Jade cultural relic digital segmentation and porosity nondestructive testing method based on CT image

Abstract

The invention provides a jade cultural relic digital segmentation and porosity nondestructive testing method based on a CT image, and relates to the technical field of cultural relic nondestructive testing and digital display. In order to solve the problems that the surface of a jade cultural relic is often covered with impurities, the internal pore structure is complex, and accurate segmentation and observation are difficult, the invention provides a double-flow processing architecture: in a calculation channel, downsampling data is combined with interactive gray profile analysis to obtain an accurate threshold value, and an entity envelope mask is constructed through three-dimensional morphological closed operation; calculating the internal porosity by taking the porosity as a constraint; in a reconstruction channel, a double-threshold contour surface extraction technology is adopted, independent three-dimensional models of a jade cultural relic body and surface attachments are constructed respectively, a key interaction control mechanism is established, and real-time state switching of overall coexistence, main body display and attachment display is achieved; according to the method, accurate quantification of the porosity is realized, an intuitive visual means for virtual segmentation of the cultural relics is provided, and researchers are assisted in deeply knowing the structure of the jade cultural relics.

Description

Technical Field

[0001] This invention belongs to the field of digital preservation and non-destructive testing technology of cultural relics, specifically involving a method for digital segmentation and non-destructive testing of porosity of jade cultural relics based on CT images. Background Technology

[0002] Jade artifacts, as important carriers of Chinese culture, rely on their internal texture, structure, and degree of staining (reflected in changes in porosity) as crucial evidence for studying their manufacturing processes, origins, and historical periods. Computed tomography (CT) technology, as a high-precision non-destructive testing method, can acquire three-dimensional density distribution information within artifacts in a non-contact manner, and has become a standard tool for the digital archiving and research of cultural relics.

[0003] However, in practical applications, unearthed jade artifacts are often covered with hardened soil, rust, or other high-density secondary deposits. These deposits often appear as irregular artifacts in CT images. Existing technologies typically use a single global threshold for segmentation, making it difficult to completely remove surface deposits while preserving the details of the jade itself. This results in rough surfaces and residual impurities in the 3D reconstruction model, severely impacting the digital display of the artifacts.

[0004] Furthermore, the quantitative calculation of porosity is a challenge in jade research. Due to the complex shapes of jade artifacts, traditional volume calculation methods struggle to automatically distinguish between "closed pores within the artifact" and "background air outside the artifact." Without precise morphological processing, external air is often misidentified as pores, or interconnected pores within the artifact are not counted, leading to significant deviations in porosity calculations. Simultaneously, existing visualization software lacks independent control mechanisms for artifact components, preventing researchers from switching between viewing the "clean body after removing adhering materials" and the "spatial distribution of adhering materials" in real-time within the same 3D scene, making it difficult to intuitively assess the erosive relationship of adhering materials on the artifact. Summary of the Invention

[0005] In view of this, the purpose of this invention is to provide a method for digital segmentation and non-destructive testing of porosity of jade artifacts based on CT images, which aims to solve the problems in the prior art, such as impure 3D reconstruction models, unintuitive jade model structures, inaccurate internal porosity calculations, and limited visualization and interaction methods caused by interference from surface deposits on artifacts.

[0006] A method for digital segmentation and non-destructive testing of porosity of jade artifacts based on CT images includes:

