Gastrointestinal surgical instrument image enhancement optimization method

Abstract

The invention relates to the technical field of image data processing, in particular to a gastrointestinal surgical instrument image enhancement optimization method, which comprises the following steps of: S1, analyzing a picture jitter degree so as to adjust picture parameters to adapt to abnormal picture jitter; s2, analyzing the movement complexity of the surgical instrument in the picture, and adjusting picture parameters to adapt to the movement of the complicated instrument; s3, analyzing a fuzzy degree index of the current frame of the surgical instrument image to judge whether the current frame of the surgical instrument image is fuzzy or not; s4, if the image is fuzzy, comprehensively analyzing the image jitter degree, the instrument movement complexity and the fuzzy degree index of the current frame of surgical instrument image to evaluate the image adjustment efficiency; s5, feedback reminding is carried out on picture adjustment according to the picture adjustment efficiency, and enhancement optimization adjustment is synchronously carried out on the gastrointestinal surgical instrument image. The gastrointestinal surgical instrument image enhancement optimization method provided by the invention can effectively improve the quality of the surgical image and adapt to a complex surgical environment.

Description

Technical Field

[0001] The present invention relates to the technical field of image data processing, and specifically to an image enhancement and optimization method for gastrointestinal surgical instruments. Background Art

[0002] With the rapid development of minimally invasive surgery, endoscopic technology, artificial intelligence, and telemedicine, the medical field's demand for high-quality images is becoming increasingly urgent. Through image enhancement and optimization, the quality of surgical images can be improved, providing doctors with clearer and more reliable visual information, thereby enhancing the safety, efficiency, and intelligence level of surgeries.

[0003] For example, the invention patent with the publication number: CN118037587A is a method for laparoscopic image enhancement and optimization. The steps include: S1. In the initialization stage of the mountain gazelle optimization algorithm, initialize the algorithm population through the good point set strategy, and improve the dolphin position update strategy in the initialization stage of the algorithm; S2. Improve the search performance control factor a of the mountain gazelle optimization algorithm through a segmented method, and improve the mathematical model of the search performance parameter Cofi of the algorithm; S3. Introduce a "changing convergence" mechanism during the convergence process of the mountain gazelle optimization algorithm to improve the optimization mechanism of the mountain gazelle optimization algorithm; S4. Use the improved mountain gazelle optimization algorithm to tune the correction coefficient α of the MSRCR algorithm to establish an IMGO-MSRCR algorithm model; S5. Input the significant point set of the abdominal cavity image into the IMGO-MSRCR algorithm model, and perform contrast enhancement processing on the abdominal cavity image according to the information of the significant points.

[0004] For example, the invention patent with the announcement number: CN107274375B is an image enhancement method applying Gaussian reverse harmony search. The Gaussian reverse harmony search algorithm is used to optimize the α and β parameters of the incomplete Beta function, and then the optimized incomplete Beta function is used to perform a non-linear transformation on the image to enhance the image quality. In Gaussian reverse harmony search, the mean information of the harmony library is fused into the Gaussian mutation operator, and the reverse learning operation is performed with a certain probability to accelerate the convergence speed of the algorithm and improve the effect of image enhancement.

[0005] However, in the process of implementing the inventive technical solution in the embodiments of the present application, it is found that the above technologies have at least the following technical problems: During the surgical process, the acquired images often suffer from motion blur due to the displacement of the instruments and the shaking of the image acquisition device, which affects the image quality. Traditional image enhancement methods are all based on the image itself in a specific mode to perform image enhancement, and have little effect when dealing with motion-blurred images in such complex surgical environments. Summary of the Invention

[0006] In view of the deficiencies of the prior art, the present invention provides an image enhancement and optimization method for gastrointestinal surgical instruments, which can effectively solve the problems involved in the above-mentioned background technology.

[0007] To achieve the above objectives, the present invention is realized through the following technical solutions: The present invention provides an image enhancement and optimization method for gastrointestinal surgical instruments, including: S1. Obtain the basic information of consecutive frame surgical images, analyze the degree of image jitter, and accordingly adjust the image parameters to adapt to abnormal image jitter.

[0008] S2. Obtain the instrument movement information in consecutive frame surgical images, analyze the complexity of the movement of surgical instruments in the images, and accordingly adjust the image parameters to adapt to complex instrument movement.

[0009] S3. After adjusting the image parameters, obtain the basic parameters of the current frame surgical instrument image, analyze the blurriness index of the current frame surgical instrument image, and thereby determine whether the current frame surgical instrument image is blurred.

[0010] S4. If it is not blurred, continue to control the display of the gastrointestinal surgical instrument image with the current image parameters; if it is blurred, comprehensively analyze the degree of image jitter, the complexity of instrument movement, and the blurriness index of the current frame surgical instrument image to evaluate the effectiveness of image adjustment.

[0011] S5. Provide feedback and reminder for image adjustment according to the effectiveness of image adjustment, and simultaneously perform enhancement and optimization adjustment on the gastrointestinal surgical instrument image.

[0012] As a further method, the analysis of the degree of image jitter is specifically as follows: The basic information of the consecutive frame surgical images includes: inter-frame difference, optical flow, and corner displacement.

[0013] Extract the extreme value of inter-frame difference, the extreme value of optical flow, and the extreme value of corner displacement from the surgical image information library, compare the inter-frame difference, optical flow, and corner displacement with the extreme value of inter-frame difference, the extreme value of optical flow, and the extreme value of corner displacement respectively, and perform coupling processing after setting corresponding weight factors to obtain the image jitter degree characteristic value.

[0014] Judge whether image adjustment is required according to the image jitter degree characteristic value.

[0015] As a further method, the process of judging whether image adjustment is required according to the image jitter degree characteristic value is specifically as follows: Extract the image jitter abnormality degree verification value from the surgical image information library, compare the image jitter degree characteristic value with the image jitter abnormality degree verification value. If the image jitter degree characteristic value is lower than or equal to the image jitter abnormality degree verification value, mark the image jitter as having no impact on the image; if the image jitter degree characteristic value is higher than the image jitter abnormality degree verification value, adjust the image parameters to adapt to abnormal image jitter.

