Skull processing material matching method, system and equipment

Abstract

The invention discloses a skull processing material matching method, system and equipment, and the matching method comprises the steps: obtaining a plurality of skull data of a to-be-processed skull and material data of a plurality of to-be-matched materials; performing point cloudization on the skull data and the material data to obtain skull point cloud data corresponding to the skull to be processed and material point cloud data corresponding to the material to be matched; performing concave-convex surface segmentation on the material point cloud data to obtain material concave-convex surface data; and according to the skull point cloud data and the material concave-convex surface data, determining a target processing material matched with the skull to be processed. Compared with the prior art, by acquiring the point cloud data of the to-be-processed skull and the to-be-matched material and performing concave-convex surface segmentation on the material point cloud data, automatic matching of the target processing material for processing the to-be-processed skull is achieved, the problems that traditional manual matching is low in efficiency, large in error and large in material waste are solved, meanwhile, human differences are avoided, and the matching efficiency is improved. The matching efficiency and precision are obviously improved, and the material cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of skull processing material matching, and in particular to a skull processing material matching method, system and device. Background Art

[0002] At present, skull repair materials mainly include titanium alloy plates, polymethyl methacrylate (PMMA), autologous bone, etc. Among them, titanium alloy is widely used due to its good biocompatibility and high mechanical strength; polyetheretherketone (PEEK) materials have a relatively fast growth rate in the PEEK material market due to advantages such as avoiding allergic rejection problems and physical properties similar to those of autologous skulls. During the rapid growth of the skull repair material market, most enterprises still use mechanical processing methods to process and shape the skull shape, which will inevitably generate chips caused by blank milling. The cost of this part of the unused material is not low.

[0003] Therefore, enterprises will design multiple processing blanks with different specifications and manually match them one by one to find the smallest specification that can just completely wrap the skull to be processed, further avoiding more blank waste.

[0004] However, the manual matching process often takes more time and may also result in matching a larger specification of processing material due to human matching differences, thereby leading to material waste and an increase in material costs. Summary of the Invention

[0005] In order to overcome the deficiencies of the prior art, the purpose of the present invention is to provide a skull processing material matching method, system and device, which do not require manual matching, thereby reducing the matching time, and at the same time reducing manual participation to avoid material waste and an increase in material costs caused by human differences.

[0006] To solve the above problems, the present invention is implemented according to the following solutions:

[0007] A skull processing material matching method is provided, including:

[0008] Obtaining a plurality of skull data of the skull to be processed and material data of a plurality of materials to be matched;

[0009] Performing point cloud conversion on the skull data and the material data to obtain skull point cloud data corresponding to the skull to be processed and material point cloud data corresponding to the materials to be matched;

[0010] Performing concave-convex surface segmentation on the material point cloud data to obtain material concave-convex surface data;

[0011] Determining a target processing material that matches the skull to be processed according to the skull point cloud data and the material concave-convex surface data.

[0012] Compared with the prior art, the beneficial effects of a method for matching cranial processing materials of the present invention are as follows: By obtaining the point cloud data of the cranial bone to be processed and the material to be matched, and performing concave-convex surface segmentation on the material point cloud data, the automatic matching of the target processing material for processing the cranial bone to be processed is realized, solving the problems of low efficiency, large error and much material waste in traditional manual matching. At the same time, human differences are avoided, the matching efficiency and accuracy are significantly improved, and the material cost is reduced.

[0013] Optionally, the point cloud conversion of the cranial bone data and the material to be matched to obtain the cranial bone point cloud data corresponding to the cranial bone to be processed and the material point cloud data corresponding to multiple materials to be matched includes:

[0014] Separate the cranial bone data and the material data respectively to obtain multiple cranial bone patch data included in the cranial bone data and multiple material patch data included in the material data;

[0015] Sample the cranial bone patch data and the material patch data respectively to obtain the cranial bone discrete data corresponding to the cranial bone patch data and the material discrete data corresponding to the material patch data;

[0016] Obtain the initial cranial bone point cloud data according to the cranial bone discrete data;

[0017] Obtain the material point cloud data according to the material discrete data;

[0018] Determine the cranial bone point cloud data according to the initial cranial bone point cloud data and the material point cloud data.