[0007] Step S1, Data Preprocessing and Dual-Stream Architecture Construction: Read the original CT image sequence data of jade artifacts, convert them into a three-dimensional voxel matrix, and then construct a dual-stream channel: (1) Based on the preset high-density artifacts (1) Perform artifact removal processing, use linear interpolation algorithm to downsample the artifact removal data, generate computational stream data for real-time interactive analysis and volume calculation; (2) retain the original resolution voxel data as rendering stream data for three-dimensional surface reconstruction and visualization. Step S2, Interactive Gray-Scale Feature Calibration: On the sliced ​​image of the computation flow, the detection path is drawn through human-computer interaction, gray-scale profile curves are generated in real time, and a four-dimensional threshold parameter set is established: the body gray-scale range. With pore gray range This four-dimensional threshold was used to quantitatively analyze the grayscale boundaries of the jade body, internal pores, and surface attachments: the grayscale range of the body distinguished the body from the attachments, and the grayscale range of the pores distinguished the body from the pores; among them, These represent the lower and upper limits of the grayscale range of the entity, respectively. These represent the lower and upper limits of the grayscale range of pores, respectively. Step S3, Volume quantization based on morphological envelope: The preliminary mask of the jade entity is extracted using the gray range of the body, and a three-dimensional binary morphological closing operation is performed on it to construct a solid envelope containing internal pores. Using this envelope as a spatial constraint mask, the volume of the jade body and the pore volume are calculated by Boolean operation, and then the porosity is obtained. Step S4, Dual-channel Independent Reconstruction and Fusion Display: Based on the rendering stream data, independent isosurface extraction and mesh optimization are performed on the "surface attachments" and the "jade body" respectively to construct two independent three-dimensional geometric objects. A logical switch is established in the rendering pipeline to realize independent visibility control and fusion rendering of the two.

[0008] Preferably, step S2 specifically includes: 1) Initialize the visualization environment: The system automatically locates the central layer slice of the computation flow data and generates a pseudo-color map and grayscale histogram for that slice. The histogram statistically analyzes the grayscale distribution from zero grayscale to the maximum grayscale value. 2) Establish a continuous interactive listening loop: The system enters an immediate response mode and continuously listens for user mouse click events; 3) Dynamic path analysis: In each iteration of the loop, a set of starting and ending coordinates of the user's click on the pseudo-color map is captured, the voxel grayscale sequence on the path is extracted in real time, and the corresponding grayscale profile curve is refreshed and displayed, so that the user can repeatedly explore the grayscale features of different areas by changing the path position. 4) Loop Termination and Parameter Establishment: Responding to the user's loop termination command, exiting the listening loop, and receiving the four-dimensional threshold parameters determined by the user based on the above repeated probing results: the body grayscale range. With pore gray range .

[0009] Preferably, step S3 specifically includes: 1) Initial entity extraction: using the main body's grayscale range Binarization thresholding is performed on the computation stream data to obtain discrete binary images; 2) Noise Removal: Perform connected component analysis on the binary image, calculate the number of voxels in all connected regions, and retain only the connected region with the largest number of voxels as the initial solid mask. Remove free noise; 3) Envelope Construction: Set the radius of the structuring element For the initial solid mask Perform morphological closing operations to fill closed voids inside the solid, generating a solid envelope mask. ; 4) Constraint Calculation: The computation flow data is then processed in... Binary results within the range and solid envelope mask Execution and logical operations, total number of ontology voxels in the statistical results A computational mask is constructed to obtain the ontology; the computational flow data is then processed in... Binary results within the range and solid envelope mask Performing logical operations, the total number of porosity voxels in the statistical results. The volume is calculated based on the physical spacing of voxels, and then applied according to the formula. To obtain porosity.

[0010] Preferably, step S4 specifically includes: 1) Attachment rendering: based on a preset high artifact threshold Isosurface extraction is performed on full-resolution CT data, and values ​​exceeding a preset threshold are considered. A three-dimensional model of the attached objects was constructed in the area and marked in yellow. This model independently represents the spatial distribution structure of the attached impurities on the surface of the cultural relic. 2) Jade body rendering: Perform high-value masking on the full-resolution CT data, reducing the grayscale value to a minimum. The voxels are set to zero to prevent high-density attachments from being counted repeatedly. This is based on the user-obtained ontology threshold. The data after zeroing is subjected to isosurface extraction to generate a preliminary body mesh model; the preliminary mesh model is subjected to three-dimensional spatial connectivity analysis to calculate the geometric scale of all independent connected regions, and only the largest connected region is retained as the three-dimensional model of the jade body, which is marked with white, and other non-connected suspended impurities and noise are automatically removed. 3) Multi-material fusion display: The 3D model of the attached object and the 3D model of the jade body are placed in the same 3D world coordinate system and overlaid and fused for rendering. This allows the form of the cultural relic body and the distribution of the attached object to be presented simultaneously in a single view, realizing the digital separation and coexistence of the two.

