Obstetric abdominal color ultrasound image display method, system and device

Abstract

The present application provides a method, system and device for displaying obstetric abdominal color ultrasound images, which collect an object medical image of a target medical display to obtain a color-gray switching medical image; determine an antagonistic typical value of the color-gray switching medical image, and extract an imaging verification sequence and a color-gray switching distribution array from the color-gray switching medical image through the antagonistic typical value; determine the antagonistic estimation tolerance of the antagonistic typical value according to the imaging verification sequence; convert the antagonistic typical value into a corresponding antagonistic estimation value through the antagonistic estimation tolerance; determine all color-gray switching channel components of the color-gray switching distribution array, and obtain the imaging significance coefficient of the color-gray switching distribution array from the antagonistic estimation tolerance and all color-gray switching channel components; use the imaging significance coefficient and the antagonistic estimation value to quickly image the medical display, thereby realizing the switching of color or grayscale imaging using personalized antagonistic values ​​for different medical images.

Description

Technical Field

[0001] The present application relates to the field of medical display imaging technology, and more specifically, to a method, system and device for displaying obstetric abdominal color ultrasound images. Background Art

[0002] Medical display imaging technology aims to use image processing, analysis and visualization technology to improve medical diagnosis, treatment and case research. Medical images often come from a variety of different modalities, such as CT scans, X-rays and ultrasound images. With the continuous advancement of imaging technology, the acquisition and analysis of three-dimensional and four-dimensional medical images have become more common. In medical display imaging technology, the quick switching of color and grayscale imaging of medical displays can provide greater flexibility in use to meet the specific needs of different medical images, which helps to improve the visualization and diagnostic efficiency of medical images.

[0003] The color ultrasound medical display in current technology usually uses a medical LCD display, adopts multi-channel technology to operate and transform each pixel, automatically detects the adaptation mode of the display source image, and performs intelligent mode selection. In practical applications, different diseases or pathologies have different requirements for the prominence of images in medical displays, which will cause image details to be lost and fidelity prominence to decrease in color imaging, and key points to be unclear in grayscale imaging, resulting in the medical display's visual prominence not being high enough when quickly switching between color or grayscale imaging. In addition, each person's visual habits are inconsistent. Therefore, setting fixed parameters for a type of disease or pathology will also, to a certain extent, cause the medical display's visual prominence to be low when quickly switching between color or grayscale imaging, thereby affecting the efficiency and quality of medical diagnosis. Summary of the invention

[0004] The present application provides a method, system and device for displaying obstetric abdominal color ultrasound images, which can use a medical display to classify and extract the color-gray antagonism values ​​of significant parts of a medical image, thereby realizing the switching of color or grayscale imaging using personalized antagonism values ​​for different medical images.

[0005] In a first aspect, the present application provides a method for displaying an obstetric abdominal color ultrasound image, comprising:

[0006] Acquire the object medical image of the target medical display to obtain the color-gray switching medical image;

[0007] Determine an antagonistic typical value of the color-gray switching medical image, and extract an imaging verification sequence and a color-gray switching distribution array from the color-gray switching medical image by using the antagonistic typical value;

[0008] Determining an antagonism estimation tolerance of the antagonism typical value according to the imaging verification sequence;

[0009] Converting the antagonism typical value into a corresponding antagonism estimation value by using the antagonism estimation tolerance;

[0010] Determine all color-gray switching channel components of the color-gray switching distribution array, and obtain the imaging significance coefficient of the color-gray switching distribution array from the antagonistic estimation tolerance and all color-gray switching channel components;

[0011] The medical display is quickly imaged using the imaging significance coefficient and the antagonism estimate.

[0012] In some embodiments, extracting the imaging verification sequence and the color-gray switching distribution array in the color-gray switching medical image by using the antagonistic typical value specifically includes:

[0013] Obtaining a historical color-gray antagonism value group corresponding to the color-gray switching medical image;

[0014] Decoupling, verifying and separating the historical color-gray antagonism value group by using the antagonism typical value to obtain an imaging verification sequence;

[0015] Pixel difference comparison is performed on the color-gray switching medical image to obtain a color-gray switching distribution array.

[0016] In some embodiments, determining the antagonism estimation tolerance of the antagonism typical value according to the imaging verification sequence specifically includes:

[0017] Determining an imaging stabilization factor and a decoupling verification approach source according to the imaging verification sequence;

[0018] The decoupling verification approach source is converted into a decoupling fluctuation degree by the imaging stability factor;

[0019] The antagonism estimation tolerance of the antagonism typical value is obtained by using the decoupling fluctuation and the imaging stability factor.

