A chronic wound depth analysis system based on image recognition

Abstract

The present invention belongs to the technical field of chronic wound depth analysis and relates to a chronic wound depth analysis system based on image recognition. By acquiring chronic wound images of target patients, the present invention provides more comprehensive and richer wound information, which helps to reduce misdiagnosis and missed diagnosis and more accurately judge the depth of chronic wounds. By analyzing the preliminary depth level of the chronic wound images of the target patients and judging the in-depth detection needs of the chronic wounds of the target patients, it helps to formulate more personalized detection plans, reduce unnecessary examinations and treatments, and reduce the economic burden and psychological pressure on patients. By determining the in-depth detection method of the chronic wounds of the target patients, performing detection and correction, and analyzing the final depth level of the chronic wound images of the target patients, it helps to identify and correct interference factors in the images, improves the accuracy of diagnosis, and provides an important basis for formulating personalized treatment plans.

Description

Technical Field

[0001] The present invention belongs to the technical field of chronic wound depth analysis, and relates to a chronic wound depth analysis system based on image recognition. Background Art

[0002] With the aging population, the rising incidence of chronic diseases like diabetes, and the frequent occurrence of accidental injuries like traffic accidents and work-related injuries, the number of patients with chronic wounds is increasing. Chronic wounds typically refer to skin tissue damage caused by various reasons that fail to heal within the expected timeframe despite conventional treatment, such as diabetic foot ulcers, pressure sores, and venous leg ulcers. These wounds not only cause physical pain and a decline in quality of life for patients, but also increase medical costs and social burdens. Therefore, a deep analysis system for chronic wounds based on image recognition is of great significance and significance.

[0003] In the prior art, there are also some related solutions involving chronic wound analysis. For example, the invention patent application for a chronic wound recognition system, method, device and medium based on multimodal fusion with Chinese patent publication number CN118570841A specifically relates to the field of image processing technology. The recognition module uses a laser camera, an RGB camera and an infrared camera to scan the chronic wound to be identified, and obtains 3D point cloud data, RGB image and infrared image of the chronic wound to be identified respectively; the processing module aligns the 3D point cloud data, RGB image and infrared image, and converts the 3D point cloud data into a depth image, and performs scale normalization processing on the depth image, RGB image and infrared image to obtain the multimodal data to be detected; the recognition result acquisition module inputs the multimodal data to be detected into the pre-trained multimodal fusion model and outputs the recognition result of the chronic wound to be identified. The above system adopts a composite data acquisition scheme, which can comprehensively utilize multiple types of image acquisition data to improve the accuracy of chronic wound recognition and timely and accurate chronic wound recognition.

[0004] Another Chinese patent application with publication number CN118213092B is an invention patent application for a remote medical supervision system for chronic wound diseases. The system includes a patient user terminal, a medical user terminal and a cloud server. The patient user terminal and the medical user terminal are respectively connected to the cloud server in communication; the patient user terminal includes a wound image acquisition module and a basic information acquisition module. The wound image acquisition module is used to obtain the patient's preliminary chronic wound image information and pre-process the patient's preliminary chronic wound image information; the basic information acquisition module is used to obtain the patient's basic information; the cloud server includes a sample acquisition module, a wound analysis model construction module, a model training module, a wound analysis module, a storage module and an electronic medical record module; the medical user terminal includes a medical diagnosis module, a patient supervision module, and a doctor-patient interaction module; the present invention can effectively monitor the wounds of patients with chronic wound diseases remotely, reduce the number of times patients go to and from the hospital, and improve the quality of life of patients.

[0005] Although the above schemes have proposed some solutions for chronic wound analysis, they still have certain limitations: on the one hand, the existing schemes use a composite data acquisition scheme, which can comprehensively utilize multiple types of image acquisition data to improve the accuracy of chronic wound identification and timely and accurate chronic wound identification. However, the existing schemes ignore the analysis from multiple angles such as the geometric features, texture features and color features of chronic wounds, and thus cannot identify chronic wounds more comprehensively and accurately based on the shape and size, subtle structure and surface changes, and color information of chronic wounds, thereby failing to reduce unnecessary examinations and treatments and reduce the economic burden and psychological pressure on patients.

[0006] On the other hand, existing solutions can effectively monitor the wounds of patients with chronic wound diseases remotely, reduce the number of times patients travel to the hospital, and improve their quality of life. However, existing solutions lack accurate judgment of the depth of chronic wounds, which is not conducive to selecting the most appropriate treatment method, cannot avoid unnecessary surgical trauma, and cannot increase patient satisfaction and trust. Summary of the Invention

[0007] In view of this, in order to solve the problems raised in the above background technology, a chronic wound depth analysis system based on image recognition is proposed.