[0011] Furthermore, it also includes a joystick camera mode: establishing a rotation response mechanism based on the duration of mouse press. When the user presses the left mouse button, the 3D model rotates continuously and automatically according to the vector direction and distance of the mouse from the click point, without the need for repeated dragging.

[0012] Furthermore, it also includes a multi-state key switching mechanism: set up a keyboard event listener and define at least three display states: State 1: render the jade body model and the surface attachment model at the same time to show the spatial attachment relationship between the two; State 2: render only the jade body model, hide the attachment model, and show the surface details of the cultural relic after removing the attachment; State 3: render only the surface attachment model, hide the jade body model, and independently show the distribution pattern of the attachment.

[0013] The present invention has the following beneficial effects: This invention provides a method for digital segmentation and non-destructive testing of porosity of jade artifacts based on CT images, achieving clean digital extraction of the artifact itself. The invention employs a dual filtering strategy combining high-value shielding and maximum connected component extraction, effectively removing high-density adhering materials from the artifact's surface while automatically filtering out surrounding suspended debris and noise, restoring the pure geometric shape of the jade artifact. The introduction of three-dimensional morphological closing operations to construct a virtual envelope creatively solves the problem of distinguishing open pores from background air, providing precise quantitative data for the study of the degree of jade patina. The visualization and interaction are intuitive and efficient: this invention breaks through the limitations of traditional single-model displays. Through dual-channel independent rendering and real-time button switching, researchers can dynamically peel off or load adhering material layers, intuitively analyzing the spatial relationship between the adhering materials and the artifact itself. Combined with joystick-style rotation interaction, it greatly enhances the convenience of digital artifact observation. Attached Figure Description

[0014] Figure 1 This is an overall flowchart of the method for digital segmentation and non-destructive testing of porosity of jade cultural relics based on CT images according to the present invention. Figure 2 This is a flowchart illustrating the specific process of porosity calculation in this invention. Figure 3 This is a flowchart illustrating the specific process of rendering a 3D model according to the present invention. Figure 4 This embodiment of the invention obtains a pseudo-color image of the central layer of a slice and its grayscale histogram; Figure 5This is a schematic diagram showing the result of obtaining the grayscale distribution of the loop path in an embodiment of the present invention; Figure 6 This is a schematic diagram illustrating the effect of obtaining the envelope mask using morphological closing operations according to an embodiment of the present invention; Figure 7 This is the final interactive visualization interface of the embodiments of the present invention, which specifically includes three three-dimensional model displays and the final porosity calculation results. Detailed Implementation

[0015] The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0016] The present invention provides a method for digital segmentation and non-destructive testing of porosity of jade artifacts based on CT images, which specifically includes the following steps: Step S1, Data Preprocessing and Dual-Stream Architecture Construction: Read the original CT image sequence data of jade artifacts, convert them into a three-dimensional voxel matrix, and then construct a dual-stream channel: (1) Computational stream, based on preset high-density artifacts (1) Perform artifact removal processing, and use linear interpolation algorithm to downsample the artifact removal data to generate low-resolution volume data for real-time interactive analysis and volume calculation; (2) Rendering stream, retain the original resolution voxel data for high-precision three-dimensional surface reconstruction and visualization. Step S2, Interactive Gray-Scale Feature Calibration: On the sliced ​​image of the computation flow, the detection path is drawn through human-computer interaction, gray-scale profile curves are generated in real time, and a four-dimensional threshold parameter set is established: the body gray-scale range. With pore gray range The four-dimensional threshold is used to quantitatively analyze the grayscale boundaries of the jade body, internal pores and surface attachments: the grayscale range of the body distinguishes the body from the attachments, and the grayscale range of the pores distinguishes the body from the pores. Step S3, Volume quantization based on morphological envelope: The preliminary mask of the jade entity is extracted using the gray range of the body, and a three-dimensional binary morphological closing operation is performed on it to construct a solid envelope containing internal pores. Using this envelope as a spatial constraint mask, the volume of the jade body and the pore volume are calculated by Boolean operation, and then the porosity is obtained. Step S4, Dual-channel Independent Reconstruction and Fusion Display: Based on the rendering stream data, independent isosurface extraction and mesh optimization are performed on the "surface attachments" and the "jade body" respectively to construct two independent three-dimensional geometric objects. A logical switch is established in the rendering pipeline to realize independent visibility control and fusion rendering of the two.

