Plastic Wound Repair Analysis System and Method Based on Image Matching

Abstract

This application relates to the technical field of wound repair analysis, and particularly to a plastic wound repair analysis system and method based on image matching. The steps of the method include: obtaining a first wound image before wound repair and a second wound image after repair; performing image registration on the first wound image and the second wound image using an image matching algorithm; evaluating the consistency of the tissue around the wound before and after repair and the consistency of the wound tissue after repair according to the image registration results; evaluating the suture strain distribution at the wound suture according to the second wound image; and analyzing the wound repair effect based on the evaluation results of the consistency of the tissue around the wound and the wound tissue and in combination with the evaluation results of the suture strain distribution. This application improves the accuracy and reliability of wound repair effect evaluation by quantitatively analyzing the consistency of the tissue around the wound, the consistency of the wound tissue, and the suture strain distribution.

Description

Technical Field

[0001] This application relates to the technical field of wound repair analysis, and particularly to a plastic wound repair analysis system and method based on image matching. Background Art

[0002] As the outermost layer of the human body, the skin has a vast area and a complex structure, and is the main barrier against external threats. Damage to the skin components and functions will lead to poor wound healing, and wound repair needs to be carried out through local wound reconstruction surgery. The treatment form of local wound reconstruction surgery mainly adopts local skin flap cutting surgery. Since the local skin flap has the same color and similar physiological tissues as the adjacent skin, it is beneficial to the healing of the local wound skin flap and is one of the main surgical methods for wound repair in plastic and reconstructive surgery.

[0003] In the early stage, the clinical detection means for wound repair effect were usually direct visual observation by doctors to evaluate the wound repair effect, and it was impossible to accurately and quantitatively evaluate the actual repair effect. With the development of non-contact optical measurement technology, digital image correlation method has developed rapidly. Because of its advantages of non-contact, non-destructive, universality, etc., it is widely used in the fields of soft tissue materials and medical biometrics.

[0004] In the process of evaluating the wound repair effect, the consistency of the tissue around the wound before and after wound repair and the consistency of the wound tissue after repair are crucial for the evaluation of the wound repair effect. It reflects the degree of recovery of the tissue around the wound and the healing of the wound itself, and is an important basis for evaluating the wound repair effect and the prognosis of the patient. However, the existing plastic wound repair analysis methods ignore the influence of the consistency index on the evaluation of the wound repair effect and the influence of the suture strain distribution at the wound suture on the wound healing quality, reducing the accuracy and precision of the wound repair effect evaluation. Summary of the Invention

[0005] In order to overcome the defects and deficiencies existing in the prior art, this application provides a plastic wound repair analysis system and method based on image matching, which improves the accuracy and reliability of the wound repair effect evaluation by quantitatively analyzing the consistency of the tissue around the wound, the consistency of the wound tissue, and the suture strain distribution.

[0006] In order to achieve the above object, this application adopts the following technical solutions:

[0007] In the first aspect, this application provides a plastic wound repair analysis method based on image matching, including the following steps:

[0008] Obtain a first wound image before wound repair and a second wound image after repair;

[0009] Use an image matching algorithm to perform image registration on the first wound image and the second wound image;

[0010] Evaluate the consistency of the tissue around the wound before and after repair and the consistency of the wound tissue after repair according to the image registration results;

[0011] Evaluate the suture strain distribution at the wound suture according to the second wound image;

[0012] Analyze the wound repair effect according to the evaluation results of the consistency of the tissue around the wound and the consistency of the wound tissue and in combination with the evaluation results of the suture strain distribution.

[0013] Preferably, evaluating the consistency of the tissue around the wound includes:

[0014] Obtain the first wound image and the second wound image after image registration;

[0015] Calculate the tissue consistency index around the wound through the first wound image and the second wound image. The tissue consistency index around the wound is used to evaluate the consistency of the tissue around the wound. The calculation formula of the tissue consistency index around the wound is as follows:

[0016]

[0017] In the formula, C(x,y) represents the RGB color value of the pixel at the position (x,y) in the tissue area around the wound in the first wound image, C′(x,y) represents the RGB color value of the pixel at the position (x,y) in the tissue area around the wound in the second wound image, ||C(x,y)-C′(x,y)|| represents the Euclidean distance of the color difference between C(x,y) and C′(x,y), Dmax represents the maximum color difference distance in the RGB color space, R represents the set of pixel positions in the tissue area around the wound, N represents the number of pixels in the tissue area around the wound, BCI represents the brightness consistency index of the tissue area around the wound, δ1 represents the first adjustment factor, and OCI represents the tissue consistency index around the wound.

