Medical image processing method and system

Abstract

The invention provides a medical image processing method and system, and the method comprises the steps: obtaining a target physiological state parameter of a target object, and determining a first working parameter corresponding to the target physiological state parameter; controlling the medical imaging equipment to image a preset position of the target object according to the first working parameter to obtain a first image; performing image segmentation on the first image to obtain a first target region and a first background region, and determining a target area ratio between the first target region and the first background region; determining a target adjustment parameter corresponding to the target area ratio, and adjusting the first working parameter according to the target adjustment parameter to obtain a second working parameter; and controlling the medical imaging equipment to image the preset position according to the second working parameter to obtain a second image.

Description

Technical Field

[0001] The present application relates to the field of image processing technology, and in particular to a medical image processing method and system. Background Art

[0002] In practical applications, medical image applications play a very important role in medical diagnosis. At present, medical diagnosis is often mainly based on the doctor's naked eye observation of medical images, thereby giving corresponding diagnostic conclusions. However, due to various reasons, it is likely that the image is not clear enough, resulting in inaccurate analysis results of medical images. Therefore, how to improve the quality of medical images to ensure the accuracy of medical image analysis results needs to be solved urgently. Summary of the invention

[0003] The embodiments of the present application provide a medical image processing method and system, which can improve the quality of medical images to ensure the accuracy of medical image analysis results.

[0004] In a first aspect, an embodiment of the present application provides a medical image processing method, which is applied to a medical imaging device, and the method includes:

[0005] Acquire a target physiological state parameter of the target object, and determine a first working parameter corresponding to the target physiological state parameter;

[0006] Controlling the medical imaging device to image a preset position of the target object with the first working parameter to obtain a first image;

[0007] Performing image segmentation on the first image to obtain a first target area and a first background area, and determining a target area ratio between the first target area and the first background area;

[0008] determining a target adjustment parameter corresponding to the target area ratio, and adjusting the first operating parameter according to the target adjustment parameter to obtain a second operating parameter;

[0009] Controlling the medical imaging device to image the preset position with the second working parameter to obtain a second image;

[0010] Performing image segmentation on the second image to obtain a second target area, and dividing the second target area into m area blocks, where m is an integer greater than 1;

[0011] Determine the feature of each of the m area blocks to obtain m feature sets; each feature set includes at least one feature;

[0012] Determine a target optimization parameter corresponding to the m feature sets, and optimize the second operating parameter according to the target optimization parameter to obtain a third operating parameter;

[0013] The medical imaging device is controlled to image the preset position with the third working parameter to obtain a third image.

[0014] In a second aspect, an embodiment of the present application provides a medical image processing system, which is applied to a medical imaging device. The system includes: an acquisition unit, a control unit, and a determination unit, wherein:

[0015] The acquisition unit is used to acquire a target physiological state parameter of the target object and determine a first working parameter corresponding to the target physiological state parameter;

[0016] The control unit is used to control the medical imaging device to image a preset position of the target object with the first working parameter to obtain a first image;

[0017] The determining unit is used to perform image segmentation on the first image to obtain a first target area and a first background area, determine a target area ratio between the first target area and the first background area; determine a target adjustment parameter corresponding to the target area ratio, and adjust the first working parameter according to the target adjustment parameter to obtain a second working parameter;

[0018] The control unit is further used to control the medical imaging device to image the preset position with the second working parameter to obtain a second image;

[0019] The determination unit is further configured to perform image segmentation on the second image to obtain a second target area, and divide the second target area into m area blocks, where m is an integer greater than 1; determine a feature of each of the m area blocks to obtain m feature sets; each feature set includes at least one feature; determine a target optimization parameter corresponding to the m feature sets, and optimize the second working parameter according to the target optimization parameter to obtain a third working parameter;

[0020] The control unit is further used to control the medical imaging device to image the preset position with the third working parameter to obtain a third image.

[0021] In a third aspect, an embodiment of the present application provides a medical imaging device, comprising a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the program includes instructions for executing the steps in the first aspect of the embodiment of the present application.

[0022] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute part or all of the steps described in the first aspect of the embodiment of the present application.

[0023] In a fifth aspect, an embodiment of the present application provides a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute some or all of the steps described in the first aspect of the embodiment of the present application. The computer program product may be a software installation package.

[0024] The implementation of the embodiments of the present application has the following beneficial effects:

[0025] It can be seen that the medical image processing method and system described in the embodiments of the present application are applied to a medical imaging device, obtain a target physiological state parameter of a target object, determine a first working parameter corresponding to the target physiological state parameter, control the medical imaging device to image a preset position of the target object with the first working parameter to obtain a first image, perform image segmentation on the first image to obtain a first target area and a first background area, determine a target area ratio between the first target area and the first background area, determine a target adjustment parameter corresponding to the target area ratio, adjust the first working parameter according to the target adjustment parameter to obtain a second working parameter, control the medical imaging device to image a preset position with the second working parameter to obtain a second image, perform image segmentation on the second image to obtain a second target area, divide the second target area into m area blocks, where m is an integer greater than 1, determine a feature of each of the m area blocks, and obtain m feature sets. Each feature set includes at least one feature, and the target optimization parameters corresponding to the m feature sets are determined. The second working parameters are optimized according to the target optimization parameters to obtain the third working parameters, and the medical imaging device is controlled to image the preset position with the third working parameters to obtain the third image. Firstly, when the working parameters corresponding to the physiological state parameters of the target object are used for imaging, the working parameters corresponding to the actual situation of the imaging object can be obtained, which helps to preliminarily ensure the imaging effect. Secondly, the working parameters can be dynamically adjusted based on the area ratio between the target area and the background area, which helps to fully present the target and also helps to ensure the overall image quality of the target. Thirdly, due to the regional characteristics and neighborhood characteristics between the target areas, the regional characteristics and neighborhood characteristics between the target areas are analyzed based on the characteristics of each area block, and then the working parameters are deeply optimized, which helps to deeply improve the quality of medical images, thereby ensuring the accuracy of the medical image analysis results. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0027] Figure 1 It is a flowchart of a medical image processing method provided in an embodiment of the present application;

[0028] Figure 2 is a structural schematic diagram of a medical imaging device provided in an embodiment of the present application;

[0029] Figure 3 It is a block diagram of the functional units of a medical image processing system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0030] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but in a possible example also includes steps or units that are not listed, or in a possible example also includes other steps or units inherent to these processes, methods, products or devices.

