Intelligent recognition and analysis system for urinary surgery calculus image

Abstract

The invention discloses an intelligent recognition and analysis system for urinary surgery calculus images, relates to the technical field of image processing, and forms a core architecture of the intelligent recognition and analysis system for urinary surgery calculus images through mutual cooperation of three algorithm units. The comprehensive image score value Ism is calculated through the comprehensive image score value algorithm unit, so that the image quality can be evaluated more comprehensively and accurately, misdiagnosis and missed diagnosis caused by a single factor are reduced, an intelligent recognition and analysis system is helped to better distinguish calculus from surrounding tissues, the calculus detection rate is increased, image recognition is better carried out, and the accuracy of calculus detection is improved. The adjusted stone segmentation threshold value Ast is calculated through the adjusted stone segmentation threshold value algorithm unit, namely, the stone segmentation threshold value can be dynamically adjusted through factors such as image quality, the boundary and range of the stone can be more accurately recognized, the stone segmentation accuracy is improved, and a scientific and reliable data basis is provided for subsequent diagnosis and treatment.

Description

Technical Field

[0001] The present invention relates to the technical field of image processing, and particularly to an intelligent recognition and analysis system for urological calculus images. Background Technique

[0002] The urology department is a hospital department mainly diagnosing and treating diseases in the "surgical" part of the urinary system, mainly treating various urinary diseases. The digital images of the urology department are generally presented in the forms of CT (Computed Tomography), MRI (Magnetic Resonance Imaging), ultrasound, etc.

[0003] In the prior art, the intelligent recognition system applied to urological calculus images still segments and recognizes the calculi in the images through the segmentation algorithm of traditional image processing. This conventional image segmentation and recognition algorithm is relatively simple, and its segmentation threshold is usually fixed and unchangeable, making it difficult to adapt to different scanning conditions (such as low dose, low scan slice thickness, obese patients). Dynamically adjusting the segmentation threshold is prone to missing small-sized calculi, which is not conducive to use.

[0004] Therefore, there is an urgent need for an intelligent recognition and analysis system for urological calculus images to solve the above problems. Summary of the Invention

[0005] The purpose of the present invention is to provide an intelligent recognition and analysis system for urological calculus images to solve the problems raised in the above background technique.

[0006] To achieve the above purpose, the present invention provides the following technical solution: An intelligent recognition and analysis system for urological calculus images, comprising: A data collection module, used to obtain the first image data of the patient through the metadata of the imaging device; A data preprocessing module, used to decode and preprocess the data information in the database to obtain the parameters participating in the calculation in the calculation processing module; A calculation processing module, and the specific calculation processing steps are as follows: S1. Substitute the parameter values obtained after decoding and preprocessing into the comprehensive image score value algorithm unit to calculate the comprehensive image score value Ism, and take the ideal value of the parameter to calculate the maximum value Ism of the comprehensive image score value max ; S2. Set the standard threshold Y1 of the image quality in the database as Ism max / 2. When the comprehensive image score value Ism > Y1, take the comprehensive image score value Ism as the input parameter and input it into the adjusted calculus segmentation threshold algorithm unit to calculate the adjusted calculus segmentation threshold Ast; S3. Input the adjusted stone segmentation threshold Ast into the segmentation algorithm in the intelligent recognition system of the stone image for stone image segmentation and recognition. S4. When the comprehensive image score value Ism < Y1, use the comprehensive image score value Ism as an input parameter to input into the adjusted image scan layer thickness algorithm unit to calculate the adjusted image scan layer thickness Thk. again ; S5. According to the adjusted image scan layer thickness Thk again , perform a secondary scan of the patient's urological stone image and re - calculate within the comprehensive image score value algorithm unit.

[0007] Optionally, obtaining the patient's first image data through the metadata of the imaging device specifically includes: Obtain the image scan layer thickness Thk, radiation dose Ed, and diameter of the stenosis segment Dus through the metadata of the imaging device; Obtain the image signal - to - noise ratio SNR and CT value gradient CTg through the image processing software built into the imaging device; Obtain the patient's BMI value from the patient's electronic medical record and upload it to the database together.