[0018] Preferably, the calculation formula of the brightness consistency index is:

[0019]

[0020] In the formula, L(x,y) represents the brightness value of the pixel at the position (x,y) in the tissue area around the wound in the first wound image, L′(x,y) represents the brightness value of the pixel at the position (x,y) in the tissue area around the wound in the second wound image, R represents the set of pixel positions in the tissue area around the wound, and BCI represents the brightness consistency index.

[0021] Preferably, evaluating the consistency of the wound tissue includes:

[0022] Obtain the second wound image and convert the second wound image into a second wound grayscale image;

[0023] Extract the wound texture feature index and the perilesional texture feature index of the second wound grayscale image using the gray-level co-occurrence matrix algorithm;

[0024] Calculate the wound tissue consistency index through the perilesional texture feature index and the wound texture feature index. The wound tissue consistency index is used to evaluate the wound tissue consistency. The calculation formula of the wound tissue consistency index is as follows:

[0025]

[0026] In the formula, T w,k represents the value of the k-th wound texture feature index, T s,k represents the value of the k-th perilesional texture feature index, n represents the number of texture feature indices, and WCI represents the wound tissue consistency index.

[0027] Preferably, the evaluation of the suture strain distribution at the wound suture includes:

[0028] Obtain the second wound image and perform image registration on the second wound images obtained at different times using an image matching algorithm;

[0029] Calculate the suture strain distribution index through the second wound image after image registration. The suture strain distribution index is used to evaluate the suture strain distribution at the wound suture. The calculation formula of the suture strain distribution index is as follows:

[0030]

[0031] In the formula, H s represents the displacement of the s-th suture pixel, represents the average displacement of the suture pixels, M represents the number of suture pixels, DDI represents the suture strain direction distribution index, δ2 represents the second adjustment factor, and SDI represents the suture strain distribution index.

[0032] Preferably, the calculation formula of the suture strain direction distribution index is:

[0033]

[0034] In the formula, D s represents the displacement vector of the s-th suture pixel, N s represents the normal vector of the suture at the s-th suture pixel, represents the average offset angle of all suture pixels, φ max represents the maximum offset angle of the suture pixel, and DDI represents the suture strain direction distribution index.

[0035] Preferably, the analysis of the wound repair effect includes:

[0036] Obtain the perilesional tissue consistency index, wound tissue consistency index, and suture strain distribution index, and calculate the wound repair index. The wound repair index is used to quantitatively analyze the wound repair effect. The calculation formula of the wound repair index is as follows:

[0037] WRI = ω1×OCI + ω2×WCI + ω3×SDI;

[0038] In the formula, OCI represents the perilesional tissue consistency index, WCI represents the wound tissue consistency index, SDI represents the suture strain distribution index, ω1 represents the perilesional tissue consistency weight, ω2 represents the wound tissue consistency weight, ω3 represents the suture strain distribution weight, and WRI represents the wound repair index.

[0039] It should be noted here that the value-taking methods of the first adjustment factor, the second adjustment factor, the perilesional tissue consistency weight, the wound tissue consistency weight, and the suture strain distribution weight are as follows: Collect 5000 groups of first wound images and second wound images, distinguish whether the wound repair effect meets the repair requirements, substitute the first wound images and the second wound images into the wound repair index calculation formula for calculation, and import the calculated wound repair index and the discrimination result into the fitting software at the same time to output the optimal first adjustment factor, second adjustment factor, perilesional tissue consistency weight, wound tissue consistency weight, and suture strain distribution weight that meet the discrimination accuracy rate of the discrimination result.

[0040] In a second aspect, the present application provides an orthopedic wound repair analysis system based on image matching, including:

[0041] An image acquisition module for acquiring a first wound image before wound repair and a second wound image after repair;

[0042] An image registration module for registering the first wound image and the second wound image using an image matching algorithm;

[0043] A first evaluation module for evaluating the perilesional tissue consistency before and after repair and the wound tissue consistency after repair according to the image registration result;

[0044] A second evaluation module for evaluating the suture strain distribution at the wound suture according to the second wound image;

[0045] A wound repair effect analysis module for analyzing the wound repair effect according to the evaluation results of the perilesional tissue consistency and the wound tissue consistency and combining the evaluation results of the suture strain distribution.

