Imaging method and system for visual laryngoscope imaging

Abstract

The present invention belongs to the technical field of visual laryngoscope photography, and specifically relates to an imaging method and system for visual laryngoscope photography. The invention can significantly improve the clarity and recognizability of the image by detecting the target image and correcting the light source, ensuring that medical personnel can more accurately diagnose the patient's laryngeal condition. In the image processing process, the blur and shadow processing of each detection area is refined to make the image correction more accurate, effectively avoiding the distortion problem that may be caused by the overall image processing. Correction can be performed in combination with the target patient's laryngeal detection data, and optimization processing can be performed on specific laryngeal abnormal areas to improve the image quality of the laryngeal abnormal areas, ensuring that medical personnel can obtain high-quality images in a timely manner, and improving the efficiency and effectiveness of clinical operations. By calculating the illumination imaging level and performing corresponding corrections, relatively consistent high-quality images can be obtained under different lighting conditions, reducing the impact of illumination changes on image quality.

Description

Technical Field

[0001] The invention belongs to the technical field of visual laryngoscope imaging, and in particular relates to an imaging method and system for visual laryngoscope imaging. Background Art

[0002] With the continuous advancement of medical technology, visualization technology is increasingly used in the medical field. Especially in the diagnosis and treatment of respiratory diseases, visual laryngoscope technology provides great convenience for doctors with its intuitive and accurate characteristics. The visual laryngoscope combines modern optical fiber and electronic imaging technology. Through the combination of camera and display screen, it can clearly display the structure deep in the throat, providing doctors with a wider and clearer field of view.

[0003] However, despite the significant advantages of video laryngoscopes in the field of tracheal intubation, their imaging methods still need to be further improved and optimized. Due to the limitations of traditional optical imaging technology, the light brightness of the laryngoscope cannot be effectively adjusted dynamically according to the imaging effect of the image, resulting in low image resolution and color reproduction, and it is difficult to clearly display abnormal images of the larynx. These problems have affected doctors' judgment of the laryngeal structure and the effect of operation to a certain extent. Summary of the invention

[0004] The purpose of the present invention is to provide an imaging method for visual laryngoscope photography, which can reduce the influence of illumination changes on image quality according to laryngeal imaging data, dynamically adjust imaging data, and improve the display of abnormal laryngeal images.

[0005] The technical solution adopted by the present invention is as follows:

[0006] An imaging method for visual laryngoscope photography, comprising:

[0007] Acquire multiple images from video data collected by a video laryngoscope;

[0008] Determine whether each image meets the preset conditions. If not, mark it as a target image;

[0009] Divide the target image to obtain multiple region images;

[0010] Perform edge detection on multiple region images based on edge detection method to obtain edge clear images and edge fuzzy images;

[0011] Acquire multiple clear inflection point coordinates in each edge clear image and multiple blurred inflection point coordinates in each edge blurred image based on a rectangular coordinate system;

[0012] Calculate the image variation value of the target image according to the multiple clear inflection point coordinates and the multiple fuzzy inflection point coordinates;

[0013] Performing image shadow detection on multiple region images to obtain multiple shadow information;

[0014] Calculating shadow compensation values ​​of target images of multiple region images according to multiple shadow information;

[0015] Collecting laryngeal detection data of the target user based on the visual laryngoscope, and calculating the detection compensation value according to the laryngeal detection data;

[0016] Acquire the brightness data of the visual laryngoscope corresponding to the target image;

[0017] Calculate the illumination imaging value according to the image variation value, the shadow compensation value, the detection compensation value and the brightness data, and obtain the illumination imaging level corresponding to the illumination imaging value;

[0018] Adjust the light intensity when the video laryngoscope collects video data according to the light imaging level.

[0019] In a preferred embodiment, the step of obtaining the coordinates of multiple blur inflection points in each edge blurred image comprises:

[0020] Obtain the edge contour of the edge blurred image and obtain the corner points of the edge contour;

[0021] The coordinates of the corner points are obtained based on the rectangular coordinate system, and the coordinates of the corner points are used as the coordinates of the fuzzy inflection points.

[0022] In a preferred embodiment, the step of determining whether each image meets the preset conditions and marking it as a target image if it does not meet the preset conditions comprises:

[0023] Get the standard image clarity threshold;

[0024] Obtaining a corresponding image clarity value according to the image;

[0025] Determine whether the image clarity value exceeds a standard image clarity threshold;

[0026] If the image clarity value does not exceed the standard image clarity threshold, the image corresponding to the value that does not exceed the standard image clarity threshold is determined as the target image.

[0027] In a preferred embodiment, in the step of calculating the image variation value of the target image according to the plurality of fuzzy information and the plurality of clear information:

[0028] The calculation formula is:

[0029]

[0030] Where T is the image variation value, i is the number of the regional image, n is the total number of regional images, g is the number of the inflection point coordinates of the fuzzy image contour in each regional image, h is the total number of the inflection point coordinates of the fuzzy image contour in each regional image, and M is g It is represented as the x-axis coordinate point of the g-th blurred image contour inflection point, M g+1 It is represented as the x-axis coordinate point of the g+1th blurred image contour inflection point, H g It is represented as the y-axis coordinate point of the g-th blurred image contour inflection point, H g+1 It is represented by the y-axis coordinate point of the g+1th blurred image contour inflection point, u is the number of the coordinate point of the clear image contour inflection point in each regional image contour, p is the total number of the coordinate points of the clear image contour inflection point in each regional image contour, Q u It is represented as the x-axis coordinate point of the u-th clear image contour inflection point, Q u+1 It is represented as the x-axis coordinate point of the u+1th clear image contour inflection point, X u It is represented as the y-axis coordinate point of the u-th clear image contour inflection point, X u+1 It is expressed as the y-axis coordinate point of the u+1th clear image contour inflection point.

[0031] In a preferred embodiment, the step of calculating the shadow compensation value of the target image of the multiple regional images according to the multiple shadow information includes:

[0032] Construct a plane rectangular coordinate system according to the target image;

[0033] Acquire coordinate points of corresponding multiple shadow image contour inflection points in a plane rectangular coordinate system according to multiple shadow information;

[0034] The shadow compensation value is calculated according to the coordinate points of the inflection points of the multiple shadow image contours, wherein the calculation formula is:

[0035]

[0036] Where A is the shadow compensation value, i is the number of the regional image, n is the total number of regional images, d is the number of the inflection point coordinates of the shadow image contour in each regional image, f is the total number of the inflection point coordinates of the shadow image contour in each regional image, S d It is represented by the x-axis coordinate point of the d-th shadow image contour inflection point, S d+1 It is represented as the x-axis coordinate point of the d+1th shadow image contour inflection point, W d It is represented as the y-axis coordinate point of the d-th shadow image contour inflection point, W d+1 Represented as the y-axis coordinate point of the d+1th shadow image contour inflection point.