[0016] As a further method, the process of adjusting the screen parameters to adapt to abnormal screen jitter is as follows: Mark the difference between the screen jitter degree characteristic value and the screen jitter abnormality degree verification value as the expected required screen jitter adjustment parameter. Extract the required reduced frame rate corresponding to the expected required screen jitter adjustment parameter from the surgical image information library. Set the difference between the initial acquisition frame rate and the required reduced frame rate as the current acquisition frame rate. After replacing the image acquisition frame rate from the initial acquisition frame rate with the current acquisition frame rate, perform image acquisition to adapt to abnormal screen jitter.

[0017] As a further method, the process of analyzing the movement complexity of surgical instruments in the screen is as follows: The instrument movement information in the continuous frame surgical screen includes: displacement amplitude, displacement direction change rate, motion trajectory curvature, and motion speed change rate.

[0018] Extract the displacement amplitude limit value, displacement direction change rate limit value, motion trajectory curvature limit value, and motion speed change rate limit value from the surgical image information library. Compare the displacement amplitude, displacement direction change rate, motion trajectory curvature, and motion speed change rate of each instrument with the corresponding limit values, and perform coupling processing after setting weight factors to obtain the instrument movement complexity characteristic value.

[0019] Judge whether screen adjustment is required according to the instrument movement complexity characteristic value.

[0020] As a further method, the process of judging whether screen adjustment is required according to the instrument movement complexity characteristic value is as follows: Extract the instrument movement complexity verification value from the surgical image information library. Compare the instrument movement complexity characteristic value with the instrument movement complexity verification value. If the instrument movement complexity characteristic value is lower than or equal to the instrument movement complexity verification value, mark the instrument movement as having no impact on instrument movement. If the instrument movement complexity characteristic value is higher than the instrument movement complexity verification value, adjust the screen parameters to adapt to complex instrument movement.

[0021] As a further method, the process of adjusting the screen parameters to adapt to complex instrument movement is as follows: Mark the difference between the instrument movement complexity characteristic value and the instrument movement complexity verification value as the expected required instrument movement adjustment parameter. Extract the contrast reference value and noise reference value corresponding to the expected required instrument movement adjustment parameter from the surgical image information library. Call the image enhancement algorithm to increase the initial contrast of the image to be equal to the contrast reference value, and at the same time reduce the initial noise of the image to be equal to the noise reference value to adapt to complex instrument movement.

[0022] As a further method, analyze the index of the blurring degree of the surgical instrument image in the current frame, and thereby determine whether the surgical instrument image in the current frame is blurred. The specific analysis process is as follows: The basic parameters of the surgical instrument image in the current frame include: the low-high frequency region energy ratio, the gradient amplitude, and the edge strength of the surgical instrument image in the current frame.

[0023] Equalize the low-high frequency region energy ratio, the gradient amplitude, and the edge strength of the consecutive frame surgical images respectively to obtain the average index of the low-high frequency region energy ratio, the average index of the gradient amplitude, and the average index of the edge strength of the consecutive frame surgical images.

[0024] Compare the low-high frequency region energy ratio, the gradient amplitude, and the edge strength of the surgical instrument image in the current frame with the average index of the low-high frequency region energy ratio, the average index of the gradient amplitude, and the average index of the edge strength of the consecutive frame surgical images respectively, and perform coupling processing after setting weight factors respectively to obtain the index of the blurring degree of the surgical instrument image in the current frame.

[0025] Extract the image blurring reference index from the surgical image information database, compare the index of the blurring degree of the surgical instrument image in the current frame with the image blurring reference index. If the index of the blurring degree of the surgical instrument image in the current frame is lower than the image blurring reference index, determine that the surgical instrument image in the current frame is not blurred and continue to control the display of the gastrointestinal surgical instrument image with the current screen parameters. If the index of the blurring degree of the surgical instrument image in the current frame is higher than or equal to the image blurring reference index, determine that the surgical instrument image in the current frame is blurred and comprehensively analyze the degree of screen jitter, the complexity of instrument movement, and the index of the blurring degree of the surgical instrument image in the current frame to evaluate the screen adjustment effectiveness.

[0026] As a further method, comprehensively analyze the degree of screen jitter, the complexity of instrument movement, and the index of the blurring degree of the surgical instrument image in the current frame to evaluate the screen adjustment effectiveness. The specific analysis process is as follows: Set the degree of screen jitter as the first influence parameter of image blurring, set the complexity of instrument movement as the second influence parameter of image blurring, perform coupling processing on the first influence parameter of image blurring and the second influence parameter of image blurring and set it as the image blurring correction factor, and jointly analyze it with the index of the blurring degree of the surgical instrument image in the current frame to obtain the screen adjustment effectiveness evaluation value.

[0027] As a further method, the system provides feedback reminders for the screen adjustment based on the screen adjustment efficiency, and simultaneously performs enhancement and optimization adjustments on the gastrointestinal surgical instrument images. The specific analysis process is as follows: Extract the screen adjustment turning value from the surgical image information library, and compare the screen adjustment efficiency evaluation value with the screen adjustment turning value. If the screen adjustment efficiency evaluation value is lower than or equal to the screen adjustment turning value, set a reminder signal to remind the image display. If the screen adjustment efficiency evaluation value is higher than the screen adjustment turning value, perform enhancement and optimization adjustments on the gastrointestinal surgical instrument images. The specific process is as follows: Extract the difference between the screen adjustment efficiency evaluation value and the screen adjustment turning value, and match the frame rate collaborative downscaling ratio, contrast collaborative upscaling ratio, and noise collaborative downscaling ratio according to the mapping set.

[0028] Adjust the image adjustment parameters based on the frame rate collaborative downscaling ratio, contrast collaborative upscaling ratio, and noise collaborative downscaling ratio, obtain the image adjustment update parameters, and input them into the surgical image information library for data update to complete the enhancement and optimization adjustments on the gastrointestinal surgical instrument images.

[0029] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects: (1) By providing a method for enhancing and optimizing gastrointestinal surgical instrument images, the present invention analyzes the screen jitter degree, instrument movement complexity, and image blur degree in real time, can accurately identify abnormal jitter and blur problems in the images, and significantly improves the clarity and detail performance of surgical images by dynamically adjusting the screen parameters, which helps to more accurately identify surgical instruments and operation areas, reduces misjudgment and misoperation, and improves the accuracy and safety of the surgery.