[0019] Optionally, determining the cranial bone point cloud data according to the initial cranial bone point cloud data and the material point cloud data includes:

[0020] Select one of the multiple material point cloud data as the target material point cloud data;

[0021] Determine the cranial bone moment of inertia for representing the moment of inertia of the initial cranial bone point cloud data and the material moment of inertia for representing the target material point cloud data;

[0022] Determine the rotation matrix according to the cranial bone moment of inertia and the material moment of inertia;

[0023] Convert the initial cranial bone point cloud data according to the rotation matrix to obtain the cranial bone point cloud data.

[0024] Optionally, the material concave-convex surface data includes material convex surface data and material concave surface data;

[0025] Perform concave and convex surface segmentation on the material point cloud data to obtain material concave and convex surface data, including:

[0026] Determine the highest point of the convex surface, the highest point of the concave surface, and the segmentation plane in the material point cloud data;

[0027] Determine the material convex surface data according to the highest point of the convex surface and the segmentation plane;

[0028] Determine the material concave surface data according to the highest point of the concave surface and the segmentation plane.

[0029] Optionally, the material point cloud data includes the point cloud data on the first axis and the point cloud data on the second axis perpendicular to the first axis;

[0030] Determining the highest point of the convex surface, the highest point of the concave surface, and the segmentation plane in the material point cloud data includes:

[0031] Determine the point cloud data with the maximum value on the first axis as the highest point of the convex surface;

[0032] Determine the highest point of the concave surface according to the highest point of the convex surface;

[0033] Determine the surface where the point cloud data with the minimum value on the second axis is located as the target surface;

[0034] Determine the point cloud data with the maximum value on the target surface and on the first axis as the segmentation point;

[0035] Determine the segmentation plane for dividing the convex surface and the concave surface according to the segmentation point.

[0036] Optionally, performing concave and convex surface segmentation on the material point cloud data to obtain material concave and convex surface data further includes:

[0037] Determine the horizontal range and the vertical range of the mis-segmented area;

[0038] Construct a correction equation with the highest point of the concave surface as the origin according to the horizontal range and the vertical range;

[0039] Determine the mis-segmented data in the material convex surface data according to the correction equation;

[0040] Determine the mis-segmented data as the material concave surface data.

[0041] Optionally, determining the target processing material matching the skull to be processed according to the skull point cloud data and the material concave and convex surface data includes:

[0042] Respectively determine the vertices of the skull point cloud data and the material concave and convex surface data on the same axis;

[0043] Determine the initial positioning parameters and the initial Euler angles based on the vertices;

[0044] Determine the amount of interference point cloud data of the skull point cloud data and the material concave-convex surface data under the initial positioning parameters and the initial Euler angles;

[0045] Adjust the initial positioning parameters and the initial Euler angles according to the amount of interference point cloud data, and determine the amount of interference point cloud data after adjustment again;

[0046] When the number of adjustments of the initial positioning parameters and the initial Euler angles is equal to the preset number of times, if the amount of interference point cloud data does not meet the preset conditions, the material concave-convex surface data does not match the skull point cloud data; if the amount of interference point cloud data meets the preset conditions, the material concave-convex surface data matches the skull point cloud data, and the material to be matched corresponding to the material concave-convex surface data is determined as the target processing material.