[0046] In a third aspect, the present application provides an electronic device, including: a processor and a memory. Among them, a computer program that can be called by the processor is stored in the memory, and the processor executes an orthopedic wound repair analysis method based on image matching by calling the computer program stored in the memory.

[0047] In a fourth aspect, the present application provides a computer-readable storage medium storing instructions that, when run on a computer, cause the computer to execute an orthopedic wound repair analysis method based on image matching.

[0048] Compared with the prior art, the present application has the following advantages and beneficial effects:

[0049] The present application performs image registration on the wound images before and after wound repair through an image matching algorithm, and quantitatively evaluates the consistency of the tissue around the wound, the consistency of the wound tissue, and the suture strain distribution at the wound suture line according to the image registration results. Furthermore, the present application analyzes the wound repair effect by comprehensively evaluating the consistency evaluation results and the suture strain distribution evaluation results, improving the accuracy and reliability of the wound repair effect evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] By reading the detailed description of the non-restrictive embodiments with reference to the following drawings, other features, objects, and advantages of the present application will become more apparent:

[0051] Figure 1 is an overall flowchart of the orthopedic wound repair analysis method based on image matching provided by an embodiment of the present application;

[0052] Figure 2 is a flowchart of the feature point matching algorithm in the orthopedic wound repair analysis method based on image matching provided by an embodiment of the present application;

[0053] Figure 3 is a structural schematic diagram of the orthopedic wound repair analysis system based on image matching provided by an embodiment of the present application;

[0054] Figure 4 is a structural schematic diagram of the electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0055] The technical solution of the present application will be described in detail below through the drawings and specific embodiments. It should be understood that the specific features in the embodiments of the present application and the embodiments are detailed descriptions of the technical solution of the present application, rather than limitations on the technical solution of the present application. Without conflict, the technical features in the embodiments of the present application and the embodiments can be combined with each other.

[0056] Please refer to Figure 1 , Figure 1It is a schematic diagram of the overall process of the plastic wound repair analysis method based on image matching provided by an embodiment of the present application, specifically including the following steps:

[0057] S110: Obtain a first wound image before wound repair and a second wound image after repair. Among them, the first wound image and the second wound image are obtained by a binocular camera, that is, two cameras take pictures of the same wound at different positions.

[0058] S120: Use an image matching algorithm to perform image registration on the first wound image and the second wound image;

[0059] The image matching algorithm includes a feature point matching algorithm and a morphological registration algorithm. Among them, the feature point matching (Feature-based Matching) algorithm is one of the commonly used techniques in the field of computer vision, which is used to find the same feature points between different images for tasks such as object recognition, image registration, and 3D reconstruction. Please refer to Figure 2 , Figure 2 It is a schematic diagram of the process of the feature point matching algorithm in the plastic wound repair analysis method based on image matching provided by an embodiment of the present application, including: (1) Feature point extraction. First, extract feature points with good distinctiveness from the first wound image and the second wound image. Feature points are usually at significant positions, corners, or local areas in the image. Commonly used features include SIFT (Scale-Invariant Feature Transform), SURF (Speeded-Up Robust Features), and ORB (Oriented FAST and Rotated BRIEF); (2) Feature point description. Describe the extracted feature points for matching, and convert the image area near the feature points into a set of vectors or descriptors that can be used for comparison. Commonly used descriptors include SIFT descriptors, SURF descriptors, and ORB descriptors, etc.; (3) Feature point matching. By comparing the feature point descriptors in different images, find similar or matching feature point pairs. The matching process usually uses distance measurement methods, such as Euclidean distance, Hamming distance, etc.; (4) Matching point verification. Verify the matching feature point pairs, and exclude false matches through geometric constraints (such as the RANSAC algorithm);

[0060] The morphological registration (Morphological Registration) algorithm is a method used for image registration (Image Registration) in image processing. Its basic principle is to adjust the shape and structure of the image through morphological operations, such as erosion, dilation, opening operation, closing operation, etc., so that the two images are as similar as possible in structure, thereby achieving alignment and matching between the images. Morphological registration is widely used in fields such as medical image registration and map registration.

