A visual obstetric imaging examination processing method

Abstract

The present invention discloses a method for visual obstetric image examination and processing, which belongs to the technical field of obstetric image examination and processing, and includes an image acquisition module, an image processing module, a circulation impact assessment module and an output and diagnosis module. The image acquisition module is used to acquire and preliminarily process the current obstetric image and transmit it to the image processing module. The image processing module is used to calculate the output image quality assessment index V, the processing effect enhancement index CZ and the circulation impact assessment index CF in sequence. Based on the result of the circulation impact assessment index CF and using the circulation impact assessment module, the adjustment of the image processing strategy according to the result of the processing effect enhancement index CZ is triggered. The present invention improves the image quality and diagnostic accuracy by comprehensively quantitatively evaluating the image quality, individually adjusting the processing method and introducing a circulation impact mechanism, thereby providing richer diagnostic information and more accurate decision support.

Description

Technical Field

[0001] The present invention relates to the technical field of obstetric image examination and processing, and in particular to a visual obstetric image examination and processing method. Background Art

[0002] With the continuous advancement of medical technology, obstetric imaging plays a vital role in prenatal diagnosis. However, traditional imaging examination methods are often limited by the volatility of image quality and the singleness of processing methods, making it difficult to meet the precise diagnostic needs of clinicians. In order to overcome these limitations, visual obstetric imaging processing methods have emerged.

[0003] Regarding the above-mentioned and existing related technologies, the inventors believe that the following defects often exist: existing image quality assessment methods often rely on a single indicator and are difficult to fully reflect the multiple dimensions of the image. This leads to the fact that in actual applications, doctors are unable to accurately judge the overall quality and processing effect of the image, and the existing image processing methods lack personalized adjustments for different image characteristics. This also leads to unsatisfactory processing effects in some cases, and even introduces new noise and blur. In addition, existing image processing methods often ignore the cyclic effects in the processing process, and thus there are cumulative errors and instability factors in the processing process. Summary of the Invention

[0004] The technical problem to be solved by the present invention is that the existing technology has limitations in image quality assessment, a single processing method, and a lack of a cyclic influence mechanism. To this end, we propose a visual obstetric image examination processing method.

[0005] The technical solution mainly includes: a visual obstetric image examination and processing method, including an image acquisition module, an image processing module, a circulation impact assessment module and an output and diagnosis module, characterized in that: the image processing module includes an image quality measurement unit, an image processing enhancement effect unit, and a circulation impact triggering unit;

[0006] The specific processing steps are as follows:

[0007] Step 1: using the image acquisition module to collect and preliminarily process the current obstetric image, and transmit it to the image processing module;

[0008] Step 2: using the image processing module, sequentially calculating the output image quality evaluation index V, the processing effect enhancement index CZ and the circulation impact evaluation index CF;

[0009] Step 3: Based on the result of the cyclic impact evaluation index CF and using the cyclic impact evaluation module, triggering the adjustment of the image processing strategy according to the result of the processing effect enhancement index CZ;

[0010] Step 4: The output and diagnosis module outputs the adjusted image processing strategy and the adjusted current obstetric image.

[0011] Preferably, the image processing module stores and summarizes the treatment effect enhancement index CZ and the circulatory impact assessment index CF, and classifies and stores the final values ​​of the treatment effect enhancement index CZ and the circulatory impact assessment index CF of different gestational periods that have completed obstetric imaging examinations to form a circulatory impact reference interval {CF l -CF h} and processing effect enhancement interval {CZ l -CZ h};

[0012] Among them, CF l The lowest index for cyclical impact assessment, CF h The highest index for circular impact assessment, CZ l The lowest index for enhancing the treatment effect, CZ h Enhances the highest index for processing effect.

