each point in point cloud set P and each point in point cloud set Q.

9. The method for analyzing the adaptability between a complete edentulous denture and a titanium alloy abutment according to claim 1, wherein The process of selecting matching points is specifically as follows: If the i-th point in the point cloud set P is marked as a resin perforation point, find the point with the smallest point-to-point difference from the points in the point cloud set Q that are all marked as titanium alloy abutment points as the matching point of the i-th point in the point cloud set P; If the i-th point is marked as a resin gingiva point, find the point with the smallest point-to-point difference from the points in the point cloud set Q that are all marked as oral tissue points as the matching point of the i-th point in the point cloud set P.

10. The method for analyzing the adaptability between a complete edentulous denture and a titanium alloy abutment according to claim 1, characterized in that, The method for obtaining the objective function of the ICP algorithm and obtaining the fitting degree between the complete edentulous denture and the titanium alloy abutment includes: The objective function of the ICP algorithm is as follows: ; where is the matching objective function of point cloud set P and point cloud set Q; represents the parameter value at which a function attains its minimum value in its domain; represents point and point of the point-to-point difference degree; is the k-th point of the point cloud set P after rotation and translation transformation; is point in the matching point in the point cloud set Q; represents the k-th point of the point cloud set P before rotation and translation transformation; M represents the total number of points in the point cloud set P; R is the rotation matrix; t is the translation vector; Use the negative correlation mapping result of the finally obtained objective function value as the fitting degree between the complete edentulous denture and the titanium alloy abutment.

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