Multi-arc-segment symmetric structural member measurement system, method and device and storage medium

Abstract

The invention relates to a multi-arc-segment symmetric structural member measurement system, method and device and a storage medium. The system comprises an OCT image acquisition device, a contour extraction module, a curve analysis module and a circle fitting module, the OCT image acquisition device is used for acquiring an original tomographic image of a to-be-detected sample by utilizing an OCT imaging technology, and the contour extraction module is used for extracting surface contour features of the preprocessed image by adopting morphology and a connected region processing algorithm; the curve analysis module is used for performing curvature analysis and segmental arc identification on the surface contour features based on a differential geometry principle and a multi-scale analysis method, and the circle fitting module is used for performing geometric analysis on segmental arc segmentation results by combining algebraic fitting and geometric fitting algorithms, judging the opening direction of each segmental arc and generating circle fitting parameters. According to the invention, an end-to-end automatic measurement process is formed by using an integrated design, the operation cost and time consumption are reduced, and the measurement precision, efficiency and reliability are improved.

Description

Technical Field

[0001] This application belongs to the field of optical measurement and imaging technology, and specifically relates to a measurement system, method, device and storage medium for multi-segment symmetrical structural components. Background Technology

[0002] Multi-segment rotationally symmetric structures are commonly found in optical components and medical devices. They are typically formed by rotating two or more circular arcs around the same axis, including a central base arc and a series of peripheral arcs. Measurement of multi-segment rotationally symmetric structures is primarily achieved through machine vision, roundness meters, and 3D scanning technology, requiring the integration of sub-pixel edge detection and multi-circle fitting algorithms to ensure accuracy. Measurement methods for this type of structure mainly include contact measurement methods and non-contact measurement methods such as optical measurement. Contact measurement methods typically use profilometers, which can easily cause surface damage to the structure and cannot capture the details of curved surfaces. While non-contact optical measurement avoids contact, it is often limited by noise interference, discontinuous contour extraction, and inaccurate arc boundary identification, resulting in insufficient measurement accuracy and efficiency.

[0003] To address the aforementioned issues, patent document CN116123996A proposes a corneal contact lens parameter measurement system based on SD-OCT (Optical Coherence Tomography). This system constructs a Michelson interferometer structure using a light source module, a reference arm module, a sample arm module, a spectral detection module, and a control processing module. The light emitted from the light source module (e.g., a superluminescent diode) is split by a fiber optic coupler and transmitted to the reference arm (containing a collimator, dispersion compensation mirror, and reflector) and the sample arm (containing an attenuator, scanning galvanometer, and imaging objective). The interference light is detected by a spectrometer and converted into a digital signal. The control processing module preprocesses the signal (e.g., dispersion compensation and image correction) and automatically calculates the geometric parameters of the corneal contact lens (e.g., radius of curvature, thickness, and diameter). This system achieves non-contact automatic measurement, which improves operational efficiency. However, it is mainly designed for single arc segments or simple contours of corneal contact lenses and lacks the ability to automatically segment multi-arc structures. Furthermore, it relies on preset design parameters and is difficult to handle cases with unknown arc boundaries, resulting in insufficient adaptability when dealing with rotationally symmetric structures composed of multiple arcs.

[0004] Another patent, CN119882268A, proposes an automatic multi-segment circular fitting method and device for corneal contact lenses. This patent focuses on a multi-segment circular fitting algorithm, achieving automatic arc segmentation through iterative search and error analysis: First, the central arc segment is fitted with a circle using empirical knowledge. Then, based on the error threshold between the data points and the fitted circle (such as the average error plus three standard deviations), the segmentation points are automatically identified, iterating step by step to the peripheral arc segments, and combining least squares optimization to calculate the center and radius of each arc segment. This method reduces the error of small circle fitting and improves robustness through symmetry constraints. However, it is only applicable to known symmetrical structures and does not integrate OCT technology for real-time image acquisition and preprocessing, thus failing to achieve a fully automatic measurement process. Furthermore, it is limited by noise and contour discontinuity issues, resulting in poor measurement accuracy and robustness. Summary of the Invention

[0005] This application provides a measurement system, method, device, and storage medium for multi-segment symmetrical structural components, aiming to at least partially solve one of the aforementioned technical problems in the prior art.