[0013] Preferably, the calculation formula for measuring the image quality unit is as follows:

[0014]

[0015] in:

[0016] V is the image quality assessment index;

[0017] Q is image clarity, which is used to measure the contrast between different tissues in current obstetric images;

[0018] YM is the image area, which reflects the proportion of the fetal area to the total area in the current obstetric image;

[0019] ZS is the noise level value, which reflects the background noise intensity in the current obstetric image;

[0020] YD is the motion blur value, which reflects the degree of blur in the current obstetric image caused by fetal and maternal movement;

[0021] T is the feature loss value, which reflects the degree of loss of key anatomical features in current obstetric images;

[0022] XJ is the detail retention value, which reflects the degree of detail information retention in the current obstetric image;

[0023] The image clarity Q is multiplied by the image area YM under the square root, which aims to emphasize the importance of clarity in image quality;

[0024] The calculation of the feature loss value T is combined with the detail retention value XJ under the square root, aiming to reflect the role of detail retention in compensating for feature loss.

[0025] Preferably, the noise level value ZS, motion blur value YD, feature loss value T and detail retention value XJ are calculated as follows:

[0026]

[0027] XJ=(XJ det[il -XJ smooth ) 2 ;

[0028] in:

[0029] N is the total number of pixels, which reflects the total number of pixels in the current obstetric image;

[0030] Z i is the noise value of the i-th pixel, Z i Represents the noise value of each pixel in the current obstetric image;

[0031] Z avg is the average pixel noise, Z avg Indicates the average degree of noise of all pixels in the current obstetric image;

[0032] The variance of the noise in the current obstetric image is calculated. The larger the variance, the higher the noise level.

[0033] D is the current frame image;

[0034] D ref is the reference frame image, D ref Indicates obstetric images one frame before and one frame after the current obstetric image;

[0035] The calculation is the current frame image D and the reference frame image D ref The difference between them, the larger the difference, the higher the degree of motion blur;

[0036] T lost is the lost feature quantity, T lost Reflects the number of key anatomical features missing in current obstetric imaging;

[0037] T total is the total feature quantity, T total Reflect the total number of key anatomical features that should be included in current obstetric imaging;

[0038] The percentage of key anatomical features missing in current obstetric imaging was calculated;

[0039] XJ detail This is the image after detail enhancement processing;

[0040] XJ smooth is the image after smoothing;

[0041] XJ=(XJ det[il -XJ smooth ) 2 The calculation is to determine the degree of detail information retention in the current obstetric image after detail enhancement processing.

[0042] Preferably, the calculation formula of the image processing enhancement effect unit is as follows:

[0043]

[0044] in:

[0045] CZ is the treatment effect enhancement index;

[0046] Indicates the direct impact of image quality and clarity on processing results;

[0047] Indicates the negative impact of feature loss on the processing effect;

[0048] It not only considers the relative influence of noise level in the processing effect, but also introduces the comprehensive effect of detail preservation and motion blur.

[0049] Preferably, the calculation formula of the cyclic impact trigger unit is as follows:

[0050]

[0051] in:

[0052] CF is the cyclic impact assessment index;

[0053] It represents the adjusted value of the processing effect enhancement index CZ after considering the influence of feature loss on the image quality evaluation index V;

[0054] Indicates the combined negative impact of noise level and motion blur on detail preservation; Consider the relative influence of image clarity Q in the proportional relationship between processing effect and image area YM.

[0055] Preferably, based on the results of the cycle impact evaluation index CF and the processing effect enhancement index CZ, the noise level value ZS, the motion blur value YD, the feature loss value T and the detail retention value XJ are adjusted as follows:

[0056] If the circulatory impact assessment index CF is in the circulatory impact reference interval {CF l -CF h}, the adjustment of the image processing strategy will not be triggered;

[0057] If the cyclical impact assessment index CF is lower than the minimum cyclical impact assessment index CF l , then the trigger process adjusts the image processing strategy. The specific image processing strategy adjustments include:

[0058] When the motion blur value YD is a major contributing factor in current obstetric images and the processing effect enhancement index CZ is low, the intensity of the motion deblurring process is increased to reduce the motion blur value YD to restore clear fetal structures;

[0059] When the feature loss value T is a major contributing factor in the current obstetric image and the value of the processing effect enhancement index CZ is low, the contrast and brightness of the current obstetric image are enhanced to reduce the value of the feature loss value T, so as to highlight and easily identify the key anatomical features of the fetus;

[0060] When the motion blur value YD and the feature loss value T are not the main contributing factors in the current obstetric images, and the value of the processing effect enhancement index CZ is low, the intensity of the detail enhancement processing should be increased to improve the detail retention value XJ, while the intensity of the noise filtering should be reduced to reduce the noise level value ZS.

