Industrial CT scanning data standardization method and device

Abstract

The invention provides an industrial CT scanning data standardization method and device, and the method comprises the steps: obtaining the voltage and current parameters of CT scanning equipment corresponding to a scanning image, the parameters of the CT scanning equipment, a CT scanning mode, and an original actual size represented by a pixel value in the scanning image based on the scanning image obtained after the CT scanning of an industrial tested sample; re-sampling the scanned image based on a proportionality coefficient calculated based on the original actual size and the pixel size to obtain a re-sampled actual size; obtaining a sample scanning image after sample contour segmentation; the voltage and current parameters of the CT scanning equipment, the parameters of the CT scanning equipment, the CT scanning mode, the original actual size, the resampling actual size and the sample scanning image are constructed into standard industrial CT scanning image data. Therefore, data differences of different CT scanning devices caused by voltage, current and pixel size factors are effectively eliminated, and usability and accuracy of CT scanning image data are improved.

Description

Technical Field

[0001] The present invention relates to the field of CT scanning technology, and in particular to a method and device for standardizing industrial CT scanning data. Background Art

[0002] In the practice of industrial computed tomography (CT) data processing, there is a lack of understanding of the characteristics of the field. Key influencing factors such as differences in experimental personnel operations, interference from environmental variables, material characteristics of the scanned samples, and deviations in sample placement orientation are ignored, and the CT scan image is simply used as a single input. This processing method is difficult to meet the high-precision requirements of industrial applications. In related technologies, the differences in voltage and current parameter settings of different CT scanning devices during actual operation, as well as the inconsistency of pixel physical size calibration in the original CT scan image, pose significant challenges to data standardization processing. In addition, due to the wide range of physical sizes of different CT inspection objects, from micron-level precision components to large components of several meters, it is very easy to cause problems such as defect positioning deviation and dimensional measurement inaccuracy. Summary of the Invention

[0003] The present invention aims to solve one of the technical problems in the related art at least to a certain extent.

[0004] To this end, the first object of the present invention is to propose an industrial CT scan data standardization method to effectively eliminate data differences caused by voltage, current, and pixel size factors among different CT scanning devices, thereby improving the availability and accuracy of CT scan image data.

[0005] The second object of the present invention is to provide an industrial CT scanning data standardization device.

[0006] A third object of the present invention is to provide an electronic device.

[0007] A fourth object of the present invention is to provide a non-transitory computer-readable storage medium storing computer instructions.

[0008] To achieve the above objectives, a first embodiment of the present invention provides a method for standardizing industrial CT scan data, the method comprising: Based on the scanned image of the industrial sample after CT scanning, the voltage and current parameters of the CT scanning device corresponding to the scanned image, the CT scanning device parameters, the CT scanning mode, and the original actual size represented by a pixel value in the scanned image are obtained; Calculate a scaling factor based on the original actual size represented by a pixel value in the scanned image and the pixel size in the scanned image; Based on the scale factor, the scanned image is resampled using a bilinear interpolation algorithm to obtain a resampled actual size represented by a pixel value after resampling; Performing sample contour segmentation on the scanned image to obtain a sample scanned image of the industrial tested sample itself after the sample contour segmentation; The voltage and current parameters of the CT scanning device, the CT scanning device parameters, the CT scanning mode, the original actual size, the resampled actual size and the sample scanning image are constructed into standard industrial CT scanning image data.

[0009] To achieve the above-mentioned objectives, a second embodiment of the present invention provides an industrial CT scanning data standardization device, the device comprising: An acquisition module is used to obtain voltage and current parameters of the CT scanning device corresponding to the scanned image, CT scanning device parameters, CT scanning mode, and the original actual size represented by a pixel value in the scanned image based on the scanned image after CT scanning of the industrial tested sample; a calculation module for calculating a scale factor based on an original actual size represented by a pixel value in the scanned image and a pixel size in the scanned image; a resampling module, configured to resample the scanned image using a bilinear interpolation algorithm based on the scale factor to obtain a resampled actual size represented by a pixel value after resampling; A segmentation module is used to perform sample contour segmentation on the scanned image to obtain a sample scanned image of the industrial sample itself after the sample contour segmentation; A construction module is used to construct the voltage and current parameters of the CT scanning device, the CT scanning device parameters, the CT scanning mode, the original actual size, the resampled actual size and the sample scanning image into standard industrial CT scanning image data.