[0017] Specifically, the implementation process of step S2 is as follows: 1) Initialize the visualization environment: The system automatically locates the central layer slice of the computation flow data and generates a pseudo-color map and grayscale histogram for that slice. The histogram statistically analyzes the grayscale distribution from zero grayscale to the maximum grayscale value. 2) Establish a continuous interactive listening loop: The system enters an immediate response mode and continuously listens for user mouse click events; 3) Dynamic path analysis: In each iteration of the loop, a set of starting and ending coordinates of the user's click on the pseudo-color map is captured, the voxel grayscale sequence on the path is extracted in real time, and the corresponding grayscale profile curve is refreshed and displayed, so that the user can repeatedly explore the grayscale features of different areas by changing the path position. 4) Loop Termination and Parameter Establishment: Responding to the user's loop termination command, exiting the listening loop, and receiving the four-dimensional threshold parameters determined by the user based on the above repeated probing results: the body grayscale range. With pore gray range .

[0018] Specifically, step S3 is the core step in achieving non-destructive testing of porosity, including: 1) Initial entity extraction: using the main body's grayscale range Binarization thresholding is performed on the computation stream data to obtain discrete binary images; 2) Noise Removal: Perform connected component analysis on the binary image, calculate the number of voxels in all connected regions, and retain only the connected region with the largest number of voxels as the initial solid mask. Remove free noise; 3) Envelope Construction: Set the radius of the structuring element For the initial solid mask Perform morphological closing operations to fill closed voids inside the solid, generating a solid envelope mask. ; 4) Constraint Calculation: The computation flow data is then processed in... Binary results within the range and solid envelope mask Execution and logical operations, total number of ontology voxels in the statistical results A computational mask is constructed to obtain the ontology; the computational flow data is then processed in... Binary results within the range and solid envelope mask Performing logical operations, the total number of porosity voxels in the statistical results. The volume is calculated based on the physical spacing of voxels, and then applied according to the formula. To obtain porosity.

[0019] Specifically, the dual-channel independent rendering reconstruction in step S4 adopts the following method: 1) Attachment rendering: based on a preset high artifact threshold Isosurface extraction is performed on full-resolution CT data, and values ​​exceeding a preset threshold are considered. A three-dimensional model of the attached objects was constructed in the area and marked in yellow. This model independently represents the spatial distribution structure of the attached impurities on the surface of the cultural relic. 2) Jade body rendering: Perform high-value masking on the full-resolution CT data, reducing the grayscale value to a minimum. The voxels are set to zero to prevent high-density attachments from being counted repeatedly. This is based on the user-obtained ontology threshold. The zeroed-out data is subjected to isosurface extraction to generate a preliminary body mesh model. Three-dimensional spatial connectivity analysis is then performed on the preliminary mesh model to calculate the geometric scale of all independent connected regions. Only the largest connected region is retained as the three-dimensional body model of the jade, marked with white, and other non-connected suspended impurities and noise are automatically removed. 3) Multi-material fusion display: The 3D model of the attached object and the 3D model of the jade body are placed in the same 3D world coordinate system and overlaid and fused for rendering. This allows the form of the cultural relic body and the distribution of the attached object to be presented simultaneously in a single view, realizing the digital separation and coexistence of the two.

