Digital human body geometric model tissue boundary segmentation, simplification and fusion generation method

Abstract

The invention relates to the technical field of digital human body models, and discloses a digital human body geometric model tissue boundary segmentation, simplification and fusion generation method. Firstly, point cloud coordinates of all tissues of a human body are obtained through a digital human body geometric model; then according to the point cloud coordinates of each tissue of the human body, determining an implicit field function of each tissue of the human body; based on the implicit field function, boundary segmentation, simplification and fusion are carried out on the human tissue through a fusion algorithm, and a curved surface model of the human tissue is obtained. And a fusion algorithm is utilized to identify the human tissue structure step by step and accurately, so that the difficulty in shape identification of tissues in complex forms is overcome, and the form fidelity and the model practicability are balanced.

Description

Technical Field

[0001] The present invention relates to the technical field of digital human body models, and in particular to a method for segmenting, simplifying and fusing tissue boundaries of a digital human body geometric model. Background Art

[0002] As an emerging direction in the field of finite element simulation, the digital human finite element model (HBM) has attracted much attention. HBM is a finite element simulation model based on a digital human body geometry model. The existing basic geometry model of HBM is generally generated by CT images of volunteers. Image data can intuitively reflect the composition of various tissues in the human body, but in the field of finite element modeling, in order to ensure the stability of simulation calculations and improve the quality of finite element models, the body tissue structure needs to be appropriately simplified during the stage of constructing the human body geometry model.

[0003] The surfaces of some tissues in the human body structure have a large number of complex shapes such as folds and grooves, which poses a challenge to the shape recognition of human tissues. For example, the surface of the cerebral cortex is densely covered with deep grooves and winding folds, the intestinal mucosa is covered with tens of thousands of circular folds and fine protrusions, and the surface of articular cartilage presents a wavy micro-texture. These structures are essentially the concrete representation of physiological functions in anatomical morphology. First of all, completely retaining the anatomical details will lead to an exponential growth in the number of model meshes, which not only greatly increases the demand for computing resources, but also may cause instability in the numerical solution due to unit distortion. However, if the surface features are over-simplified, the authenticity of biomechanical behavior may be destroyed.

[0004] Therefore, how to accurately identify human tissue and balance morphological fidelity and model practicality has become an urgent problem to be solved. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides a method for segmenting, simplifying and fusing digital human body geometric model tissue boundaries, comprising the following steps: Through the digital human body geometry model, the point cloud coordinates of each tissue of the human body are obtained; Determine the implicit field function of each tissue of the human body according to the point cloud coordinates of each tissue of the human body; the implicit field function is used to characterize the surface relationship between the tissue and the digital human body geometric model; Based on the implicit field function, the human tissue is segmented, simplified and fused by a fusion algorithm to obtain a surface model of the human tissue.

[0006] Furthermore, based on the implicit field function, the human tissue is segmented, simplified and fused by a fusion algorithm, including: Based on the implicit field function, according to the shape parameters and the offset parameters, the digital human body geometric model tissue boundary is segmented and simplified through a fusion algorithm to obtain an initial human body tissue surface model; Based on the implicit field function and according to the fusion coefficient, the tissue boundary of the digital human body geometric model is fused through a fusion algorithm to obtain a human body tissue surface model.

[0007] Furthermore, based on the implicit field function, according to the shape parameters and the offset parameters, the digital human body geometric model tissue boundary is segmented and simplified through a fusion algorithm to obtain an initial human body tissue surface model, including: Based on the implicit field function and according to the preset first shape parameter, the digital human body geometric model tissue boundary is segmented and simplified through a fusion algorithm to obtain a first human body tissue surface model; Based on the implicit field function, according to the preset second shape parameter and offset parameter, the first human tissue surface model is segmented and simplified through a fusion algorithm to obtain an initial human tissue surface model.