[0020] In some embodiments, converting the antagonism typical value into a corresponding antagonism estimation value through the antagonism estimation tolerance is to use the antagonism typical value corresponding to the antagonism estimation tolerance exceeding a preset tolerance threshold as the antagonism estimation value.

[0021] In some embodiments, determining all color-gray switching channel components of the color-gray switching distribution array specifically includes:

[0022] For each pixel point in the color-gray switching distribution array, obtain the red-green antagonism value and the blue-yellow antagonism value of each pixel point in the color-gray switching distribution array;

[0023] Performing antagonistic correction on the red-green antagonistic value and the blue-yellow antagonistic value of each pixel point according to the antagonistic estimation value, determining the antagonistic correction value of each pixel point in the color-gray switching distribution array, and then determining the antagonistic distribution space;

[0024] Channel separation is performed on the antagonistic distribution space to obtain all color-gray switching channel components.

[0025] In some embodiments, obtaining the imaging significance coefficient of the color-gray switching distribution array from the antagonistic estimation tolerance and all color-gray switching channel components specifically includes:

[0026] Extracting single channel features of the color-gray switching channel component at different scales to obtain antagonistic channel layers at different scales;

[0027] Linearly interpolating the antagonistic channel layers of different scales to obtain an antagonistic channel pyramid;

[0028] Performing edge difference comparison on the antagonistic channel pyramid through a center-periphery difference operation to obtain a multi-scale channel layer feature map;

[0029] The imaging saliency coefficient is obtained through the multi-scale channel layer feature map.

[0030] In some embodiments, using the imaging significance coefficient and the antagonism estimation value to quickly image the medical display specifically includes:

[0031] Convert all color-gray switching channel components into antagonistic significant space through the imaging verification sequence and the imaging significant coefficient;

[0032] Color or grayscale imaging of a medical display is switched according to the antagonism estimation value and the antagonism significance space.

[0033] In a second aspect, the present application provides an obstetric abdominal color ultrasound image display system, comprising a control unit, wherein the control unit comprises:

[0034] An acquisition module, used for acquiring an object medical image of a target medical display to obtain a color-gray switching medical image;

[0035] An extraction module, used for determining an antagonistic typical value of the color-gray switching medical image, and extracting an imaging verification sequence and a color-gray switching distribution array from the color-gray switching medical image through the antagonistic typical value;

[0036] A processing module, configured to determine an antagonism estimation tolerance of the antagonism typical value according to the imaging verification sequence;

[0037] The processing module is further used to convert the antagonism typical value into a corresponding antagonism estimation value through the antagonism estimation tolerance;

[0038] The processing module is further used to determine all color-gray switching channel components of the color-gray switching distribution array, and obtain the imaging significance coefficient of the color-gray switching distribution array from the antagonistic estimation tolerance and all color-gray switching channel components;

[0039] An imaging module is used to perform quick imaging on the medical display using the imaging significance coefficient and the antagonism estimation value.

[0040] In a third aspect, the present application provides a computer device, comprising a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the above-mentioned obstetric abdominal color ultrasound image display method.

[0041] In a fourth aspect, the present application provides a computer-readable storage medium, in which instructions or codes are stored. When the instructions or codes are run on a computer, the computer implements the above-mentioned obstetric abdominal color ultrasound image display method when executed.

[0042] The technical solution provided by the embodiments disclosed in this application has the following beneficial effects:

[0043] In the present application, first, the antagonistic typical value of the object medical image is obtained, and the antagonistic typical value is used to extract the imaging verification sequence in the color-gray switching medical image, and then the antagonistic estimation tolerance of the antagonistic typical value is determined, so that the antagonistic typical value is converted into the antagonistic estimation value using the antagonistic estimation tolerance, and the antagonistic contrast difference degree of the focal color value of the significant part in the object medical image can be obtained. The object medical image represents a type of image in the medical image. In the present application, the color-gray antagonistic values ​​of the medical image are classified and extracted, and the antagonistic typical value represents the most representative antagonistic value in the object medical image. The imaging verification sequence is a sequence used to verify the imaging effect. The antagonistic estimation tolerance represents the representative degree of the antagonistic typical value to the object medical image. The antagonistic typical value is converted into the antagonistic estimation value using the antagonistic estimation tolerance, which can improve the accuracy of the antagonistic contrast difference degree of the focal color value of the significant part. When switching images, the antagonistic estimated value of the focal color value of the significant part is used for rapid switching, thereby effectively improving the switching speed of color or grayscale imaging. Then, the antagonistic typical value is used to extract the color-gray switching distribution array in the color-gray switching medical image, so that the imaging significance coefficient is obtained through the color-gray switching distribution array and the antagonistic estimated tolerance, and the significance of the focal color value of the significant part in the object medical image can be obtained, thereby effectively improving the imaging effect when switching color or grayscale imaging. Finally, the antagonistic estimated value and the imaging significance coefficient are used to quickly image the medical display, which reduces the interference of noise in the color-gray switching medical image on the quick switching imaging and improves the significance of the focal pixel in the quick switching imaging. In summary, the medical display can be used to classify and extract the color-gray antagonistic value of the significant part of the medical image, so as to realize the switching of color or grayscale imaging using personalized antagonistic values ​​for different medical images. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present invention 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 invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0045] Figure 1 is an exemplary flow chart of a method for displaying obstetric abdominal color ultrasound images according to some embodiments of the present application;

[0046] Figure 2 is a schematic diagram of a process for determining an antagonistic estimation tolerance according to some embodiments of the present application;

[0047] Figure 3 is a schematic diagram of a process for determining an imaging saliency coefficient according to some embodiments of the present application;

[0048] Figure 4 is a schematic diagram of exemplary hardware and / or software of a control unit according to some embodiments of the present application;

[0049] Figure 5 It is a structural schematic diagram of a computer device applying an obstetric abdominal color ultrasound image display method as shown in some embodiments of the present application. DETAILED DESCRIPTION

[0050] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] The present application provides a method, system and device for displaying obstetric abdominal color ultrasound images, which collect an object medical image of a target medical display to obtain a color-gray switching medical image; determine an antagonistic typical value of the color-gray switching medical image, and extract an imaging verification sequence and a color-gray switching distribution array from the color-gray switching medical image through the antagonistic typical value; determine the antagonistic estimation tolerance of the antagonistic typical value according to the imaging verification sequence; convert the antagonistic typical value into a corresponding antagonistic estimation value through the antagonistic estimation tolerance; determine all color-gray switching channel components of the color-gray switching distribution array, and obtain the imaging significance coefficient of the color-gray switching distribution array from the antagonistic estimation tolerance and all color-gray switching channel components; use the imaging significance coefficient and the antagonistic estimation value to quickly image the medical display, thereby realizing the switching of color or grayscale imaging using personalized antagonistic values ​​for different medical images.

[0052] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods. Figure 1 , which is an exemplary flow chart of a method for displaying an obstetric abdominal color ultrasound image according to some embodiments of the present application, and the obstetric abdominal color ultrasound image display method mainly includes the following steps:

[0053] In step 101, an object medical image of a target medical display is acquired to obtain a color-gray switching medical image.

[0054] In specific implementation, the color-gray switching medical image is the medical image that needs to be switched and displayed on the medical display, and the original image captured by the image acquisition device supporting the medical display can be used as the color-gray switching medical image; the category of the color-gray switching medical image can be obtained through the existing classification library, and the historical antagonism value record of the medical display within a preset time period is obtained, and all antagonism value data under the category of the color-gray switching medical image are filtered in the historical antagonism value record, and the combination of all antagonism value data is used as the historical color-gray antagonism value group.

[0055] It should be noted that in the present application, the historical color-gray antagonism value refers to the contrast or color opposition between different color channels in the image. The historical color-gray antagonism value is an array used to describe the relativity and contrast differences between different colors in the color-gray switching medical image. In the grayscale imaging of the color-gray switching medical image, the historical color-gray antagonism value can be the average value of the grayscale value, that is, the average brightness. In the color imaging of the color-gray switching medical image, the historical color-gray antagonism value can be the contrast of different color channels. In the color imaging of the color-gray switching medical image, the historical color-gray antagonism value can include red-green antagonism values, blue-yellow antagonism values ​​and brightness antagonism values, all ranging from 0 to 1.

[0056] It should be noted that in the present application, typical antagonist values ​​are obtained to obtain estimated antagonist values, so that the antagonist significance space obtained by the estimated antagonist values ​​is more in line with the visual recognition habits of the human eye, reducing the difference between the typical antagonist values ​​and the estimated antagonist values, thereby reducing the time complexity of subsequent verification.