[0008] The objectives of the present invention can be achieved through the following technical solutions: The present invention provides a chronic wound in-depth analysis system based on image recognition, including: a chronic wound preliminary detection module, a chronic wound preliminary analysis module, a chronic wound in-depth detection demand judgment module, a chronic wound in-depth detection module, a chronic wound in-depth analysis module and a cloud database.

[0009] The chronic wound preliminary detection module is used to record the patient whose chronic wound depth needs to be detected as the target patient, use professional camera equipment to shoot the target patient's wound site, obtain the target patient's chronic wound images from various angles, and fuse them to obtain the target patient's chronic wound image.

[0010] The chronic wound preliminary analysis module is used to obtain the geometric feature data, texture feature data and color feature data of the chronic wound image of the target patient, analyze the geometric feature vector, texture feature vector and color feature vector of the chronic wound image of the target patient, and obtain the preliminary depth level of the chronic wound image of the target patient based on this, where the depth level includes shallow ulcer, half-thickness ulcer, full-thickness ulcer and deep ulcer.

[0011] The chronic wound in-depth detection demand judgment module is used to judge the in-depth detection demand of the chronic wound depth of the target patient. If it is necessary to conduct in-depth detection, the chronic wound in-depth detection module is executed.

[0012] The chronic wound depth detection module is used to determine the target patient's chronic wound depth detection method based on the preliminary depth level of the target patient's chronic wound image and basic medical record information, and perform real-time detection during the detection process to obtain the artifact existence index of the chronic wound area during the target patient's chronic wound depth detection process.

[0013] The chronic wound depth analysis module is used to analyze the final depth level of the chronic wound images of the target patient.

[0014] The cloud database is used to store the permitted area of ​​chronic wound images without affecting the normal life conditions of the patients, store the hue mean, saturation mean and brightness mean of the normal part images of the target patients, store the minimum preliminary depth level corresponding to the in-depth detection requirements of the chronic wound depth, and store the in-depth detection method of each chronic wound corresponding to each preliminary depth level.

[0015] Compared with the existing technology, the beneficial effects of the present invention are as follows: 1. The present invention obtains chronic wound images of the target patient from various angles and fuses them to obtain chronic wound images of the target patient. Image fusion can integrate image information from different angles into a single image, thereby providing more comprehensive and richer wound information, helping to reduce misdiagnosis and missed diagnosis, and helping to more accurately judge the depth of chronic wounds, which is conducive to selecting the most appropriate treatment method, avoiding unnecessary surgical trauma, and alleviating the patient's pain.

[0016] 2. The present invention analyzes the geometric feature vectors, texture feature vectors and color feature vectors of the chronic wound image of the target patient, thereby obtaining the preliminary depth level of the chronic wound image of the target patient and judging the in-depth detection requirements of the chronic wound depth of the target patient, which helps to more comprehensively understand the morphology, structure and color changes of the chronic wound, thereby obtaining the depth level of the chronic wound, and further helps to formulate a more personalized detection plan, which is conducive to reducing unnecessary examinations and treatments, and reducing the economic burden and psychological pressure on patients.

[0017] 3. The present invention determines the depth detection method of the chronic wound of the target patient, performs detection and correction, and analyzes the final depth level of the chronic wound image of the target patient. The artifact existence index obtained during the real-time detection process helps to identify and correct interference factors in the image, thereby improving the accuracy of diagnosis. Accurate chronic wound depth assessment provides an important basis for formulating personalized treatment plans, and precise wound assessment and personalized treatment plans can improve treatment effects, thereby increasing patient satisfaction and trust. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0019] Figure 1 This is a schematic diagram of system module connections of the present invention.

[0020] Figure 2 Schematic diagram of the system implementation flow of the present invention.

[0021] Figure 3 Schematic diagram of the texture extraction process of the gray-level co-occurrence matrix of the present invention. DETAILED DESCRIPTION

[0022] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0023] See also Figure 1As shown, the present invention provides a chronic wound in-depth analysis system based on image recognition, and the specific modules are distributed as follows: chronic wound preliminary detection module, chronic wound preliminary analysis module, chronic wound in-depth detection demand judgment module, chronic wound in-depth detection module, chronic wound in-depth analysis module and cloud database. Among them, the connection between the modules is as follows: the chronic wound preliminary detection module is connected to the chronic wound preliminary analysis module, the chronic wound preliminary analysis module is connected to the chronic wound in-depth detection demand judgment module, the chronic wound in-depth detection demand judgment module is connected to the chronic wound in-depth detection module, the chronic wound in-depth detection module is connected to the chronic wound in-depth analysis module, and the cloud database is connected to the chronic wound preliminary analysis module, the chronic wound in-depth detection demand judgment module and the chronic wound in-depth analysis module respectively.