[0008] Furthermore, when the first human tissue surface model is obtained, the expression of the fusion algorithm is: ; Among them, C 1,α represents the first human tissue surface model, α is the shape parameter, corresponding to the first shape parameter in this step, B(p i , α ) represents the point p i Centered on α is A spherical area of ​​radius, Represents the point cloud coordinate set of human tissue, p i represents the i-th point cloud point in human tissue, It means to unite the spherical area cloud points formed by various points in human tissue.

[0009] Furthermore, when the initial human tissue surface model is obtained, the fusion algorithm expression is: ; Among them, C 2，α，d represents the initial human tissue surface model, α is the shape parameter, corresponding to the second shape parameter in this step, d is the offset parameter, B(p i , α+d ) represents the point p i Centered on α is Radius, d is the spherical area of ​​the offset, Represents the point cloud coordinate set of human tissue, p i represents the i-th point cloud point in human tissue, It means to unite the spherical area cloud points formed by various points in human tissue.

[0010] Furthermore, the second shape parameter is smaller than the first shape parameter.

[0011] Furthermore, when the human tissue surface model is obtained, the expression of the fusion algorithm is: ; Among them, C 3，α，d represents the human tissue surface model, α is the shape parameter, corresponding to the second shape parameter in this step, d is the offset parameter, q is the fusion coefficient, B(p i , α+d ) represents the point p i Centered on α is Radius, d is the spherical area of ​​the offset, Represents the point cloud coordinate set of human tissue, p i represents the i-th point cloud point in human tissue, It means to unite the spherical area cloud points formed by various points in human tissue.

[0012] Furthermore, the expression of the implicit field function is: ; Among them, SDF( x ) represents any node in the coordinate system x The implicit field function at ; α represents the shape parameter, d represents the offset parameter, surface Represents the surface formed by the point cloud coordinates, distance ( x,surface ) represents a node x arrive surface The shortest distance on the surface.

[0013] Furthermore, based on the implicit field function, the human tissue is segmented, simplified and fused by a fusion algorithm to obtain a surface model of the human tissue, and the shape of the surface model of the human tissue is optimized. The optimization expression is: ; Where E is the energy function, s is the arc length of the surface, is the boundary of the surface model of the human tissue to be optimized, k(s) is the boundary curvature, and ds represents the arc length integral of the boundary of the surface model.

[0014] The embodiments of the present invention have the following technical effects: In the present invention, the point cloud coordinates of each human body tissue are first obtained through the digital human body geometric model; then, the implicit field function of each human body tissue is determined according to the point cloud coordinates of each human body tissue; then, based on the implicit field function, the human body tissue is subjected to boundary segmentation, simplification and fusion through the fusion algorithm to obtain the surface model of the human body tissue. The fusion algorithm is used to accurately identify the structure of human body tissue step by step, overcome the problem of shape recognition of complex morphological tissues, and balance the morphological fidelity and model practicality. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 It is a flowchart of the steps of the method for segmenting, simplifying and fusing the tissue boundary of a digital human body geometric model provided by an embodiment of the present invention; Figure 2 The embodiment of the present invention provides a shape parameter α Schematic diagram for controlling the shape of the package; Figure 3 The embodiment of the present invention provides an offset parameter d Schematic diagram of controlling expansion and contraction; Figure 4 is a diagram showing a geometric model of the small intestine provided by an embodiment of the present invention; Figure 5 This is a surface model display diagram of the small intestine geometric model after segmentation, simplification and fusion processing provided by an embodiment of the present invention; Figure 6 It is a comparison diagram before and after the large intestine is segmented, simplified and fused according to an embodiment of the present invention; Figure 7 It is a comparison diagram before and after the brain is segmented, simplified and fused provided by an embodiment of the present invention; Figure 8 It is a comparison diagram before and after muscle segmentation, simplification and fusion provided by an embodiment of the present invention; Fig. 9 It is a flow chart of a method for segmenting, simplifying and fusing tissue boundaries of a digital human body geometric model provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work belong to the scope of protection of the present invention.