[0010] To achieve the above-mentioned purpose, the third aspect embodiment of the present invention proposes an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method described in the first aspect.

[0011] In order to achieve the above-mentioned objectives, an embodiment of the fourth aspect of the present invention proposes a non-transitory computer-readable storage medium storing computer instructions, where the computer instructions are used to enable the computer to execute the method described in the first aspect.

[0012] The industrial CT scan data standardization method, apparatus, electronic device, and storage medium of the embodiments of the present invention obtain, based on a scanned image of an industrial sample after CT scanning, the voltage and current parameters of the CT scanning device corresponding to the scanned image, the CT scanning device parameters, the CT scanning mode, and the original actual size represented by a pixel value in the scanned image. The scanned image is then resampled based on a scale factor calculated from the original actual size and pixel size to obtain the resampled actual size. A sample scanned image of the industrial sample after sample contour segmentation is obtained. The voltage and current parameters of the CT scanning device, the CT scanning device parameters, the CT scanning mode, the original actual size, the resampled actual size, and the sample scanned image are then constructed into standard industrial CT scan image data. This effectively eliminates data differences caused by voltage, current, and pixel size between different CT scanning devices, improving the usability and accuracy of CT scan image data.

[0013] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which: Figure 1 A schematic diagram of a process for standardizing industrial CT scan data provided by an embodiment of the present invention; Figure 2 This is a structural diagram of an industrial CT scanning data standardization device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0015] The following describes embodiments of the present invention in detail, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and are not to be construed as limiting the present invention.

[0016] It should be noted that the acquisition, storage, use, and processing of data in the technical solution of the present invention comply with the relevant provisions of relevant laws and regulations.

[0017] The following describes an industrial CT scan data standardization method and apparatus according to an embodiment of the present invention with reference to the accompanying drawings.

[0018] Figure 1 A flowchart of a method for standardizing industrial CT scan data provided by an embodiment of the present invention.

[0019] like Figure 1As shown, the method includes the following steps: Step 101 , based on a scanned image of an industrial sample after CT scanning, obtain voltage and current parameters of a CT scanning device corresponding to the scanned image, CT scanning device parameters, CT scanning mode, and the original actual size represented by a pixel value in the scanned image.

[0020] In some possible implementations, in the practice of industrial computed tomography (CT) data processing, the voltage and current parameters of the CT scanning device corresponding to the scanned image, the CT scanning device parameters, the CT scanning method, and the original actual size represented by a pixel value in the scanned image are combined to reduce key influences such as experimental personnel operation differences, environmental variable interference, scanned sample material characteristics, and sample placement orientation deviation, laying a solid foundation for the efficient application of industrial CT data in the era of large models.

[0021] Step 102 : Calculate a scale factor based on the original actual size represented by a pixel value in the scanned image and the pixel size in the scanned image.

[0022] In some possible implementations, due to the wide range of physical sizes of different inspection objects, from micron-scale precision components to large structures measuring several meters, scaling factors can be used to accurately establish the correspondence between the original actual dimensions and pixel dimensions, reducing positioning deviations and dimensional measurement inaccuracies. Therefore, a precise scale mapping system is constructed using scaling factors to ensure the accuracy and reliability of industrial CT scan data processing results.

[0023] Step 103 : Based on the scale factor, the scanned image is resampled using a bilinear interpolation algorithm to obtain a resampled actual size represented by a pixel value after resampling.

[0024] In some possible implementations, based on the scale factor, a bilinear interpolation algorithm is used to resample the scanned image to obtain the actual size of the resampled pixel value after resampling, including: the corresponding continuous coordinates in the scanned image are , the scanned image is The coordinates of four adjacent pixels are 、 、 、 , and the corresponding pixel values are 、 、 、 In the case of , based on the scale factor, the bilinear interpolation algorithm is used to resample the scanned image, and the coordinates of the resampled scanned image are The pixels of the scanned image after resampling are The actual size of the resampled pixel represented by a pixel value after resampling is calculated; wherein, the bilinear interpolation algorithm is: , , , , , .