[0024] The chronic wound preliminary detection module is used to record the patient whose chronic wound depth needs to be detected as the target patient, use professional camera equipment to shoot the target patient's wound site, obtain the target patient's chronic wound images from various angles, and fuse them to obtain the target patient's chronic wound image.

[0025] It should be noted that the system implementation flow diagram is as follows Figure 2 shown.

[0026] As a preferred feasibility example, the chronic wound image of the target patient is specifically obtained in the following manner: segmenting the chronic wound images of the target patient at each angle to obtain the sub-regions to which the chronic wound images of the target patient at each angle belong, and then selecting different fusion methods according to the different texture features and color features of the sub-regions to which the chronic wound images of the target patient at each angle belong, further obtaining the fused sub-regions to which the chronic wound image of the target patient belongs, and reconstructing them to obtain the chronic wound image of the target patient.

[0027] It should be noted that the specific method of segmenting the chronic wound images of the target patient at each angle to obtain the sub-regions belonging to the chronic wound images of the target patient at each angle is: segmenting the chronic wound images of the target patient at each angle according to a plane grid form, and recording each grid in the obtained chronic wound images of the target patient at each angle as the sub-regions belonging to the chronic wound images of the target patient at each angle.

[0028] It should be further explained that the fusion method includes but is not limited to the weighted average method, the maximum method, the minimum method and the median method.

[0029] The specific content of the weighted average method is: weighted averaging is performed on each pixel of the sub-region belonging to the chronic wound image of the target patient at each angle, and the obtained average is used as the pixel value of the fusion sub-region belonging to the chronic wound image of the target patient.

[0030] The specific content of the maximum method is: taking the maximum value of each pixel in the sub-region belonging to the chronic wound image of the target patient at each angle, and the obtained maximum value is used as the pixel value of the fusion sub-region belonging to the chronic wound image of the target patient.

[0031] The specific content of the minimum method is: taking the minimum value of each pixel in the sub-region belonging to the chronic wound image of the target patient at each angle, and the obtained minimum value is used as the pixel value of each fusion sub-region belonging to the chronic wound image of the target patient.

[0032] The specific content of the median method is: taking the median of each pixel in the sub-region belonging to the chronic wound image of the target patient at each angle, and the obtained median is used as the pixel value of the fusion sub-region belonging to the chronic wound image of the target patient.

[0033] The present invention obtains chronic wound images of target patients from various angles and fuses them to obtain chronic wound images of target patients. Image fusion can integrate image information from different angles into a single image, thereby providing more comprehensive and richer wound information, helping to reduce misdiagnosis and missed diagnosis, and helping to more accurately judge the depth of chronic wounds, which in turn is conducive to selecting the most appropriate treatment method, avoiding unnecessary surgical trauma, and alleviating patients' pain.

[0034] The chronic wound preliminary analysis module is used to obtain the geometric feature data, texture feature data and color feature data of the chronic wound image of the target patient, analyze the geometric feature vector, texture feature vector and color feature vector of the chronic wound image of the target patient, and obtain the preliminary depth level of the chronic wound image of the target patient based on this, where the depth level includes shallow ulcer, half-thickness ulcer, full-thickness ulcer and deep ulcer.

[0035] As a preferred feasibility example, the geometric feature data of the chronic wound image of the target patient includes area and fractal dimension.

[0036] It should be further explained that the reason for using area and fractal dimension as geometric feature data of chronic wound images of target patients is: (1) The area size can reflect the scope of the trauma to a certain extent. A larger wound area often means deeper or more extensive tissue damage.

[0037] (2) Fractal dimension can measure the degree of irregularity of a pattern. The more complex the shape of the wound and the more jagged the edges, the higher the fractal dimension. It can reflect the geometric complexity of the wound and its relationship with depth changes from a more detailed perspective. For example, a wound with a high fractal dimension may have more significant depth differences in different local areas.

[0038] It should be further explained that the specific method of obtaining the area of ​​the chronic wound image of the target patient is: importing the chronic wound image of the target patient into the OpenCV library, and further using it to process to obtain the area of ​​the chronic wound image of the target patient.

[0039] It should be noted that OpenCV is an open-source computer vision and image processing library that is widely used in various fields, such as video surveillance, medical image processing, robotic vision, and autonomous driving. OpenCV provides a rich set of functions and tools for tasks such as image and video processing, feature detection, object recognition, and machine learning.