[0008] Optionally, the calculation and processing module includes a comprehensive image score value algorithm unit, an adjusted stone segmentation threshold algorithm unit, and an adjusted image scan layer thickness algorithm unit.

[0009] Optionally, the calculation logic of the comprehensive image score value algorithm unit is as follows: S11. Amplify the influence value of the image signal - to - noise ratio SNR on the comprehensive image score value Ism through a power function; S12. Obtain the low - contrast detectability value after balancing the spatial resolution by coupling the influence of the scan layer thickness THK and the CT value gradient CTg on the comprehensive image score value Ism; S13. Standardize the influence term of the patient's BMI value BMI on the comprehensive image score value Ism through a reference value of 25 to obtain the influence term of the patient's BMI value BMI on the comprehensive image score value Ism; S14. Multiply the influence value of the image signal - to - noise ratio SNR on the comprehensive image score value Ism, the low - contrast detectability value after balancing the spatial resolution, and the influence term of the patient's BMI value BMI on the comprehensive image score value Ism to obtain the comprehensive image score value Ism.

[0010] Optionally, the calculation logic of the adjusted stone segmentation threshold algorithm unit is as follows: S21. Through the maximum value Ism of the comprehensive image score value maxNormalize the influence term of the comprehensive image score value Ism on the stone segmentation threshold to a numerical range of 0 to 1; S22, obtain the coupling influence term of image blur and noise by dividing the radiation dose Ed by the image scan layer thickness Thk; S23, scale the coupling influence term of image blur and noise through a logarithmic function; S24, multiply the coupling influence term of image blur and noise after logarithmic function scaling by the influence term of the comprehensive image score value Ism on the stone segmentation threshold and the basic stone segmentation threshold Bst to obtain the adjusted stone segmentation threshold Ast.

[0011] Optionally, the calculation logic of the adjusted image scan layer thickness algorithm unit is as follows: S41, map the adjustment requirement value of the ureteral stenosis degree on the layer thickness through a hyperbolic tangent function; S42, through the maximum value Ism of the comprehensive image score value max Normalize the influence term of the comprehensive image score value Ism on the adjusted image scan layer thickness Thk again ; S43, multiply the table image scan layer thickness Thk by the influence term of the comprehensive image score value Ism on the adjusted image scan layer thickness Thk after normalization and the adjustment requirement value of the ureteral stenosis degree on the layer thickness to obtain the adjusted image scan layer thickness. again

[0012] Optionally, the decoding preprocessing includes data cleaning and data standardization.

[0013] Optionally, the devices used in the data collection module include ImageJ image processing software.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: First, through the mutual cooperation of the three groups of algorithm units, the present invention jointly constitutes the core architecture of an intelligent recognition and analysis system for urological calculus images. By the adjusted stone segmentation threshold algorithm unit, comprehensively considering the basic stone segmentation threshold Bst, the comprehensive image score value Ism, the radiation dose Ed, and the image scan layer thickness Thk, the adjusted stone segmentation threshold Ast is calculated, which can dynamically adjust the segmentation threshold of the stone in the image segmentation algorithm of the intelligent recognition system of the stone image under different scanning conditions (such as low dose, low scan layer thickness, obese patients), helps the system to more accurately identify the boundary and range of the stone, improves the accuracy of stone segmentation, and enhances the recognition accuracy of the stone image, providing a scientific and reliable data basis for subsequent diagnosis and treatment.

[0015] ​II. Through the comprehensive image score value algorithm unit, the present invention comprehensively considers the image signal-to-noise ratio SNR, the image scan layer thickness Thk, and the CT value gradient CTg to calculate the comprehensive image score value Ism, which can more comprehensively and accurately identify and evaluate the stone images of patients, reduce misdiagnosis and missed diagnosis caused by a single factor, enable the intelligent recognition and analysis system to better identify and distinguish the stones in the image from the surrounding tissues, and improve the detection rate of stones. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is a flowchart of an intelligent recognition and analysis system for urological stone images; Figure 2 is a schematic diagram of the overall structure of an intelligent recognition and analysis system for urological stone images. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] The technical solutions in the embodiments of the present invention will be clearly and completely described below 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 creative efforts shall fall within the protection scope of the present invention.