[0025] In addition, linear quantization or nonlinear quantization methods can be used to quantize the resampled scanned image to reduce the data volume and unify the data bit depth, obtain a quantized formatted scanned image, and store it. Data quantization processing reduces the memory usage of the formatted scanned image, improves the storage and processing efficiency of the formatted scanned image, and reduces the cost of formatted scanned image processing.

[0026] Furthermore, when the grayscale value distribution in the resampled scanned image is greater than or equal to a set threshold (when the grayscale value distribution is concentrated), the resampled scanned image is quantized using a linear quantization method to reduce the data volume and unify the data bit depth, thereby obtaining a quantized formatted scanned image and storing it, wherein: In the resampled scanned image, the N-bit grayscale value is , the range is ( , when N=16, M=65535), the n-bit gray value after quantization is , the range is ( , when n=8 and m=255), the linear mapping formula of linear quantization is , This is a rounding operation to ensure quantization accuracy.

[0027] Furthermore, when the grayscale value distribution in the resampled scanned image is less than a set threshold, a nonlinear quantization method is used to quantize the resampled scanned image to reduce the data volume and unify the data bit depth, thereby obtaining a quantized formatted scanned image and storing it. The nonlinear quantization method includes: By statistically analyzing the histogram of the resampled scanned image, the probability density function of the grayscale value in the resampled scanned image is obtained as follows: ,pass Perform histogram equalization to obtain the equalized grayscale value , the mapping relationship of histogram equalization is the cumulative distribution function (CDF), that is: .

[0028] The N-bit grayscale value after equalization Linear mapping to n-bit range, the formula is: .

[0029] The combined formulas yield the linear mapping formula for nonlinear quantization: .

[0030] in, Resample the scanned image's data bit depth (e.g. 16-bit, ), The data bit depth for formatting scanned images (e.g. 8-bit, ), is the floor function, Is the rounding function.

[0031] Step 104 : performing sample contour segmentation on the scanned image to obtain a sample scanned image of the industrial sample to be tested after the sample contour segmentation.

[0032] In some possible embodiments, the scanned image is subjected to sample contour segmentation to obtain a sample scanned image of the industrial tested sample itself after the sample contour segmentation, including: performing sample contour segmentation on the scanned image through a forward graph matching edge recognition method and a reverse graph matching edge recognition method to obtain a sample scanned image of the industrial tested sample itself after the sample contour segmentation, wherein the reverse graph matching edge recognition method adopts an edge detection algorithm or a deep learning method to reduce interference around the actual tested sample (industrial tested sample) and optimizes the ratio of the scanned image to the entire image.

[0033] Furthermore, the sample contour segmentation is performed on the scanned image by the forward graph matching edge recognition method to obtain the sample scanned image of the industrial sample itself after the sample contour segmentation, including: constructing a standard template T of different dimensions of the scanned image, wherein the standard template includes a two-dimensional template (size is , the elements are represented as ) and 3D templates (size , the elements are represented as ); Traverse the two-dimensional scanned image D row by row and column by column through the sliding window, and traverse the three-dimensional scanned image D along the horizontal axis (x), vertical axis (y), and depth axis (z) through the sliding window; calculate the traversed two-dimensional scanned image ( ) / 3D scanned image ( ) with the first similarity of each position in the two-dimensional template / three-dimensional template, locating the area containing the edge of the industrial sample to be tested in the two-dimensional scan image / three-dimensional scan image ;according to Map to the corresponding area R in the two-dimensional scanned image / three-dimensional scanned image, calculate the second similarity with each position in the two-dimensional template / three-dimensional template within R; select the second similarity greater than the set threshold ( ) / The second position with the highest similarity is used as the edge position of the industrial sample to be tested in the scanned image to perform sample contour segmentation, and the sample scan image of the industrial sample to be tested itself is obtained after the sample contour segmentation. The forward graph matching edge recognition method makes full use of the known information during sample preparation, and can more accurately identify the edge when the boundary of the sample to be tested is not clear, avoiding the limitations of traditional methods.

[0034] The first similarity between the traversed two-dimensional scanned image / three-dimensional scanned image and each position in the two-dimensional template / three-dimensional template is calculated, and the area containing the edge of the industrial sample to be tested in the two-dimensional scanned image / three-dimensional scanned image is located. Including: using multi-resolution analysis method to perform Gaussian pyramid decomposition on the two-dimensional scanned image / three-dimensional scanned image to obtain image representations of different resolutions (two-dimensional and three-dimensional), and then calculating the first similarity between the traversed two-dimensional scanned image / three-dimensional scanned image and each position in the two-dimensional template / three-dimensional template to determine the edge area of the industrial sample being tested .