[0040] The fractal dimension of the chronic wound image of the target patient is specifically obtained by directly analyzing the chronic wound image of the target patient using image analysis software to obtain the fractal dimension of the chronic wound image of the target patient.

[0041] In a specific example, the image analysis software is ImageJ, and the specific steps are as follows: first open the chronic wound image, then select the FracLac plug-in in the plug-in menu and start the corresponding analysis interface. In the interface, some parameters can be set, such as the starting value, ending value, and step size of the box size. After clicking the "Start Analysis" button, the software will cover the wound area in sequence according to the set different box sizes, and count the corresponding number of boxes. Finally, based on the collected data, the built-in mathematical model automatically calculates the fractal dimension and displays the results.

[0042] The texture feature data of the chronic wound image of the target patient includes contrast, energy, correlation and entropy.

[0043] It should be further explained that the reason for using contrast, energy, correlation and entropy as texture feature data of chronic wound images of target patients is: (1) Contrast reflects the degree of contrast of local brightness changes in the image. For chronic wound images, wounds with rough texture and deep depth often have higher contrast because the grayscale difference of tissues at different depths is large, resulting in obvious local brightness contrast.

[0044] (2) Energy indicates the uniformity of the image grayscale distribution and the coarseness of the texture. A high energy value indicates a more uniform image texture, which may correspond to a shallower wound. This is because shallower wounds mainly involve the epidermis and part of the superficial dermis, with relatively regular tissue texture and more consistent grayscale distribution.

[0045] (3) Correlation measures the linear relationship of pixel grayscale in a specific direction. Regular textures have strong correlation, while deep trauma destroys the regular structure of the tissue, the texture becomes irregular, and the correlation is weakened.

[0046] (4) Entropy can measure the randomness of image texture. For complex and irregular wound surface textures, the entropy value is usually higher, which can describe the relationship between texture complexity and depth from the perspective of information theory.

[0047] It needs to be further explained that the specific method of obtaining the contrast, energy, correlation and entropy of the chronic wound image of the target patient is: grayscale processing is performed on the chronic wound image of the target patient to obtain the grayscale image of the chronic wound of the target patient, and then the grayscale co-occurrence matrix corresponding to the grayscale image of the chronic wound of the target patient is obtained.

[0048] Among them, the gray level co-occurrence matrix is ​​a matrix that describes the spatial relationship between gray levels in an image and can be used to extract a variety of texture features.

[0049] It should be noted that the texture extraction process diagram of the gray level co-occurrence matrix is ​​as follows Figure 3 shown.

[0050] According to the calculation formula Obtain the contrast of chronic wound images of target patients ,in are the grayscale levels of the target patient’s chronic wound grayscale image, , , is the number of gray levels of the target patient’s chronic wound grayscale image, Grayscale Frequency of simultaneous occurrence.

[0051] According to the calculation formula Get the energy of chronic wound images of target patients .

[0052] According to the calculation formula Obtain the correlation of chronic wound images of target patients ,in Grayscale The average value of Grayscale The standard deviation of .

[0053] According to the calculation formula Get the entropy of the chronic wound image of the target patient ,in A small constant is set to prevent zero values ​​in logarithmic operations.

[0054] The color feature data of the chronic wound image of the target patient includes hue mean, hue variance, saturation mean, saturation variance, lightness mean and lightness variance.

[0055] It should be further explained that the specific method for obtaining the hue mean, hue variance, saturation mean, saturation variance, brightness mean and brightness variance of the chronic wound image of the target patient is as follows: the chronic wound image of the target patient is imported into the OpenCV library, and the R, G, and B three-channel values ​​corresponding to each pixel point of the chronic wound image of the target patient are obtained by processing it, which are recorded as ,in , is the number of each pixel, The number of pixels is the number of red, green and blue three-channel values ​​of each pixel of the chronic wound image of the target patient are normalized to obtain the normalized corresponding values ​​of the red, green and blue three-channel values ​​of each pixel of the chronic wound image of the target patient. .

[0056] It should be explained that the specific content of the normalization process is: according to the calculation formula Get the normalized corresponding values ​​of the red, green and blue channels of each pixel of the chronic wound image of the target patient .

[0057] According to the standard calculation formula, the hue of each pixel in the chronic wound image of the target patient can be obtained. , saturation and brightness , and the mean value of the hue, saturation and brightness of the chronic wound image of the target patient can be obtained by processing them respectively. According to the calculation formula Get the hue variance of the chronic wound image of the target patient Similarly, the saturation variance of the chronic wound image of the target patient can be obtained and brightness variance .

[0058] It should be noted that the standard formula for obtaining the brightness of each pixel corresponding to the chronic wound image of the target patient is: .