[0061] S130: Evaluate the consistency of the tissue around the wound before and after repair and the consistency of the wound surface tissue after repair according to the image registration result;

[0062] Evaluate the consistency of the tissue around the wound by comprehensively evaluating the color consistency and brightness consistency of the area around the wound before and after wound repair, and then quantifying the consistency of the tissue around the wound. The evaluation of the consistency of the tissue around the wound includes:

[0063] Obtain the first wound surface image and the second wound surface image after image registration;

[0064] Calculate the consistency index of the tissue around the wound through the first wound surface image and the second wound surface image. The consistency index of the tissue around the wound is used to evaluate the consistency of the tissue around the wound. The calculation formula of the consistency index of the tissue around the wound is as follows:

[0065]

[0066] In the formula, C(x, y) represents the RGB color value of the pixel at the position (x, y) in the tissue area around the wound in the first wound surface image, C′(x, y) represents the RGB color value of the pixel at the position (x, y) in the tissue area around the wound in the second wound surface image, ||C(x, y) - C′(x, y)|| represents the Euclidean distance of the color difference between C(x, y) and C′(x, y), R(x, y) represents the color value of the red channel in C(x, y), G(x, y) represents the color value of the green channel in C(x, y), B(x, y) represents the color value of the blue channel in C(x, y), R′(x, y) represents the color value of the red channel in C′(x, y), G′(x, y) represents the color value of the green channel in C′(x, y), B′(x, y) represents the color value of the blue channel in C′(x, y). The Euclidean distance of the color difference is used to quantify the color difference of the pixels at the same position (x, y) in the tissue area around the wound in the first wound surface image and the second wound surface image. D max represents the maximum color difference distance in the RGB color space, R represents the set of pixel positions in the tissue area around the wound, N represents the number of pixels in the tissue area around the wound, BCI represents the brightness consistency index of the tissue area around the wound, δ1 represents the first adjustment factor, and δ1 is used to control the influence degree of color consistency and brightness consistency in the consistency evaluation. OCI represents the consistency index of the tissue around the wound;

[0067] After wound repair, perilesional skin edema often occurs, and there are many manifestations of skin edema. The common feature is that the local skin is shiny. Therefore, evaluate the skin edema situation through the brightness of the skin around the wound. The calculation formula of the brightness consistency index is:

[0068]

[0069] Where \(L(x,y)\) represents the luminance value of the pixel at the position \((x,y)\) in the wound margin tissue area of the first wound image, \(L'(x,y)\) represents the luminance value of the pixel at the position \((x,y)\) in the wound margin tissue area of the second wound image, \(R\) represents the set of pixel positions in the wound margin tissue area, \(BCI\) represents the brightness consistency index, and \(BCI\) is used to comprehensively analyze the similarity of the luminance of each pixel in the wound margin tissue area and then quantify the overall change in luminance within the wound margin area;

[0070] The gray-level co-occurrence matrix algorithm is based on the statistical analysis of paired pixel gray-level values, used to describe the texture features of an image, and can describe the spatial information in terms of adjacent intervals, directions, and change amplitudes, which helps to distinguish the tissue texture consistency between the wound surface and the wound margin after wound repair and evaluate the wound tissue consistency, including:

[0071] Obtain the second wound image and convert the second wound image into a second wound gray-level image;

[0072] Use the gray-level co-occurrence matrix algorithm to extract the wound surface texture feature index and the wound margin texture feature index of the second wound gray-level image. Among them, both the wound surface texture feature index and the wound margin texture feature index include angular second moment (ASM), entropy (ENT), inverse difference moment (IDM), contrast (CON), and correlation (COR);

[0073] Calculate the wound tissue consistency index through the wound margin texture feature index and the wound surface texture feature index. The wound tissue consistency index is used to evaluate the wound tissue consistency, and the calculation formula of the wound tissue consistency index is as follows:

[0074]

[0075] In the formula, \(T\) w,k represents the value of the \(k\)th wound surface texture feature index, \(T\) s,k represents the value of the \(k\)th wound margin texture feature index, \(n\) represents the number of texture feature indexes, and \(n = 5\) is set, that is, it includes 5 texture feature indexes: angular second moment, entropy, inverse difference moment, contrast, and correlation. \(WCI\) represents the wound tissue consistency index.