[0018] Embodiment 1. Please refer to Figures 1 to 2 , the present invention provides an intelligent recognition and analysis system for urological stone images, including the following steps: A data collection module, used to obtain the first image data of the patient through the metadata of the imaging device, specifically including: Obtaining the image scan layer thickness Thk, radiation dose Ed, and diameter Dus of the stenosis section through the metadata of the imaging device; Obtaining the image signal-to-noise ratio SNR and CT value gradient CTg through the image processing software built in the imaging device; Obtaining the patient's BMI value from the patient's electronic medical record and uploading it to the database together; A data preprocessing module, used to decode and preprocess the data information in the database to obtain the parameters participating in the calculation in the calculation processing module; A calculation processing module, used to substitute the parameter values obtained after decoding and preprocessing into the comprehensive image score value algorithm unit to calculate the comprehensive image score value Ism, and calculate the maximum value Ism of the comprehensive image score value by taking the ideal values of the parameters max , and upload it to the database together; Setting the standard threshold Y1 of the image quality in the database as Ism max / 2. When the comprehensive image score value Ism > Y1, the comprehensive image score value Ism is used as an input parameter and input into the adjusted stone segmentation threshold algorithm unit to calculate the adjusted stone segmentation threshold Ast. The adjusted stone segmentation threshold Ast is input into the segmentation algorithm in the intelligent recognition system of the stone image for stone image segmentation and recognition. When the comprehensive image score value Ism < Y1, the comprehensive image score value Ism is used as an input parameter and input into the adjusted image scan layer thickness algorithm unit to calculate the adjusted image scan layer thickness Thk. again ; According to the adjusted image scan layer thickness Thk again , the urological stone image of the patient is scanned a second time and recalculated in the comprehensive image score value algorithm unit.

[0019] The specific image segmentation and recognition is a relatively mature existing technology in the field of image processing, and will not be elaborated here.

[0020] In this embodiment: Through the mutual cooperation of the three algorithm units, the present invention constitutes the core architecture of an intelligent recognition and analysis system for urological stone images. By comprehensively considering influencing factors such as the image signal-to-noise ratio SNR, the image scan layer thickness Thk, and the CT value gradient CTg in the comprehensive image score value algorithm unit, the comprehensive image score value Ism is calculated. It can more comprehensively and accurately evaluate the image quality, reduce misdiagnosis and missed diagnosis caused by a single factor, and can help the intelligent recognition and analysis system better distinguish stones from surrounding tissues, improve the detection rate of stones, and better perform image recognition. By considering the stone basic segmentation threshold Bst, the comprehensive image score value Ism, the radiation dose Ed, and the image scan layer thickness Thk in the adjusted stone segmentation threshold algorithm unit, the adjusted stone segmentation threshold Ast is calculated, that is, the segmentation threshold of the stone can be dynamically adjusted by factors such as image quality, which helps to more accurately identify the boundary and range of the stone, improve the accuracy of stone segmentation, and provide a scientific and reliable data basis for subsequent diagnosis and treatment.