[0059] The standard formula for obtaining the saturation of each pixel corresponding to the chronic wound image of the target patient is: .

[0060] The standard formula for obtaining the hue of each pixel corresponding to the chronic wound image of the target patient is: .

[0061] As a preferred feasibility example, the geometric feature vector of the chronic wound image of the target patient is specifically analyzed by extracting the area and fractal dimension of the chronic wound image of the target patient, which are respectively recorded as , analyzing the geometric feature vectors of chronic wound images of target patients ,in The permitted area of ​​chronic wound images extracted from the cloud database without affecting the patient's normal life is are the weight factors of the geometric eigenvectors corresponding to the set area and fractal dimension respectively.

[0062] A specific feasibility example, .

[0063] As a fundamental geometric characteristic, area can intuitively reflect the size and extent of a wound and is an important reference for clinical diagnosis and treatment. However, relying solely on area may not fully describe the complexity and irregularity of a wound. Therefore, a weight factor of 0.33 is assigned to the geometric eigenvector corresponding to area.

[0064] Fractal dimension can reflect the complexity and irregularity of wound edges, helping to reveal the microstructure and pathological characteristics of wounds. Changes in fractal dimension may be more sensitive than area or have higher diagnostic value. Therefore, a weight factor of 0.67 was assigned to the geometric eigenvector corresponding to the fractal dimension.

[0065] As a preferred feasibility example, the texture feature vector of the chronic wound image of the target patient is specifically analyzed by extracting the contrast, energy, correlation and entropy of the chronic wound image of the target patient, which are respectively recorded as , analyzing the texture feature vector of the chronic wound image of the target patient ,in The weight coefficients of the texture feature vector corresponding to the set contrast, energy, correlation and entropy respectively.

[0066] A specific feasibility example, 、 、 and .

[0067] Contrast reflects image clarity and the depth of texture grooves. High contrast indicates a clear image with deep texture grooves, while low contrast indicates a blurry image with shallow texture grooves. In chronic wound images, contrast helps identify the sharpness of wound edges and subtle texture changes. Therefore, a weight coefficient of 0.33 is assigned to the texture feature vector corresponding to the contrast.

[0068] Energy reflects the uniformity of an image's grayscale distribution and the coarseness of its texture. High energy indicates a stable and uniform grayscale distribution and fine texture, while low energy indicates an uneven grayscale distribution and coarse texture. In chronic wound images, energy can be used to assess the uniformity and coarseness of texture in the wound area. Therefore, a weight coefficient of 0.27 is assigned to the texture feature vector corresponding to energy.

[0069] Correlation describes the linear relationship between pixel values ​​in an image. High correlation indicates a strong linear relationship between pixel values ​​and a more regular image texture, while low correlation indicates a weaker linear relationship and a more complex image texture. In chronic wound images, correlation can be used to analyze the regularity and complexity of the texture in the wounded area. Therefore, a weight coefficient of 0.15 is assigned to the texture feature vector corresponding to the correlation.

[0070] Entropy reflects the amount of information contained in an image. High entropy indicates rich image information and complex and varied textures, while low entropy indicates less information and simple textures. In chronic wound images, entropy can be used to assess the texture complexity and information content of the wounded area. Therefore, a weight coefficient of 0.25 is assigned to the texture feature vector corresponding to the entropy.

[0071] As a preferred feasibility example, the color feature vector of the chronic wound image of the target patient is specifically analyzed by extracting the hue mean, hue variance, saturation mean, saturation variance, brightness mean and brightness variance of the chronic wound image of the target patient, which are respectively recorded as , analyze the color feature vector of the chronic wound image of the target patient ,in are the mean hue, mean saturation, and mean brightness of the normal part images of the target patient extracted from the cloud database.

[0072] As a preferred feasibility example, the preliminary depth level of the chronic wound image of the target patient is specifically obtained by recording the geometric feature vector, texture feature vector and color feature vector of the chronic wound image of the target patient as input vectors, inputting them into the chronic wound learning model, and directly obtaining the preliminary depth level of the chronic wound image of the target patient based on them.

[0073] It should be further explained that the specific acquisition method of the chronic wound learning model is as follows: (1) Dataset acquisition preparation: a large number of chronic wound images with accurate chronic wound depth level annotations are collected and recorded as the chronic wound image dataset. According to the above steps, the geometric feature vectors, texture feature vectors and color feature vectors of each chronic wound image in the chronic wound image dataset are extracted and constructed, and used as input features. The depth level of each chronic wound image in the chronic wound image dataset is used as the output label to form the training dataset.