[0031] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0032] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0033] The medical imaging equipment involved in the embodiments of the present application may include but is not limited to: X-ray machines, ultrasonic medical imaging equipment, computed tomography (CT) equipment, nuclear magnetic resonance (MRI) equipment, positron emission tomography (PET) equipment, single-photon emission computed tomography (SPECT), etc., and is not limited here.

[0034] See also Figure 1 , Figure 1 : is a flow chart of a medical image processing method provided by an embodiment of the present application. As shown in the figure, it is applied to a medical imaging device. The medical image processing method includes:

[0035] 101. Obtain a target physiological state parameter of a target object, and determine a first working parameter corresponding to the target physiological state parameter.

[0036] In the embodiments of the present application, the target object may be a human, biological tissue, other animals, etc., which is not limited here.

[0037] Among them, the first operating parameter may include at least one of the following: operating current, operating voltage, operating power, operating frequency, duty cycle, scanning mode, scanning layer thickness, scanning interval, contrast agent usage, scanning site, signal-to-noise ratio, resolution, contrast, artifacts, scanning time, imaging distance, imaging angle, etc., which are not limited here.

[0038] Among them, the target physiological state parameters may include at least one of the following: height, weight, heart rate, body fat percentage, blood temperature, blood sugar content, respiratory rate, sleep quality, basic medical history, etc., which are not limited here.

[0039] In an embodiment of the present application, the target physiological state parameters of the target object can be obtained, and the mapping relationship between the preset physiological state parameters and the working parameters can be pre-stored. The first working parameters corresponding to the target physiological state parameters are determined based on the mapping relationship. Then, the working parameters corresponding to the actual situation of the imaging object can be obtained, which helps to initially guarantee the imaging effect.

[0040] 102. Control the medical imaging device to image a preset position of the target object with the first working parameter to obtain a first image.

[0041] The preset position may be preset or set by system default.

[0042] In an embodiment of the present application, the medical imaging device can be controlled to image a preset position of the target object with a first working parameter to obtain a first image. For example, the medical imaging device can be controlled to image a preset position with a first working parameter to obtain at least one image, and the image with the best quality among the at least one image is selected as the first image. The working parameters corresponding to the actual situation of the imaging object can be obtained, which helps to initially ensure the imaging effect.

[0043] 103. Perform image segmentation on the first image to obtain a first target area and a first background area, and determine a target area ratio between the first target area and the first background area.

[0044] In an embodiment of the present application, the first image can be segmented to obtain a first target area and a first background area, determine a first area corresponding to the first target area and a second area corresponding to the first background area, and then determine the target area ratio between the first target area and the first background area, where the target area ratio = first area / second area.

[0045] 104. Determine a target adjustment parameter corresponding to the target area ratio, and adjust the first operating parameter according to the target adjustment parameter to obtain a second operating parameter.

[0046] Among them, the second operating parameter may include at least one of the following: operating current, operating voltage, operating power, operating frequency, duty cycle, scanning mode, scanning layer thickness, scanning interval, contrast agent usage, scanning site, signal-to-noise ratio, resolution, contrast, artifacts, scanning time, imaging distance, imaging angle, etc., which are not limited here.

[0047] In an embodiment of the present application, a mapping relationship between a preset area ratio and an adjustment parameter can be pre-stored, and then, a target adjustment parameter corresponding to the target area ratio can be determined based on the mapping relationship, and then the first working parameter is adjusted according to the target adjustment parameter to obtain a second working parameter, that is, the second working parameter = the first working parameter * (1 + target adjustment parameter). In this way, the working parameter can be dynamically adjusted based on the area ratio between the target area and the background area, which helps to fully present the target and also helps to ensure the overall image quality of the target, and then, ensure the quality of the medical image to ensure the accuracy of the medical image analysis results.

[0048] 105. Control the medical imaging device to image the preset position with the second working parameter to obtain a second image.

[0049] In an embodiment of the present application, the medical imaging device can be controlled to image a preset position with a second working parameter to obtain a second image. For example, the medical imaging device can be controlled to image a preset position with a second working parameter to obtain at least one image, and the image with the best quality among the at least one image is selected as the second image. That is, since the working parameters are dynamically adjusted based on the area ratio between the target area and the background area, it helps to fully present the target and also helps to ensure the overall image quality of the target, thereby ensuring the quality of the medical image and the accuracy of the medical image analysis results.

[0050] 106. Perform image segmentation on the second image to obtain a second target area, and divide the second target area into m area blocks, where m is an integer greater than 1.

[0051] In an embodiment of the present application, the second image can be segmented to obtain a second target area, and the second target area can be divided into m area blocks, where m is an integer greater than 1. In this way, the regional characteristics and neighborhood characteristics between the target areas can be analyzed, which is helpful to deeply optimize the working parameters or image effects, and thus deeply improve the quality of medical images.

[0052] The area of ​​each region block may be equal or unequal.

[0053] 107. Determine a feature of each of the m area blocks to obtain m feature sets; each feature set includes at least one feature.

[0054] The feature may include at least one of the following: feature points, feature contours, feature vectors, etc., which are not limited here.

[0055] In a specific implementation, the characteristics of each of the m regional blocks are determined to obtain m feature sets, each of which includes at least one feature. Then, the regional characteristics and neighborhood characteristics between the target regions can be analyzed based on the characteristics of each regional block, which helps to deeply optimize the working parameters or image effects, and thus deeply improve the quality of medical images.

[0056] 108. Determine target optimization parameters corresponding to the m feature sets, and optimize the second operating parameters according to the target optimization parameters to obtain third operating parameters.

[0057] Among them, the third operating parameter may include at least one of the following: operating current, operating voltage, operating power, operating frequency, duty cycle, scanning mode, scanning layer thickness, scanning interval, contrast agent usage, scanning site, signal-to-noise ratio, resolution, contrast, artifacts, scanning time, imaging distance, imaging angle, etc., which are not limited here.

[0058] In an embodiment of the present application, the m feature sets reflect the regional characteristics and neighborhood characteristics between the target areas, and thus, the target optimization parameters corresponding to the m feature sets can be determined, and the first working parameters can be optimized according to the target optimization parameters to obtain the second working parameters, that is, the second working parameters = (1 + target optimization parameters) * first working parameters, which helps to deeply optimize the working parameters and thus deeply improve the quality of medical images.