[0006] To address the above problems, this application provides the following technical solution: A measurement system for multi-segment symmetrical structural components includes an OCT image acquisition device, an image preprocessing module, a contour extraction module, a curve analysis module, and a circle fitting module; The OCT image acquisition device is used to acquire the original tomographic image of the sample to be tested using OCT imaging technology. The image preprocessing module is used to perform noise filtering and boundary trimming on the original tomographic image to obtain a preprocessed image. The contour extraction module is used to extract surface contour features from the preprocessed image using morphological and connected component processing algorithms to obtain continuous surface contour features. The curve analysis module is used to perform physical coordinate transformation and curve smoothing on the surface contour features, and then perform curvature analysis and arc segment identification on the surface contour features based on differential geometry principles and multi-scale analysis methods, and output the arc segmentation results. The circle fitting module is used to perform geometric analysis on the arc segmentation results by combining algebraic fitting and geometric fitting algorithms, determine the opening direction of each arc segment, generate circle fitting parameters based on the opening direction determination results, and output the measurement results of the sample to be tested based on the circle fitting parameters.

[0007] The technical solution adopted in this application embodiment also includes: the OCT image acquisition device is based on the principle of low coherence optical interference, and acquires the reflection intensity information of the depth direction of the sample under test using axial scan lines as basic units to generate the original tomographic image of the two-dimensional profile. ,in Represents pixel coordinates, Represents the horizontal direction. Representing the vertical direction, each frame of the image includes M A-lines.

[0008] The technical solution adopted in this application embodiment further includes: the curve analysis module performs physical coordinate transformation and curve smoothing processing on the surface contour features, specifically: The surface contour feature point set The pixel coordinates are converted to actual physical coordinates using the known horizontal resolution ( ) and axial resolution ( ) Parameters establish pixel position Compared with actual physical size The correspondence; and within a local window, a polynomial fitting is used to fit the set of surface contour feature points. Least squares fitting is performed to obtain a smooth contour curve. .

[0009] The technical solution adopted in this application embodiment also includes: the curve analysis module performs curvature analysis and arc segment recognition on surface contour features based on differential geometry principles and multi-scale analysis methods, specifically: Calculate the contour curve The second derivative is used to characterize the curvature variation, and a multi-scale analysis method combined with peak detection and threshold judgment algorithms is used to identify the arc segment points; whereby the formula for calculating curvature κ is:

[0010] Where d 2 Z, dX 2 Represents the second derivative; For the smoothed second derivative, find points that satisfy the following conditions as candidate piecewise points:

[0011] in, The standard deviation of the second derivative. This is an adjustable threshold parameter.

[0012] The technical solution adopted in this application embodiment further includes: the circle fitting module performs geometric analysis on the arc segmentation results by combining algebraic fitting and geometric fitting algorithms, determines the opening direction of each arc segment, and generates circle fitting parameters based on the opening direction determination results, specifically: Algebraic fitting is used to obtain initial estimates of the parameters for each arc segment, and the initial values ​​of the center and radius of each arc segment are obtained by solving a linear least squares problem.

[0013] Optimization is performed using geometric fitting to minimize the geometric distance from the point to the circle:

[0014] After obtaining the center and radius of each arc segment, the opening direction of each arc segment is determined by using the characteristics of the second derivative and the average positional relationship between the midpoint and the endpoint.

[0015] The technical solution adopted in this application embodiment also includes: the mathematical judgment criterion for the opening direction is: let the ordinate of the starting point of the arc segment be... The endpoint's ordinate is The midpoint ordinate is The average ordinate of the endpoints is When the second derivative is greater than zero and When the second derivative is greater than zero, it is judged to be concave upward; when the second derivative is greater than zero and When the second derivative is less than zero, it is judged to be convex downwards; when the second derivative is less than zero and When the second derivative is less than zero, it is judged to be concave downwards; when the second derivative is less than zero and When the time is right, it is judged to be convex upward.

[0016] The technical solution adopted in this application embodiment also includes: the circle fitting module is further used to display the measurement results through multi-dimensional result visualization technology; the measurement results include, but are not limited to, arc segment number and spatial position, circle center coordinates, radius of curvature, fitting quality parameters, arc segment length and central angle, and arc segment opening direction.