[0057] In step 102, an antagonistic typical value of the color-gray switching medical image is determined, and an imaging verification sequence and a color-gray switching distribution array are extracted from the color-gray switching medical image by using the antagonistic typical value.

[0058] It should be noted that the typical antagonistic value represents the gray-colored antagonistic value in the historical gray-colored antagonistic value group that best represents the current usage habits. In specific implementation, the most recent historical gray-colored antagonistic value in the historical gray-colored antagonistic value group may be used as the typical antagonistic value.

[0059] In some embodiments, extracting the imaging verification sequence and the color-gray switching distribution array in the color-gray switching medical image by using the antagonistic typical value can be achieved by using the following steps:

[0060] Obtaining a historical color-gray antagonism value group corresponding to the color-gray switching medical image;

[0061] Decoupling, verifying and separating the historical color-gray antagonism value group by using the antagonism typical value to obtain an imaging verification sequence;

[0062] Pixel difference comparison is performed on the color-gray switching medical image to obtain a color-gray switching distribution array.

[0063] In the above embodiment, the historical color-gray antagonistic value group is decoupled, verified and separated to obtain an imaging verification sequence, which can be achieved by the following steps:

[0064] Determine the antagonistic typical value and antagonistic dispersion of the historical color-gray antagonistic value group;

[0065] For each historical color-gray antagonism value in the historical color-gray antagonism value group, antagonism decoupling is performed on the historical color-gray antagonism value according to the antagonism typical value and the imaging stability factor to obtain a decoupled antagonism value;

[0066] Performing a preset credible interval verification on the decoupling antagonism value, if the decoupling antagonism value is within the preset credible interval, taking the decoupling antagonism value as the decoupling verification value, and then obtaining all the decoupling verification values;

[0067] All decoupling verification values ​​are arranged in order to obtain an imaging verification sequence.

[0068] In specific implementation, the mode of all historical color-gray antagonism values ​​in the historical color-gray antagonism value group can be used as the antagonism typical value of the historical color-gray antagonism value group, and the standard deviation of the historical color-gray antagonism value group can be used as the antagonism dispersion; for each historical color-gray antagonism value in the historical color-gray antagonism value group, the difference between the historical color-gray antagonism value and the antagonism typical value can be obtained as the antagonism difference, the ratio of the antagonism dispersion to the antagonism difference can be obtained as the antagonism ratio, and the product of the antagonism ratio and the historical color-gray antagonism value can be used as the decoupling antagonism value; a preset credible interval can be obtained based on a large amount of experimental data to determine whether the decoupling antagonism value is within the preset credible interval; if the decoupling antagonism value is within the preset credible interval, the decoupling antagonism value is used as the decoupling verification value; if the decoupling antagonism value is not within the preset credible interval, the next decoupling antagonism value is processed.

[0069] It should be noted that the imaging verification sequence refers to a sequence consisting of a series of decoupling verification values ​​arranged in order, and the imaging verification sequence can be obtained by arranging all the decoupling verification values ​​in order according to the time sequence corresponding to the decoupling verification values.

[0070] In some embodiments, pixel difference comparison is performed on the color-gray switching medical image to obtain a color-gray switching distribution array, which can be achieved by using the following steps:

[0071] Performing image segmentation on the color-gray switching medical image to obtain a focus area set;

[0072] For each focus area in the focus area set, obtaining a focus pixel value of a pixel point in the focus area, and obtaining a comparison pixel value set according to the focus pixel value;

[0073] Performing a similarity comparison between each comparison pixel value in the comparison pixel value set and the focus pixel value to obtain a focus similarity set, and then determining a focus similarity ratio;

[0074] Visualizing the focus similarity ratio to obtain a focus comparison diagram of the focus area, and then determining a focus comparison diagram of all focus areas;

[0075] The color-gray switching distribution array is obtained through the focus contrast images of all focus areas.

[0076] In specific implementation, image analysis tools can be used to extract shape features from historical images of color-gray similar images as focus features, and the color-gray switching medical images can be divided into several image partitions through the region growing method in the prior art. All image partitions that meet the focus features are screened out from all image partitions as focus areas, and the set of all focus areas is taken as a focus area set. It should be noted that color-gray similar images refer to images of the same category as the color-gray switching medical images, that is, images with the same acquisition object and acquisition purpose.