[0059] 109. Control the medical imaging device to image the preset position with the third working parameter to obtain a third image.

[0060] In a specific implementation, the medical imaging device can be controlled to image the preset position with the third working parameter to obtain a third image. For example, the medical imaging device can be controlled to image the preset position with the third working parameter to obtain at least one image, and the image with the best image quality is selected as the third image. Due to the regional characteristics and neighborhood characteristics between the target areas, the regional characteristics and neighborhood characteristics between the target areas are analyzed based on the characteristics of each area block, and then the working parameters are deeply optimized, which helps to deeply improve the quality of medical images.

[0061] Optionally, the following steps may also be included:

[0062] A1. Determine the energy mean of each of the m area blocks according to the m feature sets to obtain m energy means;

[0063] A2. determining the distribution density of feature points of each of the m area blocks according to the m feature sets to obtain m feature point distribution densities;

[0064] A3. Determine a first mean square error according to the m energy means;

[0065] A4. Determine a second mean square error according to the distribution density of the m feature points;

[0066] A5. When the first mean square error belongs to a preset first range and the second mean square error belongs to a second preset range, retain the second image.

[0067] In a specific implementation, the energy mean of each of the m area blocks can be determined based on the m feature sets to obtain m energy means, that is, the number of features in each area block can be determined, the energy value corresponding to each feature can be determined, multiple energy values ​​can be obtained, the total energy of the multiple energy values ​​can be determined, and the energy mean can be determined using the total energy and the number of features. Energy mean = total energy / number of features.

[0068] Next, the feature point distribution density of each of the m area blocks can be determined based on the m feature sets to obtain m feature point distribution densities, that is, the number of features in each area block and the area of ​​each area block are determined, then the feature point distribution density of the area block = the number of features in the area block / the area of ​​the area block.

[0069] Among them, the first mean square error can be determined according to the m energy means. Since the energy mean reflects the main body of the target area image to a certain extent, the first mean square error reflects the stability of the low-frequency features of the target area image to a certain extent. The larger the first mean square error, the worse the stability of the low-frequency features of the target area image, and the worse the main quality of the target area image. Conversely, the smaller the first mean square error, the better the stability of the low-frequency features of the target area image, and the better the main quality of the target area image.

[0070] Next, the second mean square error can be determined according to the distribution density of the m feature points. Since the distribution density of the feature points reflects the detail characteristics of the target area image to a certain extent, the second mean square error reflects the high-frequency feature stability of the target area image to a certain extent. The larger the second mean square error, the worse the high-frequency feature stability of the target area image, and the worse the detail quality of the target area image. Conversely, the smaller the second mean square error, the better the high-frequency feature stability of the target area image, and the better the detail quality of the target area image.

[0071] In a specific implementation, the first preset range can be preset or the system defaults, and the second preset range can also be preset or the system defaults. When the first mean square error belongs to the preset first range and the second mean square error belongs to the second preset range, it means that the subject and details of the second image are all okay, which means that there is no problem with the image quality, and the second image can be retained. In this way, the first mean square error can be used to reflect the stability of the low-frequency features of the target area image to a certain extent, and the second mean square error can reflect the stability of the high-frequency features of the target area image to a certain extent. The first mean square error and the second mean square error both reflect the correlation and closeness between the various regions in the target area image, so that the correlation between the various parts of the target can be evaluated more deeply, which helps to ensure the overall quality of the target area image, and helps to ensure the quality of each region of the target area image, ensuring that the details and subject of the target are all okay, and then deeply improving the quality of medical images.

[0072] Optionally, the following steps may also be included:

[0073] When the first mean square error does not belong to the preset first range and the second mean square error does not belong to the second preset range, the step of performing image segmentation on the second image to obtain a second target area is performed.

[0074] In the specific implementation, when the first mean square error does not belong to the preset first range and the second mean square error does not belong to the second preset range, it means that the details and the main body of the target are not okay, and then the regional characteristics and the neighborhood characteristics between the target areas are considered, and the regional characteristics and the neighborhood characteristics between the target areas are analyzed based on the characteristics of each area block to deeply optimize the working parameters, which helps to deeply improve the quality of medical images.

[0075] Optionally, the above step 108, determining the target optimization parameters corresponding to the m feature sets, may include the following steps:

[0076] 81. Determine a neighborhood of each of the m area blocks to obtain m neighborhood sets;

[0077] 82. Determine the energy mean corresponding to each neighborhood set in the m neighborhood sets to obtain m neighborhood energy means;

[0078] 83. Determine the weights corresponding to the m neighborhood energy means to obtain m weights;

[0079] 84. Perform a weighted operation on the m energy means and the m weights to obtain m weighted energy means;

[0080] 85. Determine a third mean square error according to the m weighted energy means;

[0081] 86. Determine the target optimization parameter corresponding to the third mean square error.

[0082] In the embodiment of the present application, the neighborhood of each of the m area blocks can be determined to obtain m neighborhood sets, and the neighborhood of a certain area block can be understood as the adjacent area blocks that share a common edge with the area block. Each of the M neighborhood sets may include one or more neighborhoods.

[0083] Next, the energy mean corresponding to each neighborhood set in the m neighborhood sets can be determined, and m neighborhood energy means can be obtained. That is, the total energy corresponding to each neighborhood set and the corresponding number of features can be determined. The ratio between the two is the neighborhood energy mean.

[0084] Furthermore, the weights corresponding to the m neighborhood energy means can be determined to obtain m weights, that is, the sum of the m neighborhood energy means can be determined to obtain the neighborhood energy sum, and then the ratio between each neighborhood energy mean among the m neighborhood energy means and the neighborhood energy sum can be determined to obtain m weights. The weight reflects the weight of the corresponding regional block, and then each energy mean in the m energy means is weighted with the corresponding weight in the m weights to obtain m weighted energy means. Since the neighborhood characteristics are considered based on the regional characteristics, each regional block is deeply connected with its surrounding neighborhood, so that the relevance of the target regional image is further improved. Then the third mean square error is determined according to the m weighted energy means. The third mean square error considers the neighborhood characteristics of each regional block, and uses the neighborhood characteristics to feedback and mine the importance of each regional block. Then, the energy weight of each regional block is weighted by importance. The third mean square error obtained on this basis deeply conforms to the individuality of each region and the commonality of the entire target regional image, and regards the depth of each regional block as an indivisible whole. The mapping relationship between the preset mean square error and the optimization parameter can also be pre-stored, and then the target optimization parameter corresponding to the third mean square error can be determined based on the mapping relationship. In the optimization process, on the basis of ensuring the individuality of each region, the overall optimization is performed in depth.