[0018] In the field of digital human body geometric model generation, simplifying or fusing certain complex tissues of the human body based on the extracted image data is a relatively new direction. Compared with manually observing the gray value of CT images and manually dividing the boundaries to simplify the human body tissue structure, using algorithms will be more efficient and the image processing will be more accurate. The human body tissue that has been divided, fused and simplified can retain the geometric appearance of the original tissue to the greatest extent and remove geometric factors that are unfavorable to finite element modeling to the greatest extent.

[0019] The present invention uses a fusion algorithm for the boundaries of tissues and organs to fuse and remove boundaries of the provided HBM geometric model of organs and other tissues, segment the boundaries of different tissues, and retain the tissue morphology of the original tissues to the greatest extent, so that each tissue in the model has a smooth appearance and a complete structure, which facilitates finite element mesh division, shortens the development cycle of HBM, and improves the quality of HBM models and operational stability.

[0020] Figure 1 is a flowchart of the steps of the method for segmenting, simplifying and fusing the tissue boundary of a digital human body geometric model provided by an embodiment of the present invention, Fig. 9 1 is a flow chart of a method for segmenting, simplifying and fusing digital human body geometric model tissue boundaries provided by an embodiment of the present invention. The method for segmenting, simplifying and fusing digital human body geometric model tissue boundaries provided by the present invention comprises the following steps: S1: Obtain the point cloud coordinates of each tissue of the human body through the digital human body geometry model; Exemplarily, a digital human body geometric model is obtained. Usually, a 3D digital model is obtained by performing tissue segmentation on a CT image and then inversely reconstructing the model, which is output in the form of an STL file to collect point cloud information of each human body tissue.

[0021] The digital human geometric model of human tissue is composed of triangular facets, and each vertex on the triangular facet can be understood as a coordinate point in space, forming a spatial coordinate point set containing the geometric information of human tissue, that is, point cloud coordinates. The purpose of the present invention is to morphologically divide, modify, and merge the organs and tissues formed by these point sets to form a surface model of human tissue organs with clear geometric features, relatively independent and closed, relatively smooth surface, and without unnecessary wrinkles, which is conducive to finite element modeling.

[0022] S2: Determine the implicit field function of each tissue of the human body according to the point cloud coordinates of each tissue of the human body; the implicit field function is used to characterize the surface relationship between the tissue and the digital human body geometric model; The expression of three-dimensional space is divided into explicit and implicit. The explicit form includes visualization methods such as point cloud and triangular patches. The implicit form is to express the three-dimensional space using functions. According to the point cloud coordinates of each piece of tissue, an implicit field function is constructed to characterize the surface relationship of the digital human body geometric model corresponding to the human tissue. This is to initialize the parameter values ​​in the fusion algorithm. The subsequent boundary segmentation, simplification and fusion of human tissues must be carried out on the basis of complying with the implicit field function.

[0023] In some embodiments, the expression of the implicit field function is: ;(Formula 1) Among them, SDF( x ) represents any node in the coordinate system x The implicit field function at ; α represents the shape parameter, d represents the offset parameter, surface Represents the surface formed by the point cloud coordinates, distance ( x,surface ) represents a node x arrive surface The shortest distance to the surface: a positive number indicates that the node x is outside the surface of the surface, and the distance is the shortest distance to the surface; a negative number indicates that the node x is inside the surface of the surface, and the absolute value represents the shortest distance to the surface; a zero value indicates that the node x is exactly on the surface.

[0024] S3: Based on the implicit field function, the human tissue is segmented, simplified and fused through the fusion algorithm to obtain the surface model of the human tissue.