[0047] Optionally, adjusting the initial positioning parameters and the initial Euler angles according to the amount of interference point cloud data, and determining the amount of interference point cloud data after adjustment again includes:

[0048] When the amount of interference point cloud data is less than the preset number of point clouds, the initial positioning parameters are used as the secondary positioning parameters, and the initial Euler angles are adjusted to obtain secondary Euler angles;

[0049] When the amount of interference point cloud data is greater than or equal to the preset number of point clouds, the initial positioning parameters and the initial Euler angles are adjusted to obtain secondary positioning parameters and secondary Euler angles;

[0050] Determine the amount of interference point cloud data of the skull point cloud data and the material concave-convex surface data under the secondary positioning parameters and the secondary Euler angles.

[0051] A skull processing material matching system is also provided, which is applied to the skull processing material matching method described above, and includes:

[0052] A point cloud conversion module, which is used for:

[0053] Obtain multiple skull data of the skull to be processed and material data of multiple materials to be matched;

[0054] Perform point cloud conversion on the skull data and the material data to obtain the skull point cloud data corresponding to the skull to be processed and the material point cloud data corresponding to the material to be matched;

[0055] A concave-convex surface segmentation module, which is used to perform concave-convex surface segmentation on the material point cloud data to obtain material concave-convex surface data;

[0056] A matching module, configured to determine a target processing material that matches the skull to be processed according to the skull point cloud data and the concave-convex surface data of the material.

[0057] A computer device is further provided, including a processor and a memory. At least one instruction, at least one program, a code set or an instruction set is stored in the memory, and the at least one instruction, at least one program, the code set or the instruction set is loaded and executed by the processor to implement the skull processing material matching method. Description of the Drawings

[0058] Figure 1 It is a flowchart of the matching method of the present invention;

[0059] Figure 2 It is a schematic diagram of the concave-convex surface data of the material of the present invention. Detailed Embodiments

[0060] The following describes the preferred embodiments of the present invention with reference to the drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0061] When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. On the contrary, they are only examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims. In the description of the present application, it should be understood that the terms "first", "second", "third", etc. are only used to distinguish similar objects, and do not have to be used to describe a specific order or sequence, nor can they be understood as indicating or implying relative importance. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific circumstances.

[0062] See Figure 1 As shown, a method for matching a skull processing material of the present invention includes:

[0063] S1: Obtain a plurality of skull data of the skull to be processed and material data of a plurality of materials to be matched; wherein, the skull to be processed is a skull with a defect area that needs to be repaired and processed, and most of the skull data comes from medical imaging devices such as CT and MRI. The skull to be processed (the skull defect area) is scanned with high precision by these medical imaging devices to obtain the corresponding skull data of the skull to be processed; in order to obtain the three-dimensional data of the skull to be processed, when scanning the skull to be processed by the medical imaging device, it is scanned from different angles, so a skull to be processed includes a plurality of skull data.

[0064] Materials for repairing and processing the skull include, but are not limited to, titanium alloy plates, polymethyl methacrylate (PMMA), and autologous bone. When selecting a processing material that matches the skull to be processed, it is necessary to consider what material matches the defect area in the skull to be processed. Therefore, when the defect areas are different, it is impossible to fix one repair and processing material, and it is necessary to select the processing material that best matches the defect area from multiple materials.

[0065] S2: Point cloud the skull data and material data to obtain the skull point cloud data corresponding to the skull to be processed and the material point cloud data corresponding to the material to be matched, including:

[0066] First, separate the skull data and material data respectively to obtain multiple skull patch data included in the skull data and multiple material patch data included in the material data. That is, one skull to be processed corresponds to multiple skull data, and each skull data includes multiple skull patch data. One material to be matched corresponds to one material data, and each material data includes multiple material patch data.

[0067] Each skull data and each material data are usually composed of multiple independent patches (such as triangular patches in a three-dimensional grid). Each patch may represent different regions or material structures. By separating the skull data and material data to obtain the corresponding patch data, the geometric details (the structure of the skull or the structure of the material) of the skull to be processed and the material to be matched can be retained, improving the accuracy of matching the skull to be processed.