[0085] Optionally, the following steps may also be included:

[0086] B1. when the first mean square error does not belong to the preset first range and the second mean square error belongs to the second preset range, determining a first image enhancement algorithm corresponding to the second mean square error;

[0087] B2. Obtaining a first algorithm control parameter corresponding to the first image enhancement algorithm;

[0088] B3. Determine a first adjustment parameter corresponding to the first mean square error;

[0089] B4. adjusting the first algorithm control parameter according to the first adjustment parameter to obtain a target first algorithm control parameter;

[0090] B5. Perform image enhancement processing on the second image according to the first image enhancement algorithm and the target first algorithm control parameters to obtain a first enhanced image.

[0091] In a specific implementation, when the first mean square error does not belong to the preset first range and the second mean square error belongs to the second preset range, it means that the main body of the second image is not okay but the details are okay, and then a first mapping relationship between the preset mean square error and the image enhancement algorithm can be pre-stored, and then, the first image enhancement algorithm corresponding to the second mean square error can be determined based on the first mapping relationship, wherein the first image enhancement algorithm can be used to achieve main body enhancement of the image on the basis of ensuring that the details remain unchanged, that is, to ensure low-frequency feature enhancement on the basis of ensuring the stability of high-frequency features.

[0092] Next, a first algorithm control parameter corresponding to the first image enhancement algorithm can also be obtained, and the first algorithm control parameter can be preset or system default. A second mapping relationship between a preset mean square error and an adjustment parameter can be pre-stored, and then a first adjustment parameter corresponding to the first mean square error is determined based on the second mapping relationship. The first mean square error reflects the low-frequency quality of the target area image to a certain extent, and then the first algorithm control parameter can be dynamically adjusted based on the low-frequency quality.

[0093] Furthermore, the first algorithm control parameter can be adjusted according to the first adjustment parameter to obtain the target first algorithm control parameter, the target first algorithm control parameter = (1 + first adjustment parameter) * first algorithm control parameter, and then the second image is enhanced according to the first image enhancement algorithm and the target first algorithm control parameter to obtain a first enhanced image, that is, when the main body of the second image is not okay but the details are okay, low-frequency feature enhancement is achieved on the basis of ensuring the stability of high-frequency features, and the low-frequency feature enhancement effect is dynamically optimized based on the low-frequency quality of the target area image, which helps to improve the image effect of the main part on the basis of the okay details of the second image, so that the main body is okay, thereby deeply improving the quality of medical images.

[0094] Optionally, the following steps may also be included:

[0095] C1. when the first mean square error belongs to the preset first range and the second mean square error does not belong to the second preset range, determining a second image enhancement algorithm corresponding to the first mean square error;

[0096] C2. Obtaining a second algorithm control parameter corresponding to the second image enhancement algorithm;

[0097] C3. Determine a second adjustment parameter corresponding to the second mean square error;

[0098] C4. Adjust the second algorithm control parameter according to the second adjustment parameter to obtain a target second algorithm control parameter;

[0099] C5. Perform image enhancement processing on the second image according to the second image enhancement algorithm and the target second algorithm control parameters to obtain a second enhanced image.

[0100] In an embodiment of the present application, when the first mean square error belongs to the preset first range and the second mean square error does not belong to the second preset range, it means that the main body of the second image is okay but the details are not okay, and a third mapping relationship between the preset mean square error and the image enhancement algorithm can be pre-stored, and then, the second image enhancement algorithm corresponding to the first mean square error can be determined based on the third mapping relationship, wherein the second image enhancement algorithm can be used to achieve detail enhancement of the image on the basis of ensuring that the main body remains unchanged, that is, to ensure high-frequency feature enhancement on the basis of ensuring the stability of low-frequency features.

[0101] Then, a second algorithm control parameter corresponding to the second image enhancement algorithm can also be obtained, and the second algorithm control parameter can be preset or system default. A fourth mapping relationship between a preset mean square error and an adjustment parameter can be pre-stored, and then a second adjustment parameter corresponding to the second mean square error is determined based on the fourth mapping relationship. The second mean square error reflects the high-frequency quality of the target area image to a certain extent, and then the second algorithm control parameter can be dynamically adjusted based on the high-frequency quality.

[0102] Furthermore, the second algorithm control parameter can be adjusted according to the second adjustment parameter to obtain the target second algorithm control parameter, which is (1+second adjustment parameter)*second algorithm control parameter. Then, the second image is enhanced according to the second image enhancement algorithm and the target second algorithm control parameter to obtain a second enhanced image. That is, when the main body of the second image is okay but the details are not okay, high-frequency feature enhancement is achieved on the basis of ensuring the stability of low-frequency features, and the high-frequency feature enhancement effect is dynamically optimized based on the high-frequency quality of the target area image, which helps to improve the image effect of the details on the basis of the main body of the second image being okay, so that the details are okay, thereby deeply improving the quality of medical images.

[0103] Optionally, the above step 101, determining the first working parameter corresponding to the target physiological state parameter, may include the following steps:

[0104] S1. Acquire a historical working parameter corresponding to the target physiological state parameter from a historical database to obtain a plurality of historical working parameters, each of which corresponds to a time point;

[0105] S2. Perform a fitting operation according to the multiple historical operating parameters and corresponding time points to obtain a fitting straight line and a fitting curve;

[0106] S3, obtaining a first slope of the fitting straight line;

[0107] S4, intercepting a target fitting curve segment of a preset time period through the fitting curve;

[0108] S5, extracting extreme value points of the target fitting curve segment to obtain multiple extreme value points, and obtaining a mean value and a fourth mean square error of the target fitting curve segment;

[0109] S6. Determine the maximum change amplitude and the minimum change amplitude corresponding to the multiple extreme value points;

[0110] S7, determining a first time length corresponding to the maximum change amplitude, and determining a second time length corresponding to the minimum change amplitude;

[0111] S8. Determine a first feedback adjustment parameter corresponding to the fourth mean square error;

[0112] S9, determining a first feedback fine-tuning parameter according to the first time length and the second time length;

[0113] S10, determining two feedback fine-tuning parameters corresponding to the first slope;

[0114] S11. Determine the first operating parameter according to the mean, the first feedback adjustment parameter, the first feedback fine-tuning parameter, and the second feedback fine-tuning parameter.