[0025] In some embodiments, based on the implicit field function, the human tissue is segmented, simplified and fused by a fusion algorithm, including: S31: Based on the implicit field function, according to the shape parameters and offset parameters, the digital human body geometric model tissue boundary is segmented and simplified through a fusion algorithm to obtain an initial human body tissue surface model; In some embodiments, based on the implicit field function, according to the shape parameters and the offset parameters, the digital human body geometric model tissue boundary is segmented and simplified through a fusion algorithm to obtain an initial human body tissue surface model, including: S31a: Based on the implicit field function and according to the preset first shape parameter, the digital human body geometric model tissue boundary is segmented and simplified by a fusion algorithm to obtain a first human body tissue surface model; In some embodiments, when the first human tissue surface model is obtained, the expression of the fusion algorithm is: ;(Formula 2) Among them, C 1,α represents the first human tissue surface model, α is the shape parameter, corresponding to the first shape parameter in this step, B(p i , α ) represents the point p i Centered on α is A spherical area of ​​radius, Represents the point cloud coordinate set of human tissue, p i represents the i-th point cloud point in human tissue, It means to unite the spherical area cloud points formed by various points in the human body tissue, that is, to unite all these spheres to form an area that wraps the surface.

[0026] S31b: Based on the implicit field function, according to the preset second shape parameter and offset parameter, the first human tissue surface model is segmented and simplified through a fusion algorithm to obtain an initial human tissue surface model.

[0027] In some embodiments, when the initial human tissue surface model is obtained, the fusion algorithm expression is: ;(Formula 3) Among them, C 2，α，d represents the initial human tissue surface model, α is the shape parameter, corresponding to the second shape parameter in this step, d is the offset parameter, B(p i , α+d ) represents the point p i Centered on α is Radius, d is the spherical area of ​​the offset, Represents the point cloud coordinate set of human tissue, p i represents the i-th point cloud point in human tissue, It means to unite the spherical area cloud points formed by various points in human tissue.

[0028] Taking the small intestine model as an example, a set of spatial coordinate points, namely point cloud coordinates, is constructed for the selected small intestine tissue, which is called a package. An offset is set for the package, and the shape of the package is iteratively optimized to obtain the ideal organ tissue shape. i , α+d ) is the point p i Centered on α is Radius, d is the spherical area of ​​the offset, by adjusting , you can control the shape details of the package, αThe larger it is, the smoother and simpler the generated surface will be. α The smaller it is, the more detail it packs in. d Represents the offset parameter, which is used to expand or reduce the shape and size of the package. d >0 means expansion outward, d <0 means inward contraction. This change is helpful to adjust the concave and convex conditions of a certain area, making the wrapped structure closer to the original model.

[0029] Still taking the small intestine model as an example, the fusion algorithm is a wrapping deformation algorithm based on two parameters, including the shape description method of the two parameters α and d. α represents the shape parameter, which controls the size of cavities or holes that cannot be traversed during fusion, such as grooves and bends in the small intestine; d Represents the offset parameter, which is used to control the distance between the vertices of the output mesh and the vertices of the original mesh.

[0030] Specifically, in the process of obtaining the initial human tissue surface model in step S31b, the preset shape parameters are firstly α and preset offset parameters d , use formula (3) to perform fine wrapping, compare the result of fine wrapping with the expected shape, if it meets the expected shape, go to step S32; if it does not meet the expected shape, it is necessary to adjust the offset parameter d , to adjust the shape of the surface model after fine wrapping so that it can meet the expected shape requirements.

[0031] For example, the model can be placed in a human tissue model to see if there is any intersection or gap between it and other tissues. If not, the expected shape requirement is met.

[0032] In some embodiments, the second shape parameter is smaller than the first shape parameter.

[0033] For example, in constructing a small intestine model, α It needs to be divided into two steps. First, the original tissue model (such as Figure 2 The small intestine model is roughly engraved to form the first human tissue surface model, whose outline is a rough range, as shown in (a). Figure 2 Then, a smaller second shape parameter is used to perform fine carving on the details to form a gap-free and smooth initial human tissue surface model, as shown in FIG. Figure 2As shown in (d), the outer edge contour is further segmented and simplified to outline a more accurate tissue contour. Compared with directly using the second shape parameter to identify and segment the boundary of the original tissue model, it is helpful to save computing time and ensure the accuracy of the model. The outer contour of the first human tissue surface model is compared with the outer contour of the initial human tissue surface model, as shown in Figure 2 As shown in (c), the dark color at the outline represents the outer contour of the first human tissue surface model, and the outline of the light-colored part close to the center is the outer contour of the initial human tissue surface model.