[0037] In a preferred embodiment, the step of collecting the target user's laryngeal detection data based on the visual laryngoscope and calculating the detection compensation value according to the laryngeal detection data includes:

[0038] Collecting target user's laryngeal detection data based on visual laryngoscope;

[0039] Acquire corresponding multiple historical laryngeal detection values ​​and current laryngeal detection values ​​according to the laryngeal detection data;

[0040] The detection compensation value is calculated according to a plurality of historical throat detection values ​​and a current throat detection value, wherein the calculation formula is:

[0041]

[0042] Where B is the detection compensation value, r is the total number of historical throat detection values, t is the number of historical throat detection values, and K is t It is represented by the t-th historical throat detection value, K d Indicates the current throat detection value.

[0043] In a preferred embodiment, the step of calculating the illumination imaging level according to the image variation value, the shadow compensation value, the detection compensation value and the brightness data includes:

[0044] Calculate the illumination imaging value according to the image variation value, the shadow compensation value, the detection compensation value and the brightness data;

[0045] Obtaining a lighting imaging table, wherein the lighting imaging table includes a plurality of lighting imaging interval values ​​and a lighting imaging level corresponding to each lighting imaging interval value;

[0046] Obtaining a target illumination imaging interval value according to the illumination imaging value;

[0047] The illumination imaging level is obtained from the illumination imaging table according to the target illumination imaging interval value.

[0048] In a preferred embodiment, the step of calculating the illumination imaging value according to the image variation value, the shadow compensation value, the detection compensation value and the brightness data includes:

[0049] Obtain corresponding brightness value according to brightness data;

[0050] The illumination imaging value is calculated according to the image variation value, the shadow compensation value, the detection compensation value and the brightness value, wherein the calculation formula is:

[0051] C = T × A × B × L;

[0052] In the formula, C represents the illumination imaging value, T represents the image variation value, A represents the shadow compensation value, B represents the detection compensation value, and L represents the brightness value.

[0053] In a preferred embodiment, after the step of calculating the illumination imaging value according to the image variation value, the shadow compensation value, the detection compensation value and the brightness value, the method further includes:

[0054] Obtaining the light imaging risk threshold;

[0055] Establish a risk monitoring cycle;

[0056] The number of times the light imaging value exceeds the light imaging risk threshold is obtained within the risk detection cycle, and marked as the number of times exceeding the risk;

[0057] Get the risk threshold times;

[0058] Determine whether the number of times exceeding the risk exceeds the risk threshold number;

[0059] If the number of times exceeding the risk threshold exceeds the number of times exceeding the risk threshold, the imaging of the visual laryngoscope is judged to be abnormal, and a warning message is issued;

[0060] If the number of times the risk is exceeded does not exceed the risk threshold number, the imaging of the video laryngoscope is determined to be normal.

[0061] The present invention also provides an imaging system for visual laryngoscope photography, which is used in the imaging method for visual laryngoscope photography, comprising:

[0062] An image module, used to obtain multiple images from the video data collected by the visual laryngoscope;

[0063] A judgment module is used to judge whether each image meets the preset conditions. If not, it is marked as a target image;

[0064] A partitioning module is used to divide the target image into multiple area images;

[0065] An image detection module, used to perform edge detection on multiple regional images based on an edge detection method to obtain edge-clear images and edge-fuzzy images, and also used to obtain multiple clear inflection point coordinates in each edge-clear image and multiple fuzzy inflection point coordinates in each edge-fuzzy image based on a rectangular coordinate system;

[0066] An image variation module, used for calculating an image variation value of a target image according to a plurality of clear inflection point coordinates and a plurality of fuzzy inflection point coordinates;

[0067] A shadow detection module is used to perform image shadow detection on multiple area images to obtain multiple shadow information;

[0068] A shadow compensation module, used for calculating a shadow compensation value of a target image of a plurality of area images according to a plurality of shadow information;

[0069] A detection compensation module, used to collect laryngeal detection data of a target user based on a visual laryngoscope, and calculate a detection compensation value based on the laryngeal detection data;

[0070] A brightness module, used to obtain brightness data of the visual laryngoscope corresponding to the target image;

[0071] An imaging level module is used to calculate the illumination imaging value according to the image variation value, the shadow compensation value, the detection compensation value and the brightness data, and obtain the illumination imaging level corresponding to the illumination imaging value;

[0072] The correction module is used to adjust the light intensity when the visual laryngoscope collects video data according to the light imaging level.

[0073] The technical effects achieved by the present invention are:

[0074] The present invention can significantly improve the clarity and recognizability of the image through detection of the target image and correction of the light source, ensuring that medical personnel can diagnose the patient's laryngeal condition more accurately. In the image processing process, the blur and shadow processing of each detection area is refined to make the image correction more accurate, effectively avoiding the distortion problem that may be caused by the overall image processing. Correction can be performed in combination with the laryngeal detection data of the target patient, and optimization processing can be performed on specific laryngeal abnormal areas to improve the image quality of the laryngeal abnormal areas, which is conducive to precision medicine. Processing and correction can be performed in real time during image acquisition to ensure that medical personnel can obtain high-quality images in a timely manner, and the efficiency and effectiveness of clinical operations can be improved. By calculating the light imaging level and performing corresponding corrections, relatively consistent high-quality images can be obtained under different lighting conditions, reducing the impact of light changes on image quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 is a flow chart of the method provided by the present invention;

[0076] Figure 2 It is a system module diagram provided by the present invention. DETAILED DESCRIPTION

[0077] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0078] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0079] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure or characteristic that may be included in at least one implementation of the present invention. The phrase "in a preferred embodiment" that appears in different places in this specification does not refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments.

[0080] Secondly, the present invention is described in detail in conjunction with schematic diagrams. When describing the embodiments of the present invention in detail, for the convenience of explanation, the schematic diagrams are only examples and should not limit the scope of protection of the present invention.