[0035] 2D:

[0036] 3D:

[0037] Where s is the downsampling factor and x, y, z are the low-resolution image coordinates.

[0038] Alternatively, the two-dimensional normalized cross correlation (NCC) formula is: .

[0039] The two-dimensional template mean formula is: .

[0040] The formula for the mean value of the two-dimensional scanned image area is: .

[0041] The three-dimensional normalized cross correlation (NCC) formula is: .

[0042] The three-dimensional template mean formula is: .

[0043] The formula for the regional mean of a 3D scanning image is: .

[0044] Furthermore, the reverse graph matching edge recognition method can use conventional edge detection algorithms or deep learning methods. These methods are relatively computationally complex, and their accuracy depends on the algorithm architecture. They are suitable for edge detection of samples of unknown shapes or deformations. Through multi-resolution analysis, they significantly reduce the computational complexity of edge recognition, improve processing speed, and make CT scan data processing more efficient. Specifically, edge detection algorithms include: 1. Feature extraction and edge candidate point detection: Use a multi-level edge detection algorithm (Canny operator) or a discrete differential operator for edge detection (Sobel operator) to calculate the gradient magnitude and direction.

[0045] The Sobel operator is used to calculate the gradient of the scanned image: the gradient in the horizontal and vertical directions is calculated , : , .

[0046] Gradient magnitude and direction , : , .

[0047] 2. Feature point enhancement: Apply non-maximum suppression and double threshold method to refine edge candidate points.

[0048] Non-maximum suppression: , yes Local maximum in the gradient direction, 0 otherwise.

[0049] Double thresholding: .

[0050] 3. Initial edge model construction: construct the initial edge model based on the extracted edge candidate points, parametric representation: use parametric curves (such as B-spline, NURBS) or non-parametric representation (such as point set).

[0051] Parameterized initial edge model B-spline curve: .

[0052] in, is the k-order B-spline basis function, It is a control point.

[0053] 4. Define the energy function, combining the scanned image gradient, smoothness constraint and prior knowledge to define the energy function (snake model energy function): .

[0054] in: is the B-spline parameterization, Control flexibility, Control rigidity, is the image energy term, defined as a function of the gradient magnitude.

[0055] 5. Iteratively optimize the initial edge model, use the gradient descent algorithm to minimize the energy function, and update the initial edge model parameters based on the optimization results.

[0056] The gradient descent algorithm formula is: .

[0057] in, is the learning rate and t is the number of iterations.

[0058] Step 105 : constructing standard industrial CT scanning image data from voltage and current parameters of the CT scanning device, CT scanning device parameters, CT scanning mode, original actual size, resampled actual size, and sample scanning image.

[0059] Achieve precise correspondence between actual size and pixel size, reduce analytical errors in size effect research, and lay a solid foundation for the efficient application of industrial CT data in the era of large models.

[0060] The industrial CT scan data standardization method of an embodiment of the present invention obtains the voltage and current parameters of the CT scanning device corresponding to the scanned image, the CT scanning device parameters, the CT scanning mode, and the original actual size represented by a pixel value in the scanned image based on the scanned image after CT scanning of the industrial sample under test. The scanned image is then resampled based on a scale factor calculated from the original actual size and the pixel size to obtain the resampled actual size. A sample scanned image of the industrial sample under test is obtained after sample contour segmentation. The voltage and current parameters of the CT scanning device, the CT scanning device parameters, the CT scanning mode, the original actual size, the resampled actual size, and the sample scanned image are then constructed into standard industrial CT scan image data. This effectively eliminates data differences caused by voltage, current, and pixel size between different CT scanning devices, improving the usability and accuracy of CT scan image data.

[0061] In order to implement the above embodiment, the present invention also provides an industrial CT scanning data standardization device.

[0062] Figure 2 This is a structural diagram of an industrial CT scanning data standardization device provided by an embodiment of the present invention.

[0063] like Figure 2As shown, the industrial CT scanning data standardization device 20 includes: an acquisition module 21, a calculation module 22, a resampling module 23, a segmentation module 24, and a construction module 25.