[0077] In specific implementation, the red-green antagonism value of each pixel in the color-gray switching distribution array can be determined according to the difference between the red chromaticity value and the green chromaticity value, and the blue-yellow antagonism value of each pixel in the color-gray switching distribution array can be determined according to the following formula:

[0078]

[0079] in, Represents the blue-yellow antagonism value of each pixel in the color-gray switching distribution array, represents the red chromaticity value, represents the green chromaticity value, Represents a blue chromaticity value, where the chromaticity value may be determined based on a color extraction tool.

[0080] In specific implementation, the pixel values ​​of all pixel points in the focus area can be obtained according to the pixel acquisition tool, the mode of all pixel values ​​can be used as the focus pixel value, and all pixel values ​​are deduplicated after removing the focus pixel value to obtain a comparison pixel value set; the difference between each comparison pixel value and the focus pixel value in the comparison pixel value set is used as the focus difference corresponding to each comparison pixel value to obtain the focus difference corresponding to all comparison pixel values, the set of focus difference values ​​corresponding to all comparison pixel values ​​is used as a focus similarity set, and the average value of all focus differences in the focus similarity set is used as the focus similarity ratio; the focus contrast maps of all focus areas can be combined to obtain a color-gray switching distribution array.

[0081] It should be noted that in the present application, the color-gray switching medical images are subjected to difference comparison and verification separation, which reduces the data complexity of the historical color-gray antagonism value group, improves the estimation accuracy of the antagonism estimation value obtained later, and makes the color space obtained by the antagonism estimation value more in line with the recognition habits of the human eye, thereby improving the significance of the final generated image.

[0082] In step 103, the antagonism estimation tolerance of the antagonism typical value is determined according to the imaging verification sequence.

[0083] In some embodiments, reference Figure 2 As described above, the figure is a schematic diagram of a process for determining the tolerance of antagonistic estimation in some embodiments of the present application. In this embodiment, the determination of the tolerance of antagonistic estimation can be implemented by the following steps:

[0084] In step 1031, an imaging stability factor and a decoupling verification approach source are determined according to the imaging verification sequence;

[0085] In step 1032, the decoupling verification approach source is converted into a decoupling fluctuation degree by the imaging stability factor;

[0086] In step 1033, the antagonism estimation tolerance of the antagonism typical value is obtained through the decoupling fluctuation and the imaging stability factor.

[0087] In some embodiments of step 1031, determining the imaging stability factor and the decoupling verification approach source according to the imaging verification sequence is implemented by the following steps:

[0088] Determining a decoupling stability parameter and an antagonism verification angle according to the imaging verification sequence;

[0089] Obtaining an imaging stability factor through the decoupling stability parameter and the antagonism verification angle;

[0090] The decoupling verification approach source is obtained by the first decoupling verification value and the last decoupling verification value in the imaging verification sequence.

[0091] It should be noted that in the present application, the imaging stability factor represents the degree of dispersion of the imaging verification sequence relative to the current decoupling verification value. The lower the imaging stability factor, the lower the degree of dispersion of the imaging verification sequence relative to the current decoupling verification value, and the lower the tolerance for antagonistic estimation. The decoupling verification convergence source represents the overall change trend of the decoupling verification value in the imaging verification sequence. The higher the decoupling verification convergence source, the more drastic the overall change of the decoupling verification value, and the lower the tolerance for antagonistic estimation.

[0092] In specific implementation, the The decoupling verification value is used as the mean of the imaging verification sequence and the standard deviation is used as the decoupling stability parameter; The antagonistic verification angle of a decoupling verification value refers to the angle formed by the line connecting the previous decoupling verification value of the decoupling verification value and the next decoupling verification value at the decoupling verification value; the slope of the line connecting the first decoupling verification value and the last decoupling verification value of the imaging verification sequence can be used as the decoupling verification approach source; the imaging stability factor can be determined according to the following formula:

[0093]

[0094] in, Indicates the first Imaging stability factor of decoupling verification value, Represents the imaging verification sequence relative to the The decoupling stability parameter of the decoupling verification value, Indicates the first Decoupled verification values, Indicates the first The antagonistic verification angle of the decoupled verification value,

[0095] In some embodiments of step 1032, the decoupling verification approach source is converted into a decoupling fluctuation degree by the imaging stability factor, wherein the decoupling fluctuation degree can be determined according to the following formula:

[0096]

[0097] in, Indicates the first The decoupling volatility of the decoupling verification value, It means that the decoupled verification approaches the source, Indicates the first The imaging stability factor of the decoupling verification value. It should be noted that the decoupling fluctuation in the present application represents the changing trend of the decoupling verification value in the imaging verification sequence. The higher the decoupling fluctuation, the more drastic the change of the decoupling verification value and the lower the tolerance of the antagonism estimation.