[0061] Preferably, the equipment used by the image acquisition module includes ultrasound equipment and MRI equipment;

[0062] The equipment used by the image processing module and the circulation impact assessment module includes an image processing workstation and image processing software;

[0063] The devices used by the output and diagnosis module include a display device.

[0064] Technical effects and advantages of the present invention:

[0065] In the present invention, by measuring the image quality unit, a comprehensive quantitative evaluation of image quality is achieved, wherein by integrating multiple key parameters, the overall quality and processing effect of the image can be more accurately reflected, thereby providing richer diagnostic information.

[0066] In the present invention, according to the calculation results of the enhancement effect unit and the cyclic influence trigger unit after image processing, personalized adjustments can be made for different image characteristics, including different adjustment strategies when the motion blur value YD and the feature loss value T are respectively the main contributing factors in the current obstetric image, and when the motion blur value YD and the feature loss value T are not the main contributing factors in the current obstetric image. This personalized adjustment strategy can ensure the optimization of the processing effect and reduce unnecessary noise and blur.

[0067] In the present invention, through iterative calculation and cyclic influence mechanism, changes in the image processing process can be dynamically reflected, which helps to timely discover and deal with potential problems, thereby ensuring the stability and accuracy of the processing process. At the same time, this mechanism can also provide more accurate decision support, thereby improving the accuracy and reliability of diagnosis. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 This is a flowchart of the method for visualizing the obstetric imaging examination processing method;

[0069] Figure 2 This is a schematic diagram of the overall structure of the visual obstetric imaging examination processing method;

[0070] Figure 3 Schematic diagram of the structure of the image processing module in the present invention;

[0071] Figure 4 Schematic diagram of triggering adjustment of the Circulation Impact Assessment Index CF in the present invention. DETAILED DESCRIPTION

[0072] The present invention will now be described in further detail with reference to the accompanying drawings and preferred embodiments.

[0073] Reference Figure 1-4 As shown, the present invention provides a technical solution: a visual obstetric image examination and processing method, including an image acquisition module, an image processing module, a circulation impact assessment module and an output and diagnosis module, characterized in that: the image processing module includes an image quality measurement unit, an image processing enhancement effect unit, and a circulation impact triggering unit;

[0074] The specific processing steps are as follows:

[0075] Step 1: Use the image acquisition module to collect and preliminarily process the current obstetric images and transmit them to the image processing module;

[0076] Step 2: Using the image processing module, calculate the output image quality evaluation index V, the processing effect enhancement index CZ and the circulation impact evaluation index CF in sequence;

[0077] Step 3: Based on the result of the cycle impact assessment index CF, and using the cycle impact assessment module, trigger the adjustment of the image processing strategy according to the result of the processing effect enhancement index CZ;

[0078] Step 4: The output and diagnosis module outputs the adjusted image processing strategy and the adjusted current obstetric image;

[0079] The equipment used in the image acquisition module includes ultrasound equipment and MRI equipment;

[0080] The equipment used in the image processing module and the cycle impact assessment module includes an image processing workstation and image processing software;

[0081] The devices used by the output and diagnostic module include display devices.

[0082] The method steps, modules, units and equipment used in the visual obstetric image examination and processing method of this embodiment constitute a complete system, which can achieve high-quality acquisition, processing, evaluation and diagnosis of obstetric images. By continuously optimizing and adjusting the image processing method, the image quality and diagnostic accuracy can be further improved. More importantly, the result value of the cyclic impact trigger unit, the cyclic impact evaluation index CF, can cyclically influence the calculation process of the image quality evaluation index V in the image quality measurement unit, forming a closed-loop feedback system. This cyclic impact mechanism can more accurately reflect the dynamic changes of the obstetric image examination and processing method in actual application, thereby providing more accurate decision support, and by continuously optimizing and adjusting the processing strategy, it can ensure that the image quality is always maintained in the best state, thereby improving the accuracy and reliability of prenatal diagnosis.