[0081] Please refer to the attached Figure 1 As shown, a method for imaging by visual laryngoscope is provided, comprising:

[0082] S1, obtaining multiple images from video data collected by a visual laryngoscope;

[0083] S2, judging whether each image meets the preset conditions, if not, marking it as a target image;

[0084] S3, dividing the target image to obtain multiple region images;

[0085] S4, performing edge detection on multiple region images based on an edge detection method to obtain edge-clear images and edge-fuzzy images;

[0086] Acquire multiple clear inflection point coordinates in each edge clear image and multiple blurred inflection point coordinates in each edge blurred image based on a rectangular coordinate system;

[0087] S5, calculating an image variation value of the target image according to the multiple clear inflection point coordinates and the multiple fuzzy inflection point coordinates;

[0088] S6, performing image shadow detection on multiple region images to obtain multiple shadow information;

[0089] S7, calculating shadow compensation values ​​of target images of multiple region images according to multiple shadow information;

[0090] S8. Collecting laryngeal detection data of the target user based on the visual laryngoscope, and calculating a detection compensation value according to the laryngeal detection data;

[0091] S9, obtaining brightness data of the visual laryngoscope corresponding to the target image;

[0092] S10, calculating the illumination imaging value according to the image variation value, the shadow compensation value, the detection compensation value and the brightness data, and obtaining the illumination imaging level corresponding to the illumination imaging value;

[0093] S11. Adjust the light intensity when the visual laryngoscope collects video data according to the light imaging level.

[0094] As in the above steps S1 to S11, multiple images are extracted from the video data collected by the visual laryngoscope as a basis for subsequent processing, and it is determined whether each extracted image meets the preset conditions (for example, clarity, brightness, etc.). If not, the image is marked as a target image, indicating that it needs further processing. In the marked target image, multiple image detection areas are divided (which can be rectangular areas of fixed size or other forms of detection areas), and then the regional images in each detection area are obtained respectively, and image blur detection and image clarity detection are performed on each regional image, and multiple blur information and multiple clarity information are extracted. By analyzing the multiple blur information and multiple clarity information, an image variation value of a target image is calculated for quantifying the degree of image blur, and shadow detection is performed on the regional image of each image detection area, and multiple shadow information is extracted. According to the detected multiple shadow information, a shadow compensation value is calculated for subsequent visual laryngoscope correction, based on the throat detection data of the target user collected by the visual laryngoscope, which usually includes information such as the abnormal part or type of the throat, and the detection compensation value is calculated based on these throat abnormality data, which is very important for the correction of a specific throat abnormality area, and the corresponding visual laryngoscope is obtained based on the target image. The brightness data of the laryngoscope is used to analyze the lighting conditions in the image. According to the image variation value, shadow compensation value, detection compensation value and brightness data, a lighting imaging level is calculated comprehensively. This level reflects the imaging quality of the current image. According to the calculated lighting imaging level, the brightness of the light source for collecting video data by the visual laryngoscope is adjusted to improve the overall quality of the image. By detecting the target image and correcting the light source, the clarity and recognizability of the image can be significantly improved, ensuring that medical personnel can diagnose the patient's throat more accurately. In the image processing process, the blur and shadow processing of each detection area is refined to make the image correction more accurate, effectively avoiding the distortion problem that may be caused by the overall image processing. Combined with the target patient's throat detection data for correction, the specific laryngeal abnormal area can be optimized and processed to improve the image quality of the laryngeal abnormal area, which is conducive to precision medicine. It can be processed and corrected in real time during the image acquisition process to ensure that medical personnel can obtain high-quality images in a timely manner and improve the efficiency and effectiveness of clinical operations. By calculating the lighting imaging level and making corresponding corrections, relatively consistent high-quality images can be obtained under different lighting conditions, reducing the impact of lighting changes on image quality.

[0095] In a specific embodiment, the step of obtaining the coordinates of multiple blur inflection points in each edge blurred image includes:

[0096] Obtain the edge contour of the edge blurred image and obtain the corner points of the edge contour;

[0097] The coordinates of the corner points are obtained based on the rectangular coordinate system, and the coordinates of the corner points are used as the coordinates of the fuzzy inflection points.

[0098] In the above steps, the Canny algorithm is first used to detect the edge of the blurred image to obtain clear edge information; then, the Harris corner detection is used to further detect corners on the edge image, and these corners are the desired inflection points. Through the high accuracy of the Canny algorithm in edge positioning and the stability and reliability of the Harris detector in corner recognition, the inflection point information can be quickly determined. Of course, the acquisition of the inflection point of the edge-clear image can also be determined by the Harris corner detection method. At the same time, considering that the edge detection effect of the Canny algorithm blurred image has certain defects, pre-trained CNN models such as VGG16, ResNet and Inception can also be used here. The above models have been trained on large-scale image data sets and have learned common visual features. By extracting local features such as edges and textures at different levels of abstraction, more complex patterns can be recognized. For example, the first layer of the VGG16 model usually extracts simple edge and color information, while the deeper layers gradually extract more complex shape and texture information. This multi-layer feature extraction ensures that the model can automatically learn and represent edge features in complex images. For example, load the VGG16 model in Keras. Then, the input image is properly preprocessed to make it conform to the input format expected by the model. Then, the preprocessed image is input into the model and the feature map is obtained through forward propagation. Finally, the feature output of a specific layer can be selected as needed for further task processing.

[0099] In a specific embodiment, the step of determining whether each image meets the preset condition and marking it as a target image if it does not meet the preset condition comprises:

[0100] S201, obtaining a standard image clarity threshold;

[0101] S202, obtaining a corresponding image clarity value according to the image;

[0102] S203, determining whether the image clarity value exceeds a standard image clarity threshold;

[0103] If the image clarity value does not exceed the standard image clarity threshold, the image corresponding to the value that does not exceed the standard image clarity threshold is determined as the target image.