[0064] An acquisition module 21 is configured to obtain, based on a scanned image of an industrial sample after CT scanning, voltage and current parameters of the CT scanning device corresponding to the scanned image, CT scanning device parameters, CT scanning mode, and the original actual size represented by a pixel value in the scanned image; a calculation module 22 for calculating a scale factor based on the original actual size represented by a pixel value in the scanned image and the pixel size in the scanned image; A resampling module 23 is configured to resample the scanned image using a bilinear interpolation algorithm based on the scale factor to obtain a resampled actual size represented by a pixel value after resampling; a segmentation module 24 for performing sample contour segmentation on the scanned image to obtain a sample scanned image of the industrial sample to be tested after the sample contour segmentation; The construction module 25 is used to construct the voltage and current parameters of the CT scanning device, the CT scanning device parameters, the CT scanning mode, the original actual size, the resampled actual size and the sample scanning image into standard industrial CT scanning image data.

[0065] Furthermore, in a possible implementation of the embodiment of the present invention, the resampling module 23 is specifically configured to: The corresponding continuous coordinates in the scanned image are , the scanned image is The coordinates of four adjacent pixels are 、 、 、 , and the corresponding pixel values are 、 、 、 In the case of, based on the scale factor, the scanned image is resampled using a bilinear interpolation algorithm to obtain the coordinates of the resampled scanned image. pixels; According to the resampling, the coordinates in the scanned image are , calculate the actual size of the resampled pixel represented by the resampled pixel value; Among them, the bilinear interpolation algorithm is: , , , , , .

[0066] Furthermore, in a possible implementation of the embodiment of the present invention, the apparatus further includes: The quantization storage module is used to quantize the resampled scanned image by using a linear quantization method or a nonlinear quantization method to reduce the data volume and unify the data bit depth, obtain a quantized formatted scanned image, and store it.

[0067] Furthermore, in a possible implementation of the embodiment of the present invention, the quantization storage module is specifically configured to quantize the resampled scanned image using a linear quantization method when the grayscale value distribution in the resampled scanned image is greater than or equal to a set threshold, so as to reduce the data volume and unify the data bit depth, obtain a quantized formatted scanned image, and store the quantized formatted scanned image, wherein: In the resampled scanned image, the N-bit grayscale value is , the range is ( ), after quantization, the n-bit gray value is , the range is ( ), the linear mapping formula of linear quantization is , This is a rounding operation.

[0068] Furthermore, in a possible implementation of the embodiment of the present invention, the quantization storage module is further specifically configured to, when the grayscale value distribution in the resampled scanned image is less than a set threshold, quantize the resampled scanned image using a nonlinear quantization method to reduce the data volume and unify the data bit depth, obtain a quantized formatted scanned image, and store the quantized formatted scanned image. The nonlinear quantization method includes: By statistically analyzing the histogram of the resampled scanned image, the probability density function of the grayscale value in the resampled scanned image is obtained as follows: ,pass Perform histogram equalization to obtain the equalized grayscale value , the mapping relationship of histogram equalization is the cumulative distribution function, that is: ; The N-bit grayscale value after equalization Linear mapping to n-bit range, the formula is: ; The combined formulas yield the linear mapping formula for nonlinear quantization: ; in, Resample the data bit depth of the scanned image, To format the data bit depth of the scanned image, is the floor function, Is the rounding function.

[0069] Furthermore, in a possible implementation of the embodiment of the present invention, the segmentation module 24 is specifically configured to: The scanned image is subjected to sample contour segmentation by a forward graph matching edge recognition method and a reverse graph matching edge recognition method to obtain a sample scanned image of the industrial tested sample itself after sample contour segmentation, wherein the reverse graph matching edge recognition method adopts an edge detection algorithm or a deep learning method.