[0083] Reference Figure 1-4 As shown, in this embodiment: the calculation formula for measuring the image quality unit is as follows:

[0084]

[0085] in:

[0086] V is the image quality assessment index;

[0087] Q is image clarity, which is used to measure the contrast between different tissues in current obstetric images;

[0088] YM is the image area, which reflects the proportion of the fetal area to the total area in the current obstetric image;

[0089] ZS is the noise level value, which reflects the background noise intensity in the current obstetric image;

[0090] YD is the motion blur value, which reflects the degree of blur in the current obstetric image caused by fetal and maternal movement;

[0091] T is the feature loss value, which reflects the degree of loss of key anatomical features in current obstetric images;

[0092] XJ is the detail retention value, which reflects the degree of detail information retention in the current obstetric image;

[0093] The image clarity Q is multiplied by the image area YM under the square root, which aims to emphasize the importance of clarity in image quality;

[0094] The calculation of the feature loss value T is combined with the detail retention value XJ under the square root, aiming to reflect the role of detail retention in compensating for feature loss;

[0095] The calculation formulas for the noise level value ZS, motion blur value YD, feature loss value T and detail retention value XJ are as follows:

[0096]

[0097] XJ=(XJ det[il -XJ smooth ) 2 ;

[0098] in:

[0099] N is the total number of pixels, which reflects the total number of pixels in the current obstetric image;

[0100] Z i is the noise value of the i-th pixel, Z i Represents the noise value of each pixel in the current obstetric image;

[0101] Z avg is the average pixel noise, Z avg Indicates the average degree of noise of all pixels in the current obstetric image;

[0102] The variance of the noise in the current obstetric image is calculated. The larger the variance, the higher the noise level.

[0103] D is the current frame image;

[0104] D ref is the reference frame image, D ref Indicates obstetric images one frame before and one frame after the current obstetric image;

[0105] The calculation is the current frame image D and the reference frame image D ref The difference between them, the larger the difference, the higher the degree of motion blur;

[0106] T lost is the lost feature quantity, T lost Reflects the number of key anatomical features missing in current obstetric imaging;

[0107] T total is the total feature quantity, T total Reflect the total number of key anatomical features that should be included in current obstetric imaging;

[0108] The percentage of key anatomical features missing in current obstetric imaging was calculated;

[0109] XJ detail This is the image after detail enhancement processing;

[0110] XJ smooth is the image after smoothing;

[0111] XJ=(XJ det[il -XJ smooth ) 2 The calculation is to determine the degree of detail information retention in the current obstetric image after detail enhancement processing.

[0112] In the algorithm of this embodiment, the image clarity Q measures the contrast between different tissues in the current obstetric image and is the basis for image quality assessment. In the formula, The multiplication of is intended to emphasize the importance of clarity in image quality and combine it with the area ratio to consider the overall performance of the image. The image area YM reflects the proportion of the fetal area in the image to the total area, which helps to evaluate the focus of the image and the effectiveness of the information. In the formula, The multiplication of the area ratio reflects the direct impact of the area ratio on the image quality. The noise level value ZS and the motion blur value YD are both negative factors in the image quality assessment, and the noise level value ZS and the motion blur value YD are added as The denominator of is intended to emphasize their negative impact on image quality, and further highlights their adverse effects by dividing them by the product of sharpness and area ratio;

[0113] The feature loss value T reflects the degree of loss of key anatomical features in the image, and It aims to quantify the relative impact of feature loss on image quality, while considering the compensatory effect of detail retention on feature loss. The detail retention value XJ measures the degree of detail information retention in current obstetric images.

[0114] The image quality measurement unit of this embodiment can comprehensively consider multiple dimensions of image clarity, area ratio, noise level, motion blur, feature loss and detail retention, thereby achieving a comprehensive quantitative evaluation of image quality, which helps to more accurately understand the overall quality of the image and provide a reliable basis for subsequent image processing.

[0115] This algorithm unit can judge the quality performance of images in different aspects based on the value of the image quality assessment index V, and then guide the formulation of image processing strategies. When the image quality assessment index V value is low, it can adjust the key factors affecting the quality, including increasing the intensity of the clarity enhancement processing and reducing the intensity of the noise filtering.

[0116] By optimizing image quality, measuring image quality units can help improve the accuracy of prenatal diagnosis, while clearer images, less noise and blur, and more complete preservation of anatomical features will help to more accurately judge the health of the fetus during obstetric examinations.