[0074] (2) Model selection and training: Select a suitable machine learning model or deep learning model, use the training data set to train the selected model, adjust the model parameters, and enable the model to learn the mapping relationship between the geometric feature vectors, texture feature vectors, and color feature vectors of the chronic wound image and the depth level of the chronic wound. The trained model is then recorded as the chronic wound learning model.

[0075] It should be explained that the machine learning model includes but is not limited to support vector machines, decision trees and multi-layer perceptrons, and the deep learning model includes but is not limited to convolutional neural networks.

[0076] The chronic wound in-depth detection demand judgment module is used to judge the in-depth detection demand of the chronic wound depth of the target patient. If it is necessary to conduct in-depth detection, the chronic wound in-depth detection module is executed.

[0077] As a preferred feasibility example, the in-depth detection requirement of the chronic wound depth of the target patient is specifically judged in the following manner: comparing the preliminary depth level of the chronic wound image of the target patient with the minimum preliminary depth level corresponding to the in-depth detection requirement of the chronic wound depth stored in the cloud database; if the preliminary depth level of the chronic wound image of the target patient is higher than or equal to the minimum preliminary depth level corresponding to the in-depth detection requirement of the chronic wound depth, then the in-depth detection requirement of the chronic wound depth of the target patient is recorded as requiring in-depth detection requirement; otherwise, the in-depth detection requirement of the chronic wound depth of the target patient is recorded as not requiring in-depth detection requirement.

[0078] The present invention analyzes the geometric feature vectors, texture feature vectors and color feature vectors of the chronic wound image of the target patient, thereby obtaining the preliminary depth level of the chronic wound image of the target patient and judging the in-depth detection requirements of the chronic wound depth of the target patient. This helps to more comprehensively understand the morphology, structure and color changes of the chronic wound, thereby obtaining the depth level of the chronic wound, and further helps to formulate a more personalized detection plan, which is conducive to reducing unnecessary examinations and treatments, and reducing the economic burden and psychological pressure on patients.

[0079] The chronic wound depth detection module is used to determine the target patient's chronic wound depth detection method based on the preliminary depth level of the target patient's chronic wound image and basic medical record information, and perform real-time detection during the detection process to obtain the artifact existence index of the chronic wound area during the target patient's chronic wound depth detection process.

[0080] As a preferred feasibility example, the basic medical record information of the target patient includes an implant presence index at a chronic wound site.

[0081] It should be further explained that the specific method of obtaining the implant presence index in the chronic wound area of ​​the target patient is: the medical staff directly obtains from the target patient's medical record whether there is an implant in the chronic wound area of ​​the target patient during the in-depth detection process of the chronic wound of the target patient. If there is an implant in the chronic wound area of ​​the target patient, the implant presence index in the chronic wound area of ​​the target patient during the in-depth detection process of the chronic wound of the target patient is recorded as 1; otherwise, the implant presence index in the chronic wound area of ​​the target patient during the in-depth detection process of the chronic wound of the target patient is recorded as 0.

[0082] It needs to be further explained that the specific method of obtaining the artifact existence index of the chronic wound part of the target patient's chronic wound in-depth detection process is: the medical staff directly observes from the chronic wound in-depth detection result image of the target patient. If there is an artifact in the chronic wound part of the target patient's chronic wound in-depth detection process, the artifact existence index of the chronic wound part of the target patient's chronic wound in-depth detection process is recorded as 1; otherwise, the artifact existence index of the chronic wound part of the target patient's chronic wound in-depth detection process is recorded as 0.

[0083] It should be noted that artifacts are common in medical imaging and can be caused by a variety of factors, including radiation hardening, volume effects, interference from metal objects, patient movement during the examination, improper scanning parameter settings, and equipment failure. The Artifact Presence Index quantifies the extent of artifacts in an image, thereby reflecting image quality. High-quality images are crucial for accurate diagnosis in the in-depth examination of chronic wounds. Artifacts can affect image interpretation and diagnostic accuracy. By assessing the Artifact Presence Index, physicians can determine whether images are affected by artifacts, thereby avoiding misdiagnosis or missed diagnosis.

[0084] As a preferred feasibility example, the method for determining the depth detection method of the chronic wound of the target patient includes: matching the preliminary depth level of the chronic wound image of the target patient with the chronic wound depth detection methods corresponding to each preliminary depth level stored in the cloud database to obtain the depth detection methods of each chronic wound of the target patient.

[0085] It should be further explained that the in-depth detection methods for the chronic wounds include but are not limited to ultrasound examination, magnetic resonance imaging, CT scanning and OCT scanning.