[0030] (2) By comprehensively analyzing parameters such as inter-frame differences, optical flow, corner displacements, instrument displacement amplitudes, and motion trajectory curvatures of the images, the present invention provides a basis for dynamically adjusting image acquisition and processing parameters, helps to adapt to complex image environment changes caused by rapid instrument movement or equipment jitter during the surgery, and is beneficial to ensuring the stability and clarity of surgical images.

[0031] (3) By evaluating the effectiveness of the screen adjustment and providing intelligent feedback reminders, the present invention not only reduces the need for manual intervention but also improves the adaptive ability of the system, which is beneficial to ensuring the continuous optimization of the image enhancement effect and providing the best image quality in different surgical scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The present invention is further described with reference to the accompanying drawings. However, the embodiments in the drawings do not constitute any limitation to the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained according to the following drawings.

[0033] Figure 1Schematic diagram of the method flow of the present invention. Detailed implementation manners

[0034] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without making creative efforts shall fall within the protection scope of the present invention.

[0035] Refer to Figure 1 As shown, the present invention provides a method for enhancing and optimizing the image of gastrointestinal surgical instruments, including: S1. Obtain the basic information of consecutive frame surgical images, analyze the degree of image jitter, and thereby adjust the image parameters to adapt to abnormal image jitter.

[0036] It should be explained that the consecutive frame surgical images refer to the images obtained by extracting frames from the surgical videos captured by the gastroscope.

[0037] Specifically, to analyze the degree of image jitter, the specific analysis process is as follows: The basic information of the consecutive frame surgical images includes: inter-frame difference, optical flow, and corner displacement.

[0038] Extract the extreme values of inter-frame difference, optical flow, and corner displacement from the surgical image information database, compare the inter-frame difference, optical flow, and corner displacement with the extreme values of inter-frame difference, optical flow, and corner displacement respectively, and perform coupling processing after setting corresponding weight factors to obtain the feature value of the image jitter degree.

[0039] Judge whether image adjustment is required according to the feature value of the image jitter degree.

[0040] In a specific embodiment, the numerical expression of the feature value of the image jitter degree is: ; In the formula, represents the feature value of the image jitter degree, e represents the natural constant, represents the inter-frame difference, represents the optical flow, represents the corner displacement, represents the extreme value of the inter-frame difference, represents the extreme value of the optical flow, represents the extreme value of the corner displacement, represents the preset weight factor of the image jitter influence corresponding to the inter-frame difference, represents the preset weight factor of the image jitter influence corresponding to the optical flow, represents the preset weight factor of the image jitter influence corresponding to the corner displacement.

[0041] It should be noted that the inter-frame difference refers to the degree of difference between two adjacent frames of images at the pixel level. It can be measured by calculating the average value of the differences in the color values of the corresponding pixel points of the two frames of images. The larger the inter-frame difference, the greater the change between two adjacent frames of images, which may be caused by instrument movement, tissue deformation, or image jitter during the surgical process. In this embodiment, the inter-frame difference refers to the cumulative value of the degree of difference between all adjacent two frames of images at the pixel level among consecutive frames. Optical flow refers to the movement of pixel points caused by the movement of objects in the image on the image plane. It reflects the displacement vector of each pixel point in the image between consecutive frames. Corners are points with obvious features in the image, such as the edges and corners of objects. Corner displacement is the change in the position of these corners between consecutive frames. First, a corner detection algorithm (such as the Harris corner detection) is used to detect corners in the image. Then, corner matching is performed between consecutive frames through feature descriptors (such as SIFT, SURF, etc.). For successfully matched corner pairs, the coordinate differences in the image coordinate system are calculated, which is the corner displacement. The magnitude of the corner displacement can be quantified by the Euclidean distance.

[0042] It should be noted that the optical flow of pixel points in the image can be calculated through the built-in Lucas - Kanade optical flow algorithm (Lucas - Kanade Optical Flow Algorithm) in the system, that is, by analyzing the brightness changes of pixel points between adjacent frames in the image sequence to estimate the motion vector of each pixel point on the image plane. In this embodiment, the optical flow is only the length of the motion trajectory of each pixel point on the image plane, without considering the direction.

[0043] It should be noted that the more obvious the optical flow will be when the inter-frame difference is larger. The main reason for the generation of the inter-frame difference is the movement of objects in the image or the jitter of the image, and the optical flow is exactly the vector field describing the object movement. A large inter-frame difference may lead to a relatively large corner displacement. Because when the overall image changes greatly, as the corner points are the feature points in the image, their positions will also change accordingly. The optical flow can be regarded as a description of the movement of pixel points in the entire image, and the corner displacement is the manifestation of the optical flow at these special pixel points of the corners. The direction and magnitude of the corner displacement are closely related to the vector direction and magnitude of the optical flow at the corners. Generally, the corners will be displaced along the direction of the optical flow, and the magnitude of the optical flow will also affect the amplitude of the corner displacement.

[0044] It should be noted that in this embodiment represents the weight factor of the influence of image jitter corresponding to the preset inter-frame difference, represents the weight factor of the influence of image jitter corresponding to the preset optical flow, Denote the weight factor of the screen jitter effect corresponding to the preset corner displacement, which are the values representing the influence degrees of the inter-frame difference, optical flow, and unit value of corner displacement on the screen jitter respectively. They can be directly obtained from the surgical image information library during use, and their corresponding relationships can be pre-set mapping relationships. For example, the inter-frame difference, optical flow, and corner displacement respectively form a mapping set with the weight factors of the screen jitter effect corresponding to the preset inter-frame difference, optical flow, and corner displacement in the surgical image information library. Input the real-time inter-frame difference, optical flow, and corner displacement into the mapping set to obtain the corresponding weight factor of the screen jitter effect, where the mapping relationships can be one-to-one or many-to-one relationships. The value ranges of the above influence weight factors are all between 0 and 1.