[0104] As in the above steps S201 to S203, a standard image clarity threshold is set. This threshold is pre-defined based on clinical needs and equipment capabilities, and is used to measure whether the image clarity meets the diagnostic requirements. The image is analyzed by an algorithm to calculate the clarity value of the current image. The image clarity value can usually be achieved through image processing technologies such as edge detection and contrast analysis. The calculated image clarity value is compared with the preset standard image clarity threshold. If the image clarity value exceeds the standard image clarity threshold, the imaging of the camera data is determined to be normal. If the image clarity value does not exceed the standard image clarity threshold, the imaging of the camera data is determined to be abnormal and marked as a target image. By setting the clarity threshold and automatically calculating the image clarity value, it is possible to quickly and accurately determine whether the image quality meets the requirements. This automated processing reduces the subjectivity and errors of human judgment. , the process of automatically judging the target image speeds up work efficiency, allowing doctors to focus more on diagnosis and treatment instead of spending time on image quality checks. Through strict image quality control, ensuring that the images input into the doctor's field of view are always clear is crucial to improving the accuracy and reliability of diagnosis. Clear images can provide more details of laryngeal abnormalities, helping doctors make more accurate diagnoses. Image quality can be monitored in real time and feedback can be provided immediately when abnormalities are found, which helps to adjust equipment parameters or operating methods in time during surgery or examinations to avoid delays in diagnosis and treatment due to image quality problems. By ensuring image quality in the first examination, the need for secondary examinations or operations due to unclear images is reduced, thereby reducing patients' pain and risks. Clear images not only help doctors diagnose, but also make it easier for patients and their families to understand when explaining their condition, improving the quality and efficiency of doctor-patient communication.

[0105] In a specific embodiment, the step of calculating the image variation value of the target image according to the multiple clear inflection point coordinates and the multiple fuzzy inflection point coordinates includes:

[0106] The image variation is calculated based on multiple fuzzy inflection point coordinates and multiple clear inflection point coordinates, where the calculation formula is:

[0107]

[0108] Where T is the image variation value, i is the number of the regional image, n is the total number of regional images, g is the number of the inflection point coordinates of the fuzzy image contour in each regional image, h is the total number of the inflection point coordinates of the fuzzy image contour in each regional image, and M is g It is represented as the x-axis coordinate point of the g-th blurred image contour inflection point, M g+1 It is represented as the x-axis coordinate point of the g+1th blurred image contour inflection point, H g It is represented as the y-axis coordinate point of the g-th blurred image contour inflection point, Hg+1 It is represented by the y-axis coordinate point of the g+1th blurred image contour inflection point, u is the number of the coordinate point of the clear image contour inflection point in each regional image contour, p is the total number of the coordinate points of the clear image contour inflection point in each regional image contour, Q u It is represented as the x-axis coordinate point of the u-th clear image contour inflection point, Q u+1 It is represented as the x-axis coordinate point of the u+1th clear image contour inflection point, X u It is represented as the y-axis coordinate point of the u-th clear image contour inflection point, X u+1 It is expressed as the y-axis coordinate point of the inflection point of the u+1th clear image contour, where when the value of g is h+1, the coordinate point when g=1 is input, and similarly when the value of u is equal to p+1, the coordinate point when u=1 is input.

[0109] As in the above steps S501 to S504, image blur detection is performed on multiple area images, color images are converted into grayscale images, Gaussian blur is applied to reduce noise and details, a Canny edge detector is used to detect image edges, morphological operations (such as dilation and erosion) are used to connect disconnected edges and remove small noise points, a contour detection algorithm (such as OpenCV's findContours) is used to detect possible blurred area contours, features (such as shape, position, etc.) are extracted for each detected area, these features are used in combination with a pre-set threshold or a machine learning model to confirm which areas are truly blurred, a blank image of the same size as the original image is created, the detected blurred areas are drawn on the blank image, the target image is mapped to a standard plane rectangular coordinate system so that images of different areas can be compared and analyzed in the same coordinate system, and the coordinate points of multiple blurred image contour inflection points are obtained according to the mapped plane rectangular coordinate system, and the coordinate points of multiple blurred image contour inflection points are obtained according to the mapped plane rectangular coordinate system, and the coordinate points of the clear image contour inflection points are extracted using an algorithm that is the same as or similar to the blurred image contour extraction algorithm, and the coordinate points of the blurred image contour inflection points reflect the The boundary and shape of the blurred area. Similarly, the coordinate points of the inflection points of the clear image contour reflect the boundary and shape of the clear area. According to the coordinate points of the inflection points of the blurred image contour and the clear image contour, the image variation value is calculated. In order to facilitate the calculation method of the formula, for example, there are two area image data, that is, n=2, and the coordinate points of the inflection points of the blurred image contour of the first area are 3, namely (M1, H1)=(0,0), (M2, H2)=(0,1), (M3, H3)=(1,0), that is, h=3 for the first area, and the coordinate points of the inflection points of the clear image contour of the first area There are 3 points, namely (Q1, X1) = (0, 0), (Q2, X2) = (0, 1), (Q3, X3) = (-1, 0), that is, p = 3 in the first area, and the coordinate points of the inflection points of the blurred image contour in the second area are (M1, H1) = (0, 0), (M2, H2) = (0, -1), (M3, H3) = (-1, 0), that is, h = 3 in the second area, and the coordinate points of the inflection points of the clear image contour in the second area are (Q1, X1) = (0, 0), (Q2, X2) = (0, -2), (Q3, X3) = (1,0), that is, p=3 in the first region, and is brought into the calculation. The numerator is {1 / 2×|(0×1-0×0+0×0-1×1+1×0-0×0)|+1 / 2×|(0×(-1)-0×0+0×0-(-1)×(-1)+(-1)×0-0×0)|}×{1 / 2×|(0×1-0×0+0×0-(-1)×1+(-1)×0-0×0)|+1 / 2×|(0×(-2)-0×0+0×0-1×(-2)+1×0-0×0)|}=3 / 2, and the denominator is √{[1 / 2×|(0×1-0×0+0×0-1×1+1×0-0×0)|], 2 +[1 / 2×|(0×(-1)-0×0+0×0-(-1)×(-1)+(-1)×0-0×0)|] 2}×√{[1 / 2×|(0×1-0×0+0×0-(-1)×1+(-1)×0-0×0)|] 2 +[1 / 2×|(0×(-2)-0×0+0×0-1×(-2)+1×0-0×0)|] 2}=√10 / 4, the final result is T=(3 / 2) / (√10 / 4)≈1.8974. The image variation value is used to quantify the blurriness of the image. By obtaining the coordinate points of the contour inflection points of the blurred and clear images and calculating the image variation value, the blurriness of the image can be accurately quantified. A plane rectangular coordinate system is constructed and the image is processed in different regions, making the image processing more refined. The image clarity can be significantly improved, providing doctors with clearer laryngeal images to assist in diagnosis and treatment. The image quality can be monitored in real time to ensure that high-quality images are always obtained during surgery or examination. Clear images help doctors accurately identify and analyze laryngeal abnormalities, reduce the risk of misdiagnosis, and improve the accuracy and reliability of diagnosis and treatment.