[0070] Furthermore, in a possible implementation of the embodiment of the present invention, the segmentation module 24 is further specifically configured to: Constructing standard templates of different dimensions of the scanned image, wherein the standard templates include two-dimensional templates and three-dimensional templates; The two-dimensional scanned image is traversed row by row and column by column through the sliding window, and the three-dimensional scanned image is traversed along the horizontal axis, vertical axis, and depth axis through the sliding window; Calculate the first similarity between the traversed 2D scan image / 3D scan image and each position in the 2D template / 3D template, and locate the area in the 2D scan image / 3D scan image that contains the edge of the industrial sample being tested ; according to Mapping to a corresponding region R in the two-dimensional scanned image / three-dimensional scanned image, and calculating a second similarity within R with each position in the two-dimensional template / three-dimensional template; The position where the second similarity is greater than the set threshold / the second similarity is the highest is selected as the edge position of the industrial sample in the scanned image to perform sample contour segmentation, and obtain a sample scanned image of the industrial sample itself after sample contour segmentation.

[0071] The industrial CT scan data standardization device of an embodiment of the present invention obtains, based on a scanned image of an industrial sample after CT scanning, the voltage and current parameters of the CT scanning device corresponding to the scanned image, the CT scanning device parameters, the CT scanning mode, and the original actual size represented by a pixel value in the scanned image. The device then resamples the scanned image based on a scale factor calculated from the original actual size and pixel size to obtain a resampled actual size. The device then obtains a sample scanned image of the industrial sample after sample contour segmentation. The device then constructs standard industrial CT scan image data from the voltage and current parameters of the CT scanning device, the CT scanning device parameters, the CT scanning mode, the original actual size, the resampled actual size, and the sample scanned image. This effectively eliminates data differences caused by voltage, current, and pixel size between different CT scanning devices, improving the usability and accuracy of CT scan image data.

[0072] In order to implement the above embodiment, the present invention further provides an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor. The instructions are executed by the at least one processor to enable the at least one processor to perform the aforementioned method.

[0073] In order to implement the above embodiment, the present invention further proposes a non-transitory computer-readable storage medium storing computer instructions, where the computer instructions are used to enable the computer to execute the above method.

[0074] In the description of this specification, the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0075] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "plurality" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0076] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention pertain.

[0077] The logic and / or steps represented in a flowchart or otherwise described herein, for example, can be considered a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" is any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (not exhaustive) of computer-readable media include: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and a portable compact disc read-only memory (CDROM). Furthermore, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing it in another suitable manner if necessary, and then storing it in a computer memory.

[0078] It should be understood that various components of the present invention may be implemented using hardware, software, firmware, or a combination thereof. In the aforementioned embodiments, multiple steps or methods may be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any of the following technologies known in the art, or a combination thereof, may be used: a discrete logic circuit having logic gates for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gates, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0079] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0080] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing module, or each unit may exist physically separately, or two or more units may be integrated into a single module. The aforementioned integrated modules may be implemented in the form of hardware or in the form of software functional modules. If the integrated modules are implemented in the form of software functional modules and sold or used as independent products, they may also be stored in a computer-readable storage medium.

[0081] The storage medium mentioned above may be a read-only memory, a magnetic disk, or an optical disk, etc. Although the embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and are not to be construed as limiting the present invention. Persons skilled in the art may make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for standardizing industrial CT scanning data, characterized in that: The method comprises: Based on the scanned image of the industrial sample after CT scanning, the voltage and current parameters of the CT scanning device corresponding to the scanned image, the CT scanning device parameters, the CT scanning mode, and the original actual size represented by a pixel value in the scanned image are obtained; Calculate a scaling factor based on the original actual size represented by a pixel value in the scanned image and the pixel size in the scanned image; Based on the scale factor, the scanned image is resampled using a bilinear interpolation algorithm to obtain a resampled actual size represented by a pixel value after resampling; Performing sample contour segmentation on the scanned image to obtain a sample scanned image of the industrial tested sample itself after the sample contour segmentation; The voltage and current parameters of the CT scanning device, the CT scanning device parameters, the CT scanning mode, the original actual size, the resampled actual size and the sample scanning image are constructed into standard industrial CT scanning image data.

2. The method according to claim 1, characterized in that The step of resampling the scanned image based on the scale coefficient using a bilinear interpolation algorithm to obtain a resampled actual size represented by a pixel value after resampling includes: The corresponding continuous coordinates in the scanned image are , the scanned image is The coordinates of four adjacent pixels are 、 、 、 , and the corresponding pixel values are 、 、 、 In the case of, based on the scale factor, the scanned image is resampled using a bilinear interpolation algorithm to obtain the coordinates of the resampled scanned image. pixels; According to the resampling, the coordinates in the scanned image are , calculate the actual size of the resampled pixel represented by the resampled pixel value; Among them, the bilinear interpolation algorithm is: ， ， ， ， ， 。 3. The method according to claim 2, characterized in that The method further comprises: The resampled scanned image is quantized by using a linear quantization method or a nonlinear quantization method to reduce the data volume and unify the data bit depth, thereby obtaining a quantized formatted scanned image and storing it.