[0076] S140: Evaluate the suture strain distribution at the wound suture according to the second wound image;

[0077] Judge the uniformity and stability of the suture by analyzing the strain distribution at the suture. Uniform strain distribution indicates good suture effect and helps to avoid complications, while non-uniform strain distribution may lead to local excessive pulling, which is not conducive to wound healing. Evaluating the suture strain distribution at the wound suture includes:

[0078] Obtain the second wound image and use the image matching algorithm to perform image registration on the second wound images obtained at different times;

[0079] Calculate the suture strain distribution index from the second wound image after image registration. The suture strain distribution index is used to evaluate the suture strain distribution at the wound suture. The calculation formula of the suture strain distribution index is as follows:

[0080]

[0081] where H s represents the displacement of the s-th suture pixel, represents the mean displacement of the suture pixels, M represents the number of suture pixels, DDI represents the suture strain direction distribution index, δ2 represents the second adjustment factor, and SDI represents the suture strain distribution index;

[0082] The uniform distribution of the suture strain direction can reflect the stability and uniformity of the forces acting on the wound during the healing process. If there are significant deviations in the strain direction at the suture, it may indicate insecure suturing or poor wound healing, increasing the risk of complications (such as wound dehiscence or infection). The calculation formula of the suture strain direction distribution index is:

[0083]

[0084] where D s represents the displacement vector of the s-th suture pixel, N s represents the normal vector of the suture at the s-th suture pixel. The normal vector is a vector that passes through the s-th suture pixel and is perpendicular to the tangent of the suture curve, which is used to calculate the relative deviation angle between the displacement direction of the s-th suture pixel in the second wound images obtained at different times and the normal vector, represents the mean deviation angle of all suture pixels, φ max represents the maximum deviation angle of the suture pixels, and DDI represents the suture strain direction distribution index.

[0085] S150: Analyze the wound repair effect based on the evaluation results of the consistency of the tissue around the wound and the consistency of the wound tissue and in combination with the evaluation results of the suture strain distribution;

[0086] By comprehensively considering the consistency of the tissue around the wound, the consistency of the wound tissue, and the suture strain distribution, the overall effect of wound repair is quantitatively evaluated. Analyze the wound repair effect, including:

[0087] Obtain the index of the consistency of the tissue around the wound, the index of the consistency of the wound tissue, and the suture strain distribution index and calculate the wound repair index. The wound repair index is used to quantitatively analyze the wound repair effect. The calculation formula of the wound repair index is as follows:

[0088] WRI = ω1×OCI + ω2×WCI + ω3×SDI;

[0089] In the formula, OCI represents the perilesional tissue consistency index, WCI represents the wound tissue consistency index, SDI represents the suture strain distribution index, ω1 represents the perilesional tissue consistency weight, ω2 represents the wound tissue consistency weight, ω3 represents the suture strain distribution weight. By assigning corresponding weights to different indexes to reflect their relative importance in the evaluation of wound repair effect, WRI represents the wound repair index.

[0090] Please refer to Figure 3 , Figure 3 Figure 3 is a schematic structural diagram of a plastic wound repair analysis system based on image matching provided by an embodiment of the present application. The present embodiment provides a plastic wound repair analysis system based on image matching, including:

[0091] An image acquisition module 210, configured to acquire a first wound image before wound repair and a second wound image after repair;

[0092] An image registration module 220, configured to perform image registration on the first wound image and the second wound image using an image matching algorithm;

[0093] A first evaluation module 230, configured to evaluate the perilesional tissue consistency before and after repair and the wound tissue consistency after repair according to the image registration result;

[0094] A second evaluation module 240, configured to evaluate the suture strain distribution at the wound suture according to the second wound image;

[0095] A repair effect analysis module 250, configured to analyze the wound repair effect according to the evaluation results of the perilesional tissue consistency and the wound tissue consistency and in combination with the evaluation result of the suture strain distribution.

[0096] In the embodiment of the present application, the first evaluation module 230 is configured to evaluate the perilesional tissue consistency before and after repair and the wound tissue consistency after repair according to the image registration result. Evaluating the perilesional tissue consistency includes:

[0097] Acquire the first wound image and the second wound image after image registration;

[0098] Calculate the perilesional tissue consistency index through the first wound image and the second wound image. The perilesional tissue consistency index is used to evaluate the perilesional tissue consistency. The calculation formula of the perilesional tissue consistency index is as follows:

[0099]