[0110] In a specific embodiment, the step of calculating the shadow compensation value of the target image of the multiple region images according to the multiple shadow information includes:

[0111] S701, constructing a plane rectangular coordinate system according to the target image;

[0112] S702, acquiring coordinate points of corresponding inflection points of multiple shadow image contours in a plane rectangular coordinate system according to multiple shadow information;

[0113] S703, calculating the shadow compensation value according to the coordinate points of the inflection points of the multiple shadow image contours, wherein the calculation formula is:

[0114]

[0115] Where A is the shadow compensation value, i is the number of the regional image, n is the total number of regional images, d is the number of the inflection point coordinates of the shadow image contour in each regional image, f is the total number of the inflection point coordinates of the shadow image contour in each regional image, S d It is represented by the x-axis coordinate point of the d-th shadow image contour inflection point, S d+1 It is represented as the x-axis coordinate point of the d+1th shadow image contour inflection point, W d It is represented as the y-axis coordinate point of the d-th shadow image contour inflection point, W d+1 It is represented as the y-axis coordinate point of the inflection point of the d+1th shadow image contour, where when d is f+1, the coordinate point when d=1 is input.

[0116] As in the above steps S701 to S703, image shadow detection is performed on multiple area images, the color image is converted into a grayscale image, Gaussian blur is applied to reduce noise and details, histogram equalization and other methods can be used to enhance image contrast and highlight shadow areas, a Canny edge detector is used to detect image edges, morphological operations (such as dilation and erosion) are used to connect disconnected edges and remove small noise points, a contour detection algorithm (such as OpenCV's findContours) is used to detect possible shadow area contours, features (such as shape, position, etc.) are extracted for each detected area, these features are used in combination with a pre-set threshold or a machine learning model to confirm which areas are true shadows, a blank image of the same size as the original image is created, the detected shadow area is drawn on the blank image, the target image is mapped to a standard plane rectangular coordinate system, so that images of different areas can be compared and analyzed in the same coordinate system, and the coordinates of multiple shadow image contour inflection points are obtained according to the mapped plane rectangular coordinate system (the inflection points are obtained in the same way as the above steps of obtaining the coordinates of multiple blurred inflection points in each edge blurred image, in This will not be described in detail). These coordinate points reflect the boundary and shape of the shadow area. According to the coordinate points of the inflection points of the multiple shadow image contours, the shadow compensation value is calculated. The shadow compensation value is used to measure and compensate for the degree of shadow in the image to improve the overall brightness and contrast of the image. By obtaining the coordinate points of the inflection points of the shadow image contour and calculating the shadow compensation value, in order to facilitate the explanation of the calculation method of the formula, for example, there are two area image data, that is, n=2, and the coordinate points of the inflection points of the shadow image contour of the first area are 3, namely (S1, W1)=(0,0), (S2, W2)=(0, 1), (S3, W3) = (1, 0), that is, f = 3 in the first area, the coordinate points of the inflection points of the shadow image contour in the second area are (S1, W1) = (0, 0), (S2, W2) = (0, -1), (S3, W3) = (-1, 0), that is, f = 3 in the second area, put it into the calculation, A = 1 / 2 × {1 / 2 × | (0 × 1-0 × 0 + 0 × 0-1 × 1 + 1 × 0-0 × 0) | + 1 / 2 × | (0 × (-1)-0 × 0 + 0 × 0-(-1) × (-1) + (-1) × 0-0 × 0) |} = 0.5. It can accurately quantify the degree of shadows in the image, construct a plane rectangular coordinate system and process the image by region, making the image processing more refined, and can perform targeted corrections on local shadow areas, improving the overall image quality. By accurately calculating and compensating for shadow areas, it can significantly improve the brightness and contrast of the image, providing doctors with clearer laryngeal images, helping diagnosis and treatment. It can monitor the shadow situation in the image in real time, and immediately process and compensate for the shadow area after it is found, ensuring that high-quality images are always obtained during surgery or examination, reducing the impact of shadows on image quality, helping doctors to accurately identify and analyze laryngeal abnormalities, reduce the risk of misdiagnosis, and improve the accuracy and reliability of diagnosis and treatment. .

[0117] In a specific embodiment, the step of collecting the target user's laryngeal detection data based on the visual laryngoscope and calculating the detection compensation value according to the laryngeal detection data includes:

[0118] S801, collecting throat detection data of the target user based on the visual laryngoscope;

[0119] S802, acquiring corresponding multiple historical laryngeal detection values ​​and current laryngeal detection values ​​according to the laryngeal detection data;

[0120] S803, calculating a detection compensation value according to a plurality of historical throat detection values ​​and a current throat detection value, wherein the calculation formula is:

[0121]

[0122] Where B is the detection compensation value, r is the total number of historical throat detection values, t is the number of historical throat detection values, and K is t Represented as the tth historical throat detection value, K d It represents the current laryngeal detection value;

[0123] Wherein, t=0, 1…r, when r is 0, it indicates that the historical laryngeal detection value of the target patient is not obtained.

[0124] As in the above steps S801 to S803, the target patient's laryngeal detection data is extracted from the video data collected by the visual laryngoscope. These data may come from electronic medical records, historical examination records, etc. According to the extracted laryngeal detection data, a corresponding plurality of laryngeal detection values ​​are obtained. These detection values ​​include a plurality of historical laryngeal detection values ​​(such as previous detection data, etc.) and current laryngeal detection values ​​(detection data during the current examination, etc.). According to the plurality of historical laryngeal detection values ​​and the current laryngeal detection value, a detection compensation value is calculated. This compensation value is used to adjust the image to compensate for the image quality problem caused by laryngeal abnormality. By using the target patient's laryngeal detection data for compensation, it is possible to Image adjustment is performed according to the specific situation of the individual, which improves the pertinence and accuracy of the image. Combined with historical test data and current test data, comprehensive analysis and calculation of test compensation values ​​make the compensation more comprehensive and accurate. Through the calculation and application of test compensation values, the image quality problems caused by laryngeal abnormalities can be significantly improved, ensuring that doctors obtain clearer and more accurate images during observation and diagnosis. By improving the clarity and accuracy of the image, misdiagnosis or missed diagnosis caused by image quality problems is reduced, and the accuracy and reliability of diagnosis are improved. The image quality can be dynamically adjusted during real-time inspections, and compensation is performed according to the latest test data to ensure that the best image effect is always obtained.