[0020] Furthermore, the system is also equipped with an interactive visual control mechanism, including: Joystick camera mode: Establish a rotation response mechanism based on the duration of mouse press. When the user holds down the left mouse button, the 3D model rotates continuously and automatically according to the vector direction and distance of the mouse from the click point, without the need for repeated dragging. Multi-state key switching mechanism: Set up a keyboard event listener and define at least three display states. State 1 (Key 0): Simultaneously render the jade body model and the surface attachment model, showing the spatial attachment relationship between the two; State 2 (Key 1): Only render the jade body model, hide the attachment model, and show the surface details of the artifact after removing the attachment; State 3 (Key 2): Only render the surface attachment model, hide the jade body model, and independently show the distribution pattern of the attachment.

[0021] Example 1: like Figure 1 As shown, this embodiment provides a method for digital segmentation and non-destructive testing of porosity of jade artifacts based on CT images. The method includes the following core steps: Step S1: Data Preprocessing and Dual-Stream Architecture Construction. Using the SimpleITK library, CT sequence images are read, and a dual-channel data stream is constructed: one channel undergoes downsampling for fast computation, while the other retains full resolution for high-precision reconstruction.

[0022] Step S2: Interactive grayscale feature calibration. On the downsampling slice, a grayscale profile curve is drawn in real time through a click-draw-analyze interactive method to accurately locate the grayscale boundary between the jade body and the attached material.

[0023] Step S3: Porosity calculation based on envelope constraints. A virtual envelope of the jade entity is constructed using morphological closing operations, and this envelope is used as a constraint to accurately calculate the internal pore volume.

[0024] Step S4: Dual-channel independent reconstruction and fusion display. Based on the VTK engine, 3D mesh models of the attached material and the pure jade body are constructed separately. The construction of the body model combines high-value masking and connected component denoising technology, and finally achieves fusion display in the same view window.

[0025] Example 2: This embodiment provides a specific code implementation logic based on the Python language for steps S1 and S2 in the above embodiment one.

[0026] S101 Data Loading: The sitk.ImageSeriesReader class is used to read the CT image folder path, GetGDCMSeriesFileNames is called to obtain the sequence file name, and the original three-dimensional voxel matrix is ​​constructed through the Execute() method.

[0027] S102 Downsampling Processing: To improve the response speed of subsequent interactive analysis, a sitk.ResampleImageFilter filter is created. The target size is set to half of the original size, i.e., the downsampling factor is 2, and the interpolator SetInterpolator is set to linear interpolation sitkLinear, thereby generating computational stream data.

[0028] S201 Visualization Environment Initialization: Extract the Z-axis center layer slice of the computation flow data and convert it to a NumPy array. Create two subplot windows using matplotlib.pyplot: the left side uses imshow to display the slices (configured with the turbo color map), and the right side uses hist to plot the logarithmic scale histogram.

[0029] S202 establishes a recursive interactive listening loop: The `plt.ginput` function is called to suspend the program, continuously listening for mouse click events, waiting for the user to select a starting point P1 and an ending point P2 on the left-hand slice image. If valid coordinates are captured, the `skimage.draw.line` algorithm is used to extract all pixel indices along the path connecting P1 and P2. The grayscale value sequence along this path is indexed from the slice array, and the grayscale profile curve is dynamically refreshed in the right-hand window. The user repeatedly clicks on different areas (such as areas with dense impurities or pores) to observe the peaks and troughs of the curve until confirmation is achieved, at which point the loop exits, and the four-dimensional threshold parameter—the body grayscale range—is input. With pore gray range .

[0030] Example 3: This embodiment is a specific implementation of step S3 in embodiment 1 above, which is based on the SimpleITK morphological algorithm.