[0017] Another technical solution adopted in this application embodiment is: a measurement method for multi-segment symmetrical structural components, including: The original tomographic images of the sample to be tested are acquired using OCT imaging technology, and noise filtering and boundary trimming are performed on the original tomographic images to obtain preprocessed images; Morphological and connected component processing algorithms are used to extract surface contour features from the preprocessed image to obtain continuous surface contour features. After performing physical coordinate transformation and curve smoothing on the surface contour features, curvature analysis and arc segment identification are performed on the surface contour features based on differential geometry principles and multi-scale analysis methods, and arc segmentation results are output. The arc segmentation results are geometrically analyzed by combining algebraic fitting and geometric fitting algorithms to determine the opening direction of each arc segment. Based on the opening direction determination results, circle fitting parameters are generated, and the measurement results of the test sample are output based on the circle fitting parameters.

[0018] Another technical solution adopted in this application embodiment is: a device, the device including a processor and a memory coupled to the processor, wherein, The memory stores program instructions for implementing the measurement method for the multi-segment symmetrical structural component; The processor is used to execute the program instructions stored in the memory to control the measurement method for multi-segment symmetrical structures.

[0019] Another technical solution adopted in this application embodiment is: a storage medium storing processor-executable program instructions, the program instructions being used to execute the multi-segment symmetrical structure measurement method.

[0020] Compared to existing technologies, the beneficial effects of the embodiments of this application are as follows: The multi-segment symmetrical structural component measurement system, method, device, and storage medium of the embodiments of this application integrate OCT image acquisition, image preprocessing, contour extraction, automatic arc segmentation, and hybrid circle fitting mechanisms to form an end-to-end automated measurement process. This eliminates manual intervention, significantly reduces operating costs and time consumption, and improves measurement accuracy, efficiency, and reliability. The present invention utilizes an automatic arc segmentation algorithm to overcome the problems of noise sensitivity and inaccurate segmentation in existing technologies. By smoothing the contour curve and calculating the second derivative, combined with multi-mode detection to automatically identify arc segmentation points, it not only has strong adaptability but also accurately captures complex surface details, ensuring that the segmentation boundaries are consistent with the actual geometric features, avoiding the defects of contour discontinuity in existing technologies. The present invention combines algebraic fitting and geometric fitting, and introduces opening direction judgment to ensure that the generated symmetrical arcs are consistent with the spatial relationship of the measured data, significantly reducing fitting errors and improving the robustness and accuracy of parameter measurement. Finally, the present invention uses multi-dimensional result visualization technology to intuitively display the measurement results, which can help users quickly identify anomalies and enhance the practicality and reliability of the measurement. Attached Figure Description

[0021] Figure 1 This is a schematic diagram of the multi-segment symmetrical structural component measurement system according to an embodiment of this application; Figure 2 This is a schematic diagram of the structure of the OCT image acquisition device in the embodiments of this application; Figure 3 This is a schematic diagram of the original tomographic image acquired by the OCT image acquisition device according to an embodiment of this application; Figure 4 This is a diagram showing the curvature analysis and arc segmentation effect of the curve analysis module in this embodiment of the application; Figure 5 This is a schematic diagram of the arc segment measurement results output by the circle fitting module in this embodiment of the application; Figure 6 This is a schematic flowchart of the measurement method for multi-segment symmetrical structural components according to an embodiment of this application; Figure 7 This is a schematic diagram of the device structure according to an embodiment of this application; Figure 8 This is a schematic diagram of the structure of the storage medium according to an embodiment of this application. Detailed Implementation

[0022] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0023] The terms "first," "second," and "third" in this application are for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first," "second," or "third" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified. All directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of this application are only used to explain the relative positional relationships and movements between components in a specific orientation (as shown in the figures). If the specific orientation changes, the directional indications also change accordingly. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0024] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0025] Specifically, please refer to Figure 1This is a schematic diagram of the multi-segment symmetrical structural component measurement system according to an embodiment of this application. The multi-segment symmetrical structural component measurement system 100 according to this embodiment includes an OCT image acquisition device 101, an image preprocessing module 102, a contour extraction module 103, a curve analysis module 104, and a circle fitting module 104. The OCT image acquisition device 101 is used to acquire the original tomographic image of the sample to be tested using OCT imaging technology. The image preprocessing module 102 is used to perform preprocessing such as noise filtering and boundary trimming on the original tomographic image of the sample to be tested to obtain a preprocessed image. The contour extraction module 103 is used to process the preprocessed image using morphological and connected component processing algorithms. The system extracts surface contour features to obtain continuous surface contour features. The curve analysis module 104 performs physical coordinate transformation and curve smoothing on the extracted surface contour features, and then performs curvature analysis and arc segment identification on the surface contour features based on differential geometry principles and multi-scale analysis methods, and outputs the arc segmentation results. The circle fitting module 105 combines algebraic fitting and geometric fitting algorithms to perform geometric analysis on the arc segmentation results, determines the opening direction of each arc segment, generates the optimal circle fitting parameters based on the opening direction determination results, and outputs the measurement results of the test sample based on the circle fitting parameters.