[0045] It should be noted that in this embodiment, by comprehensively analyzing the inter-frame difference, optical flow, and corner displacement, the jitter degree of consecutive-frame surgical images can be more comprehensively understood. The inter-frame difference provides information on the overall change of the screen, the optical flow further distinguishes the changes caused by motion and non-motion factors, and the corner displacement focuses on the jitter of local feature points of the screen. They complement and corroborate each other, providing a strong basis for accurately obtaining the characteristic value of the screen jitter degree, thus helping to adapt to the complex environmental changes of the images caused by the rapid movement of instruments or equipment jitter during the surgical process, which is beneficial to ensuring the stability and clarity of surgical images.

[0046] In a specific embodiment, the surgical image information library is used to store the relevant parameters during the enhancement process of surgical instrument images, including the inter-frame difference threshold, optical flow extreme value, and corner displacement extreme value, as well as the data extracted from the surgical image information library in the above embodiment. The relevant parameters can be obtained from the historical images acquired by the image acquisition device, or from the log files of the image enhancement algorithm, or from the metadata of the image files.

[0047] Furthermore, it is judged whether the screen needs to be adjusted according to the characteristic value of the screen jitter degree. The specific process is as follows: extract the verification value of the screen jitter abnormality degree from the surgical image information library, compare the characteristic value of the screen jitter degree with the verification value of the screen jitter abnormality degree. If the characteristic value of the screen jitter degree is lower than or equal to the verification value of the screen jitter abnormality degree, mark the screen jitter as having no impact on the screen; if the characteristic value of the screen jitter degree is higher than the verification value of the screen jitter abnormality degree, adjust the screen parameters to adapt to the abnormal screen jitter.

[0048] Specifically, the screen parameters are adjusted to adapt to abnormal screen jitter. The specific process is as follows: The difference between the characteristic value of the screen jitter degree and the verification value of the abnormal degree of the screen jitter is marked as the predicted required screen jitter adjustment parameter. The required reduced frame rate corresponding to the predicted required screen jitter adjustment parameter is extracted from the surgical image information database. The difference between the initial acquisition frame rate and the required reduced frame rate is set as the current acquisition frame rate. After replacing the image acquisition frame rate from the initial acquisition frame rate with the current acquisition frame rate, image acquisition is performed again to adapt to the abnormal screen jitter.

[0049] It should be noted that the required reduced frame rate corresponding to the predicted required screen jitter adjustment parameter is obtained by matching the mapping set of the required reduced frame rates corresponding to the predicted required screen jitter adjustment parameter intervals stored in the surgical image information database. The real-time predicted required screen jitter adjustment parameter interval is input to match the corresponding required reduced frame rate, and the mapping relationship therein is a one-to-one relationship.

[0050] S2. Obtain the instrument movement information in the continuous-frame surgical images, analyze the complexity of the surgical instrument movement in the images, and thus adjust the screen parameters to adapt to the complex instrument movement.

[0051] Specifically, analyzing the complexity of the surgical instrument movement in the images, the specific process is as follows: The instrument movement information in the continuous-frame surgical images includes: displacement amplitude, displacement direction change rate, motion trajectory curvature, and motion speed change rate.

[0052] The displacement amplitude limit value, displacement direction change rate limit value, motion trajectory curvature limit value, and motion speed change rate limit value are extracted from the surgical image information database. The displacement amplitude, displacement direction change rate, motion trajectory curvature, and motion speed change rate of each instrument are respectively compared with the corresponding limit values, and after setting the weight factors, coupling processing is performed to obtain the instrument movement complexity characteristic value.

[0053] Judge whether screen adjustment is required according to the instrument movement complexity characteristic value.

[0054] In a specific embodiment, the numerical expression of the instrument movement complexity characteristic value is: ; In the formula, represents the instrument movement complexity characteristic value, represents the number of each instrument, , m represents the total number of instruments, represents the displacement amplitude of the nth instrument, represents the displacement direction change rate of the nth instrument, represents the motion trajectory curvature of the nth instrument, represents the motion speed change rate of the nth instrument, Indicates the displacement amplitude limit value, Indicates the limit value of the rate of change of displacement direction, represents the curvature limit of the motion trajectory, Indicates the limit value of the rate of change of motion speed. Indicates the weight factor affecting the complexity of the movement of the device corresponding to the preset displacement direction change rate, Indicates the weight factor affecting the complexity of the movement of the device corresponding to the preset motion trajectory curvature, Indicates the weight factor affecting the complexity of the movement of the device corresponding to the preset rate of change of movement speed.

[0055] It should be explained that the displacement amplitude in this embodiment refers to the distance that the surgical instrument moves between consecutive frames. It can be calculated using the Euclidean distance formula. The displacement direction change rate is used to measure the change in the direction of the instrument's movement. A high direction change rate indicates that the instrument changes direction frequently during movement. The motion trajectory curvature refers to the degree of curvature of the instrument's motion trajectory. The greater the curvature, the more curved the trajectory, indicating that the instrument has made more drastic turns during movement. The motion speed change rate indicates the change in the instrument's motion speed between consecutive frames, which can be obtained by calculating the ratio of the absolute value of the difference in the speed of adjacent frames to the time interval. The greater the speed change rate, the more unstable the instrument's motion speed.

[0056] It should be explained that the displacement direction can be expressed by the angle between the displacement vector and the positive direction of the x-axis. The displacement vector from the i-th frame to the i+1-th frame is , then the displacement direction , where arctan2 is the four-quadrant inverse tangent function. The angle change between adjacent frames . Let the time interval between adjacent frames be , then the displacement direction change rate is .

[0057] It should be explained that the position points of the surgical instrument in the continuous frames ( , ) is fitted into a curve, assuming that the equation of the fitted curve is y = f(x). For the plane curve y = f(x), the calculation formula for its curvature k is , where y' and y'' are the first and second derivatives of the curve respectively. y' and y'' can be approximated by numerical differentiation.

[0058] It should be noted that the displacement amplitude of the instrument is closely related to the movement speed, and speed is the rate of change of displacement with respect to time. The rate of change of movement speed directly affects the displacement amplitude. If the rate of change of speed is large, the displacement amplitude of the instrument may increase within the same time interval; conversely, if the speed changes relatively smoothly, the increase in displacement amplitude will be relatively small. The rate of change of displacement direction and the curvature of the movement trajectory are both related to the change in the direction of the instrument movement. Generally speaking, the greater the rate of change of displacement direction, the greater the curvature of the movement trajectory, because frequent changes in direction will cause the trajectory to be more curved.