[0031] S301 Entity Initial Extraction and Denoising: Using sitk.BinaryThreshold based on user settings. A preliminary binary mask is generated. To remove free noise in the background, sitk.ConnectedComponentImageFilter is applied to label connected components, and sitk.LabelShapeStatisticsImageFilter is used to count the number of pixels in each connected component. Only the connected component with the largest number of pixels (i.e., the jade body) is retained, and the other areas are set to zero.

[0032] S302 Envelope Construction: Selecting a structuring element radius The function calls `sitk.BinaryMorphologicalClosing` to perform a 3D morphological closing operation on the denoised solid mask. This step fills in the closed voids inside the solid, generating a solid envelope mask.

[0033] S303 Volume Calculation: Perform a logical AND operation between the original binary image that meets the ontology threshold and the envelope mask, and count the total number of ontology voxels in the result. The volume of the bulk is obtained. A logical AND operation is performed between the original binary image satisfying the pore threshold and the envelope mask, and the total number of pore voxels in the result is counted. The pore volume is obtained. The monomer volume is calculated based on the spacing property of the CT image data, and finally converted to physical volume (mm³) according to the formula. Calculate the porosity.

[0034] Example 4: This embodiment is a dual-channel reconstruction implementation method based on VTK pipeline technology provided in step S4 of embodiment 1 above.

[0035] S401 constructs the attachment channel: Input the raw full-resolution data and use the vtkMarchingCubes class to extract isosurfaces. Call SetValue to set the extraction threshold to the high artifact threshold. The generated vtkPolyData data is mapped to vtkActor through vtkPolyDataMapper for independent representation of attachments, and the color attribute is set to yellow.

[0036] S402 Constructing the Jade Body Channel: First, the raw data is processed using the vtkImageThreshold filter. ThresholdByUpper is called to select the area with attachments, and SetInValue is used to force its grayscale value to 0. This step is a crucial preprocessing step to prevent attachments from sticking to the body. The masked data is input into vtkMarchingCubes, and SetValue is called to extract the jade body surface. The extracted mesh data is input into the vtkPolyDataConnectivityFilter filter, and the SetExtractionModeToLargestRegion() function is called. The system will automatically analyze the mesh topology, retaining only the independent connected components with the largest number of vertices, thus automatically removing small impurities and noise floating around the body. The final clean mesh constructs the jade body model, and its color attribute is set to white.

[0037] S403 Multi-Model Fusion Assembly: Create a vtkRenderer object and call renderer.AddActor to add the jade body model and the attached object model to the same rendering scene. Since both are built based on CT data in the same coordinate system, they automatically achieve precise registration and fusion in 3D space, intuitively presenting the state of the attached object covering the body.

[0038] Example 5: This embodiment provides a specific implementation method for the interaction logic of step S4 in embodiment 1.

[0039] S501 Mouse Interaction: Create a vtkRenderWindowInteractor and set its style to vtkInteractorStyleJoystickCamera to achieve continuous and smooth rotation based on mouse press.

[0040] S502 Key Monitoring and Interaction: Key 0 simultaneously makes both the jade body and the attached object visible, i.e., both are merged; Key 1 makes only the jade body visible; Key 2 makes only the attached object visible. The interactive system constantly monitors the key press status and displays the corresponding model based on the key presses. Some terms involved in this invention are explained below: SimpleITK (SITK) is a simplified layer interface built on Insight Segmentation and Registration Toolkit (ITK), primarily used for efficient processing and analysis of medical images. In this invention, SITK is mainly responsible for loading DICOM data, denoising, downsampling preprocessing, and morphology-based complex volume calculations.

[0041] sitkBinaryMorphologicalClosing (3D Morphological Closing Operation): This is a morphological filter in SimpleITK used for processing binary images. Its operation is "dilation followed by erosion," capable of filling closed cavities inside an object and smoothing boundaries without changing the object's overall dimensions. This invention utilizes this algorithm to construct a "solid envelope" of jade, which forms the mathematical basis for porosity calculation.

[0042] Matplotlib is a plotting library for the Python programming language. In this invention, it is used to generate pseudo-color slices and grayscale profile curves during the interactive calibration process.