[0026] Further, please refer to Figure 2This is a schematic diagram of the structure of the OCT image acquisition device 101 in this embodiment of the application. In the figure, the thin solid lines represent optical paths and the thick solid lines represent circuits. Specifically, the OCT image acquisition device 101 includes a light source 1, a coupler / beam splitter 2, a reference arm 3, a sample arm 6, a processor 13, and a detection system 14, wherein the light source 1, the coupler / beam splitter 2, the reference arm 3, and the sample arm 6 are connected in sequence by optical paths. Light source 1 is any laser source suitable for OCT imaging, including but not limited to broadband sources for spectral domain OCT (SD-OCT) (such as superluminescent laser diodes (SLDs) and frequency-sweeping laser sources for scanning source OCT (SS-OCT)); reference arm 3 also includes a first collimating lens 4 and a reflector 5, and sample arm 6 also includes a second collimating lens 7, a beam scanning mechanism 8, a scanning controller 9, a scanning lens 10, and a stage 11, wherein the beam scanning mechanism 8, the scanning controller 9, the processor 13, and the detection system 14 are electrically connected in sequence. Light source 1 is used to emit light, and coupler / beam splitter 2 is used to receive the light emitted by light source 1 and send the light to reference arm 3 and sample arm 6 respectively; after the light source is collimated by the first collimating lens 4, the light is returned to the reference light by the reflector 5; after the beam in sample arm 6 is collimated by the second collimating lens 7, the beam scanning mechanism is controlled by the scanning controller 9. The guide beam 8 performs a non-contact cross-sectional scan of the sample 12 on the stage 11 and returns the sample light. The scan controller 9 receives scan control commands from the processor 13 and provides specific current / voltage signals to the beam scanning mechanism 8 to drive it to scan the sample 12 on the stage 11. The reference light and sample light converge at the coupler / beam splitter 2 to generate an interference signal, which is then propagated to the detection system 14. The detection system 14 includes, but is not limited to, an SD-OCT spectrometer or an SS-OCT-based photodiode detector system, used to convert the interference signal into an electrical signal and send it to the processor 13. The processor 13 is a computer device with signal acquisition, data processing, and storage capabilities, used to simultaneously control the output and input of the electrical signals to the detection system 14, and outputs the reconstructed original tomographic image based on the electrical signals sent by the detection system 14.

[0027] Based on the above results, the OCT image acquisition device 101 of this application is based on the principle of low-coherence optical interference, and uses the A-line (axial scan line) as the basic unit to acquire the reflection intensity information of the sample in the depth direction, and generates the original tomographic image of the two-dimensional profile. ,in Represents pixel coordinates, Represents the horizontal direction. Representing the vertical direction, each frame of the image includes M A-lines, and the image resolution is determined by the horizontal resolution of the OCT image acquisition device ( ) and axial resolution ( (Confirmed. Specifically, as follows...) Figure 3 The image shown is a schematic diagram of the original tomographic image acquired by the OCT image acquisition device according to an embodiment of this application.

[0028] Furthermore, after the OCT image acquisition device 101 acquires the original tomographic image of the sample to be tested, the image preprocessing module 102 performs noise filtering and boundary trimming on the original tomographic image according to the set region of interest, and then outputs the preprocessed image. It can be understood that in this embodiment, the image preprocessing module 102 performs noise filtering and boundary trimming on the original tomographic image to remove irrelevant background interference, thereby improving the signal-to-noise ratio and enhancing the accuracy and efficiency of subsequent image processing.