[0115] Among them, the historical working parameters may include at least one of the following: working current, working voltage, working power, working frequency, duty cycle, scanning mode, scanning layer thickness, scanning interval, contrast agent usage, scanning site, signal-to-noise ratio, resolution, contrast, artifacts, scanning time, imaging distance, imaging angle, etc., which are not limited here.

[0116] In the embodiment of the present application, different physiological state parameters may correspond to different working parameters, and then, the historical working parameters corresponding to the target physiological state parameters may be obtained from the historical database to obtain multiple historical working parameters, each of which corresponds to a time point. Then, a fitting operation is performed based on the multiple historical working parameters and the corresponding time points to obtain a fitting line and a fitting curve, the horizontal axis of the fitting line and the fitting curve are both time and the vertical axis are both working parameters, specifically, each historical working parameter and its corresponding time point may be regarded as a coordinate point, and then these coordinate points may be mapped to the coordinate system, and after the multiple coordinate points are fitted, the fitting line and the fitting curve may be obtained.

[0117] In a specific implementation, the first slope of the fitting line can be obtained, and the slope reflects the decay trend of the medical imaging device. The preset time period can be preset or the system defaults. The target fitting curve segment of the preset time period can be intercepted by the fitting curve, and then the extreme points of the target fitting curve segment can be extracted to obtain multiple extreme points, the multiple extreme points include maximum values ​​and minimum values, and the mean and the fourth mean square error of the target fitting curve segment are obtained, and then the maximum change amplitude and the minimum change amplitude corresponding to the multiple extreme points are determined, the maximum change amplitude is the difference between the maximum maximum value and the minimum minimum value among the multiple extreme points, and the minimum change amplitude is the difference between the minimum maximum value and the maximum minimum value among the multiple maximum values.

[0118] Next, the first time length corresponding to the maximum change amplitude can be determined. Since the maximum change amplitude corresponds to the maximum maximum value and the minimum minimum value, the maximum maximum value and the minimum minimum value both correspond to one time point, and the difference between the two time points is the first time length. Accordingly, the second time length corresponding to the minimum change amplitude is determined. Since the minimum change amplitude corresponds to the minimum maximum value and the maximum minimum value, the minimum maximum value and the maximum minimum value both correspond to one time point, and the difference between the two time points is the second time length.

[0119] Furthermore, a mapping relationship between a preset mean square error and a feedback fine-tuning parameter can be pre-stored, and then, a first feedback adjustment parameter corresponding to a fourth mean square error can be determined based on the mapping relationship. The fourth mean square error reflects the working stability of the medical imaging device to a certain extent, and then, the working parameters are adjusted based on the device characteristic feedback of the medical imaging device, thereby compensating for the imaging effect of the medical imaging device.

[0120] In addition, the first feedback fine-tuning parameter can also be determined according to the first time length and the second time length. The first time length reflects the stable period length to a certain extent, and the second time length reflects the unstable period length to a certain extent. Then, the target stability factor can be determined, and the target stability factor = the first time length / (the first time length+the second time length). Then, based on the mapping relationship between the target stability factor and the feedback fine-tuning parameter, the first feedback fine-tuning parameter corresponding to the target stability factor can be determined based on the mapping relationship. The mapping relationship between the preset slope and the feedback adjustment parameter can also be pre-stored, and then the second feedback fine-tuning parameter corresponding to the first slope can be determined based on the mapping relationship. Finally, according to the mean, the first feedback adjustment parameter, and the first feedback The first working parameter is determined by the feedback fine-tuning parameter and the second feedback fine-tuning parameter, that is, the first working parameter = mean * (1 + first feedback adjustment parameter) * (1 + first feedback fine-tuning parameter) * (1 + second feedback fine-tuning parameter). Since the medical imaging equipment is not necessarily in the on state all the time, the on and off of the equipment conforms to the work and rest habits of the medical staff, and its stability also has certain periodic changes. Therefore, the stability of the equipment can be deeply analyzed based on the difference in the amplitude of the change, and the working parameters can be adjusted based on the feedback of the equipment stability. In addition, the equipment attenuation is also considered, and the working parameters are further feedback-adjusted. In this way, the final working parameters are deeply consistent with the actual situation, and the working parameters corresponding to the actual situation of the imaging object can be deeply obtained. In this way, it is helpful to initially guarantee the imaging effect.

[0121] It can be seen that the medical image processing method described in the embodiment of the present application is applied to a medical imaging device, obtains a target physiological state parameter of a target object, determines a first working parameter corresponding to the target physiological state parameter, controls the medical imaging device to image a preset position of the target object with the first working parameter to obtain a first image, performs image segmentation on the first image to obtain a first target area and a first background area, determines a target area ratio between the first target area and the first background area, determines a target adjustment parameter corresponding to the target area ratio, adjusts the first working parameter according to the target adjustment parameter to obtain a second working parameter, controls the medical imaging device to image a preset position with the second working parameter to obtain a second image, performs image segmentation on the second image to obtain a second target area, divides the second target area into m area blocks, where m is an integer greater than 1, determines a feature of each of the m area blocks, and obtains m feature sets; each A feature set includes at least one feature, and a target optimization parameter corresponding to m feature sets is determined. The second working parameter is optimized according to the target optimization parameter to obtain a third working parameter, and the medical imaging device is controlled to image a preset position with the third working parameter to obtain a third image. Firstly, when imaging is performed with the working parameters corresponding to the physiological state parameters of the target object, the working parameters corresponding to the actual situation of the imaging object can be obtained, which helps to preliminarily ensure the imaging effect. Secondly, the working parameters can be dynamically adjusted based on the area ratio between the target area and the background area, which helps to fully present the target and also helps to ensure the overall image quality of the target. Thirdly, due to the regional characteristics and neighborhood characteristics between the target areas, the regional characteristics and neighborhood characteristics between the target areas are analyzed based on the characteristics of each area block, and then the working parameters are deeply optimized, which helps to deeply improve the quality of medical images, thereby ensuring the accuracy of the medical image analysis results.