[0068] The patch data is a continuous surface, and it is necessary to convert the patch data into discrete data through sampling. The discrete data is specifically coordinate points distributed in three-dimensional space to represent the geometric features of the patch data. Therefore, after obtaining the skull patch data and material patch data, sample the skull patch data and material patch data respectively to obtain the skull discrete data corresponding to the skull patch data and the material discrete data corresponding to the material patch data.

[0069] According to the skull discrete data, obtain the initial skull point cloud data; the skull discrete data is a three-dimensional point set obtained by sampling the skull patch data. This three-dimensional point set includes the geometric information on the surface of the skull to be processed, and multiple skull discrete data constitute the initial skull point cloud data. That is, the initial skull point cloud data is specifically a set of discrete points (skull discrete data).

[0070] According to the material discrete data, obtain the material point cloud data; the material discrete data is a three-dimensional point set obtained by sampling the material patch data. This three-dimensional point set includes the geometric information on the structure of the material to be matched, and multiple material discrete data constitute the material point cloud data. That is, the material point cloud data is specifically a set of discrete points (material discrete data).

[0071] Different point cloud data may have spatial direction differences, and the moment of inertia reflects the principal axis direction of an object. By aligning the moments of inertia of the two, the rotation matrix can be obtained through calculation. Since there are cranial data obtained by photographing a cranial bone to be processed at different angles, cranial patch data is obtained from the cranial data, and then cranial discrete data is obtained from the cranial patch data. Finally, initial cranial point cloud data is obtained from the cranial discrete data. Different initial cranial point cloud data may have angular differences, that is, there are differences in the principal axis directions of different initial cranial point cloud data; and there are also differences in the principal axis directions of the cranial bone to be processed and the material to be matched. Therefore, after aligning the moments of inertia of the initial cranial point cloud data and the material point cloud data, the cranial point cloud can be rotated to be consistent with the principal axis direction of the material point cloud, ensuring that the coordinate systems of the cranial bone and the material are aligned during matching or processing, and the shape error between the cranial bone and the material can be reduced.

[0072] Therefore, after obtaining the initial cranial point cloud data and the material point cloud data, it is necessary to align the moments of inertia of the initial cranial point cloud data and the material point cloud data to rotate the cranial point cloud to be consistent with the principal axis direction of the material point cloud. Specifically, according to the initial cranial point cloud data and the material point cloud data, the cranial point cloud data is determined, including:

[0073] First, select one point cloud data from multiple material point cloud data as the target material point cloud data; then, determine the cranial moment of inertia representing the moment of inertia of the initial cranial point cloud data and the material moment of inertia representing the target material point cloud data; finally, determine the rotation matrix according to the cranial moment of inertia and the material moment of inertia; according to the rotation matrix, transform the initial cranial point cloud data to obtain cranial point cloud data consistent with the principal axis direction of the material point cloud data.

[0074] S3: Perform concave-convex surface segmentation on the material point cloud data to obtain material concave-convex surface data; among them, the material concave-convex surface data includes material convex surface data and material concave surface data, and the material point cloud data includes point cloud data on the first axis and point cloud data on the second axis perpendicular to the first axis; by performing concave-convex surface segmentation on the material point cloud data, the material concave-convex surface data is obtained, including:

[0075] First, determine the highest convex point, the highest concave point and the segmentation surface in the material point cloud data, including: determine the point cloud data with the maximum value on the first axis as the highest convex point; determine the highest concave point according to the highest convex point; determine the surface where the point cloud data with the minimum value on the second axis is located as the target surface; determine the segmentation point as the point cloud data with the maximum value on the first axis on the target surface; determine the segmentation surface for separating the convex surface and the concave surface according to the segmentation point.