[0029] Furthermore, the extracted surface contour features are input into the curve analysis module 104, and the curve analysis module 104 processes the surface contour feature point set. The pixel coordinates are converted to actual physical coordinates to establish the correspondence between pixel positions and actual physical dimensions. Then, within a local window, a polynomial fit is used to fit the data points to the surface contour feature point set. Least squares fitting is performed to obtain a smooth contour curve. Then, calculate the profile curve. The second derivative is used to automatically identify the segmentation points of each arc segment based on the zero-crossing points, extreme points, or set thresholds of the second derivative, using a multi-scale analysis method combined with peak detection and threshold judgment algorithms.

[0030] Specifically, the curve analysis module 104 uses a known lateral resolution ( ) and axial resolution ( ) Parameters establish pixel position Compared with actual physical size The correspondence is represented as follows: (2) (3) Since the surface contour features extracted by the contour extraction module 103 often contain measurement noise and high-frequency fluctuations, they are not conducive to accurate curvature calculation. Therefore, the curve analysis module 104 uses a polynomial fitting method to fit the surface contour feature point set within a local window. Least square fitting is performed to smooth the data while preserving the high-frequency characteristics of the signal. Specifically, for depth coordinate sequences... Smoothed values It is obtained from the following convolution operation: (4) in To fit the weighting coefficients, the window length is used. and polynomial order The decision was made. The fitting weight coefficients were obtained by solving the local polynomial fitting problem using the least squares method.

[0031] Furthermore, the curve analysis module 104, based on the principles of differential geometry, calculates the contour curve. The second derivative is used to characterize its curvature variation features, and a multi-scale analysis method combined with peak detection and threshold judgment algorithms is used to automatically identify the arc segmentation points on the surface contour. The curvature analysis and arc segmentation effect diagram of curve analysis module 104 is shown in the figure. Figure 4 As shown. The formula for calculating curvature κ is: (5) Where d 2 Z, dX 2 It represents the second derivative.

[0032] Specifically, for the smoothed second derivative, points satisfying the following conditions are identified as candidate segmentation points: (6) in, The standard deviation of the second derivative. As an adjustable threshold parameter, it can simultaneously consider the minimum distance constraint between each segment point, ensuring the rationality of the segmentation.

[0033] It is understood that in this embodiment, the curve analysis module 104 converts the pixel coordinates of the surface contour features into actual physical coordinates, ensuring that subsequent geometric analyses are performed at a real physical scale. Smoothing processing provides a high-quality data foundation for subsequent curvature analysis, thereby effectively suppressing noise while preserving curve features.

[0034] It should be noted that, in another embodiment of this application, other algorithms such as moving average filtering or wavelet denoising can also be used for curve smoothing filtering. Moving average filtering smooths data through a sliding window mean, but may lead to over-smoothing of the signal; wavelet denoising processes high-frequency noise through multi-scale threshold decomposition, suitable for non-stationary signals, but parameter selection is complex. Machine learning algorithms can also be used for arc segment point identification. By collecting a large amount of contour data with known segmentation results as a training set, a classifier (such as a support vector machine, random forest, or convolutional neural network) is trained to automatically identify arc boundary features. This method can learn complex segmentation patterns and adapt to various special-shaped contour structures, especially showing stronger adaptability when handling non-standard arc transitions.

[0035] Furthermore, the arc segmentation results are input into the circle fitting module 105. The circle fitting module 105 performs independent geometric analysis on each identified arc segment to determine the opening direction of each arc segment. Based on the opening direction determination results, a theoretical arc consistent with the concave and convex characteristics of the measured data is generated, and the optimal circle fitting parameters are generated. The fitting quality is evaluated through residual analysis. Finally, the final measurement results are displayed through multi-dimensional result visualization technology. The measurement results include, but are not limited to, key parameters such as arc segment number and spatial position (starting point, ending point coordinates), center coordinates, radius of curvature, fitting quality parameters (RMSE, maximum error), arc segment length and central angle, and arc segment opening direction (concave upward / concave downward / convex upward / convex downward).