[0059] It should be noted that in this embodiment represents the influence weight factor of the instrument movement complexity corresponding to the preset displacement amplitude, represents the influence weight factor of the instrument movement complexity corresponding to the preset rate of change of displacement direction, represents the influence weight factor of the instrument movement complexity corresponding to the preset curvature of the movement trajectory, represents the influence weight factor of the instrument movement complexity corresponding to the preset rate of change of movement speed, which respectively represent the values of the influence degree of the unit values of displacement amplitude, rate of change of displacement direction, curvature of the movement trajectory, and rate of change of movement speed on the movement complexity of the surgical instrument. When used, they can be directly obtained from the surgical image information database, and their corresponding relationships can be pre-set mapping relationships. For example, the displacement amplitude, rate of change of displacement direction, curvature of the movement trajectory, and rate of change of movement speed of the instrument respectively form a mapping set with the influence weight factors of the instrument movement complexity corresponding to the preset displacement amplitude, rate of change of displacement direction, curvature of the movement trajectory, and rate of change of movement speed in the surgical image information database. Inputting the real-time displacement amplitude, rate of change of displacement direction, curvature of the movement trajectory, and rate of change of movement speed of the instrument into the mapping set to obtain the corresponding influence weight factors of the instrument movement complexity, and the mapping relationships therein can be one-to-one or many-to-one relationships. The value ranges of the above influence weight factors are all between 0 and 1.

[0060] It should be noted that in this embodiment, the displacement amplitude, rate of change of displacement direction, curvature of the movement trajectory, and rate of change of movement speed of the instrument are interrelated and interact with each other, jointly describing the complexity eigenvalue of the instrument movement in consecutive frame surgical images. By analyzing these parameters, the movement state and operation characteristics of the surgical instrument can be deeply understood, providing a basis for reducing the influence of these factors on the image quality, thereby facilitating the improvement of image quality.

[0061] Further, it is determined whether image adjustment is required according to the eigenvalue of the instrument movement complexity. The specific process is as follows: The verification value of the instrument movement complexity is extracted from the surgical image information database, and the eigenvalue of the instrument movement complexity is compared with the verification value of the instrument movement complexity. If the eigenvalue of the instrument movement complexity is lower than or equal to the verification value of the instrument movement complexity, the instrument movement is marked as having no impact on the instrument movement. If the eigenvalue of the instrument movement complexity is higher than the verification value of the instrument movement complexity, the image parameters are adjusted to adapt to the complex instrument movement.

[0062] Specifically, to adjust the image parameters to adapt to the complex instrument movement, the specific process is as follows: The difference between the eigenvalue of the instrument movement complexity and the verification value of the instrument movement complexity is marked as the predicted required instrument movement adjustment parameter. The corresponding contrast reference value and noise reference value of the predicted required instrument movement adjustment parameter are extracted from the surgical image information database. The image enhancement algorithm is called to increase the initial contrast of the image to be equal to the contrast reference value, and at the same time, the initial noise of the image is decreased to be equal to the noise reference value to adapt to the complex instrument movement.

[0063] It should be explained that the image enhancement algorithm improves the visual quality of the image by processing the image, highlights the useful information in the image, and improves the contrast, brightness, color, etc. of the image, making the image more suitable for human eye observation or subsequent computer processing. In this embodiment, the image enhancement algorithm includes: contrast enhancement algorithm, sharpening algorithm, and color enhancement algorithm. Among them, the contrast enhancement algorithm can expand the difference between different gray levels in the image, make the details of the image clearer, and improve the visual effect of the image. The sharpening algorithm can enhance the edges and details in the image, make the image look clearer and sharper, and improve the resolution and readability of the image. The color enhancement algorithm is used to adjust parameters such as the color saturation, hue, and brightness of the image, making the color of the image more vivid and natural, and enhancing the visual attraction of the image.

[0064] It should be explained that the corresponding contrast reference value and noise reference value of the predicted required instrument movement adjustment parameter are obtained by matching the mapping set of the contrast reference value and noise reference value corresponding to the predicted required instrument movement adjustment parameter interval stored in the surgical image information database. The real-time predicted required instrument movement adjustment parameter interval is input to match the corresponding contrast reference value and noise reference value, and the mapping relationship is a one-to-one relationship.

[0065] S3. After adjusting the image parameters, obtain the basic parameters of the current frame of the surgical instrument image, analyze the blurriness index of the current frame of the surgical instrument image, and thus determine whether the current frame of the surgical instrument image is blurred.

[0066] Specifically, analyze the blurriness index of the surgical instrument image in the current frame, and thereby determine whether the surgical instrument image in the current frame is blurred. The specific analysis process is as follows: The basic image parameters include: the low-high frequency region energy ratio, gradient amplitude, and edge intensity of the surgical instrument image in the current frame.

[0067] Equalize the low-high frequency region energy ratio, gradient amplitude, and edge intensity of the consecutive frame surgical images respectively to obtain the average index of the low-high frequency region energy ratio, average gradient amplitude index, and average edge intensity index of the consecutive frame surgical images.

[0068] Compare the low-high frequency region energy ratio, gradient amplitude, and edge intensity of the surgical instrument image in the current frame with the average index of the low-high frequency region energy ratio, average gradient amplitude index, and average edge intensity index of the consecutive frame surgical images respectively, and perform coupling processing after setting weight factors respectively to obtain the blurriness index of the surgical instrument image in the current frame.

[0069] In a specific embodiment, the numerical expression of the blurriness index of the surgical instrument image in the current frame is: ; In the formula, represents the blurriness index of the surgical instrument image in the current frame, represents the low-high frequency region energy ratio of the surgical instrument image in the current frame, F represents the gradient amplitude of the surgical instrument image in the current frame, Q represents the edge intensity of the surgical instrument image in the current frame, represents the average index of the low-high frequency region energy ratio of the consecutive frame surgical images, represents the average gradient amplitude index of the consecutive frame surgical images, represents the average edge intensity index of the consecutive frame surgical images, represents the image blurriness influence weight factor corresponding to the preset low-high frequency region energy ratio, represents the image blurriness influence weight factor corresponding to the preset gradient amplitude, represents the image blurriness influence weight factor corresponding to the preset edge intensity.