[0122] In accordance with the above embodiment, please refer to Figure 2 , Figure 2 : is a schematic diagram of the structure of a medical imaging device provided in an embodiment of the present application. As shown in the figure, the device includes a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor. In the embodiment of the present application, the program includes instructions for executing the following steps:

[0123] Acquire a target physiological state parameter of the target object, and determine a first working parameter corresponding to the target physiological state parameter;

[0124] Controlling the medical imaging device to image a preset position of the target object with the first working parameter to obtain a first image;

[0125] Performing image segmentation on the first image to obtain a first target area and a first background area, and determining a target area ratio between the first target area and the first background area;

[0126] determining a target adjustment parameter corresponding to the target area ratio, and adjusting the first operating parameter according to the target adjustment parameter to obtain a second operating parameter;

[0127] Controlling the medical imaging device to image the preset position with the second working parameter to obtain a second image;

[0128] Performing image segmentation on the second image to obtain a second target area, and dividing the second target area into m area blocks, where m is an integer greater than 1;

[0129] Determine the feature of each of the m area blocks to obtain m feature sets; each feature set includes at least one feature;

[0130] Determine a target optimization parameter corresponding to the m feature sets, and optimize the second operating parameter according to the target optimization parameter to obtain a third operating parameter;

[0131] The medical imaging device is controlled to image the preset position with the third working parameter to obtain a third image.

[0132] Optionally, the program further includes instructions for executing the following steps:

[0133] Determine the energy mean of each of the m area blocks according to the m feature sets to obtain m energy means;

[0134] Determine the feature point distribution density of each of the m area blocks according to the m feature sets to obtain m feature point distribution densities;

[0135] Determine a first mean square error according to the m energy means;

[0136] Determine a second mean square error according to the distribution density of the m feature points;

[0137] When the first mean square error belongs to a preset first range and the second mean square error belongs to a second preset range, the second image is retained.

[0138] Optionally, the program further includes instructions for executing the following steps:

[0139] When the first mean square error does not belong to the preset first range and the second mean square error does not belong to the second preset range, the step of performing image segmentation on the second image to obtain a second target area is performed.

[0140] Optionally, in determining the target optimization parameters corresponding to the m feature sets, the program includes instructions for performing the following steps:

[0141] Determine the neighborhood of each of the m area blocks to obtain m neighborhood sets;

[0142] Determine the energy mean corresponding to each neighborhood set in the m neighborhood sets to obtain m neighborhood energy means;

[0143] Determine the weights corresponding to the m neighborhood energy means to obtain m weights;

[0144] Performing a weighted operation on the m energy means and the m weights to obtain m weighted energy means;

[0145] Determining a third mean square error according to the m weighted energy means;

[0146] The target optimization parameter corresponding to the third mean square error is determined.

[0147] Optionally, the program further includes instructions for executing the following steps:

[0148] When the first mean square error does not belong to the preset first range and the second mean square error belongs to the second preset range, determining a first image enhancement algorithm corresponding to the second mean square error;

[0149] Acquire a first algorithm control parameter corresponding to the first image enhancement algorithm;

[0150] determining a first adjustment parameter corresponding to the first mean square error;

[0151] Adjusting the first algorithm control parameter according to the first adjustment parameter to obtain a target first algorithm control parameter;

[0152] Performing image enhancement processing on the second image according to the first image enhancement algorithm and the target first algorithm control parameter to obtain a first enhanced image;

[0153] or,

[0154] When the first mean square error belongs to the preset first range and the second mean square error does not belong to the second preset range, determining a second image enhancement algorithm corresponding to the first mean square error;

[0155] Acquire a second algorithm control parameter corresponding to the second image enhancement algorithm;

[0156] determining a second adjustment parameter corresponding to the second mean square error;

[0157] Adjust the second algorithm control parameter according to the second adjustment parameter to obtain a target second algorithm control parameter;

[0158] The second image is subjected to image enhancement processing according to the second image enhancement algorithm and the target second algorithm control parameters to obtain a second enhanced image.

[0159] It can be seen that the medical imaging device described in the embodiment of the present application obtains the target physiological state parameter of the target object, determines the first working parameter corresponding to the target physiological state parameter, controls the medical imaging device to image the preset position of the target object with the first working parameter to obtain a first image, performs image segmentation on the first image to obtain a first target area and a first background area, determines the target area ratio between the first target area and the first background area, determines the target adjustment parameter corresponding to the target area ratio, adjusts the first working parameter according to the target adjustment parameter to obtain a second working parameter, controls the medical imaging device to image the preset position with the second working parameter to obtain a second image, performs image segmentation on the second image to obtain a second target area, divides the second target area into m area blocks, where m is an integer greater than 1, determines the features of each of the m area blocks to obtain m feature sets; each feature set includes At least one feature is used to determine the target optimization parameters corresponding to the m feature sets, the second working parameters are optimized according to the target optimization parameters to obtain the third working parameters, and the medical imaging device is controlled to image the preset position with the third working parameters to obtain the third image. Firstly, when the working parameters corresponding to the physiological state parameters of the target object are used for imaging, the working parameters corresponding to the actual situation of the imaging object can be obtained, which helps to preliminarily ensure the imaging effect. Secondly, the working parameters can be dynamically adjusted based on the area ratio between the target area and the background area, which helps to fully present the target and also helps to ensure the overall image quality of the target. Thirdly, due to the regional characteristics and neighborhood characteristics between the target areas, the regional characteristics and neighborhood characteristics between the target areas are analyzed based on the characteristics of each area block, and then the working parameters are deeply optimized, which helps to deeply improve the quality of medical images, thereby ensuring the accuracy of the medical image analysis results.

[0160] Figure 3 300 is a functional unit block diagram of a medical image processing system 300 involved in an embodiment of the present application, which is applied to a medical imaging device. The medical image processing system 300 includes: an acquisition unit 301, a control unit 302 and a determination unit 303, wherein:

[0161] The acquisition unit 301 is used to acquire a target physiological state parameter of a target object and determine a first working parameter corresponding to the target physiological state parameter;

[0162] The control unit 302 is used to control the medical imaging device to image a preset position of the target object with the first working parameter to obtain a first image;

[0163] The determining unit 303 is used to perform image segmentation on the first image to obtain a first target area and a first background area, determine a target area ratio between the first target area and the first background area; determine a target adjustment parameter corresponding to the target area ratio, and adjust the first working parameter according to the target adjustment parameter to obtain a second working parameter;

[0164] The control unit 302 is further used to control the medical imaging device to image the preset position with the second working parameter to obtain a second image;

[0165] The determination unit 303 is further configured to perform image segmentation on the second image to obtain a second target area, and divide the second target area into m area blocks, where m is an integer greater than 1; determine a feature of each of the m area blocks to obtain m feature sets; each feature set includes at least one feature; determine a target optimization parameter corresponding to the m feature sets, and optimize the second operating parameter according to the target optimization parameter to obtain a third operating parameter;

[0166] The control unit 302 is further used to control the medical imaging device to image the preset position with the third working parameter to obtain a third image.