[0036] Specifically, for each arc segment identified by the curve analysis module 104, the circle fitting module 105 employs a hybrid circle fitting algorithm combining algebraic and geometric fitting, and incorporates the opening direction judgment to obtain the measurement parameters of each arc segment. The final output arc segment measurement results are as follows: Figure 5 As shown. The circle fitting module 105 uses algebraic fitting to obtain initial estimates of the parameters for each arc segment, and quickly obtains the initial values ​​of the circle center and radius by solving a linear least squares problem: (7) Geometric fitting, as an exact optimization, employs a nonlinear least squares method to minimize the geometric distance from the point to the circle: (8) After obtaining the center and radius information of each arc segment, the opening direction of each arc segment is further determined using the second derivative characteristics of the arc segment and the average positional relationship between the midpoint and the endpoints. This includes concave upward, concave downward, convex upward, and convex downward, ensuring that the generated arc segment curves are consistent with the spatial positional relationship of the original data. The second derivative characterizes the concavity / convexity of the arc segment: a positive value indicates concave upward, and a negative value indicates concave downward. The average positional relationship between the midpoint and the endpoints is determined by comparing the ordinate of the midpoint with the average ordinate of the endpoints to determine the position of the data point relative to the center. Specifically, the mathematical criterion for determining the opening direction of each arc segment is as follows: Let the ordinate of the arc segment's starting point be... The endpoint's ordinate is The midpoint ordinate is The average ordinate of the endpoints is When the second derivative is greater than zero and When the second derivative is greater than zero, it is judged to be concave upward; when the second derivative is greater than zero and When the second derivative is less than zero, it is judged to be convex downwards; when the second derivative is less than zero and When the second derivative is less than zero, it is judged to be concave downwards; when the second derivative is less than zero and When the time is right, it is judged to be convex upward.

[0037] It should be noted that, in another embodiment of this application, the RANSAC (Random Sample Consensus) algorithm can also be used to randomly extract a minimum sample set (3 points) from the data points for circle fitting, and then evaluate the consistency between all data points and the fitted model. Iterative optimization is then used to find the model with the most interior points. This scheme has good robustness to outliers and is suitable for measurement data containing noisy points. Alternatively, the Hough transform can be used to accumulate votes in the parameter space (center coordinates, radius) to find peak points in the parameter space as circle fitting parameters. This method does not require initial values ​​and can detect multiple arcs simultaneously, but its computational complexity is high, making it suitable for use when computational resources are sufficient.

[0038] Please see Figure 6 This is a schematic flowchart of a method for measuring multi-segment symmetrical structural components according to an embodiment of this application. The method for measuring multi-segment symmetrical structural components according to an embodiment of this application includes the following steps: S100: Acquire raw tomographic images of the sample under test using OCT imaging technology; S110: Perform noise filtering and boundary trimming on the original tomographic image of the sample to be tested to obtain a preprocessed image; S120: Morphological and connected component processing algorithms are used to extract surface contour features from the preprocessed image to obtain continuous surface contour features; S130: After performing physical coordinate transformation and curve smoothing on the extracted surface contour features, the curvature analysis and arc segment identification of the surface contour features are performed based on the principles of differential geometry and multi-scale analysis methods, and the arc segmentation results are output. S140: Combine algebraic fitting and geometric fitting algorithms to perform geometric analysis on the arc segmentation results, determine the opening direction of each arc segment, generate the best circle fitting parameters based on the opening direction determination results, and output the measurement results of the test sample based on the circle fitting parameters.

[0039] It should be noted that since the information interaction and execution process between the method embodiments of this application and the above-mentioned system / device / module / unit are based on the same concept, the specific functions and technical effects can be found in the system embodiments section, and will not be repeated here.

[0040] Based on the above, the multi-segment symmetrical structural component measurement system and method of this application integrates OCT image acquisition, image preprocessing, contour extraction, automatic arc segmentation, and a hybrid circle fitting mechanism to form an end-to-end automated measurement process. This eliminates manual intervention, significantly reduces operating costs and time consumption, and improves measurement accuracy, efficiency, and reliability. This invention utilizes an automatic arc segmentation algorithm to overcome the problems of noise sensitivity and inaccurate segmentation in existing technologies. By smoothing the contour curve and calculating the second derivative, combined with multi-mode detection to automatically identify arc segmentation points, it not only has strong adaptability but also accurately captures complex surface details, ensuring that the segmentation boundaries are consistent with the actual geometric features, avoiding the defects of contour discontinuity in existing technologies. This invention combines algebraic fitting and geometric fitting, and introduces opening direction judgment to ensure that the generated symmetrical arcs are consistent with the spatial relationship of the measured data, significantly reducing fitting errors and improving the robustness and accuracy of parameter measurement. Finally, this invention uses multi-dimensional result visualization technology to intuitively display the measurement results, helping users quickly identify anomalies and enhancing the practicality and reliability of the measurement.