[0076] Among them, the first axis is specifically the Z-axis, and the second axis is specifically the X-axis. That is, to determine the highest convex point, the highest concave point, and the segmentation plane in the material point cloud data. Specifically, first, the point cloud data with the maximum value on the Z-axis in the material point cloud data is determined as the highest convex point. Also, since the highest convex point (the maximum value on the Z-axis) is usually located at the top of the material point cloud data, and the highest concave point is the corresponding point of the highest convex point in the concave area. Therefore, after finding the highest convex point, the local highest point in the concave area can be found by moving down along the Z-axis, and this point is the highest concave point. So, after determining the highest convex point, the highest concave point can be determined simultaneously through the highest convex point.

[0077] Next, determine the plane where the point cloud data with the minimum value on the X-axis in the material point cloud data is located, and determine this plane as the target plane. Determine the point cloud data with the maximum value on the Z-axis on the target plane as the segmentation point, that is, determine the Z values of all the point cloud data on the target plane in the Z-axis direction, and use the point cloud data with the largest Z value as the segmentation point.

[0078] Finally, use the plane where the Z value of the segmentation point is located as the segmentation plane, that is, the segmentation plane is a plane perpendicular to the Z-axis. In the Z-axis direction, this segmentation plane divides the point cloud data of the material to be matched into upper and lower parts. See Figure 2 As shown, the upper part in the figure is the convex surface data of the material, and the lower part in the figure is the concave surface data of the material.

[0079] Therefore, after determining the highest convex point, the highest concave point, and the segmentation plane, the material point cloud data can be segmented into the convex surface data of the material and the concave surface data of the material. Specifically, according to the highest convex point and the segmentation plane, determine the convex surface data of the material; according to the highest concave point and the segmentation plane, determine the concave surface data of the material.

[0080] When performing convex-concave surface segmentation, due to the complex geometric shape of the material point cloud data, the segmentation plane may not be able to completely and accurately separate the convex surface and the concave surface, resulting in some point cloud data being mis-segmented. For example, some point cloud data that should originally belong to the concave surface may be wrongly classified into the convex surface data. To ensure the accuracy of the segmentation of the material point cloud data, it is necessary to correct the mis-segmented area to reclassify the mis-segmented point cloud data into the correct point cloud data.

[0081] Therefore, after segmenting the material point cloud data through the highest convex point, the highest concave point, and the segmentation plane, it is also necessary to correct the convex surface data of the material and the concave surface data of the material. That is, when performing convex-concave surface segmentation on the material point cloud data to obtain the convex-concave surface data of the material, it also includes: determining the horizontal range and the vertical range of the mis-segmented area. The horizontal range is specifically the mis-segmented range on the X-axis, and the vertical range is specifically the mis-segmented range on the Z-axis; according to the horizontal range and the vertical range, construct a correction equation with the highest concave point as the origin. The correction equation is specifically an ellipse equation, and the expression of the ellipse equation is as follows:

[0082]

[0083] Wherein, X is the value on the X-axis, Z is the value on the Z-axis, a and b are the parameters of the ellipse equation, which are the semi-axis lengths of the ellipse in the horizontal (X-axis) and vertical (Z-axis) directions respectively. According to the horizontal range and vertical range of the mis-segmented area, the parameters a and b of the ellipse equation are adjusted so that the area represented by the ellipse equation is the same as the area represented by the actual convex point cloud data of the material to be matched; when there is point cloud data outside the area of the ellipse equation (corrected equation) in the material point cloud data, this point cloud data is the concave surface data mis-segmented to the convex surface. Therefore, according to the corrected equation, the mis-segmented data in the material convex surface data is determined; the mis-segmented data is determined as the material concave surface data, that is, the mis-segmented data is divided into the concave surface area.

[0084] S4: Determine the target processing material that matches the skull to be processed according to the skull point cloud data and the concave and convex surface data of the material, including:

[0085] First, determine the vertices of the skull point cloud data and the concave and convex surface data of the material on the same axis respectively. Specifically, determine the vertex of the skull point cloud data in the Z-axis direction and the vertex of the concave and convex surface data of the material in the Z-axis direction. The vertices of the concave and convex surface data of the material are the highest points of the convex surface and the highest points of the concave surface.