[0117] Reference Figure 1-4 As shown, in this embodiment: the calculation formula of the image processing enhancement effect unit is as follows:

[0118]

[0119] in:

[0120] CZ is the treatment effect enhancement index;

[0121] Indicates the direct impact of image quality and clarity on processing results;

[0122] Indicates the negative impact of feature loss on the processing effect;

[0123] It not only considers the relative influence of noise level in the processing effect, but also introduces the comprehensive effect of detail preservation and motion blur.

[0124] In the algorithm of this embodiment, the image quality evaluation index V serves as the basis of the image enhancement effect unit after image processing. The image quality evaluation index V reflects the original quality of the image. The image quality evaluation index V is multiplied by the image clarity Q under the square root to emphasize the importance of clarity in the processing effect. The feature loss value T is similar to the image quality measurement unit. The feature loss value T also represents the degree of loss of key anatomical features in the image enhancement effect unit after image processing. The feature loss value T is divided by The aim is to quantify the relative impact of feature loss on processing results and consider the compensatory effects of image quality assessment index V and area ratio on feature loss;

[0125] In the image processing enhancement unit, the noise level value ZS is processed by taking into account the combined effect of the detail retention value XJ and the motion blur value YD. The noise level value ZS is divided by The aim is to evaluate the relative impact of noise level in the processing effect, and to consider the compensatory effect of detail preservation on noise level and the negative impact of motion blur on noise level;

[0126] The processing effect enhancement index CZ value of this embodiment can intuitively reflect the degree of improvement in image quality achieved by the processing strategy. By comparing the processing effect enhancement index CZ values ​​under different processing strategies, it is possible to evaluate which strategy is more effective, thereby optimizing the processing flow. Specifically, when the processing effect enhancement index CZ value is low, it indicates that the current processing strategy has limited effect on improving image quality. In this case, the parameters and methods of the processing strategy can be adjusted according to the changing trend of the processing effect enhancement index CZ value to improve the processing effect. For example, if the processing effect enhancement index CZ value is mainly affected by the noise level, it is possible to consider increasing the intensity of noise filtering. If it is mainly affected by feature loss, it is possible to consider increasing the intensity of detail enhancement processing.

[0127] This algorithm evaluates the processing effect, so that the image processing enhancement effect unit helps to allocate medical resources more reasonably. For higher-quality images, it can reduce unnecessary processing steps and resource consumption, and for lower-quality images, it can give priority to processing and optimization.

[0128] Reference Figure 1-4 As shown, in this embodiment: the calculation formula of the cyclic impact trigger unit is as follows:

[0129]

[0130] in:

[0131] CF is the cyclic impact assessment index;

[0132] It represents the adjusted value of the processing effect enhancement index CZ after considering the influence of feature loss on the image quality evaluation index V;

[0133] Indicates the combined negative impact of noise level and motion blur on detail preservation; Consider the relative influence of image clarity Q in the proportional relationship between processing effect and image area YM.

[0134] In the algorithm of this embodiment, the processing effect enhancement index CZ serves as the basis for the cyclic impact trigger unit. The processing effect enhancement index CZ reflects the enhancement effect of the image processing method. The processing effect enhancement index CZ is related to The product of the calculation part is intended to emphasize the adjustment value of the processing effect after considering the influence of feature loss, where the noise level value ZS and the motion blur value YD are in the loop influence trigger unit. The square root division calculation is performed to evaluate the combined negative impact of noise level and motion blur on detail preservation. Finally, the image clarity Q is divided by The calculation part aims to consider the relative influence of clarity in the relationship between processing effect and image area ratio;

[0135] The cyclic impact assessment index CF value of this embodiment can cyclically influence the calculation process of the image quality assessment index V, forming a continuous feedback mechanism. This helps to promptly discover problems in the processing strategy and make adjustments, and ensure that the processing effect is always maintained at the optimal state. By iteratively calculating the cyclic impact assessment index CF value, it is possible to more accurately understand the long-term impact of the processing strategy on image quality, which helps to formulate more reasonable processing strategies and avoid the negative impact of short-term optimization measures on long-term effects.