[0034] As for the first shape parameter, the second shape parameter and the offset parameter, they need to be determined according to the overall size of the model, the recognition accuracy requirement and the computational efficiency. Specifically, the second shape parameter must first be smaller than the first shape parameter, and then the larger the overall size of the model, the larger the shape parameter and the offset parameter can be set, and vice versa; if the recognition accuracy requirement is high, the shape parameter and the offset parameter can be set smaller, and vice versa; if the computational efficiency requirement is fast, the shape parameter and the offset parameter can be set larger, and vice versa.

[0035] S32: Based on the implicit field function and the fusion coefficient, the digital human body geometric model tissue boundary is fused through a fusion algorithm to obtain a human body tissue surface model.

[0036] In some embodiments, based on the implicit field function and according to the fusion coefficient, the digital human body geometric model tissue boundary is fused through a fusion algorithm to obtain a human body tissue surface model. The expression of the fusion algorithm used is: ;(Formula 4) Among them, C 3，α，d represents the human tissue surface model, α is the shape parameter, corresponding to the second shape parameter in this step, d is the offset parameter, q is the fusion coefficient, B(p i , α+d ) represents the point p i Centered on α is Radius, d is the spherical area of ​​the offset, Represents the point cloud coordinate set of human tissue, p i represents the i-th point cloud point in human tissue, It means to combine the spherical area cloud points formed by various points in human tissue. The fusion coefficient q value is greater than or equal to 1. The larger the q is, the larger the fat volume that needs to be fused is.

[0037] The outside of the human intestine is wrapped with abdominal fat. The fat layer in a normal human body is fluid and has no fixed shape. If the intestine and periintestinal fat are modeled separately, forming an intestinal model and a fat block model, it does not conform to the real human anatomical structure. However, during CT image segmentation, fat and internal organs have different refractive indices for radiation, so it is impossible to clearly segment fat tissue in imaging, and it is impossible to generate a three-dimensional model of fat. However, fat really exists in human tissues and is wrapped around organs and occupies a certain volume, especially in organs such as the intestine.

[0038] Based on this problem, the present invention fuses the tissue boundary of the digital human body geometric model by adding a fusion coefficient q to simulate the mutual wrapping state between the internal organs and the surrounding fat and other tissues. Therefore, a fusion coefficient is added during the tissue and organ fusion process, and the volume of fat is added to the organ to increase the biological simulation of the model.

[0039] Taking the small intestine as an example, the anatomical structure of the intestine is surrounded by fat, which blends with the small intestine. Therefore, when only the small intestine is fused, the volume of fat should also be considered, and the intestinal model and the periintestinal fat model should be integrated for modeling. q can be set to 1.3, which is more in line with the real anatomical structure of the human body, while also considering the volume of periintestinal fat in the human body.

[0040] In some embodiments, based on the implicit field function, the human body tissue is segmented, simplified and fused by a fusion algorithm to obtain a surface model of the human body tissue, and further includes: S4: Shape optimization of the surface model of human tissue is performed, and the optimization expression is: ;(Formula 5) Where E is the energy function, s is the arc length of the surface, is the boundary of the surface model of the human tissue to be optimized, k(s) is the boundary curvature, and ds represents the arc length integral of the boundary of the surface model, that is, the differential arc length element along the surface boundary.

[0041] In order to optimize the wrapped shape, an energy function E can be defined to make the shape smoother. The energy function is defined as when the energy value reaches the minimum, the system reaches a stable state. In this model, when the energy value reaches the minimum, the model reaches the best smooth state. During the calculation process, by minimizing E, a smooth offset wrapped surface can be obtained, and the self-overlap between patches can be removed, while repairing the holes formed after the offset.