[0167] Optionally, the medical image processing system 300 is further specifically used for:

[0168] Determine the energy mean of each of the m area blocks according to the m feature sets to obtain m energy means;

[0169] Determine the feature point distribution density of each of the m area blocks according to the m feature sets to obtain m feature point distribution densities;

[0170] Determine a first mean square error according to the m energy means;

[0171] Determine a second mean square error according to the distribution density of the m feature points;

[0172] When the first mean square error belongs to a preset first range and the second mean square error belongs to a second preset range, the second image is retained.

[0173] Optionally, the medical image processing system 300 is further specifically used for:

[0174] When the first mean square error does not belong to the preset first range and the second mean square error does not belong to the second preset range, the step of performing image segmentation on the second image to obtain a second target area is performed.

[0175] Optionally, in determining the target optimization parameters corresponding to the m feature sets, the determining unit 303 is specifically configured to:

[0176] Determine the neighborhood of each of the m area blocks to obtain m neighborhood sets;

[0177] Determine the energy mean corresponding to each neighborhood set in the m neighborhood sets to obtain m neighborhood energy means;

[0178] Determine the weights corresponding to the m neighborhood energy means to obtain m weights;

[0179] Performing a weighted operation on the m energy means and the m weights to obtain m weighted energy means;

[0180] Determining a third mean square error according to the m weighted energy means;

[0181] The target optimization parameter corresponding to the third mean square error is determined.

[0182] Optionally, the medical image processing system 300 is further specifically used for:

[0183] When the first mean square error does not belong to the preset first range and the second mean square error belongs to the second preset range, determining a first image enhancement algorithm corresponding to the second mean square error;

[0184] Acquire a first algorithm control parameter corresponding to the first image enhancement algorithm;

[0185] determining a first adjustment parameter corresponding to the first mean square error;

[0186] Adjusting the first algorithm control parameter according to the first adjustment parameter to obtain a target first algorithm control parameter;

[0187] Performing image enhancement processing on the second image according to the first image enhancement algorithm and the target first algorithm control parameter to obtain a first enhanced image;

[0188] or,

[0189] When the first mean square error belongs to the preset first range and the second mean square error does not belong to the second preset range, determining a second image enhancement algorithm corresponding to the first mean square error;

[0190] Acquire a second algorithm control parameter corresponding to the second image enhancement algorithm;

[0191] determining a second adjustment parameter corresponding to the second mean square error;

[0192] Adjust the second algorithm control parameter according to the second adjustment parameter to obtain a target second algorithm control parameter;

[0193] The second image is subjected to image enhancement processing according to the second image enhancement algorithm and the target second algorithm control parameters to obtain a second enhanced image.

[0194] It can be seen that the medical image processing system described in the embodiment of the present application is applied to a medical imaging device, obtains a target physiological state parameter of a target object, determines a first working parameter corresponding to the target physiological state parameter, controls the medical imaging device to image a preset position of the target object with the first working parameter to obtain a first image, performs image segmentation on the first image to obtain a first target area and a first background area, determines a target area ratio between the first target area and the first background area, determines a target adjustment parameter corresponding to the target area ratio, adjusts the first working parameter according to the target adjustment parameter to obtain a second working parameter, controls the medical imaging device to image a preset position with the second working parameter to obtain a second image, performs image segmentation on the second image to obtain a second target area, divides the second target area into m area blocks, where m is an integer greater than 1, determines a feature of each of the m area blocks, and obtains m feature sets; each A feature set includes at least one feature, and a target optimization parameter corresponding to m feature sets is determined. The second working parameter is optimized according to the target optimization parameter to obtain a third working parameter, and the medical imaging device is controlled to image a preset position with the third working parameter to obtain a third image. Firstly, when imaging is performed with the working parameters corresponding to the physiological state parameters of the target object, the working parameters corresponding to the actual situation of the imaging object can be obtained, which helps to preliminarily ensure the imaging effect. Secondly, the working parameters can be dynamically adjusted based on the area ratio between the target area and the background area, which helps to fully present the target and also helps to ensure the overall image quality of the target. Thirdly, due to the regional characteristics and neighborhood characteristics between the target areas, the regional characteristics and neighborhood characteristics between the target areas are analyzed based on the characteristics of each area block, and then the working parameters are deeply optimized, which helps to deeply improve the quality of medical images, thereby ensuring the accuracy of the medical image analysis results.

[0195] It can be understood that the functions of each program module of the medical image processing system of this embodiment can be specifically implemented according to the method in the above method embodiment, and its specific implementation process can refer to the relevant description of the above method embodiment, which will not be repeated here.

[0196] An embodiment of the present application also provides a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute part or all of the steps of any method recorded in the above method embodiments, and the above computer includes a medical imaging device.

[0197] The present application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute some or all of the steps of any method described in the method embodiment. The computer program product may be a software installation package, and the computer includes a medical imaging device.

[0198] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0199] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0200] In the several embodiments provided in the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the above-mentioned units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0201] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0202] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0203] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a memory, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the above-mentioned methods of each embodiment of the present application. The aforementioned memory includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or CD-ROM and other media that can store program codes.

[0204] A person skilled in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable memory, and the memory can include: a flash drive, a read-only memory (English: Read-Only Memory, abbreviated as: ROM), a random access memory (English: Random Access Memory, abbreviated as: RAM), a magnetic disk or an optical disk, etc.

[0205] The embodiments of the present application are introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core idea of ​​the present application. At the same time, for general technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A medical image processing method, characterized in that: Applied to medical imaging equipment, the method comprises: Acquire a target physiological state parameter of the target object, and determine a first working parameter corresponding to the target physiological state parameter; Controlling the medical imaging device to image a preset position of the target object with the first working parameter to obtain a first image; Performing image segmentation on the first image to obtain a first target area and a first background area, and determining a target area ratio between the first target area and the first background area; determining a target adjustment parameter corresponding to the target area ratio, and adjusting the first operating parameter according to the target adjustment parameter to obtain a second operating parameter; Controlling the medical imaging device to image the preset position with the second working parameter to obtain a second image; Performing image segmentation on the second image to obtain a second target area, and dividing the second target area into m area blocks, where m is an integer greater than 1; Determine the feature of each of the m area blocks to obtain m feature sets; each feature set includes at least one feature; Determine a target optimization parameter corresponding to the m feature sets, and optimize the second operating parameter according to the target optimization parameter to obtain a third operating parameter; The medical imaging device is controlled to image the preset position with the third working parameter to obtain a third image.