[0136] The cyclic impact mechanism of this embodiment can enhance the stability of the entire evaluation system. Even if some unforeseen problems and changes are encountered during the processing, the system can maintain the stability and optimization of the overall performance through iterative calculation and adjustment.

[0137] Reference Figure 1-4 As shown, in this embodiment: the image processing module stores and summarizes the treatment effect enhancement index CZ and the circulatory impact assessment index CF, and classifies and stores the final values ​​of the treatment effect enhancement index CZ and the circulatory impact assessment index CF of different gestational periods that have completed obstetric imaging examinations, forming a circulatory impact reference interval {C characteristic loss value T-CF h} and processing effect enhancement interval {CZ l -CZ h};

[0138] Among them, CF l The lowest index for cyclical impact assessment, CF h The highest index for circular impact assessment, CZ l The lowest index for enhancing the treatment effect, CZ h Enhance the highest index for treatment effect;

[0139] Based on the results of the cycle impact evaluation index CF and the processing effect enhancement index CZ, the noise level value ZS, motion blur value YD, feature loss value T and detail retention value XJ are adjusted as follows:

[0140] If the circulatory impact assessment index CF is in the circulatory impact reference interval {CF l -CF h}, the adjustment of the image processing strategy will not be triggered;

[0141] If the cyclical impact assessment index CF is lower than the minimum cyclical impact assessment index CF l , then the trigger process adjusts the image processing strategy. The specific image processing strategy adjustments include:

[0142] When the motion blur value YD is a major contributing factor in current obstetric images and the processing effect enhancement index CZ is low, the intensity of the motion deblurring process is increased to reduce the motion blur value YD to restore clear fetal structures;

[0143] When the feature loss value T is a major contributing factor in the current obstetric image and the value of the processing effect enhancement index CZ is low, the contrast and brightness of the current obstetric image are enhanced to reduce the value of the feature loss value T, so as to highlight and easily identify the key anatomical features of the fetus;

[0144] When the motion blur value YD and the feature loss value T are not the main contributing factors in the current obstetric images, and the value of the processing effect enhancement index CZ is low, the intensity of the detail enhancement processing should be increased to improve the detail retention value XJ, while the intensity of the noise filtering should be reduced to reduce the noise level value ZS.

[0145] In this embodiment, by iteratively calculating the cyclic impact assessment index CF value and feeding it back into the calculation process of the image quality assessment index V, the cyclic impact trigger unit can continuously optimize the image quality assessment results. This helps to more accurately understand the quality performance of the image at different processing stages and provides a more reliable basis for the subsequent formulation of processing strategies. Because the cyclic impact assessment index CF value can reflect the long-term impact of the processing strategy on image quality, the cyclic impact trigger unit can help formulate more adaptive processing strategies. These strategies can be adjusted and optimized according to changes in image quality at different stages, ensuring that the processing effect always meets clinical needs. By introducing the cyclic impact mechanism, the cyclic impact trigger unit can enhance the overall performance of the entire obstetric imaging examination processing method.

[0146] In addition, the final values ​​of the treatment effect enhancement index CZ and the circulatory impact assessment index CF at different gestational stages of the completed obstetric imaging examination are classified and stored to form a circulatory impact reference interval {CF l -CF h} and processing effect enhancement interval {CZ l -CZ hThrough continuous adjustment and optimization, obstetric images can be made clearer, with richer details, lower noise, and less motion blur, thereby improving the diagnostic value of images. High-quality images can help more accurately identify fetal structures and abnormalities, thereby reducing the risk of misdiagnosis and missed diagnosis. Clear images and accurate diagnostic results can improve patients' satisfaction and trust in medical services. Through continuous iteration and optimization of the feedback loop, it can promote the development and innovation of obstetric image processing technology and provide better support and services for clinicians.

[0147] In summary, the final values ​​of the treatment effect enhancement index CZ and circulatory impact assessment index CF at different gestational stages of obstetric imaging examinations were classified and stored, and the circulatory impact reference interval {CF l -CF h} and processing effect enhancement interval {CZ l -CZ h}, and the feedback loop formed by adjusting the examination and processing methods not only improves image quality and reduces the risk of misdiagnosis and missed diagnosis, but also improves patient satisfaction and promotes technological development.