[0042] To further illustrate the effect of the method for segmenting, simplifying and fusing the tissue boundary of the digital human body geometric model in the present invention, the following is taken as an example for demonstration: Figure 4This is a display diagram of the small intestine geometry model provided by an embodiment of the present invention. The structure of the small intestine is complex and there are a large number of groove structures on the surface, which is very unfavorable for finite element modeling and model simulation. The shrink wrap method is used to wrap the small intestine model into a bounding box. Starting from the bounding box, it is continuously refined and gradually "carved" out a mesh close to the input geometry. The volume of fat is considered and merged with the small intestine. The first shape parameter is set to 0.002m, the second shape parameter is 0.001m, the offset parameter is 0.001m, and q is 1.3. After segmenting, simplifying and fusing the small intestine geometry model, the surface model of the fused and smoothed small intestine model is obtained. The result is as follows: Figure 5 shown.

[0043] In the present invention, the point cloud coordinates of each human tissue are first obtained through a digital human body geometric model; then, the implicit field function of each human tissue is determined according to the point cloud coordinates of each human tissue; then, based on the implicit field function, the human tissue is subjected to boundary segmentation, simplification and fusion through a fusion algorithm to obtain a surface model of the human tissue. Using the fusion algorithm, a relatively rough first human tissue surface model is first outlined through shape parameters; then, the shape parameters are optimized, and preset offset parameters are added to obtain a relatively detailed initial human tissue surface model; the offset parameters can also be adjusted to further adjust the shape of the initial human tissue surface model to meet the expected shape requirements, accurately identify human tissue, and overcome the shape recognition problem of complex morphological tissues such as a large number of wrinkles and gullies; and a fusion coefficient is added to add the volume of fat to the organ to increase the biological simulation of the model; finally, the energy function E is used to optimize the package shape to make the shape smoother. Based on the above steps, the human tissue structure is accurately identified step by step, the shape recognition problem of complex morphological tissues is overcome, and the morphological fidelity and model practicality are balanced.

[0044] This method can be applied to small intestine models, brain models, muscle models and other soft tissues. Through this method, they can be quickly integrated and simplified into expected geometric shapes that are conducive to finite element modeling, protect the appearance of the original model, and eliminate grooves, making the surface of organs or tissues smooth and non-interfering with each other, shortening the finite element modeling development cycle.

[0045] For example, Figure 6 This is a comparison diagram of the large intestine before and after segmentation, simplification and fusion provided by an embodiment of the present invention. Figure 6 The middle left picture shows the large intestine before fusion. Figure 6 The middle right picture shows the fused image.

[0046] For example, Figure 7 This is a comparison diagram of the brain before and after segmentation, simplification and fusion provided by an embodiment of the present invention. Figure 7 The middle left picture shows the brain before fusion. Figure 7The middle right picture shows the fused image.

[0047] For example, Figure 8 This is a comparison diagram of muscles before and after segmentation, simplification and fusion provided by an embodiment of the present invention. Figure 8 The middle left picture shows the muscle before fusion. Figure 8 The middle right picture shows the fused image.

[0048] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the technical solutions of the embodiments of the present invention.

Claims

1. A method for segmenting, simplifying and fusing tissue boundaries of a digital human body geometric model, characterized in that: The steps include: Through the digital human body geometry model, the point cloud coordinates of each tissue of the human body are obtained; Determine the implicit field function of each tissue of the human body according to the point cloud coordinates of each tissue of the human body; the implicit field function is used to characterize the surface relationship between the tissue and the digital human body geometric model; Based on the implicit field function, the human tissue is segmented, simplified and fused by a fusion algorithm to obtain a surface model of the human tissue.