[0041] Please see Figure 7 This is a schematic diagram of the device structure according to an embodiment of this application. The device 50 includes: Memory 51 storing executable program instructions; Processor 52 connected to memory 51; The processor 52 is used to call the executable program instructions stored in the memory 51 and perform the following steps: acquire the original tomographic image of the sample to be tested using OCT imaging technology, and perform noise filtering and boundary trimming on the original tomographic image to obtain a preprocessed image; extract surface contour features from the preprocessed image using morphological and connected component processing algorithms to obtain continuous surface contour features; perform physical coordinate transformation and curve smoothing on the surface contour features, and perform curvature analysis and arc segment recognition on the surface contour features based on differential geometry principles and multi-scale analysis methods, and output the arc segmentation results; perform geometric analysis on the arc segmentation results using algebraic fitting and geometric fitting algorithms, determine the opening direction of each arc segment, generate circle fitting parameters based on the opening direction determination results, and output the measurement results of the sample to be tested based on the circle fitting parameters.

[0042] The processor 52 can also be referred to as a CPU (Central Processing Unit). The processor 52 may be an integrated circuit chip with signal processing capabilities. The processor 52 can also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. A general-purpose processor can be a microprocessor or any conventional processor.

[0043] Please see Figure 8 This is a schematic diagram of the structure of the storage medium in an embodiment of this application. The storage medium in this embodiment stores program instructions 61 capable of implementing the following steps: acquiring the original tomographic image of the sample to be tested using OCT imaging technology, and performing noise filtering and boundary trimming on the original tomographic image to obtain a preprocessed image; extracting surface contour features from the preprocessed image using morphological and connected component processing algorithms to obtain continuous surface contour features; performing physical coordinate transformation and curve smoothing on the surface contour features, and performing curvature analysis and arc segment recognition on the surface contour features based on differential geometry principles and multi-scale analysis methods, and outputting arc segmentation results; performing geometric analysis on the arc segmentation results using algebraic fitting and geometric fitting algorithms, determining the opening direction of each arc segment, generating circle fitting parameters based on the opening direction determination results, and outputting the measurement results of the sample to be tested based on the circle fitting parameters. The program instructions 61 can be stored in the above-mentioned storage medium in the form of a software product, including several instructions to cause a device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods of various embodiments of this application. The aforementioned storage media include: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program instructions, or terminal devices such as computers, servers, mobile phones, and tablets. Servers can be independent servers or cloud servers providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0044] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interfaces, apparatuses, or units, and may be electrical, mechanical, or other forms.

[0045] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated units described above can be implemented in hardware or as software functional units. The above are merely embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made based on the description and drawings of this application, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A measurement system for multi-segment symmetrical structural components, characterized in that, It includes an OCT image acquisition device, an image preprocessing module, a contour extraction module, a curve analysis module, and a circle fitting module; The OCT image acquisition device is used to acquire the original tomographic image of the sample to be tested using OCT imaging technology. The image preprocessing module is used to perform noise filtering and boundary trimming on the original tomographic image to obtain a preprocessed image. The contour extraction module is used to extract surface contour features from the preprocessed image using morphological and connected component processing algorithms to obtain continuous surface contour features. The curve analysis module is used to perform physical coordinate transformation and curve smoothing on the surface contour features, and then perform curvature analysis and arc segment identification on the surface contour features based on differential geometry principles and multi-scale analysis methods, and output the arc segmentation results. The circle fitting module is used to perform geometric analysis on the arc segmentation results by combining algebraic fitting and geometric fitting algorithms, determine the opening direction of each arc segment, generate circle fitting parameters based on the opening direction determination results, and output the measurement results of the sample to be tested based on the circle fitting parameters.

2. The measurement system for multi-segment symmetrical structural components according to claim 1, characterized in that, The OCT image acquisition device is based on the principle of low-coherence optical interference. It acquires the reflection intensity information of the sample under test along the depth direction using axial scan lines as the basic unit, and generates the original tomographic image of the two-dimensional profile. ,in Represents pixel coordinates, Represents the horizontal direction. Representing the vertical direction, each frame of the image includes M A-lines.