[0148] It should be noted that any modification, equivalent replacement, improvement, etc. within the spirit and principles of the present invention should also be within the scope of protection of the present invention.

Claims

1. A visual obstetric imaging examination and processing method, comprising an image acquisition module, an image processing module, a circulatory impact assessment module, and an output and diagnosis module, characterized in that: The image processing module includes an image quality measurement unit, an image processing enhancement effect unit, and a cycle impact triggering unit; The specific processing steps are as follows: Step 1: using the image acquisition module to collect and preliminarily process the current obstetric image, and transmit it to the image processing module; Step 2: using the image processing module, sequentially calculating the output image quality evaluation index V, the processing effect enhancement index CZ and the circulation impact evaluation index CF; Step 3: Based on the result of the cyclic impact evaluation index CF and using the cyclic impact evaluation module, triggering the adjustment of the image processing strategy according to the result of the processing effect enhancement index CZ; Step 4: the output and diagnosis module outputs the adjusted image processing strategy and the adjusted current obstetric image; The calculation formula for measuring the image quality unit is as follows: ； in: V is the image quality assessment index; Q is the image clarity; YM is the image area; ZS is the noise level value; YD is the motion blur value; T is the feature loss value; XJ is the detail retention value; The calculation formulas of the noise level value ZS, motion blur value YD, feature loss value T and detail retention value XJ are as follows: ； ； ； ； in: N is the total number of pixels; Z i is the noise value of the i-th pixel; Z avg is the average pixel noise; D is the current frame image; D ref is the reference frame image; T lost is the lost feature quantity; T total is the total feature quantity; XJ detail This is the image after detail enhancement processing; XJ smooth is the image after smoothing; The calculation formula of the image processing enhancement effect unit is as follows: ； in: CZ is the treatment effect enhancement index; The calculation formula of the cyclic impact trigger unit is as follows: ； in: CF is the cyclical impact assessment index.

2. The method for visual obstetric imaging examination and processing according to claim 1, characterized in that: The image processing module stores and summarizes the treatment effect enhancement index CZ and the circulatory impact assessment index CF, and classifies and stores the final values ​​of the treatment effect enhancement index CZ and the circulatory impact assessment index CF of different gestational periods that have completed obstetric imaging examinations to form a circulatory impact reference interval {CF l -CF h } and processing effect enhancement interval {CZ l -CZ h }; Among them, CF l The lowest index for cyclical impact assessment, CF h The highest index for circular impact assessment, CZ l The lowest index for enhancing the treatment effect, CZ h Enhances the highest index for processing effect.

3. The method for visual obstetric imaging examination and processing according to claim 2, characterized in that: Based on the results of the cycle impact evaluation index CF and the processing effect enhancement index CZ, the noise level value ZS, the motion blur value YD, the feature loss value T and the detail retention value XJ are adjusted as follows: If the circulatory impact assessment index CF is in the circulatory impact reference interval {CF l -CF h }, the adjustment of the image processing strategy will not be triggered; If the cyclical impact assessment index CF is lower than the minimum cyclical impact assessment index CF l , then the trigger process adjusts the image processing strategy. The specific image processing strategy adjustments include: When the motion blur value YD is a major contributing factor in the current obstetric image, and the processing effect enhancement index CZ is lower than CZ l When , the intensity of the motion deblurring process is increased to reduce the value of the motion blur value YD to restore a clear fetal structure; When the feature loss value T is a major contributing factor in the current obstetric image, and the value of the processing effect enhancement index CZ is lower than CZ l When , the contrast and brightness of the current obstetric image are enhanced to reduce the value of the feature loss value T, so as to highlight and easily identify the key anatomical features of the fetus; When the motion blur value YD and the feature loss value T are not the main contributing factors in the current obstetric image, and the processing effect enhancement index CZ is lower than CZ l When , the intensity of detail enhancement processing is increased to improve the detail retention value XJ, while the intensity of noise filtering is reduced to reduce the noise level value ZS.

4. The method for visual obstetric imaging examination and processing according to claim 1, characterized in that: The equipment used in the image acquisition module includes ultrasound equipment and MRI equipment; The equipment used by the image processing module and the circulation impact assessment module includes an image processing workstation and image processing software; The devices used by the output and diagnosis module include a display device.

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