[0043] VTK (Visualization Toolkit) is an open-source, cross-platform computer graphics and visualization library that supports parallel processing. This invention utilizes VTK to build a rendering pipeline to achieve 3D reconstruction, mesh processing, and interactive display of CT data.

[0044] vtkMarchingCubes (moving cube algorithm): This is the core class in VTK used to extract isosurfaces from a 3D scalar field (volume data). It constructs a triangular mesh surface by traversing each voxel in the volume data and based on a set threshold. In this invention, this class is used to construct a high-density attachment model and a jade body model.

[0045] vtkPolyDataConnectivityFilter (connectivity filter): This is a class in VTK used for analyzing the topology of geometric meshes. It can identify and extract independent connected regions in the mesh. This invention utilizes its SetExtractionModeToLargestRegion() method to automatically identify and retain the connected component with the largest geometric volume (i.e., the jade itself), thereby eliminating surrounding floating non-connected noise points.

[0046] vtkImageThreshold (Image Threshold Processor): This is a class in VTK used for binarizing or thresholding image data. In this invention, its SetInValue(0) method is used to implement "high-value masking," that is, to forcibly set the grayscale value of high-density attachments to zero, preventing them from sticking together during body reconstruction.

[0047] vtkInteractorStyleJoystickCamera: This is an interactive control mode provided by VTK. Unlike traditional drag-and-drop rotation, in this mode, the user presses and holds the left mouse button, and the camera will automatically and smoothly rotate continuously according to the direction of the vector direction of the mouse's deviation from the center. It is suitable for comprehensive and non-destructive observation of complex cultural relics.

[0048] In summary, the above are merely preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for digital segmentation and non-destructive testing of porosity of jade artifacts based on CT images, characterized in that, include: Step S1, Data Preprocessing and Dual-Stream Architecture Construction: Read the original CT image sequence data of jade artifacts, convert them into a three-dimensional voxel matrix, and then construct a dual-stream channel: (1) Based on the preset high-density artifacts (1) Perform artifact removal processing, use linear interpolation algorithm to downsample the artifact removal data, generate computation stream data for real-time interactive analysis and volume calculation; (2) retain the original resolution voxel data as rendering stream data for three-dimensional surface reconstruction and visualization. Step S2, Interactive Gray-Scale Feature Calibration: On the sliced ​​image of the computation flow, the detection path is drawn through human-computer interaction, gray-scale profile curves are generated in real time, and a four-dimensional threshold parameter set is established: the body gray-scale range. With pore gray range This four-dimensional threshold was used to quantitatively analyze the grayscale boundaries of the jade body, internal pores, and surface attachments: the grayscale range of the body distinguished the body from the attachments, and the grayscale range of the pores distinguished the body from the pores; among them, These represent the lower and upper limits of the grayscale range of the entity, respectively. These represent the lower and upper limits of the grayscale range of pores, respectively. Step S3, Volume quantization based on morphological envelope: The preliminary mask of the jade entity is extracted using the gray range of the body, and a three-dimensional binary morphological closing operation is performed on it to construct a solid envelope containing internal pores. Using this envelope as a spatial constraint mask, the volume of the jade body and the pore volume are calculated by Boolean operation, and then the porosity is obtained. Step S4, Dual-channel Independent Reconstruction and Fusion Display: Based on the rendering stream data, separate isosurface extraction and mesh optimization are performed for "surface attachments" and "jade body" to construct two independent 3D geometric objects. A logical switch is established in the rendering pipeline to achieve independent visibility control and fusion rendering of the two.