[0086] Extract the implant presence index of the chronic wound site of the target patient. If it is 1, screen the chronic wound in-depth detection methods in which the implant may be present from the chronic wound in-depth detection methods of the target patient, record them as the preferred chronic wound in-depth detection methods of the target patient, and further randomly select a chronic wound in-depth detection method from them as the chronic wound in-depth detection method of the target patient.

[0087] If it is 0, a chronic wound in-depth detection method is randomly selected from various chronic wound in-depth detection methods for the target patient as the chronic wound in-depth detection method for the target patient.

[0088] The chronic wound depth analysis module is used to analyze the final depth level of the chronic wound images of the target patient.

[0089] As a preferred feasibility example, the final depth level of the chronic wound image of the target patient is specifically analyzed in the following manner: extracting the artifact existence index of the chronic wound part of the target patient in the in-depth detection process of the chronic wound; if it is 1, it means that there are artifacts in the in-depth detection image of the chronic wound part of the target patient, and the in-depth detection correction method of the chronic wound of the target patient is recorded as image artifact correction; if it is 0, it means that there are no artifacts and implants in the chronic wound part of the target patient, and the in-depth detection correction method of the chronic wound of the target patient does not need to be corrected.

[0090] Based on the determined target patient's chronic wound depth detection method, the target patient's chronic wound site is detected to obtain the detection result data of the target patient's chronic wound site. Further based on the target patient's chronic wound depth detection correction method, the detection result data of the target patient's chronic wound site is corrected to obtain the final depth level of the target patient's chronic wound image.

[0091] The cloud database is used to store the permitted area of ​​chronic wound images without affecting the patient's normal life conditions, store the hue mean, saturation mean and brightness mean of the normal part images of the target patient, store the minimum preliminary depth level corresponding to the in-depth detection requirement of the chronic wound depth, and store the in-depth detection method of each chronic wound corresponding to each preliminary depth level.

[0092] The present invention determines the depth detection method of the chronic wound of the target patient, performs detection and correction, and analyzes the final depth level of the chronic wound image of the target patient. The artifact existence index obtained during the real-time detection process helps to identify and correct interference factors in the image, thereby improving the accuracy of diagnosis. Accurate chronic wound depth assessment provides an important basis for formulating personalized treatment plans, and precise wound assessment and personalized treatment plans can improve treatment effects, thereby increasing patient satisfaction and trust.

[0093] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they should all fall within the scope of protection of the present invention.

Claims

1. A chronic wound depth analysis system based on image recognition, characterized by: include: The chronic wound preliminary detection module is used to record the patient whose chronic wound depth needs to be detected as the target patient, use professional camera equipment to shoot the target patient's wound site, obtain the target patient's chronic wound images from various angles, and perform fusion processing on the images to obtain the target patient's chronic wound image; A chronic wound preliminary analysis module is used to obtain geometric feature data, texture feature data, and color feature data of the chronic wound image of the target patient, analyze the geometric feature vectors, texture feature vectors, and color feature vectors of the chronic wound image of the target patient, and obtain the preliminary depth level of the chronic wound image of the target patient based on the data, where the depth level includes shallow ulcer, half-thickness ulcer, full-thickness ulcer, and deep ulcer; A chronic wound in-depth detection demand judgment module is used to judge the target patient's chronic wound depth in-depth detection demand. If it is necessary to in-depth detection demand, the chronic wound in-depth detection module is executed; A chronic wound depth detection module is used to determine the target patient's chronic wound depth detection method based on the preliminary depth level of the chronic wound image and basic medical history information of the target patient, and to perform real-time detection during the detection process to obtain the artifact presence index of the chronic wound site during the target patient's chronic wound depth detection process; A chronic wound depth analysis module is used to analyze the final depth level of chronic wound images of target patients; The cloud database is used to store the permitted area of ​​chronic wound images without affecting the normal life conditions of the patients, store the hue mean, saturation mean and brightness mean of the normal part images of the target patients, store the minimum preliminary depth level corresponding to the in-depth detection requirements of the chronic wound depth, and store the in-depth detection method of each chronic wound corresponding to each preliminary depth level.

2. The chronic wound depth analysis system based on image recognition according to claim 1, characterized in that: The chronic wound image of the target patient is obtained in the following specific manner: The chronic wound images of the target patient at each angle are segmented to obtain the sub-regions to which the chronic wound images of the target patient at each angle belong. Then, different fusion methods are selected according to the different texture features and color features of the sub-regions to which the chronic wound images of the target patient at each angle belong. The fused sub-regions to which the chronic wound images of the target patient belong are further obtained, and they are reconstructed to obtain the chronic wound images of the target patient.