[0086] Next, according to the vertices, determine the initial positioning parameters and the initial Euler angles, and determine the amount of interfering point cloud data of the skull point cloud data and the concave and convex surface data of the material under the initial positioning parameters and the initial Euler angles. The interfering point cloud data is the amount of point cloud data where the skull point cloud data and the concave and convex surface data of the material interfere.

[0087] Next, adjust the initial positioning parameters and the initial Euler angles according to the amount of interfering point cloud data, and determine the amount of interfering point cloud data after adjustment again; when the number of adjustments of the initial positioning parameters and the initial Euler angles is equal to the preset number of times, if the amount of interfering point cloud data does not meet the preset conditions, the concave and convex surface data of the material does not match the skull point cloud data; if the amount of interfering point cloud data meets the preset conditions, the concave and convex surface data of the material matches the skull point cloud data, and the material to be matched corresponding to the concave and convex surface data of the material is determined as the target processing material.

[0088] Wherein, adjusting the initial positioning parameters and the initial Euler angles according to the amount of interfering point cloud data and determining the amount of interfering point cloud data after adjustment again includes:

[0089] When the amount of interference point cloud data is less than the preset point cloud quantity, the initial positioning parameters are used as the secondary positioning parameters to adjust the initial Euler angles to obtain the secondary Euler angles; when the amount of interference point cloud data is greater than or equal to the preset point cloud quantity, the initial positioning parameters and the initial Euler angles are adjusted to obtain the secondary positioning parameters and the secondary Euler angles; finally, the cranial point cloud data and the material concave-convex surface data are determined, and the amount of interference point cloud data under the secondary positioning parameters and the secondary Euler angles is obtained.

[0090] The method for determining whether the cranial bone to be processed matches the material to be matched is to calculate whether there is point cloud data within a specified radius between the cranial point cloud data and the material concave-convex surface data of the material to be matched. In an embodiment of the present invention, the material concave-convex surface data (point cloud) of the material to be matched is initialized with a KDTree, and the radius range is set to 0.5 mm. By searching the established KDTree, it is determined whether there is the point cloud of the cranial bone to be processed (cranial point cloud data) within the set radius range, so as to realize the judgment of whether the cranial bone to be processed matches the material to be matched successfully.

[0091] Among them, the matching process is specifically as follows: The cranial point cloud data of the cranial bone to be processed is roughly registered into the material point cloud data of the material to be matched, so that the vertices of the cranial bone to be processed and the material to be matched are in the same direction of the Z axis, and the vertex of the cranial bone to be processed is lower than the vertex of the material to be matched; within the range of ±1 mm of the Z value in the Z-axis direction at the current position, a value is randomly obtained for the Z value (initial positioning parameter) of the first match. At the same time, the numerical value of the Euler angle (initial Euler angle) is also randomly obtained based on the range of ±2 of the current position.

[0092] If the amount of interference point cloud data between the cranial bone to be processed and the material to be matched is less than 100, it is considered that this Z value (positioning parameter) is a better option, and the value of the Z axis is not changed anymore, that is, this value is still used as the positioning parameter in the second match, and only the numerical value of the Euler angle is changed; that is, the preset point cloud quantity is 100. When the amount of interference point cloud data is less than 100, the initial positioning parameters are used as the secondary positioning parameters to adjust the initial Euler angles to obtain the secondary Euler angles. When the amount of interference point cloud data is greater than or equal to 100, the initial positioning parameters and the initial Euler angles are adjusted to obtain the secondary positioning parameters and the secondary Euler angles.

[0093] In an embodiment of the present invention, the number of iterations is set to 200 times, that is, the cranial bone to be processed and each material to be matched are matched at most 200 times. If the amount of interference point cloud data is 0 during the 200 iterations, it means that the matching is successful; if the amount of interference point cloud data is not 0 after 200 iterations, it means that the matching fails; that is, when the amount of interference point cloud data is 0, the amount of interference point cloud data meets the preset conditions, and when the amount of interference point cloud data is not 0, the amount of interference point cloud data does not meet the preset conditions.