[0098] In some embodiments of step 1033, obtaining the antagonism estimation tolerance of the antagonism typical value by using the decoupling fluctuation and the imaging stability factor can be implemented by the following steps:

[0099] Obtaining an imaging stability factor for each decoupling verification value in an imaging verification sequence;

[0100] Determine the difference value of each decoupling verification value relative to the antagonist typical value in the imaging verification sequence;

[0101] Obtaining the antagonistic verification angle composed of adjacent decoupling verification values ​​in the imaging verification sequence;

[0102] Obtaining the decoupling fluctuation of each decoupling verification value in the imaging verification sequence;

[0103] The antagonism estimation tolerance is determined according to the imaging stability factor of each decoupling verification value in the imaging verification sequence, the differential value of each decoupling verification value in the imaging verification sequence relative to the antagonism typical value, the antagonism verification angle composed of adjacent decoupling verification values ​​in the imaging verification sequence, and the decoupling fluctuation of each decoupling verification value in the imaging verification sequence, wherein the antagonism estimation tolerance can be determined according to the following formula:

[0104]

[0105] in, represents the tolerance of antagonistic estimation, represents the number of decoupled verification values ​​in the imaging verification sequence, Indicates the first Imaging stability factor of decoupling verification value, Indicates the first The difference between the decoupling verification value and the antagonistic typical value, Indicates the first The decoupling fluctuation between a decoupling verification value and the adjacent decoupling verification value, Indicates the first The antagonism verification angle of the decoupled verification value. It should be noted that the antagonism estimation tolerance in this application represents the representativeness of the antagonism typical value in the imaging verification sequence, which is used to ensure the significance of the final generated image. The lower the antagonism estimation tolerance, the more the antagonism typical value can represent the imaging verification sequence, and the higher the significance of the final generated image.

[0106] It should be noted that in the present application, the antagonism estimation tolerance of the antagonism typical value is obtained through the imaging verification sequence, which indicates the representativeness of the antagonism typical value in the final generated image, ensures the antagonism estimation tolerance, and effectively improves the saliency of the final image.

[0107] In step 104, the antagonism typical value is converted into a corresponding antagonism estimation value by using the antagonism estimation tolerance.

[0108] In some embodiments, converting the antagonism typical value into the corresponding antagonism estimation value through the antagonism estimation tolerance is to use the antagonism typical value corresponding to the antagonism estimation tolerance that exceeds the preset tolerance threshold as the antagonism estimation value. It should be noted that the antagonism estimation value is an array used to describe the relativity and contrast differences between different colors, including red-green antagonism values, blue-yellow antagonism values ​​and brightness antagonism values.

[0109] In specific implementation, if the antagonism estimate tolerance exceeds the preset tolerance threshold, the antagonism typical value is used as the antagonism estimate value; if the antagonism estimate tolerance is lower than the preset tolerance threshold, the antagonism typical value is adjusted by the preset tolerance coefficient ratio until the antagonism estimate tolerance exceeds the preset tolerance threshold.

[0110] In step 105, all color-gray switching channel components of the color-gray switching distribution array are determined, and the imaging significance coefficient of the color-gray switching distribution array is obtained from the antagonistic estimation tolerance and all color-gray switching channel components.

[0111] In some embodiments, determining all color-gray switching channel components of the color-gray switching distribution array may be implemented by the following steps:

[0112] For each pixel point in the color-gray switching distribution array, obtain the red-green antagonism value and the blue-yellow antagonism value of each pixel point in the color-gray switching distribution array;

[0113] Performing antagonistic correction on the red-green antagonistic value and the blue-yellow antagonistic value of each pixel point according to the antagonistic estimation value, determining the antagonistic correction value of each pixel point in the color-gray switching distribution array, and then determining the antagonistic distribution space;

[0114] Channel separation is performed on the antagonistic distribution space to obtain all color-gray switching channel components.

[0115] In some embodiments, antagonism correction is performed on the red-green antagonism value and the blue-yellow antagonism value of each pixel point according to the antagonism estimation value to determine the antagonism correction value. In specific implementation, the average of the red-green antagonism value and the red-green antagonism value in the antagonism estimation value can be used as the red-green antagonism correction value, the average of the blue-yellow antagonism value and the blue-yellow antagonism value in the antagonism estimation value can be used as the blue-yellow antagonism correction value, and the red-green antagonism correction value and the blue-yellow antagonism correction value can be filled into an array as the antagonism correction value.