[0021] Please refer to Figures 1 to 2 , the comprehensive image score value algorithm unit is as follows: ; Among them: Ism represents the comprehensive image score value: SNR represents the image signal-to-noise ratio, which is the ratio of the average value of the image signal to the standard deviation of the background noise, and is obtained through the ImageJ image processing software built into the imaging device; Thk represents the image scan layer thickness, which is the slice thickness during the scan of the imaging device and is obtained from the metadata of the imaging device. CTg represents the CT value gradient, which is the CT value gradient between the stone and the surrounding tissues and is obtained through the built-in image processing software of the imaging device; BMI represents the patient's BMI value and is obtained from the patient's electronic medical record; In the formula calculation: This part reflects the non-linear influence of the image signal-to-noise ratio SNR on the comprehensive image score value through a power function. As the image signal-to-noise ratio SNR increases, the calculated comprehensive image score value Ism increases, but the growth rate gradually slows down, which is used to slow down the excessive influence on the comprehensive image score value Ism when the image signal-to-noise ratio SNR is too large; Part of it reflects the comprehensive influence of the coupling of the scan layer thickness THK and the CT value gradient CTg on the comprehensive image score value Ism, which is used to balance the spatial resolution and low-contrast detectability. Specifically: As the molecular scan layer thickness THK decreases, This part of the value will increase, and the calculated comprehensive image score value Ism will increase; When the CT value gradient CTG is high, it means the image edge is clear, the denominator part approaches 1, and the value of this part of the coupling term is close to the value of the scan layer thickness THK; When the CT value gradient CTG is low, it means the image is blurred, the denominator increases, this part of the coupling term decreases, and the calculated comprehensive image score value Ism decreases. In this case, to ensure the image quality, the system will select a thinner layer thickness when the CT value gradient CTG is low; When the patient's BMI value BMI is greater than or equal to 25, it is considered overweight, and artifacts will appear during image shooting, reducing the image quality. The standard value of the BMI value is set to 25 at the molecular part, This part standardizes the influence of the patient's BMI value BMI on the comprehensive image score value through the reference value 25. Specifically: When the patient's BMI value BMI is equal to 25, The value of this part is 1, indicating that the patient's current weight will not affect the calculation of the comprehensive image score value Ism; When the patient's BMI value BMI is greater than 25, The value of this part is greater than 1. As the patient's BMI value BMI increases, The value of this part will decrease to between 0 and 1, thereby reducing the calculated comprehensive image score value Ism; When the patient's BMI value BMI is less than 25, Part of the value is less than 1. As the patient's BMI value BMI decreases, The value of this part will increase, thereby increasing the calculated comprehensive image score value Ism.

[0022] In this embodiment, the image signal-to-noise ratio SNR reflects the clarity of the image. The higher the image signal-to-noise ratio SNR, the less noise in the image, and the clearer the characteristics of the stone can be distinguished. The image scan layer thickness Thk affects the observation of stone details. A thinner layer thickness can provide more detailed stone morphology information. The CT value gradient CTG reflects the density difference between the stone and the surrounding tissues, which helps to accurately identify the stone. The patient's BMI value is related to the absorption and scattering of X-rays by body tissues and will affect the image quality. The comprehensive image score value algorithm unit comprehensively considers the image signal-to-noise ratio SNR, the image scan layer thickness Thk, the CT value gradient CTg, and the patient's BMI value BMI to comprehensively calculate the comprehensive image score value Ism, which can more comprehensively and accurately evaluate the image quality, reduce misdiagnosis and missed diagnosis caused by a single factor. The imaging manifestations of urological stones may vary due to factors such as stone composition, size, and location. The comprehensive image score value Ism, as a comprehensive index in the system database, can help the intelligent recognition and analysis system better distinguish the stone from the surrounding tissues, improve the detection rate of the stone, and better identify the image.