2. The method according to claim 1, characterized in that The method further comprises: Determine the energy mean of each of the m area blocks according to the m feature sets to obtain m energy means; Determine the feature point distribution density of each of the m area blocks according to the m feature sets to obtain m feature point distribution densities; Determine a first mean square error according to the m energy means; Determine a second mean square error according to the distribution density of the m feature points; When the first mean square error belongs to a preset first range and the second mean square error belongs to a second preset range, the second image is retained.

3. The method according to claim 2, characterized in that The method further comprises: When the first mean square error does not belong to the preset first range and the second mean square error does not belong to the second preset range, the step of performing image segmentation on the second image to obtain a second target area is performed.

4. The method according to claim 3, characterized in that The determining of the target optimization parameters corresponding to the m feature sets includes: Determine the neighborhood of each of the m area blocks to obtain m neighborhood sets; Determine the energy mean corresponding to each neighborhood set in the m neighborhood sets to obtain m neighborhood energy means; Determine the weights corresponding to the m neighborhood energy means to obtain m weights; Performing a weighted operation on the m energy means and the m weights to obtain m weighted energy means; Determining a third mean square error according to the m weighted energy means; The target optimization parameter corresponding to the third mean square error is determined.

5. The method according to claim 3 or 4, characterized in that: The method further comprises: When the first mean square error does not belong to the preset first range and the second mean square error belongs to the second preset range, determining a first image enhancement algorithm corresponding to the second mean square error; Acquire a first algorithm control parameter corresponding to the first image enhancement algorithm; determining a first adjustment parameter corresponding to the first mean square error; Adjusting the first algorithm control parameter according to the first adjustment parameter to obtain a target first algorithm control parameter; Performing image enhancement processing on the second image according to the first image enhancement algorithm and the target first algorithm control parameter to obtain a first enhanced image; or, When the first mean square error belongs to the preset first range and the second mean square error does not belong to the second preset range, determining a second image enhancement algorithm corresponding to the first mean square error; Acquire a second algorithm control parameter corresponding to the second image enhancement algorithm; determining a second adjustment parameter corresponding to the second mean square error; Adjust the second algorithm control parameter according to the second adjustment parameter to obtain a target second algorithm control parameter; The second image is subjected to image enhancement processing according to the second image enhancement algorithm and the target second algorithm control parameters to obtain a second enhanced image.

6. A medical image processing system, characterized in that: Applied to medical imaging equipment, the system comprises: an acquisition unit, a control unit and a determination unit, wherein: The acquisition unit is used to acquire a target physiological state parameter of the target object and determine a first working parameter corresponding to the target physiological state parameter; The control unit is used to control the medical imaging device to image a preset position of the target object with the first working parameter to obtain a first image; The determining unit is used to perform image segmentation on the first image to obtain a first target area and a first background area, determine a target area ratio between the first target area and the first background area; determine a target adjustment parameter corresponding to the target area ratio, and adjust the first working parameter according to the target adjustment parameter to obtain a second working parameter; The control unit is further used to control the medical imaging device to image the preset position with the second working parameter to obtain a second image; The determination unit is further configured to perform image segmentation on the second image to obtain a second target area, and divide the second target area into m area blocks, where m is an integer greater than 1; determine a feature of each of the m area blocks to obtain m feature sets; each feature set includes at least one feature; determine a target optimization parameter corresponding to the m feature sets, and optimize the second working parameter according to the target optimization parameter to obtain a third working parameter; The control unit is further used to control the medical imaging device to image the preset position with the third working parameter to obtain a third image.

7. The system according to claim 6, characterized in that The system is also specifically used for: Determine the energy mean of each of the m area blocks according to the m feature sets to obtain m energy means; Determine the feature point distribution density of each of the m area blocks according to the m feature sets to obtain m feature point distribution densities; Determine a first mean square error according to the m energy means; Determine a second mean square error according to the distribution density of the m feature points; When the first mean square error belongs to a preset first range and the second mean square error belongs to a second preset range, the second image is retained.

8. The system according to claim 7, characterized in that The system is also specifically used for: When the first mean square error does not belong to the preset first range and the second mean square error does not belong to the second preset range, the step of performing image segmentation on the second image to obtain a second target area is performed.

9. The system according to claim 8, characterized in that In determining the target optimization parameters corresponding to the m feature sets, the determining unit is specifically used to: Determine the neighborhood of each of the m area blocks to obtain m neighborhood sets; Determine the energy mean corresponding to each neighborhood set in the m neighborhood sets to obtain m neighborhood energy means; Determine the weights corresponding to the m neighborhood energy means to obtain m weights; Performing a weighted operation on the m energy means and the m weights to obtain m weighted energy means; Determining a third mean square error according to the m weighted energy means; The target optimization parameter corresponding to the third mean square error is determined.

10. The system according to claim 8 or 9, characterized in that The system is also specifically used for: When the first mean square error does not belong to the preset first range and the second mean square error belongs to the second preset range, determining a first image enhancement algorithm corresponding to the second mean square error; Acquire a first algorithm control parameter corresponding to the first image enhancement algorithm; determining a first adjustment parameter corresponding to the first mean square error; Adjusting the first algorithm control parameter according to the first adjustment parameter to obtain a target first algorithm control parameter; Performing image enhancement processing on the second image according to the first image enhancement algorithm and the target first algorithm control parameter to obtain a first enhanced image; or, When the first mean square error belongs to the preset first range and the second mean square error does not belong to the second preset range, determining a second image enhancement algorithm corresponding to the first mean square error; Acquire a second algorithm control parameter corresponding to the second image enhancement algorithm; determining a second adjustment parameter corresponding to the second mean square error; Adjust the second algorithm control parameter according to the second adjustment parameter to obtain a target second algorithm control parameter; The second image is subjected to image enhancement processing according to the second image enhancement algorithm and the target second algorithm control parameters to obtain a second enhanced image.

Images