[0100] Where \(C(x,y)\) represents the RGB color value of the pixel at the position \((x,y)\) in the wound edge tissue area of the first wound image, \(C'(x,y)\) represents the RGB color value of the pixel at the position \((x,y)\) in the wound edge tissue area of the second wound image, \(\|C(x,y)-C'(x,y)\|\) represents the Euclidean distance of the color difference between \(C(x,y)\) and \(C'(x,y)\), \(D\) max represents the maximum color difference distance in the RGB color space, \(R\) represents the set of pixel positions in the wound edge tissue area, \(N\) represents the number of pixels in the wound edge tissue area, \(BCI\) represents the brightness consistency index of the wound edge tissue area, \(\delta_1\) represents the first adjustment factor, and \(OCI\) represents the wound edge tissue consistency index;

[0101] The calculation formula of the brightness consistency index is:

[0102]

[0103] Where \(L(x,y)\) represents the brightness value of the pixel at the position \((x,y)\) in the wound edge tissue area of the first wound image, \(L'(x,y)\) represents the brightness value of the pixel at the position \((x,y)\) in the wound edge tissue area of the second wound image, \(R\) represents the set of pixel positions in the wound edge tissue area, and \(BCI\) represents the brightness consistency index;

[0104] Evaluating the wound tissue consistency includes:

[0105] Obtaining the second wound image and converting the second wound image into a second wound grayscale image;

[0106] Using the gray-level co-occurrence matrix algorithm to extract the wound texture feature index and the wound edge texture feature index of the second wound grayscale image;

[0107] Calculating the wound tissue consistency index through the wound edge texture feature index and the wound texture feature index. The wound tissue consistency index is used to evaluate the wound tissue consistency. The calculation formula of the wound tissue consistency index is as follows:

[0108]

[0109] Where \(T\) w,k represents the value of the \(k\)th wound texture feature index, \(T\) s,k represents the value of the \(k\)th wound edge texture feature index, \(n\) represents the number of texture feature indexes, and \(WCI\) represents the wound tissue consistency index.

[0110] In the embodiment of the present application, the second evaluation module 240 is used to evaluate the suture strain distribution at the wound suture according to the second wound image. Evaluating the suture strain distribution at the wound suture includes:

[0111] Obtain the second wound surface image, and perform image registration on the second wound surface images obtained at different times using an image matching algorithm;

[0112] Calculate the suture strain distribution index through the second wound surface image after image registration. The suture strain distribution index is used to evaluate the suture strain distribution at the wound suture. The calculation formula of the suture strain distribution index is as follows:

[0113]

[0114] In the formula, H s represents the displacement of the s-th suture pixel, represents the average displacement of the suture pixels, M represents the number of suture pixels, DDI represents the suture strain direction distribution index, δ2 represents the second adjustment factor, and SDI represents the suture strain distribution index;

[0115] The calculation formula of the suture strain direction distribution index is:

[0116]

[0117] In the formula, D s represents the displacement vector of the s-th suture pixel, N s represents the normal vector of the suture at the s-th suture pixel, represents the average offset angle of all suture pixels, φ max represents the maximum offset angle of the suture pixel, and DDI represents the suture strain direction distribution index.

[0118] In the embodiment of the present application, the repair effect analysis module 250 is used to analyze the wound repair effect according to the evaluation results of the perilesional tissue consistency and the wound tissue consistency and in combination with the suture strain distribution evaluation result. Analyzing the wound repair effect includes:

[0119] Obtain the perilesional tissue consistency index, the wound tissue consistency index, and the suture strain distribution index, and calculate the wound repair index. The wound repair index is used to quantitatively analyze the wound repair effect. The calculation formula of the wound repair index is as follows:

[0120] WRI = ω1×OCI + ω2×WCI + ω3×SDI;

[0121] In the formula, OCI represents the perilesional tissue consistency index, WCI represents the wound tissue consistency index, SDI represents the suture strain distribution index, ω1 represents the perilesional tissue consistency weight, ω2 represents the wound tissue consistency weight, ω3 represents the suture strain distribution weight, and WRI represents the wound repair index.

[0122] For the steps of each parameter and each unit module in the above-mentioned plastic surgery wound repair analysis system based on image matching of the present application to implement corresponding functions, reference may be made to the parameters and steps in the embodiments of the plastic surgery wound repair analysis method based on image matching in the above text, which will not be elaborated here.

[0123] As Figure 4 shown, an embodiment of the present invention further provides an electronic device 300, including a memory 310, a processor 320, and a communication bus 330; the memory 310 and the processor 320 are connected through the communication bus 330. The memory 310 stores an analysis method based on image matching for plastic surgery wound repair that can be loaded and executed by the processor 320 as provided in the above embodiment.