2. The method for segmenting, simplifying and fusing tissue boundaries of a digital human body geometric model according to claim 1, characterized in that: Based on the implicit field function, the human tissue is segmented, simplified and fused by a fusion algorithm, including: Based on the implicit field function, according to the shape parameters and the offset parameters, the digital human body geometric model tissue boundary is segmented and simplified through a fusion algorithm to obtain an initial human body tissue surface model; Based on the implicit field function and according to the fusion coefficient, the initial human tissue surface model is fused through a fusion algorithm to obtain a human tissue surface model.

3. The method for segmenting, simplifying and fusing tissue boundaries of a digital human body geometric model according to claim 2, characterized in that: Based on the implicit field function, according to the shape parameters and the offset parameters, the digital human body geometric model tissue boundary is segmented and simplified through a fusion algorithm to obtain an initial human body tissue surface model, including: Based on the implicit field function and according to the preset first shape parameter, the digital human body geometric model tissue boundary is segmented and simplified through a fusion algorithm to obtain a first human body tissue surface model; Based on the implicit field function, according to the preset second shape parameter and offset parameter, the first human tissue surface model is segmented and simplified through a fusion algorithm to obtain an initial human tissue surface model.

4. The method for segmenting, simplifying and fusing tissue boundaries of a digital human body geometric model according to claim 3, characterized in that: When the first human tissue surface model is obtained, the expression of the fusion algorithm is: ； Among them, C 1,α represents the first human tissue surface model, α is the shape parameter, corresponding to the first shape parameter in this step, B(p i , α ) represents the point p i Centered on α is A spherical area of ​​radius, Represents the point cloud coordinate set of human tissue, p i represents the i-th point cloud point in human tissue, It means to unite the spherical area cloud points formed by various points in human tissue.

5. The method for segmenting, simplifying and fusing tissue boundaries of a digital human body geometric model according to claim 3, characterized in that: When the initial human tissue surface model is obtained, the fusion algorithm expression is: ； Among them, C 2，α，d represents the initial human tissue surface model, α is the shape parameter, corresponding to the second shape parameter in this step, d is the offset parameter, B(p i , α+d ) represents the point p i Centered on α is Radius, d is the spherical area of ​​the offset, Represents the point cloud coordinate set of human tissue, p i represents the i-th point cloud point in human tissue, It means to unite the spherical area cloud points formed by various points in human tissue.

6. The method for segmenting, simplifying and fusing tissue boundaries of a digital human body geometric model according to claim 3, characterized in that: The second shape parameter is smaller than the first shape parameter.

7. The method for segmenting, simplifying and fusing tissue boundaries of a digital human body geometric model according to claim 2, characterized in that: When the human tissue surface model is obtained, the expression of the fusion algorithm is: ； Among them, C 3，α，d represents the human tissue surface model, α is the shape parameter, corresponding to the second shape parameter in this step, d is the offset parameter, q is the fusion coefficient, B(p i , α+d ) represents the point p i Centered on α is Radius, d is the spherical area of ​​the offset, Represents the point cloud coordinate set of human tissue, p i represents the i-th point cloud point in human tissue, It means to unite the spherical area cloud points formed by various points in human tissue.

8. The method for segmenting, simplifying and fusing tissue boundaries of a digital human body geometric model according to claim 1, characterized in that: The expression of the implicit field function is: ； Among them, SDF( x ) represents any node in the coordinate system x The implicit field function at ; α represents the shape parameter, d represents the offset parameter, surface Represents the surface formed by the point cloud coordinates, distance ( x,surface ) represents a node x arrive surface The shortest distance on the surface.

9. The method for segmenting, simplifying and fusing tissue boundaries of a digital human body geometric model according to claim 1, characterized in that: Based on the implicit field function, the human tissue is segmented, simplified and fused by a fusion algorithm to obtain a surface model of the human tissue, and the shape of the surface model of the human tissue is optimized. The optimization expression is: ； Where E is the energy function, s is the arc length of the surface, is the boundary of the surface model of the human tissue to be optimized, k(s) is the boundary curvature, and ds represents the arc length integral of the boundary of the surface model.

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