[0070] It should be noted that in this embodiment represents the image blurriness influence weight factor corresponding to the preset low-high frequency region energy ratio, represents the image blurriness influence weight factor corresponding to the preset gradient amplitude, The image blur influence weight factors corresponding to the preset edge intensity respectively represent the numerical values ​​of the influence of the low-high frequency area energy ratio, gradient amplitude and edge intensity unit value on the image blur degree. When used, they can be directly obtained from the surgical image information library. The corresponding relationship can be a pre-set mapping relationship. For example, the low-high frequency area energy ratio, gradient amplitude and edge intensity of the image are respectively mapped with the image blur influence weight factors corresponding to the preset low-high frequency area energy ratio, gradient amplitude and edge intensity in the surgical image information library to form a mapping set. The real-time image low-high frequency area energy ratio, gradient amplitude and edge intensity are input into the mapping set to obtain the corresponding image blur influence weight factors. The mapping relationship can be one-to-one or many-to-one. The value range of the above-mentioned influence weight factors is between 0 and 1.

[0071] It should be explained that, in this embodiment, the low-frequency area represents the smooth part or background information in the image. The high-frequency area represents the detailed part in the image, such as edges, textures, etc. The low-high frequency area energy ratio refers to the ratio between the energy of the low-frequency component and the energy of the high-frequency component in the image, which can be obtained by frequency domain conversion. The gradient amplitude represents the rate of change of the brightness of each pixel in the image, and is often used to detect edges and boundaries in the image. The larger the gradient amplitude, the more drastic the change at that position. The gradient field of the image can be calculated by Sobel operator, Prewitt operator or Laplacian operator, and then the gradient amplitude of each pixel is obtained. Edge strength is an indicator of edge clarity in an image, and is usually associated with gradient amplitude. High edge strength means that the edges in the image are more obvious and sharp. The edge strength is calculated by the Canny edge detection algorithm or other edge detection methods.

[0072] It needs to be explained that image blurring will relatively reduce the energy of high-frequency components and relatively increase the energy of low-frequency components. Because the blurring operation is similar to a low-pass filtering process, it will smooth out the details in the image, and the details are mainly concentrated in the high-frequency part. Therefore, the blurrier the image, the larger the energy ratio of the low-high frequency area is usually. Image blurring will cause the overall gradient amplitude to decrease. Because blurring makes the brightness change of pixels in the image smooth, the originally sharp edges become smooth, and the brightness difference between pixels becomes smaller, the calculated gradient amplitude will also decrease accordingly. Edge strength is closely related to gradient amplitude, and image blurring will reduce edge strength. Because blurring makes the edges of the image unclear, the originally sharp edges become blurred, resulting in a decrease in edge strength. Images with high edge strength have clear edges and distinct object contours; once the image is blurred, the clarity of the edge will decrease and the edge strength will also weaken.

[0073] It should be noted that when the energy ratio in the low-frequency region is relatively high, there are more smooth parts in the image; while when the energy ratio in the high-frequency region is relatively high, the detailed parts of the image are more prominent. The gradient magnitude directly reflects the degree of brightness change in the image, and the edge intensity is a comprehensive evaluation based on the gradient magnitude. The edge intensity is greater where the gradient magnitude is larger.

[0074] It should be noted that in this embodiment, by analyzing the energy ratio of the low-high frequency regions, the gradient magnitude, and the edge intensity of the image, not only can it help evaluate the quality and characteristics of the image, but also it can provide valuable information in medical image analysis. By comparing the parameter changes between consecutive frames, the analysis of the dynamic characteristics in the image sequence can also be achieved.

[0075] S4. If it is not blurred, continue to control the display of the gastrointestinal surgical instrument image with the current screen parameters. If it is blurred, comprehensively analyze the degree of screen jitter, the complexity of instrument movement, and the blurring degree index of the surgical instrument image in the current frame to evaluate the screen adjustment efficiency.

[0076] Specifically, extract the image blurring reference index from the surgical image information database, compare the blurring degree index of the surgical instrument image in the current frame with the image blurring reference index. If the blurring degree index of the surgical instrument image in the current frame is lower than the image blurring reference index, determine that the surgical instrument image in the current frame is not blurred and continue to control the display of the gastrointestinal surgical instrument image with the current screen parameters. If the blurring degree index of the surgical instrument image in the current frame is higher than or equal to the image blurring reference index, determine that the surgical instrument image in the current frame is blurred and comprehensively analyze the degree of screen jitter, the complexity of instrument movement, and the blurring degree index of the surgical instrument image in the current frame to evaluate the screen adjustment efficiency.

[0077] Specifically, comprehensively analyze the degree of screen jitter, the complexity of instrument movement, and the blurring degree index of the surgical instrument image in the current frame to evaluate the screen adjustment efficiency. The specific analysis process is as follows: Set the degree of screen jitter as the first influencing parameter of image blurring, set the complexity of instrument movement as the second influencing parameter of image blurring, add the first influencing parameter of image blurring and the second influencing parameter of image blurring and set it as the image blurring correction factor, and multiply it by the blurring degree index of the surgical instrument image in the current frame to obtain the screen adjustment efficiency evaluation value.

[0078] S5. Give feedback reminders for the screen adjustment according to the screen adjustment efficiency, and simultaneously perform enhancement and optimization adjustments on the gastrointestinal surgical instrument image.

[0079] Specifically, feedback reminders are given for the screen adjustment according to the screen adjustment efficiency, and at the same time, the images of gastrointestinal surgical instruments are enhanced and optimized. The specific analysis process is as follows: The screen adjustment turning value is extracted from the surgical image information library, and the screen adjustment efficiency evaluation value is compared with the screen adjustment turning value. If the screen adjustment efficiency evaluation value is lower than or equal to the screen adjustment turning value, a reminder signal is set to remind the image display. If the screen adjustment efficiency evaluation value is higher than the screen adjustment turning value, the images of gastrointestinal surgical instruments are enhanced and optimized. The specific process is as follows: The difference between the screen adjustment efficiency evaluation value and the screen adjustment turning value is extracted, and the frame rate collaborative downscaling ratio, contrast collaborative upscaling ratio, and noise collaborative downscaling ratio are obtained according to the mapping set.