[0023] Please refer to Figures 1 to 2 , the adjusted stone segmentation threshold algorithm unit is as follows: ; Where: Ast represents the adjusted stone segmentation threshold; Bst represents the basic stone segmentation threshold, which is the preset threshold for identifying stones in the image segmentation algorithm; Ism represents the comprehensive image score value; Ism max represents the maximum value of the comprehensive image score value, which is the theoretical upper limit of the image quality and represents the image score value in the ideal state; Ed represents the radiation dose, which is obtained from the metadata of the imaging device; Thk represents the image scan layer thickness; α represents the compensation coefficient, and α ∈ (0.5, 3) can be self-adjusted in the adjusted stone segmentation threshold algorithm unit. Specifically: When the radiation dose Ed used in the CT examination is low, the value of the compensation coefficient α will be larger (2 - 3) to increase the calculated adjusted stone segmentation threshold Ast, which is used to reduce the misjudgment of noise in the image when the radiation dose Ed is low, thereby avoiding the missed detection of small stones; In the formula calculation: This part is through the maximum value Ism of the comprehensive image score valuemax Normalize the influence of the comprehensive image score value Ism on the stone segmentation threshold to the mathematical range of 0 to 1. Specifically: As the comprehensive image score value Ism increases, when the comprehensive image score value Ism is closer to the maximum value Ism of the image score max , This term is approximately equal to 1, indicating that the image quality of the patient's captured image is close to the ideal value and no adjustment of the stone segmentation threshold is required; As the comprehensive image score value Ism decreases, This term increases, indicating that the image quality of the patient's captured image is poor and the stone segmentation threshold needs to be increased to avoid missing small stones in the image; Regarding the image scan layer thickness Thk: As the image scan layer thickness THK increases, the spatial resolution will be reduced and the stone edge will be blurred. Therefore, the threshold needs to be increased to reduce false positives; Regarding the radiation dose Ed: Reducing the radiation dose ED will increase the noise image during image capture. Therefore, the threshold needs to be increased to suppress noise misjudgment; This part actually represents the coupled influence of image blur and noise by dividing the radiation dose Ed by the image scan layer thickness Thk. Specifically: When the scan layer thickness Thk increases and the radiation dose Ed decreases, The ratio of this part increases significantly, and the calculated adjusted stone segmentation threshold represents an increase in the adjusted stone segmentation threshold Ast, indicating low spatial resolution and more noise in the image capture, and a large compensation for the segmentation threshold is required, and the calculated adjusted stone segmentation threshold Ast increases; It should be noted that the use of the logarithmic function here can map the coupled image of this part of image blur and noise to the logarithmic space, so that the threshold compensation increases progressively rather than linearly with the increase of this part.

[0024] In this embodiment: In the intelligent recognition and analysis system for urological calculi images, in the traditional stone image algorithm segmentation, the style threshold of the stone is usually fixed and cannot adapt to different scanning conditions (such as low dose, low scan layer thickness, obese patients), which is prone to false positives or missed detections. By using the adjusted stone segmentation threshold algorithm unit to comprehensively consider the basic stone segmentation threshold Bst, the comprehensive image score value Ism, the radiation dose Ed, and the image scan layer thickness Thk, and calculating the adjusted stone segmentation threshold Ast, it is possible to dynamically adjust the stone segmentation threshold according to factors such as image quality, which helps to more accurately identify the boundary and scope of the stone, improve the accuracy of stone segmentation, and provide a scientific and reliable data basis for subsequent diagnosis and treatment.

[0025] The intelligent recognition and analysis system for stone images can automatically calculate and adjust the post-adjustment stone segmentation threshold Ast, which can reduce the time and effort required for doctors to manually adjust the segmentation threshold. This makes the image analysis process more efficient, enabling doctors to obtain accurate stone information faster, improving the efficiency of medical work. Moreover, by incorporating the radiation dose Ed into the calculation of the post-adjustment stone segmentation threshold algorithm unit, it is possible to reduce the radiation exposure to patients while ensuring image quality, which helps protect the health of patients and reduce potential risks caused by radiation.