3. The measurement system for multi-segment symmetrical structural components according to claim 2, characterized in that, The curve analysis module performs physical coordinate transformation and curve smoothing on the surface contour features, specifically as follows: The surface contour feature point set The pixel coordinates are converted to actual physical coordinates using the known horizontal resolution ( ) and axial resolution ( ) Parameters establish pixel position Compared with actual physical size The correspondence; and within a local window, a polynomial fitting is used to fit the set of surface contour feature points. Least squares fitting is performed to obtain a smooth contour curve. .

4. The measurement system for multi-segment symmetrical structural components according to claim 3, characterized in that, The curve analysis module performs curvature analysis and arc segment identification on surface contour features based on differential geometry principles and multi-scale analysis methods, specifically: Calculate the contour curve The second derivative is used to characterize its curvature variation. A multi-scale analysis method combined with peak detection and threshold judgment algorithms is used to identify arc segmentation points; the formula for calculating curvature κ is: Where d 2 Z, dX 2 Represents the second derivative; For the smoothed second derivative, find points that satisfy the following conditions as candidate piecewise points: in, The standard deviation of the second derivative. This is an adjustable threshold parameter.

5. The measurement system for multi-segment symmetrical structural components according to claim 4, characterized in that, The circle fitting module combines algebraic and geometric fitting algorithms to perform geometric analysis on the arc segmentation results, determines the opening direction of each arc segment, and generates circle fitting parameters based on the opening direction determination results, specifically: Algebraic fitting is used to obtain initial estimates of the parameters for each arc segment, and the initial values ​​of the center and radius of each arc segment are obtained by solving a linear least squares problem. Optimization is performed using geometric fitting to minimize the geometric distance from the point to the circle: After obtaining the center and radius of each arc segment, the opening direction of each arc segment is determined by using the characteristics of the second derivative and the average positional relationship between the midpoint and the endpoint.

6. The measurement system for multi-segment symmetrical structural components according to claim 5, characterized in that, The mathematical criterion for determining the direction of the opening is: Let the ordinate of the starting point of the arc segment be... The endpoint ordinate is The midpoint ordinate is The average ordinate of the endpoints is ; When the second derivative is greater than zero and When the concave direction is upward, it is determined that the concave direction is upward. When the second derivative is greater than zero and When the second derivative is less than zero, it is judged to be convex downwards; when the second derivative is less than zero and When the second derivative is less than zero, it is judged to be concave downwards; when the second derivative is less than zero and When the time is right, it is judged to be convex upward.

7. The measurement system for multi-arc segment symmetrical structural components according to any one of claims 1 to 6, characterized in that, The circle fitting module is also used to display the measurement results through multi-dimensional result visualization technology; the measurement results include, but are not limited to, arc segment number and spatial position, circle center coordinates, radius of curvature, fitting quality parameters, arc segment length and central angle, and arc segment opening direction.

8. A method for measuring multi-segment symmetrical structural components, characterized in that, include: The original tomographic images of the sample to be tested are acquired using OCT imaging technology, and noise filtering and boundary trimming are performed on the original tomographic images to obtain preprocessed images; Morphological and connected component processing algorithms are used to extract surface contour features from the preprocessed image to obtain continuous surface contour features. After performing physical coordinate transformation and curve smoothing on the surface contour features, curvature analysis and arc segment identification are performed on the surface contour features based on differential geometry principles and multi-scale analysis methods, and arc segmentation results are output. The arc segmentation results are geometrically analyzed by combining algebraic fitting and geometric fitting algorithms to determine the opening direction of each arc segment. Based on the opening direction determination results, circle fitting parameters are generated, and the measurement results of the test sample are output based on the circle fitting parameters.

9. A device, characterized in that, The device includes a processor and a memory coupled to the processor, wherein, The memory stores program instructions for implementing the measurement method for the multi-segment symmetrical structural component; The processor is used to execute the program instructions stored in the memory to control the measurement method for multi-segment symmetrical structures.

10. A storage medium, characterized in that, The system stores processor-executable program instructions for performing the measurement method for the multi-segment symmetrical structure.

Images