3. The chronic wound depth analysis system based on image recognition according to claim 1, characterized in that: The geometric feature data of the chronic wound image of the target patient includes area and fractal dimension; The texture feature data of the chronic wound image of the target patient includes contrast, energy, correlation and entropy; The color feature data of the chronic wound image of the target patient includes hue mean, hue variance, saturation mean, saturation variance, lightness mean and lightness variance; The basic medical record information of the target patient includes an implant presence index at the chronic wound site.

4. The chronic wound depth analysis system based on image recognition according to claim 3, characterized in that: The specific analysis method of the geometric feature vector of the chronic wound image of the target patient includes: The area and fractal dimension of the chronic wound image of the target patient are extracted and recorded as , analyzing the geometric feature vectors of chronic wound images of target patients ,in The permitted area of ​​chronic wound images extracted from the cloud database without affecting the patient's normal life is are the weight factors of the geometric eigenvectors corresponding to the set area and fractal dimension respectively.

5. The chronic wound depth analysis system based on image recognition according to claim 4, characterized in that: The texture feature vector of the chronic wound image of the target patient is specifically analyzed in the following manner: The contrast, energy, correlation and entropy of the chronic wound image of the target patient are extracted and recorded as , analyzing the texture feature vector of the chronic wound image of the target patient ,in The weight coefficients of the texture feature vector corresponding to the set contrast, energy, correlation and entropy respectively.

6. The chronic wound depth analysis system based on image recognition according to claim 5, characterized in that: The specific analysis method of the color feature vector of the chronic wound image of the target patient includes: The hue mean, hue variance, saturation mean, saturation variance, brightness mean and brightness variance of the chronic wound image of the target patient are extracted and recorded as , analyze the color feature vector of the chronic wound image of the target patient ,in are the mean hue, mean saturation, and mean brightness of the normal part images of the target patient extracted from the cloud database.

7. The chronic wound depth analysis system based on image recognition according to claim 6, characterized in that: The preliminary depth level of the chronic wound image of the target patient is obtained in the following manner: The geometric feature vector, texture feature vector and color feature vector of the chronic wound image of the target patient are recorded as input vectors and input into the chronic wound learning model. Based on them, the preliminary depth level of the chronic wound image of the target patient can be directly obtained.

8. The chronic wound depth analysis system based on image recognition according to claim 1, characterized in that: The in-depth detection requirements for the depth of chronic wounds of the target patients include: The preliminary depth level of the chronic wound image of the target patient is compared with the minimum preliminary depth level corresponding to the in-depth detection requirement of the chronic wound depth stored in the cloud database. If the preliminary depth level of the chronic wound image of the target patient is higher than or equal to the minimum preliminary depth level corresponding to the in-depth detection requirement of the chronic wound depth, the in-depth detection requirement of the chronic wound depth of the target patient is recorded as requiring in-depth detection requirement; otherwise, the in-depth detection requirement of the chronic wound depth of the target patient is recorded as not requiring in-depth detection requirement.

9. The chronic wound depth analysis system based on image recognition according to claim 3, characterized in that: The method for determining the in-depth detection of chronic wounds in target patients specifically includes: Matching the preliminary depth level of the chronic wound image of the target patient with the chronic wound depth detection methods corresponding to the preliminary depth levels stored in the cloud database to obtain the chronic wound depth detection methods of the target patient; Extracting the implant presence index of the chronic wound site of the target patient; if the index is 1, screening the chronic wound depth detection methods of the target patient for the presence of implants from the chronic wound depth detection methods, recording them as the preferred chronic wound depth detection methods of the target patient, and further randomly selecting a chronic wound depth detection method from them as the chronic wound depth detection method of the target patient; If it is 0, a chronic wound in-depth detection method is randomly selected from various chronic wound in-depth detection methods for the target patient as the chronic wound in-depth detection method for the target patient.

10. The chronic wound depth analysis system based on image recognition according to claim 9, characterized in that: The final depth level of the chronic wound image of the target patient is analyzed in the following specific manner: Extract the artifact presence index of the chronic wound site of the target patient during the in-depth detection process of the chronic wound site. If the index is 1, it means that artifacts exist in the in-depth detection image of the chronic wound site of the target patient, and the in-depth detection correction method of the chronic wound site of the target patient is recorded as image artifact correction. If the index is 0, it means that there are no artifacts and implants in the chronic wound site of the target patient, and the in-depth detection correction method of the chronic wound site of the target patient is not required, and is recorded as no correction is required. Based on the determined target patient's chronic wound depth detection method, the target patient's chronic wound site is detected to obtain the detection result data of the target patient's chronic wound site. Further based on the target patient's chronic wound depth detection correction method, the detection result data of the target patient's chronic wound site is corrected to obtain the final depth level of the target patient's chronic wound image.

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