[0080] Based on the frame rate collaborative downscaling ratio, contrast collaborative upscaling ratio, and noise collaborative downscaling ratio, the image adjustment parameters are adjusted to obtain the image adjustment update parameters and input them into the surgical image information library for data update, completing the enhancement and optimization of the images of gastrointestinal surgical instruments.

[0081] Specifically, the process of adjusting the image adjustment parameters based on the frame rate collaborative downscaling ratio, contrast collaborative upscaling ratio, and noise collaborative downscaling ratio is as follows: The required decreased frame rate corresponding to each required screen jitter adjustment parameter preset in the surgical image information library is successively subtracted by the frame rate collaborative downscaling ratio to obtain the adjusted value of the required decreased frame rate corresponding to each required screen jitter adjustment parameter, and the required decreased frame rate corresponding to each required screen jitter adjustment parameter is subtracted by the corresponding adjusted value of the required decreased frame rate to obtain the updated value of the required decreased frame rate corresponding to each required screen jitter adjustment parameter.

[0082] The contrast reference value corresponding to each required instrument movement adjustment parameter preset in the surgical image information library is successively superimposed with the contrast collaborative upscaling ratio to obtain the adjusted value of the required increased contrast corresponding to each required instrument movement adjustment parameter, and the contrast reference value corresponding to each required instrument movement adjustment parameter is superimposed with the corresponding adjusted value of the required increased contrast to obtain the updated contrast reference value corresponding to each required instrument movement adjustment parameter.

[0083] The noise reference value corresponding to each required instrument movement adjustment parameter preset in the surgical image information library is successively subtracted by the noise collaborative downscaling ratio to obtain the adjusted value of the required decreased noise corresponding to each required instrument movement adjustment parameter, and the noise reference value corresponding to each required instrument movement adjustment parameter is subtracted by the corresponding adjusted value of the required decreased noise to obtain the updated noise reference value corresponding to each required instrument movement adjustment parameter.

[0084] The updated value of the required decreased frame rate, the updated contrast reference value, and the updated noise reference value of the gastrointestinal surgical instrument image are jointly recorded as the image adjustment update parameters and input into the surgical image information library for data update to complete the enhancement and optimization of the gastrointestinal surgical instrument image.

[0085] It should be explained that in this embodiment, by evaluating the effectiveness of the screen adjustment and thereby providing intelligent feedback reminders, this feedback mechanism not only reduces the need for manual intervention but also improves the system's adaptability, which is conducive to ensuring the continuous optimization of the image enhancement effect and providing the best image quality in different surgical scenarios.

[0086] It should be explained that this application provides a method for optimizing the image enhancement of gastrointestinal surgical instruments. By analyzing the degree of screen jitter, the complexity of instrument movement, and the degree of image blurring in real time, it can accurately identify abnormal jitter and blurring problems in the image. By dynamically adjusting the screen parameters, it significantly improves the clarity and detail performance of the surgical image, helps to more accurately identify surgical instruments and the operation area, reduces misjudgment and misoperation, and improves the accuracy and safety of the surgery.

[0087] The above content is only an example and explanation of the structure of the present invention. Those skilled in the art of this technology can make various modifications or supplements to the described specific embodiments or use similar methods for substitution. As long as they do not deviate from the structure of the invention or exceed the scope defined by this claim book, they should fall within the protection scope of the present invention.

Claims

1. An image enhancement and optimization method for gastrointestinal surgical instruments, characterized in that, Including: S1. Obtain the basic information of consecutive-frame surgical images, analyze the degree of image jitter, and accordingly adjust the image parameters to adapt to abnormal image jitter; S2. Obtain the instrument movement information in consecutive-frame surgical images, analyze the complexity of surgical instrument movement in the images, and accordingly adjust the image parameters to adapt to complex instrument movement; S3. After adjusting the image parameters, obtain the basic parameters of the current-frame surgical instrument image, analyze the blurriness index of the current-frame surgical instrument image, and thereby determine whether the current-frame surgical instrument image is blurred; S4. If it is not blurred, continue to control the display of the gastrointestinal surgical instrument image with the current image parameters. If it is blurred, comprehensively analyze the degree of image jitter, the complexity of instrument movement, and the blurriness index of the current-frame surgical instrument image to evaluate the image adjustment efficiency; S5. Provide feedback reminders for the image adjustment according to the image adjustment efficiency, and simultaneously perform enhancement and optimization adjustments on the gastrointestinal surgical instrument image.

2. The image enhancement and optimization method for a gastrointestinal surgical instrument according to claim 1, wherein: The analysis of the degree of image jitter, the specific analysis process is as follows: The basic information of consecutive-frame surgical images includes: inter-frame difference, optical flow, and corner displacement; Extract the inter-frame difference extreme value, optical flow extreme value, and corner displacement extreme value from the surgical image information library. Compare the inter-frame difference, optical flow, and corner displacement with the inter-frame difference extreme value, optical flow extreme value, and corner displacement extreme value respectively, and perform coupling processing after setting the corresponding weight factors to obtain the image jitter degree characteristic value; Judge whether image adjustment is required according to the image jitter degree characteristic value.

3. The image enhancement optimization method for a gastrointestinal surgical instrument according to claim 2, wherein: The process of judging whether image adjustment is required according to the image jitter degree characteristic value is as follows: Extract the image jitter abnormality degree verification value from the surgical image information library. Compare the image jitter degree characteristic value with the image jitter abnormality degree verification value. If the image jitter degree characteristic value is lower than or equal to the image jitter abnormality degree verification value, mark the image jitter as non-influential image jitter. If the image jitter degree characteristic value is higher than the image jitter abnormality degree verification value, adjust the image parameters to adapt to the abnormal image jitter.