[0094] The present invention also provides a cranial bone processing material matching system, which is applied to a cranial bone processing material matching method, and includes:

[0095] A point cloud conversion module, configured to:

[0096] Obtain a plurality of cranial bone data of the cranial bone to be processed and material data of a plurality of materials to be matched;

[0097] Perform point cloud conversion on the cranial bone data and the material data to obtain cranial bone point cloud data corresponding to the cranial bone to be processed and material point cloud data corresponding to the materials to be matched;

[0098] A concave-convex surface segmentation module, configured to perform concave-convex surface segmentation on the material point cloud data to obtain material concave-convex surface data;

[0099] A matching module, configured to determine a target processing material that matches the cranial bone to be processed according to the cranial bone point cloud data and the material concave-convex surface data.

[0100] It further includes a processing material library for storing the material data of a plurality of materials to be matched, which is managed through a json file, and records the names, sizes, and current path information of the materials to be matched.

[0101] The present invention also provides a computer device, including a processor and a memory. At least one instruction, at least one program, a code set, or an instruction set is stored in the memory, and the at least one instruction, at least one program, the code set, or the instruction set is loaded and executed by the processor to implement the above matching method.

[0102] The so-called processor may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0103] The memory can be used to store the computer programs or modules. By running or executing the computer programs or modules stored in the memory and calling the data stored in the memory, the processor realizes various functions of the matching method. The memory mainly includes a program storage area and a data storage area. Among them, the program storage area can store the operating system, application programs required for at least one function, etc.; the data storage area can store the data created according to the use of the mobile phone, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage devices.

[0104] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A skull processing material matching method, characterized in that: include: Acquire multiple skull data of skulls to be processed and multiple material data of materials to be matched; Performing point cloud conversion on the skull data and the material data to obtain skull point cloud data corresponding to the skull to be processed and material point cloud data corresponding to the material to be matched; Performing concave-convex surface segmentation on the material point cloud data to obtain material concave-convex surface data; A target processing material matching the skull to be processed is determined according to the skull point cloud data and the material concave-convex surface data.

2. A skull processing material matching method according to claim 1, characterized in that: The step of converting the skull data and the materials to be matched into point cloud data to obtain skull point cloud data corresponding to the skull to be processed and material point cloud data corresponding to a plurality of materials to be matched includes: Separating the skull data and the material data respectively to obtain a plurality of skull surface data included in the skull data and a plurality of material surface data included in the material data; The skull surface patch data and the material surface patch data are sampled respectively to obtain skull discrete data corresponding to the skull surface patch data and material discrete data corresponding to the material surface patch data; Obtaining initial skull point cloud data according to the skull discrete data; Obtaining the material point cloud data according to the material discrete data; The skull point cloud data is determined according to the initial skull point cloud data and the material point cloud data.

3. A skull processing material matching method according to claim 2, characterized in that: Determining the skull point cloud data according to the initial skull point cloud data and the material point cloud data includes: Select one point cloud data from multiple material point cloud data as target material point cloud data; determining a skull moment of inertia for representing the moment of inertia of the initial skull point cloud data, and a material moment of inertia for representing the target material point cloud data; Determining a rotation matrix based on the skull moment of inertia and the material moment of inertia; The initial skull point cloud data is transformed according to the rotation matrix to obtain the skull point cloud data.

4. A skull processing material matching method according to claim 2, characterized in that: The material concave-convex surface data includes material convex surface data and material concave surface data; Performing concave-convex surface segmentation on the material point cloud data to obtain material concave-convex surface data, including: Determine the highest point of the convex surface, the highest point of the concave surface and the split surface in the material point cloud data; Determining the convex surface data of the material according to the highest point of the convex surface and the segmentation surface; The concave surface data of the material is determined according to the highest point of the concave surface and the dividing surface.