[0026] Please refer to Figures 1 to 2 , the post-adjustment image scan slice thickness algorithm unit is as follows: ; Where: Thk again represents the post-adjustment image scan slice thickness; Thk represents the image scan slice thickness; Dus represents the diameter of the stenosis segment, which is the diameter of the stenosis segment of the patient's ureter and is obtained from the metadata of the imaging device; Dus ref represents the normal value of the diameter of the stenosis segment, which is the diameter of the stenosis segment of the ureter of a normal person without urinary diseases; Ism represents the comprehensive image score value; Ism max represents the maximum value of the comprehensive image score value β represents a constant for division by zero, with a value of 0.1; In the formula calculation: The smaller the diameter Dus of the stenosis segment of the patient, the greater the potential risk brought by the stone to the patient. Therefore, a thinner CT slice thickness is required for scanning to improve image quality and more clearly capture small stones to reduce the potential risk brought by small stones to the patient. This part reflects the adjustment requirement of the slice thickness due to the degree of ureteral stenosis through the hyperbolic tangent function. Specifically: When the diameter Dus of the stenosis segment is close to the normal value Dus of the diameter of the stenosis segment ref , the function value of this part approaches 0, this part of the value approaches 1, indicating that the diameter Dus of the stenosis segment of the patient is normal and the slice thickness does not need to be adjusted; When the diameter Dus of the stenosis segment is less than the normal value Dus ref , the function value of this part decreases, this part of the value decreases to reduce the calculated post-adjustment image scan slice thickness Thk again for capturing finer image details; Partially through the maximum value Ism of the comprehensive image score value max , standardize the influence of the comprehensive image score value Ism on the adjusted image scan layer thickness Thk again . Specifically: As the comprehensive image score value Ism increases, when the comprehensive image score value Ism approaches the maximum value Ism of the comprehensive image score value max , it represents good image quality, the value of this part decreases, thereby reducing the calculated adjusted image scan layer thickness Thk again . As the comprehensive image score value Ism decreases, it represents poor image quality, then the calculated adjusted image scan layer thickness Thk increases again , meaning that a thinner scan layer thickness is required to capture finer image details

[0027] In this embodiment: The adjusted image scan layer thickness algorithm unit comprehensively considers the diameter Dus of the stenotic segment of the patient's ureter and the comprehensive image score value Ism to calculate the adjusted image scan layer thickness Thk again . On the one hand, according to the adjusted image scan layer thickness Thk again , patients with image quality lower than the standard threshold Y1 can be re-shot for urological stone images, realizing the automatic adjustment of the image scan layer thickness Thk, reducing the time and effort required for doctors to manually adjust scan parameters, improving the efficiency of medical work, which enables doctors to focus more on the diagnosis and treatment of patients, and improving the quality and level of medical services; On the other hand, through this algorithm formula, the image scan layer thickness can be adjusted according to the conditions of different patients. For patients with a smaller diameter of the stenotic segment, the intelligent recognition and analysis system of urological stone images can select a thinner scan layer thickness to capture small stones more clearly, so as to reduce the risk of missed diagnosis caused by blurred images of small stones. Specifically: When the diameter Dus of the stenotic segment of the patient is small, the adjusted image scan layer thickness algorithm unit will forcibly reduce the layer thickness through the hyperbolic tangent function (tanh) (for example, when Dus = 1mm, the layer thickness will be compressed to 30% of the original value), significantly improving the image resolution at the stenosis, and avoiding detail blur caused by too thick a layer thickness, so as to improve the image quality and diagnostic accuracy

[0028] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents

Claims

1. An intelligent recognition and analysis system for urological lithiasis imaging, characterized in that, Including: A data collection module, which is used to obtain the first imaging data of a patient through the metadata of an imaging device; A data preprocessing module, which is used to decode and preprocess the data information in the database to obtain the parameters participating in the calculation in the calculation processing module; A calculation processing module, and the specific calculation processing steps are as follows: S1. Substitute the parameter values obtained after decoding preprocessing into the comprehensive image scoring value algorithm unit to calculate the comprehensive image scoring value Ism, and use the ideal values of the parameters to calculate the maximum value Ism of the comprehensive image scoring value max ; S2. Set the standard threshold Y1 of the image quality in the database to Ism max / 2. When the comprehensive image score value Ism > Y1, use the comprehensive image score value Ism as the input parameter and input it into the adjusted stone segmentation threshold algorithm unit to calculate the adjusted stone segmentation threshold Ast; S3. Input the adjusted stone segmentation threshold Ast into the segmentation algorithm in the intelligent recognition system of the stone image for stone image segmentation and recognition; S4, When the comprehensive image score value Ism < Y1, use the comprehensive image score value Ism as the input parameter and input it into the adjusted image scan layer thickness algorithm unit to calculate the adjusted image scan layer thickness Thk again ; S5, according to the adjusted imaging scan slice thickness Thk again , perform a second scan of the patient's urological calculus image and recalculate within the algorithm unit of the comprehensive image scoring value.