2. The method for digital segmentation and non-destructive testing of porosity of jade artifacts based on CT images as described in claim 1, characterized in that, Step S2 specifically includes: 1) Initialize the visualization environment: The system automatically locates the central layer slice of the computation flow data and generates a pseudo-color map and grayscale histogram for that slice. The histogram statistically analyzes the grayscale distribution from zero grayscale to the maximum grayscale value. 2) Establish a continuous interactive listening loop: The system enters a timely response mode and continuously listens for user mouse click events; 3) Dynamic path analysis: In each iteration of the loop, a set of starting and ending coordinates of the user's click on the pseudo-color map is captured, the voxel grayscale sequence on the path is extracted in real time, and the corresponding grayscale profile curve is refreshed and displayed, so that the user can repeatedly explore the grayscale features of different areas by changing the path position. 4) Loop Termination and Parameter Establishment: Responding to the user's loop termination command, exiting the listening loop, and receiving the four-dimensional threshold parameter determined by the user based on the above repeated probing results: the body grayscale range. With pore gray range .

3. The method for digital segmentation and non-destructive testing of porosity of jade artifacts based on CT images as described in claim 1, characterized in that, Step S3 specifically includes: 1) Initial entity extraction: using the grayscale range of the main body Binarization thresholding is performed on the computation stream data to obtain discrete binary images; 2) Noise Removal: Perform connected component analysis on the binary image, calculate the number of voxels in all connected regions, and retain only the connected region with the largest number of voxels as the initial solid mask. Remove free noise; 3) Envelope Construction: Set the radius of the structuring element For the initial solid mask Perform morphological closing operations to fill closed voids inside the solid, generating a solid envelope mask. ; 4) Constraint Calculation: The computation flow data is then processed in... Binary results within the range and solid envelope mask Execution and logical operations, total number of ontology voxels in the statistical results A computational mask is constructed to obtain the ontology; the computational flow data is then processed in... Binary results within the range and solid envelope mask Performing logical operations, the total number of porosity voxels in the statistical results. The volume is calculated based on the physical spacing of voxels, and then applied according to the formula. To obtain porosity.

4. The method for digital segmentation and non-destructive testing of porosity of jade artifacts based on CT images as described in claim 1, characterized in that, Step S4 specifically includes: 1) Attachment rendering: based on a preset high artifact threshold Isosurface extraction is performed on full-resolution CT data, and values ​​exceeding a preset threshold are considered. A three-dimensional model of the attached objects was constructed in the area and marked in yellow. This model independently represents the spatial distribution structure of the attached impurities on the surface of the cultural relic. 2) Jade body rendering: Perform high-value masking on the full-resolution CT data, reducing the grayscale value to a minimum. The voxels are set to zero to prevent high-density attachments from being counted repeatedly. This is based on the user-obtained ontology threshold. The data after zeroing is subjected to isosurface extraction to generate a preliminary body mesh model; the preliminary mesh model is subjected to three-dimensional spatial connectivity analysis to calculate the geometric scale of all independent connected regions, and only the largest connected region is retained as the three-dimensional model of the jade body, which is marked with white, and other non-connected suspended impurities and noise are automatically removed. 3) Multi-material fusion display: The 3D model of the attached object and the 3D model of the jade body are placed in the same 3D world coordinate system and overlaid and fused for rendering. This allows the form of the cultural relic body and the distribution of the attached object to be presented simultaneously in a single view, realizing the digital separation and coexistence of the two.

5. The method for digital segmentation and non-destructive testing of porosity of jade artifacts based on CT images as described in claim 1, characterized in that, It also includes a joystick camera mode: establishing a rotation response mechanism based on the duration of mouse press. When the user presses the left mouse button, the 3D model rotates continuously and automatically according to the vector direction and distance of the mouse from the click point, without the need for repeated dragging.

6. The method for digital segmentation and non-destructive testing of porosity of jade artifacts based on CT images as described in claim 1, characterized in that, It also includes a multi-state key switching mechanism: set up a keyboard event listener and define at least three display states: State 1: render the jade body model and the surface attachment model at the same time to show the spatial attachment relationship between the two; State 2: render only the jade body model, hide the attachment model, and show the surface details of the cultural relic after removing the attachment; State 3: render only the surface attachment model, hide the jade body model, and independently show the distribution pattern of the attachment.

Images