5. A skull processing material matching method according to claim 4, characterized in that: The material point cloud data includes point cloud data on a first axis and point cloud data on a second axis perpendicular to the first axis; Determining the convex highest point, the concave highest point and the split surface in the material point cloud data includes: Determine the point cloud data of the maximum value on the first axis as the highest point of the convex surface; Determine the highest point of the concave surface according to the highest point of the convex surface; The surface where the point cloud data with the minimum value on the second axis is located is determined as the target surface; Determine the point cloud data with the maximum value on the target surface and the first axis as the segmentation point; A dividing surface for dividing the convex surface and the concave surface is determined according to the dividing point.

6. A skull processing material matching method according to claim 5, characterized in that: Performing concave-convex surface segmentation on the material point cloud data to obtain material concave-convex surface data also includes: Determine the horizontal and vertical ranges of the mis-segmented area; According to the lateral range and the longitudinal range, a correction equation is constructed with the highest point of the concave surface as the origin; According to the correction equation, determining the mis-segmented data in the convex surface data of the material; The mis-segmented data is determined as the material concave surface data.

7. A skull processing material matching method according to claim 6, characterized in that: Determining a target processing material matching the skull to be processed according to the skull point cloud data and the material concave-convex surface data, including: Respectively determine the vertices of the skull point cloud data and the material concave-convex surface data on the same axis; According to the vertices, determining initial positioning parameters and initial Euler angles; Determine the amount of interference point cloud data of the skull point cloud data and the material concave-convex surface data under the initial positioning parameters and the initial Euler angles; According to the amount of interference point cloud data, the initial positioning parameters and the initial Euler angles are adjusted, and the adjusted amount of interference point cloud data is determined again; When the number of adjustments to the initial positioning parameters and the initial Euler angles is equal to the preset number, if the amount of interference point cloud data does not meet the preset conditions, the material concave and convex surface data does not match the skull point cloud data; if the amount of interference point cloud data meets the preset conditions, the material concave and convex surface data matches the skull point cloud data, and the material to be matched corresponding to the material concave and convex surface data is determined as the target processing material.

8. A skull processing material matching method according to claim 7, characterized in that: According to the amount of interference point cloud data, the initial positioning parameters and the initial Euler angles are adjusted, and the adjusted amount of interference point cloud data is determined again, including: When the amount of interference point cloud data is less than the preset point cloud quantity, the initial positioning parameter is used as a secondary positioning parameter, and the initial Euler angle is adjusted to obtain a secondary Euler angle; When the amount of interference point cloud data is greater than or equal to the preset point cloud quantity, the initial positioning parameters and the initial Euler angles are adjusted to obtain secondary positioning parameters and secondary Euler angles; Determine the amount of interference point cloud data of the skull point cloud data and the material concave-convex surface data under the secondary positioning parameters and the secondary Euler angles.

9. A skull processing material matching system, applied to a skull processing material matching method as described in claims 1 to 8, characterized in that: include: Point cloud module, used for: Acquire multiple skull data of skulls to be processed and multiple material data of materials to be matched; Performing point cloud conversion on the skull data and the material data to obtain skull point cloud data corresponding to the skull to be processed and material point cloud data corresponding to the material to be matched; A concave-convex surface segmentation module is used to perform concave-convex surface segmentation on the material point cloud data to obtain material concave-convex surface data; A matching module is used to determine a target processing material that matches the skull to be processed based on the skull point cloud data and the material concave-convex surface data.

10. A computer device, characterized in that: The computer device includes a processor and a memory, wherein the memory stores at least one instruction, at least one program, code set or instruction set, and the at least one instruction, at least one program, code set or instruction set is loaded and executed by the processor to implement the skull processing material matching method as described in any one of claims 1 to 8.

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