2. The intelligent recognition and analysis system for urological lithiasis imaging according to claim 1, wherein: Obtaining the first imaging data of a patient through the metadata of an imaging device specifically includes: Obtaining the imaging scan layer thickness Thk, radiation dose Ed, and diameter Dus of the stenosis segment through the metadata of the imaging device; Obtaining the imaging signal-to-noise ratio SNR and CT value gradient CTg through the image processing software built in the imaging device; Obtaining the BMI value of the patient from the electronic medical record of the patient and uploading it to the database together.

3. An intelligent recognition and analysis system for urological lithiasis imaging according to claim 1, characterized in that: The calculation processing module includes a comprehensive image score value algorithm unit, an adjusted stone segmentation threshold algorithm unit, and an adjusted imaging scan layer thickness algorithm unit.

4. An intelligent recognition and analysis system for urological calculi imaging according to claim 3, characterized in that: The calculation logic of the comprehensive image score value algorithm unit is as follows: S11. Amplify the influence value of the imaging signal-to-noise ratio SNR on the Ism of the comprehensive image score value through a power function; S12. Obtain the low-contrast detectability value after balancing the spatial resolution by coupling the scanning layer thickness THK and the CT value gradient CTg on the comprehensive image score value Ism; S13. Standardize the influence term of the patient's BMI value BMI on the comprehensive image score value Ism through a reference value of 25 to obtain the influence term of the patient's BMI value BMI on the comprehensive image score value Ism; S14. Multiply the influence value of the imaging signal-to-noise ratio SNR on the Ism of the comprehensive image score value, the low-contrast detectability value after balancing the spatial resolution, and the influence term of the patient's BMI value BMI on the comprehensive image score value Ism to obtain the comprehensive image score value Ism.

5. The intelligent recognition and analysis system for urological calculi imaging according to claim 3, characterized in that: The calculation logic of the adjusted stone segmentation threshold algorithm unit is as follows: S21, normalize the influence term of the comprehensive image score value Ism on the stone segmentation threshold to the numerical range of 0 to 1 by using the maximum value Ism of the comprehensive image score value max Normalize the influence term of the comprehensive image score value Ism on the stone segmentation threshold to the numerical range of 0 to 1; S22. Obtain the coupled influence term of image blur and noise by dividing the radiation dose Ed by the imaging scan layer thickness Thk; S23. Scale the coupled influence term of image blur and noise through a logarithmic function; S24. Multiply the coupled influence term of image blur and noise scaled by the logarithmic function by the influence term of the comprehensive image score value Ism on the stone segmentation threshold and the basic stone segmentation threshold Bst to obtain the adjusted stone segmentation threshold Ast.

6. The intelligent recognition and analysis system for urological calculi imaging according to claim 4, wherein: The calculation logic of the adjusted imaging scan layer thickness algorithm unit is as follows: S41. Map the adjustment requirement value of the ureteral stenosis degree on the layer thickness through a hyperbolic tangent function; S42, by the maximum value Ism of the comprehensive image score value max Normalize the influencing item of the adjusted image scan layer thickness Thk again with respect to the comprehensive image score value Ism; S43, the influencing term of the adjusted image scan thickness Thk by multiplying the table image scan thickness Thk by the comprehensive image score value Ism after normalization processing, and the adjusted demand value of the layer thickness due to the degree of ureteral stricture, to obtain the adjusted image scan thickness. again ​ 7. An intelligent recognition and analysis system for urological lithiasis imaging according to claim 1, characterized in that, The decoding preprocessing includes data cleaning and data standardization.

8. An intelligent recognition and analysis system for urological lithiasis imaging according to claim 1, characterized in that, The devices used in the data collection module include ImageJ image processing software.

Images