[0124] The memory 310 can be used to store instructions, programs, codes, code sets, or instruction sets. The memory 310 may include a program storage area and a data storage area. Among them, the program storage area can store instructions for implementing an operating system, instructions for at least one function, and instructions for implementing the analysis method based on image matching for plastic surgery wound repair provided in the above embodiment, etc.; the data storage area can store data involved in the analysis method based on image matching for plastic surgery wound repair provided in the above embodiment, etc.

[0125] The processor 320 may include one or more processing cores. The processor 320 runs or executes instructions, programs, code sets, or instruction sets stored in the memory 310, calls data stored in the memory 310, and executes various functions of the present application and processes data. The processor 320 may be at least one of an application specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field programmable gate array (FPGA), a central processing unit (CPU), a controller, a microcontroller, and a microprocessor. It can be understood that for different devices, the electronic devices for implementing the functions of the above-mentioned processor 320 may be others, and the embodiments of the present application do not make specific limitations.

[0126] The communication bus 330 may include a path for transmitting information between the above components. The communication bus 330 may be a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The communication bus 330 may be divided into an address bus, a data bus, a control bus, etc. For the sake of convenience of representation, Figure 4 only a double arrow is used in Figure 4 , but it does not mean that there is only one bus or one type of bus.

[0127] An embodiment of the present application provides a computer-readable storage medium storing a computer program that can be loaded and executed by a processor to perform the method for analyzing and repairing a shaped wound surface based on image matching provided in the above embodiment.

[0128] In an embodiment of the present application, the computer-readable storage medium may be a tangible device that holds and stores instructions used by an instruction execution device. The computer-readable storage medium may be, but is not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination of the above. Specifically, the computer-readable storage medium may be a portable computer disk, a hard disk, a USB flash drive, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a podium random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, an optical disk, a magnetic disk, a mechanical encoding device, and any combination of the above.

[0129] The term "including", "comprising", or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article, or device including a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or device.

[0130] The above description is only a preferred embodiment of the present application and an explanation of the applied technical principles. Those skilled in the art should understand that the scope of the application involved in the present application is not limited to the technical solution formed by the specific combination of the above technical features, and should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the foregoing application concept. For example, a technical solution formed by mutually replacing the above features with (but not limited to) technical features having similar functions applied in the present application.

Claims

1. A plastic wound repair analysis method based on image matching, characterized in that It includes the following steps: Obtain a first wound image before wound repair and a second wound image after repair; Perform image registration on the first wound image and the second wound image using an image matching algorithm; Evaluate the consistency of the tissue around the wound before and after repair and the consistency of the wound tissue after repair according to the image registration result; Evaluate the suture strain distribution at the wound suture according to the second wound image; Analyze the wound repair effect according to the evaluation results of the consistency of the tissue around the wound and the wound tissue and in combination with the evaluation result of the suture strain distribution; The analysis of the wound repair effect includes: Obtain the tissue consistency index around the wound, the wound tissue consistency index, and the suture strain distribution index and calculate the wound repair index. The wound repair index is used to quantitatively analyze the wound repair effect. The calculation formula of the wound repair index is as follows: WRI = ω1×OCI + ω2×WCI + ω3×SDI; In the formula, OCI represents the tissue consistency index around the wound, WCI represents the wound tissue consistency index, SDI represents the suture strain distribution index, ω1 represents the weight of the tissue consistency around the wound, ω2 represents the weight of the wound tissue consistency, ω3 represents the weight of the suture strain distribution, and WRI represents the wound repair index.

2. The method for analyzing plastic wound repair based on image matching according to claim 1, wherein Evaluating the consistency of the tissue around the wound includes: Obtain the first wound image and the second wound image after image registration; Calculate the tissue consistency index around the wound through the first wound image and the second wound image. The tissue consistency index around the wound is used to evaluate the consistency of the tissue around the wound. The calculation formula of the tissue consistency index around the wound is as follows: Where \(C(x,y)\) represents the RGB color value of the pixel at the position \((x,y)\) in the wound margin tissue area in the first wound image, \(C'(x,y)\) represents the RGB color value of the pixel at the position \((x,y)\) in the wound margin tissue area in the second wound image, \(\|C(x,y)-C'(x,y)\|\) represents the Euclidean distance of the color difference between \(C(x,y)\) and \(C'(x,y)\), \(D\) max represents the maximum color difference distance in the RGB color space, \(R\) represents the set of pixel positions in the wound margin tissue area, \(N\) represents the number of pixels in the wound margin tissue area, \(BCI\) represents the brightness consistency index of the wound margin tissue area, \(\delta_1\) represents the first adjustment factor, and \(OCI\) represents the wound margin tissue consistency index.