4. The image enhancement and optimization method for a gastrointestinal surgical instrument according to claim 3, wherein: The process of adjusting the image parameters to adapt to the abnormal image jitter is as follows: Mark the difference between the image jitter degree characteristic value and the image jitter abnormality degree verification value as the predicted required image jitter adjustment parameter. Extract the required reduced frame rate corresponding to the predicted required image jitter adjustment parameter from the surgical image information library. Set the difference between the initial acquisition frame rate and the required reduced frame rate as the current acquisition frame rate, and replace the image acquisition frame rate from the initial acquisition frame rate with the current acquisition frame rate for image acquisition to adapt to the abnormal image jitter.

5. The image enhancement and optimization method of a gastrointestinal surgical instrument according to claim 1, wherein: The analysis of the complexity of surgical instrument movement in the image, the specific process is as follows: The instrument movement information in consecutive-frame surgical images includes: displacement amplitude, displacement direction change rate, motion trajectory curvature, and motion speed change rate; Extract the displacement amplitude limit value, displacement direction change rate limit value, motion trajectory curvature limit value, and motion speed change rate limit value from the surgical image information library. Compare the instrument displacement amplitude, displacement direction change rate, motion trajectory curvature, and motion speed change rate with the corresponding limit values respectively, and perform coupling processing after setting weight factors to obtain the instrument movement complexity characteristic value; Judge whether screen adjustment is required according to the eigenvalue of instrument movement complexity.

6. The image enhancement and optimization method of a gastrointestinal surgical instrument according to claim 5, wherein: The process of judging whether screen adjustment is required according to the eigenvalue of instrument movement complexity is as follows: Extract the verification value of instrument movement complexity from the surgical image information database, compare the eigenvalue of instrument movement complexity with the verification value of instrument movement complexity. If the eigenvalue of instrument movement complexity is lower than or equal to the verification value of instrument movement complexity, mark the instrument movement as having no impact on instrument movement. If the eigenvalue of instrument movement complexity is higher than the verification value of instrument movement complexity, adjust the screen parameters to adapt to complex instrument movement.

7. The image enhancement and optimization method of a gastrointestinal surgical instrument according to claim 6, characterized in that: The process of adjusting the screen parameters to adapt to complex instrument movement is as follows: Mark the difference between the eigenvalue of instrument movement complexity and the verification value of instrument movement complexity as the predicted required instrument movement adjustment parameter. Extract the corresponding contrast reference value and noise reference value of the predicted required instrument movement adjustment parameter from the surgical image information database. Call the image enhancement algorithm to increase the initial contrast of the image to be equal to the contrast reference value, and at the same time reduce the initial noise of the image to be equal to the noise reference value to adapt to complex instrument movement.

8. The image enhancement and optimization method for a gastrointestinal surgical instrument according to claim 1, characterized in that: Analyze the blurriness index of the current frame surgical instrument image, and thus judge whether the current frame surgical instrument image is blurry. The specific analysis process is as follows: The basic parameters of the current frame surgical instrument image include: the low-high frequency region energy ratio, gradient amplitude, and edge strength of the current frame surgical instrument image; Equalize the low-high frequency region energy ratio, gradient amplitude, and edge strength of consecutive frame surgical images to obtain the average index of low-high frequency region energy ratio, gradient amplitude average index, and edge strength average index of consecutive frame surgical images; Compare the low-high frequency region energy ratio, gradient amplitude, and edge strength of the current frame surgical instrument image with the average index of low-high frequency region energy ratio, gradient amplitude average index, and edge strength average index of consecutive frame surgical images respectively, and set weight factors respectively and then perform coupling processing to obtain the blurriness index of the current frame surgical instrument image; Extract the image blurriness reference index from the surgical image information database, compare the blurriness index of the current frame surgical instrument image with the image blurriness reference index. If the blurriness index of the current frame surgical instrument image is lower than the image blurriness reference index, judge that the current frame surgical instrument image is not blurry and continue to control the display of the gastrointestinal surgical instrument image with the current screen parameters. If the blurriness index of the current frame surgical instrument image is higher than or equal to the image blurriness reference index, judge that the current frame surgical instrument image is blurry and comprehensively analyze the screen jitter degree, instrument movement complexity, and the blurriness index of the current frame surgical instrument image to evaluate the screen adjustment efficiency.

9. The image enhancement and optimization method for a gastrointestinal surgical instrument according to claim 1, characterized in that: The specific analysis process of comprehensively analyzing the screen jitter degree, instrument movement complexity, and the blurriness index of the current frame surgical instrument image to evaluate the screen adjustment efficiency is as follows: Set the degree of screen jitter as the first influencing parameter of image blur, set the complexity of instrument movement as the second influencing parameter of image blur, couple the first influencing parameter of image blur and the second influencing parameter of image blur and set it as the image blur correction factor, and jointly analyze it with the current frame surgical instrument image blur degree index to obtain the screen adjustment efficiency evaluation value.

10. The image enhancement and optimization method for a gastrointestinal surgical instrument according to claim 1, characterized in that: Give feedback and reminder on the screen adjustment according to the screen adjustment efficiency, and synchronously perform enhancement and optimization adjustment on the gastrointestinal surgical instrument image. The specific analysis process is as follows: Extract the screen adjustment turning value from the surgical image information library, compare the screen adjustment efficiency evaluation value with the screen adjustment turning value. If the screen adjustment efficiency evaluation value is lower than or equal to the screen adjustment turning value, set a reminder signal to remind the image display. If the screen adjustment efficiency evaluation value is higher than the screen adjustment turning value, perform enhancement and optimization adjustment on the gastrointestinal surgical instrument image. The specific process is as follows: Extract the difference between the screen adjustment efficiency evaluation value and the screen adjustment turning value, and match the frame rate collaborative down - adjustment ratio, contrast collaborative up - adjustment ratio, and noise collaborative down - adjustment ratio according to the mapping set; Adjust the image adjustment parameters based on the frame rate collaborative down - adjustment ratio, contrast collaborative up - adjustment ratio, and noise collaborative down - adjustment ratio, obtain the image adjustment updated parameters and input them into the surgical image information library for data update, and complete the enhancement and optimization adjustment of the gastrointestinal surgical instrument image.

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