[0125] In a specific embodiment, the step of calculating the illumination imaging level according to the image variation value, the shadow compensation value, the detection compensation value and the brightness data includes:

[0126] S1001, calculating the illumination imaging value according to the image variation value, the shadow compensation value, the detection compensation value and the brightness data;

[0127] S1002, obtaining a light imaging table, wherein the light imaging table includes a plurality of light imaging interval values ​​and a light imaging level corresponding to each light imaging interval value;

[0128] S1003, obtaining a target illumination imaging interval value according to the illumination imaging value;

[0129] S1004. Obtain the illumination imaging level from the illumination imaging table according to the target illumination imaging interval value.

[0130] As in the above steps S1001 to S1004, a lighting imaging value is comprehensively calculated based on the image variation value, shadow compensation value, detection compensation value and brightness data. This value reflects the lighting quality of the current image. A lighting imaging table is prepared, which includes multiple lighting imaging interval values ​​and lighting imaging levels corresponding to each interval value. These interval values ​​and levels are pre-set based on a large number of experiments and clinical experience. According to the calculated lighting imaging value, the corresponding lighting imaging interval value is found in the lighting imaging table. This interval value represents the range to which the current lighting imaging value belongs. According to the target lighting imaging interval value, the corresponding lighting imaging level is obtained from the lighting imaging table. This level is used to evaluate the lighting quality of the current image and guide subsequent image adjustments. By comprehensively considering the image variation value, shadow compensation value, detection compensation value, The compensation value and light brightness data are measured, and the calculated light imaging value can fully reflect the light quality of the image, making the evaluation more accurate. The light imaging table provides multiple light imaging interval values ​​and corresponding levels, making the light quality evaluation more accurate and standardized, which is helpful to standardize image quality control. The light imaging value is calculated in real time and the corresponding light imaging level is obtained. It can quickly respond to and adjust the light conditions of the image to ensure that the best image effect is always obtained during surgery or examination. By accurately evaluating and adjusting the light imaging level, the brightness and contrast of the image can be significantly improved, and the clarity and readability of the image can be improved, providing doctors with better quality images. High-quality images can help doctors observe and diagnose laryngeal abnormalities more accurately, improve the accuracy of diagnosis and the effectiveness of treatment, and ultimately enhance the patient's treatment experience and effect.

[0131] In a specific embodiment, the step of calculating the illumination imaging value according to the image variation value, the shadow compensation value, the detection compensation value and the brightness data includes:

[0132] S10011. Obtain corresponding brightness value according to brightness data;

[0133] S10012. Calculate the illumination imaging value according to the image variation value, the shadow compensation value, the detection compensation value and the brightness value, wherein the calculation formula is:

[0134] C = T × A × B × L;

[0135] In the formula, C represents the illumination imaging value, T represents the image variation value, A represents the shadow compensation value, B represents the detection compensation value, and L represents the brightness value.

[0136] As in the above steps S10011 to S10012, brightness data is obtained from the camera data. These data may be measured and calculated by a sensor or an image processing algorithm to reflect the light intensity in the image. The light imaging value C is calculated according to the following formula: C=T×A×B×L. These values ​​are multiplied in the formula to obtain the light imaging value C, which can comprehensively evaluate the light quality of the image, including image clarity, shadow compensation effect, laryngeal abnormality correction effect and overall light intensity. By comprehensively considering the image variation value, shadow compensation value, detection compensation value and brightness data, the calculated light imaging value can comprehensively evaluate the light quality of the image and determine Ensure that doctors obtain clear and accurate image information. The calculation of illumination imaging values ​​allows precise adjustment of images according to actual conditions, including compensation for shadows, abnormal laryngeal areas and adjustment of illumination intensity, thereby optimizing image observation and analysis effects. By setting illumination imaging tables and formulas, the illumination management process and evaluation criteria are standardized, which helps to improve the consistency and comparability of medical images. Real-time calculation of illumination imaging values ​​allows for instant adjustments while acquiring images, so as to maximize image quality and the diagnostic effect of medical images. High-quality images can provide more accurate information on laryngeal abnormalities, helping doctors reduce misdiagnosis and missed diagnosis, and improve diagnostic accuracy and treatment effects.

[0137] In a specific embodiment, after the step of calculating the illumination imaging value according to the image variation value, the shadow compensation value, the detection compensation value and the brightness value, the step further includes:

[0138] S10013. Obtaining the light imaging risk threshold;

[0139] S10014. Establish a risk monitoring cycle;

[0140] S10015. Obtain the number of times that the light imaging value exceeds the light imaging risk threshold value during the risk monitoring period, and mark it as the number of times that the light imaging value exceeds the risk threshold value;

[0141] S10016. Get the risk threshold number;

[0142] S10017. Determine whether the number of times the risk exceeds the risk threshold number;

[0143] If the number of times exceeding the risk threshold exceeds the number of times exceeding the risk threshold, the imaging of the visual laryngoscope is judged to be abnormal, and a warning message is issued;

[0144] If the number of times the risk is exceeded does not exceed the risk threshold number, the imaging of the video laryngoscope is determined to be normal.

[0145] As in the above steps S10013 to S10017, in actual applications, based on clinical experience and technical specifications, a risk threshold for light imaging is set. This threshold can be based on historical data analysis or expert evaluation and is used to determine the degree of abnormality in image illumination. A risk detection cycle is set, which is usually performed within a certain time or number of operations. This cycle can be adjusted according to specific needs to ensure timely monitoring of light imaging quality. In each risk monitoring cycle, light imaging values ​​are obtained, and the number of times the preset light imaging risk threshold is exceeded is counted. These exceeding times reflect the abnormality of light quality. The risk threshold number is determined, which is a preset threshold used to determine whether light imaging is abnormal. It is usually set according to medical standards and clinical practice. The number of times the light imaging risk threshold is exceeded is compared with the preset risk threshold number. According to the judgment result, if the risk number is exceeded, the risk threshold number is determined. If the number does not reach or equal to the risk threshold, the illumination imaging is considered normal. On the contrary, if the number exceeds the risk threshold, it is considered as abnormal imaging and a warning message is issued in time so that medical staff can take further measures or adjustments, or the visual laryngoscope camera has an abnormality and needs to be repaired. By setting the risk threshold and detection cycle, real-time monitoring and evaluation of the illumination imaging quality is achieved. This real-time nature can help medical staff discover and deal with abnormal illumination imaging in the first place, reduce misdiagnosis or delays caused by image quality problems, and set up a warning information system to promptly notify relevant personnel when illumination imaging is abnormal, prompting them to take appropriate measures, such as readjusting equipment, re-collecting images or further inspections. By counting and analyzing situations that exceed the threshold, quality control evaluation can be performed, and imaging equipment and operating procedures can be continuously improved to optimize illumination imaging quality and clinical application effects.