3. The plastic wound repair analysis method based on image matching according to claim 2, wherein The calculation formula of the brightness consistency index is: In the formula, L(x,y) represents the brightness value of the pixel at the position (x,y) in the tissue area around the wound in the first wound image, L′(x,y) represents the brightness value of the pixel at the position (x,y) in the tissue area around the wound in the second wound image, R represents the set of pixel positions in the tissue area around the wound, and BCI represents the brightness consistency index.

4. The method for analyzing plastic wound repair based on image matching according to claim 1, wherein Evaluating the consistency of the wound tissue includes: Obtain the second wound image and convert the second wound image into a second wound grayscale image; Use the gray-level co-occurrence matrix algorithm to extract the wound texture feature index and the tissue texture feature index around the wound of the second wound grayscale image; Calculate the wound tissue consistency index through the tissue texture feature index around the wound and the wound texture feature index. The wound tissue consistency index is used to evaluate the consistency of the wound tissue. The calculation formula of the wound tissue consistency index is as follows: where T w,k represents the value of the k-th wound surface texture feature index, T s,k represents the value of the k-th wound perimeter texture feature index, n represents the number of texture feature indices, and WCI represents the wound tissue consistency index.

5. The method for analyzing plastic wound repair based on image matching according to claim 1, wherein The evaluation of the suture strain distribution at the wound suture includes: Obtain the second wound image and perform image registration on the second wound images obtained at different times using an image matching algorithm; Calculate the suture strain distribution index through the second wound image after image registration. The suture strain distribution index is used to evaluate the suture strain distribution at the wound suture. The calculation formula of the suture strain distribution index is as follows: where H s represents the displacement of the s-th suture pixel, represents the average displacement of the suture pixels, M represents the number of suture pixels, DDI represents the suture strain direction distribution index, δ2 represents the second adjustment factor, and SDI represents the suture strain distribution index.

6. The method for analyzing plastic wound repair based on image matching according to claim 5, wherein The calculation formula of the suture strain direction distribution index is: where D s represents the displacement vector of the s-th suture pixel, N s represents the normal vector of the suture at the s-th suture pixel, φ represents the mean offset angle of all suture pixels, φ max represents the maximum offset angle of the suture pixel, and DDI represents the suture strain direction distribution index.

7. An orthoplastic wound repair analysis system based on image matching, which is applied to the orthoplastic wound repair analysis method based on image matching according to any one of claims 1-6, characterized in that The system includes: An image acquisition module for obtaining a first wound image before wound repair and a second wound image after repair; An image registration module for performing image registration on the first wound image and the second wound image using an image matching algorithm; The first evaluation module is used to evaluate the consistency of the tissue around the wound before and after repair and the consistency of the wound tissue after repair according to the image registration result; The second evaluation module is used to evaluate the suture strain distribution at the wound suture according to the second wound image; The repair effect analysis module is used to analyze the wound repair effect according to the evaluation results of the consistency of the tissue around the wound and the wound tissue and in combination with the evaluation result of the suture strain distribution; Analyzing the wound repair effect includes: Obtaining the perilesional tissue consistency index, the wound tissue consistency index and the suture strain distribution index and calculating the wound repair index. The wound repair index is used to quantitatively analyze the wound repair effect. The calculation formula of the wound repair index is as follows: WRI = ω1×OCI + ω2×WCI + ω3×SDI; In the formula, OCI represents the perilesional tissue consistency index, WCI represents the wound tissue consistency index, SDI represents the suture strain distribution index, ω1 represents the perilesional tissue consistency weight, ω2 represents the wound tissue consistency weight, ω3 represents the suture strain distribution weight, and WRI represents the wound repair index.

8. An electronic device, comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; characterized in that the processor executes the image matching-based plastic wound repair analysis method according to any one of claims 1-6 by calling the computer program stored in the memory.

9. A computer-readable storage medium, characterized in that, Stored with instructions that, when run on a computer, cause the computer to execute the image matching-based plastic wound repair analysis method according to any one of claims 1-6.

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