4. The method according to claim 3, characterized in that When the grayscale value distribution in the resampled scanned image is greater than or equal to a set threshold, the resampled scanned image is quantized using a linear quantization method to reduce the data volume and unify the data bit depth, thereby obtaining a quantized formatted scanned image and storing the image, wherein: In the resampled scanned image, the N-bit grayscale value is , the range is ( ), after quantization, the n-bit gray value is , the range is ( ), then the linear mapping formula of linear quantization is , This is a rounding operation.

5. The method according to claim 4, characterized in that When the grayscale value distribution in the resampled scanned image is less than a set threshold, a nonlinear quantization method is used to quantize the resampled scanned image to reduce the data volume and unify the data bit depth, thereby obtaining a quantized formatted scanned image and storing the image. The nonlinear quantization method includes: By statistically analyzing the histogram of the resampled scanned image, the probability density function of the grayscale value in the resampled scanned image is obtained as follows: ,pass Perform histogram equalization to obtain the equalized grayscale value , the mapping relationship of histogram equalization is the cumulative distribution function, that is: ; The N-bit grayscale value after equalization Linear mapping to n-bit range, the formula is: ； The combined formulas yield the linear mapping formula for nonlinear quantization: ； in, Resample the data bit depth of the scanned image, To format the data bit depth of the scanned image, is the floor function, Is the rounding function.

6. The method according to claim 1, characterized in that The performing of sample contour segmentation on the scanned image to obtain a sample scanned image of the industrial sample to be tested after the sample contour segmentation, comprises: The scanned image is subjected to sample contour segmentation by a forward graph matching edge recognition method and a reverse graph matching edge recognition method to obtain a sample scanned image of the industrial tested sample itself after sample contour segmentation, wherein the reverse graph matching edge recognition method adopts an edge detection algorithm or a deep learning method.

7. The method according to claim 6, characterized in that The scanned image is subjected to sample contour segmentation by a forward graph matching edge recognition method to obtain a sample scanned image of the industrial sample to be tested after the sample contour segmentation, including: Constructing standard templates of different dimensions of the scanned image, wherein the standard templates include two-dimensional templates and three-dimensional templates; The two-dimensional scanned image is traversed row by row and column by column through the sliding window, and the three-dimensional scanned image is traversed along the horizontal axis, vertical axis, and depth axis through the sliding window; Calculate the first similarity between the traversed 2D scan image / 3D scan image and each position in the 2D template / 3D template, and locate the area in the 2D scan image / 3D scan image that contains the edge of the industrial sample being tested ; according to Mapping to a corresponding region R in the two-dimensional scanned image / three-dimensional scanned image, and calculating a second similarity within R with each position in the two-dimensional template / three-dimensional template; The position where the second similarity is greater than the set threshold / the second similarity is the highest is selected as the edge position of the industrial sample in the scanned image to perform sample contour segmentation, and obtain a sample scanned image of the industrial sample itself after sample contour segmentation.

8. An industrial CT scanning data standardization device, characterized in that: The device comprises: An acquisition module is used to obtain voltage and current parameters of the CT scanning device corresponding to the scanned image, CT scanning device parameters, CT scanning mode, and the original actual size represented by a pixel value in the scanned image based on the scanned image after CT scanning of the industrial tested sample; a calculation module for calculating a scale factor based on an original actual size represented by a pixel value in the scanned image and a pixel size in the scanned image; a resampling module, configured to resample the scanned image using a bilinear interpolation algorithm based on the scale factor to obtain a resampled actual size represented by a pixel value after resampling; A segmentation module is used to perform sample contour segmentation on the scanned image to obtain a sample scanned image of the industrial sample itself after the sample contour segmentation; A construction module is used to construct the voltage and current parameters of the CT scanning device, the CT scanning device parameters, the CT scanning mode, the original actual size, the resampled actual size and the sample scanning image into standard industrial CT scanning image data.

9. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 7.

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