[0146] Please refer to the attached Figure 2 As shown, the present invention also provides an imaging system for visual laryngoscope photography, which is used in the imaging method for visual laryngoscope photography, comprising:

[0147] An image module, used to obtain multiple images from the video data collected by the visual laryngoscope;

[0148] A judgment module is used to judge whether each image meets the preset conditions. If not, it is marked as a target image;

[0149] A partitioning module is used to divide the target image into multiple image detection areas and obtain regional images within the multiple image detection areas;

[0150] An image detection module is used to perform image blur detection and image clarity detection on multiple area images to obtain multiple blur information and multiple clarity information;

[0151] An image variation module, used for calculating an image variation value of a target image according to a plurality of fuzzy information and a plurality of clear information;

[0152] A shadow detection module is used to perform image shadow detection on multiple area images to obtain multiple shadow information;

[0153] A shadow compensation module, used for calculating a shadow compensation value of a target image of a plurality of area images according to a plurality of shadow information;

[0154] A detection compensation module, used to collect laryngeal detection data of the target user based on the visual laryngoscope, and calculate a detection compensation value based on the laryngeal detection data;

[0155] A brightness module, used to obtain brightness data of the visual laryngoscope corresponding to the target image;

[0156] An imaging grade module is used to calculate the illumination imaging grade according to the image variation value, the shadow compensation value, the detection compensation value and the brightness data;

[0157] The correction module is used to adjust the light intensity when the visual laryngoscope collects video data according to the light imaging level.

[0158] As mentioned above, the image module is responsible for extracting multiple images from the video data collected by the visual laryngoscope, the judgment module analyzes each extracted image to determine whether it meets the preset image clarity and other quality conditions, and marks the image that does not meet the conditions as the target image, the partition module divides the target image into multiple image detection areas, and obtains the regional image of each area, the image detection module performs image blur detection and image clarity detection on each regional image, extracts multiple blur information and multiple clear information, the image variation module analyzes the blur information and clear information in each regional image, calculates the image variation value of the target image, and reflects the change in clarity in the image, the shadow detection module performs image shadow detection on each regional image, extracts multiple shadow information, the shadow compensation module analyzes the shadow information in each regional image, and calculates the shadow compensation value, the detection compensation module obtains the target patient's laryngeal detection data, analyzes and calculates the detection compensation value, which is used to adjust and correct the performance of abnormal laryngeal parts in the image, the brightness module evaluates the overall light intensity in the image according to the brightness data obtained by the visual laryngoscope corresponding to the target image, and the imaging level module integrates the image variation value, shadow compensation value, detection compensation value and brightness data, or regional illumination imaging level The correction module makes necessary corrections and adjustments to the illumination intensity of the video data collected by the visual laryngoscope according to the calculated illumination imaging level, ensuring that the system can obtain real-time images that need to be analyzed and processed, and providing input for subsequent modules. Through automated judgment, it can quickly identify and process image quality abnormalities, improve the stability and reliability of the system, allow the system to perform more refined analysis and processing on specific areas, improve the ability of subsequent modules to identify and correct local problems, help the system identify and measure blur in the image, and provide important data support for subsequent laryngeal abnormality and illumination quality assessment, optimize the visual clarity and readability of the image, eliminate or reduce the interference of shadows on image diagnosis, and improve diagnostic accuracy. The image is adjusted according to the individualized laryngeal abnormality of the patient, so that the doctor can observe and diagnose laryngeal abnormalities more accurately, improve the quality of medical services, provide quantitative analysis of illumination quality, provide basic data for subsequent illumination imaging level assessment, ensure the visual effect and diagnostic accuracy of the image, provide accurate illumination quality feedback to doctors through standardized evaluation methods, help optimize images and improve diagnostic accuracy, ensure that doctors obtain the best visual image effect during real-time observation and diagnosis, and improve the quality and efficiency of medical services.

[0159] The above is only a preferred embodiment of the present invention. It should be noted that, for those skilled in the art, several improvements and modifications can be made without departing from the principles of the present invention, and these improvements and modifications should also be considered as the protection scope of the present invention. The structures, devices and operating methods not specifically described and explained in the present invention shall be implemented according to the conventional means in the art unless otherwise specified and limited.

Claims

1. An imaging method for visual laryngoscope photography, characterized in that: include: Acquire multiple images from video data collected by a video laryngoscope; Determine whether each image meets the preset conditions. If not, mark it as a target image; Divide the target image to obtain multiple regional images; Perform edge detection on multiple region images based on edge detection method to obtain edge clear images and edge fuzzy images; Acquire multiple clear inflection point coordinates in each edge clear image and multiple blurred inflection point coordinates in each edge blurred image based on a rectangular coordinate system; Calculate the image variation value of the target image according to the multiple clear inflection point coordinates and the multiple fuzzy inflection point coordinates; Performing image shadow detection on multiple region images to obtain multiple shadow information; Calculating shadow compensation values ​​of target images of multiple region images according to multiple shadow information; Based on the visual laryngoscope, the laryngeal detection data of the target user is collected, the laryngeal detection data includes information on the abnormal position or type of the laryngeal part, and the detection compensation value is calculated according to the laryngeal detection data; Acquire the brightness data of the visual laryngoscope corresponding to the target image; Calculate the illumination imaging value according to the image variation value, the shadow compensation value, the detection compensation value and the brightness data, and obtain the illumination imaging level corresponding to the illumination imaging value; Adjust the light intensity when the video laryngoscope collects video data according to the light imaging level; In the step of calculating the image variation value of the target image according to the multiple clear inflection point coordinates and the multiple fuzzy inflection point coordinates: The calculation formula is: ； Where T represents the image variation value, i represents the number of the regional image, n represents the total number of regional images, g represents the number of the inflection point coordinates of the fuzzy image contour in each regional image, h represents the total number of the inflection point coordinates of the fuzzy image contour in each regional image, It is represented as the x-axis coordinate point of the g-th blurred image contour inflection point, It is represented as the x-axis coordinate point of the g+1th blurred image contour inflection point, It is represented as the y-axis coordinate point of the g-th blurred image contour inflection point, It is represented as the y-axis coordinate point of the g+1th blurred image contour inflection point, u is represented as the number of the coordinate point of the clear image contour inflection point in each regional image contour, and p is represented as the total number of the coordinate points of the clear image contour inflection point in each regional image contour. It is represented as the x-axis coordinate point of the u-th clear image contour inflection point, It is represented as the x-axis coordinate point of the u+1th clear image contour inflection point, It is represented as the y-axis coordinate point of the inflection point of the u-th clear image contour, It is expressed as the y-axis coordinate point of the u+1th clear image contour inflection point.

2. The imaging method of video laryngoscope according to claim 1, characterized in that: The step of obtaining the coordinates of multiple blur inflection points in each edge blurred image comprises: Obtain the edge contour of the edge blurred image and obtain the corner points of the edge contour; The coordinates of the corner points are obtained based on the rectangular coordinate system, and the coordinates of the corner points are used as the coordinates of the fuzzy inflection points.

3. The imaging method of video laryngoscope according to claim 1, characterized in that: The step of judging whether each image meets the preset conditions and marking it as a target image if it does not meet the preset conditions comprises: Get the standard image clarity threshold; Obtaining a corresponding image clarity value according to the image; Determine whether the image clarity value exceeds a standard image clarity threshold; If the image clarity value does not exceed the standard image clarity threshold, the image corresponding to the value that does not exceed the standard image clarity threshold is determined as the target image.

4. The imaging method of video laryngoscope according to claim 1, characterized in that: The step of calculating the shadow compensation value of the target image of the multiple region images according to the multiple shadow information comprises: Construct a plane rectangular coordinate system according to the target image; Acquire coordinate points of corresponding multiple shadow image contour inflection points in a plane rectangular coordinate system according to multiple shadow information; The shadow compensation value is calculated according to the coordinate points of the inflection points of the multiple shadow image contours, wherein the calculation formula is: ； Where A represents the shadow compensation value, i represents the number of the regional image, n represents the total number of regional images, d represents the number of the inflection point coordinate points of the shadow image contour in each regional image, and f represents the total number of the inflection point coordinate points of the shadow image contour in each regional image. It is represented as the x-axis coordinate point of the d-th shadow image contour inflection point, It is represented as the x-axis coordinate point of the d+1th shadow image contour inflection point, It is represented as the y-axis coordinate point of the inflection point of the d-th shadow image contour. Represented as the y-axis coordinate point of the d+1th shadow image contour inflection point.

5. The imaging method of video laryngoscope according to claim 1, characterized in that: The step of collecting the target user's laryngeal detection data based on the visual laryngoscope and calculating the detection compensation value according to the laryngeal detection data includes: Collecting target user's laryngeal detection data based on visual laryngoscope; Acquire corresponding multiple historical laryngeal detection values ​​and current laryngeal detection values ​​according to the laryngeal detection data; The detection compensation value is calculated according to a plurality of historical throat detection values ​​and a current throat detection value, wherein the calculation formula is: Where B represents the detection compensation value, r represents the total number of historical throat detection values, and t represents the number of historical throat detection values. It is represented as the t-th historical throat detection value, Indicates the current throat detection value.

6. The imaging method of video laryngoscope according to claim 1, characterized in that: The step of calculating the illumination imaging level according to the image variation value, the shadow compensation value, the detection compensation value and the brightness data comprises: Calculate the illumination imaging value according to the image variation value, the shadow compensation value, the detection compensation value and the brightness data; Obtaining a lighting imaging table, wherein the lighting imaging table includes a plurality of lighting imaging interval values ​​and a lighting imaging level corresponding to each lighting imaging interval value; Obtaining a target illumination imaging interval value according to the illumination imaging value; The illumination imaging level is obtained from the illumination imaging table according to the target illumination imaging interval value.

7. The imaging method of video laryngoscope according to claim 6, characterized in that: The step of calculating the illumination imaging value according to the image variation value, the shadow compensation value, the detection compensation value and the brightness data comprises: Obtain corresponding brightness value according to brightness data; The illumination imaging value is calculated according to the image variation value, the shadow compensation value, the detection compensation value and the brightness value, wherein the calculation formula is: C = T × A × B × L; In the formula, C represents the illumination imaging value, T represents the image variation value, A represents the shadow compensation value, B represents the detection compensation value, and L represents the brightness value.

8. The imaging method of video laryngoscope according to claim 6, characterized in that: After the step of calculating the illumination imaging value according to the image variation value, the shadow compensation value, the detection compensation value and the brightness value, the method further includes: Obtaining the light imaging risk threshold; Establish a risk monitoring cycle; The number of times the light imaging value exceeds the light imaging risk threshold is obtained within the risk detection cycle, and marked as the number of times exceeding the risk; Get the risk threshold times; Determine whether the number of times exceeding the risk exceeds the risk threshold number; If the number of times the risk is exceeded exceeds the risk threshold, the imaging of the visual laryngoscope is judged to be abnormal and a warning message is issued; If the number of times the risk is exceeded does not exceed the risk threshold number, the imaging of the video laryngoscope is determined to be normal.

9. An imaging system for video laryngoscope photography, applied to the imaging method for video laryngoscope photography according to any one of claims 1 to 8, characterized in that: include: An image module, used to obtain multiple images from the video data collected by the visual laryngoscope; A judgment module is used to judge whether each image meets the preset conditions. If not, it is marked as a target image; A partitioning module is used to divide the target image into multiple area images; An image detection module, used to perform edge detection on multiple regional images based on an edge detection method to obtain edge-clear images and edge-blurred images, and also used to obtain multiple clear inflection point coordinates in each edge-clear image and multiple fuzzy inflection point coordinates in each edge-blurred image based on a rectangular coordinate system; An image variation module, used for calculating an image variation value of a target image according to a plurality of clear inflection point coordinates and a plurality of fuzzy inflection point coordinates; A shadow detection module is used to perform image shadow detection on multiple area images to obtain multiple shadow information; A shadow compensation module, used for calculating a shadow compensation value of a target image of a plurality of area images according to a plurality of shadow information; A detection compensation module, used to collect laryngeal detection data of a target user based on a visual laryngoscope, and calculate a detection compensation value based on the laryngeal detection data; A brightness module, used to obtain brightness data of the visual laryngoscope corresponding to the target image; An imaging level module is used to calculate the illumination imaging value according to the image variation value, the shadow compensation value, the detection compensation value and the brightness data, and obtain the illumination imaging level corresponding to the illumination imaging value; The correction module is used to adjust the light intensity when the visual laryngoscope collects video data according to the light imaging level.

Images