[0116] It should be noted that the antagonism correction value is an array including red-green antagonism correction values ​​and blue-yellow antagonism correction values; the antagonism correction value of each pixel point in the antagonism estimation value is used as the distribution value of the pixel point to obtain the antagonism distribution space.

[0117] In specific implementation, according to the prior art, the antagonistic distribution space can be channel-separated to obtain a brightness channel, a chromaticity channel and a saturation channel, and the brightness channel, the chromaticity channel and the saturation channel are used as color-gray switching channel components.

[0118] In some embodiments, reference Figure 3 The figure is a schematic diagram of a process for determining an imaging saliency coefficient in some embodiments of the present application. In this embodiment, determining the imaging saliency coefficient can be implemented by the following steps:

[0119] In step 1051, the single channel features of the color-gray switching channel component at different scales are extracted to obtain antagonistic channel layers at different scales;

[0120] In step 1052, linear interpolation is performed on the antagonistic channel layers of different scales to obtain an antagonistic channel pyramid;

[0121] In step 1053, edge difference comparison is performed on the antagonistic channel pyramid through a center-periphery difference operation to obtain a multi-scale channel layer feature map;

[0122] In step 1054, an imaging saliency coefficient is obtained through the multi-scale channel layer feature map.

[0123] In specific implementation, the antagonistic channel layers of different scales include brightness channel layer, color channel layer and direction channel layer. The channel layers at different scales can be obtained by downsampling. The channel layers at different scales represent the channel layers at different sampling levels for the color-gray switching channel components. The lower the sampling level, the smaller the generated channel layer is, and the less image information it contains. Among them, the average brightness value of the three primary colors of red, green and blue can be used as the brightness channel layer, and the antagonistic values ​​of the two groups of antagonistic colors of red-green and blue-yellow can be used as the color channel; the Gabor (Gabor, wavelet) function can be used to sample 0 0 , 45 0 , 90 0 , 135 0 Filtering is performed in four directions to obtain directional features, and all directional features are used as directional channel layers.

[0124] In some embodiments of step 1052, the antagonistic channel layers of different scales are linearly interpolated to obtain an antagonistic channel pyramid. In specific implementation, the antagonistic channel layers of different scales are linearly interpolated by the linear interpolation method in the prior art until the spatial sizes of all antagonistic channel layers are the same, and all antagonistic channel layers are combined as an antagonistic channel pyramid, which refers to the antagonistic transformation process of the color-gray switching channel component from the center to the periphery.

[0125] In specific implementation, sampling levels 2, 3, and 4 can be used as the central scale, and the peripheral scale can be determined according to the following formula:

[0126]

[0127] in, Indicates the surrounding scale, represents the central scale, Representing the scale difference, performing central-peripheral pairwise difference on the antagonistic channel pyramids at the central scale and the peripheral scale can obtain 6 brightness channel feature maps, 12 color channel feature maps and 24 direction channel feature maps, and merging the above brightness channel feature maps, color channel feature maps and direction channel feature maps in each channel to obtain a brightness channel feature map, a color channel feature map and a direction channel feature map, and then merging them to obtain a multi-scale channel layer feature map, wherein the weights can be preset through the existing image technology, and the weighted average of the pixels in different channel feature maps can be used to achieve merging.

[0128] In some embodiments of step 1054, obtaining the imaging saliency coefficient through the multi-scale channel layer feature map may be achieved by using the following steps:

[0129] Determine the visual imperfections of multi-scale channel layer feature maps;

[0130] Determine the color contrast between the aggregation points of the pixel block and the aggregation points of similar neighboring pixel blocks of the pixel block in the multi-scale channel layer feature map;

[0131] Determine the spatial scale of the multi-scale channel layer feature map;

[0132] Determine the contrast difference between a pixel block and similar neighboring pixel blocks of the pixel block in the multi-scale channel layer feature map;

[0133] An imaging saliency coefficient is obtained according to the visual defect degree of the multi-scale channel layer feature map, the color contrast between the aggregation points of the pixel block in the multi-scale channel layer feature map and the aggregation points of the similar neighboring pixel blocks of the pixel block, the spatial scale of the multi-scale channel layer feature map and the contrast difference between the pixel block in the multi-scale channel layer feature map and the similar neighboring pixel blocks of the pixel block, wherein the imaging saliency coefficient can be determined according to the following formula:

[0134]

[0135] in, represents the imaging significance coefficient, Represents the number of antagonistic channel layers in the color-gray switching channel component, Represents the number of similar neighboring pixel blocks, represents the spatial scale of the multi-scale channel layer feature map, Represents the visual imperfection degree of the multi-scale channel layer feature map, Represents a pixel block Similar neighboring pixel blocks The contrast difference, Indicated in scale The next The pixel blocks in the antagonistic channel layer, Indicates The spatial scale corresponding to the antagonistic channel layer is Indicated in scale The next Similar neighboring pixel blocks, Indicates The spatial scale corresponding to the antagonistic channel layer where similar neighboring pixel blocks are located, Indicates The total pixel step length between the aggregation point of a pixel block and the aggregation points of all similar neighboring pixel blocks of this pixel block, The aggregation point representing the first pixel block With The aggregation point of the pixel block The pixel step size between Indicates The aggregation point of the pixel block The color contrast of clustered points with similar neighboring pixel blocks.

[0136] It should be noted that the imaging significance coefficient indicates the significance of the color-gray switching channel component in describing the key stable feature information of the color-gray switching medical image. The higher the imaging significance coefficient, the more significant the color-gray switching channel component is in describing the key stable feature information of the color-gray switching medical image, and the higher the significance of the final medical display imaging.

[0137] In specific implementation, pixel blocks in the multi-scale channel layer feature map can be obtained by dividing according to the focus area set, and similarity analysis of the pixel blocks can be performed according to the image analysis tool to obtain all similar neighboring pixel blocks corresponding to each pixel block. The mode of the color values ​​of all pixel points in the pixel block can be used as the aggregate color value of the pixel block, and the center point of all aggregate color values ​​can be used as the aggregation point of the pixel block, wherein the coordinates of the center point of the aggregate color value can be determined according to the coordinate average value of all pixel points whose color values ​​in the pixel block are the aggregate color value, and the color value can be described by a hexadecimal color code; the product of the number of central scales and scale differences can be used as the spatial scale of the multi-scale channel feature map, and all antagonistic channel layers in the color-gray switching channel component are arranged in order from the central scale to the peripheral scale, and the arrangement sequence number is used as the scale of each antagonistic channel layer in the color-gray switching channel component; the difference between the aggregate color value of the pixel block and the aggregate color value of a single similar neighboring pixel block of the pixel block can be used as the pixel step, and the total pixel step refers to the total value of all pixel steps.

[0138] It should be noted that the visual defect degree represents the perceptual sensitivity of the multi-scale channel layer feature map under human vision, and the visual defect degree can be obtained according to the human visual sensor in the prior art; the contrast difference between the pixel block and the similar neighboring pixel block in the multi-scale channel layer feature map can be determined according to the following formula:

[0139]

[0140] in, Represents pixel blocks in the multi-scale channel layer feature map Similar neighboring pixel blocks The contrast difference, express and The color distance between Represents the aggregated color value of the pixel block in the multi-scale channel layer feature map, express and It should be noted that the contrast difference represents the spatial difference and color difference between the pixel block and the similar neighboring pixel block in the multi-scale channel layer feature map. The higher the contrast difference, the greater the spatial difference and color difference between the pixel blocks, and the higher the imaging significant coefficient. In addition, the color contrast between the aggregation point of the pixel block and the aggregation point of the similar neighboring pixel block in the multi-scale channel layer feature map can be determined according to the following formula:

[0141]

[0142] in, Represents the first The aggregation point of the pixel block The color contrast between clusters of similar neighboring pixels, Represents the total number of color categories in the multi-scale channel layer feature map, that is, the number of non-repeated aggregated color values. Indicates The frequency of the aggregated color values ​​of the pixel blocks appearing in the multi-scale channel layer feature map, Represents the aggregation point of a pixel block The aggregation point with the pixel block In this application, color contrast is a metric used to measure the obvious difference and distinction between two colors. The higher the color contrast, the more obvious the difference between the two colors, the higher the distinction, and the higher the imaging significance coefficient.

[0143] It should be noted that in the present application, the antagonistic correction obtains the antagonistic distribution space, which reduces the interference of noise in the color-gray switching medical image on the quick switching imaging, improves the significance of the focus pixel in the quick switching imaging, and performs multi-scale edge difference comparison on the antagonistic channel to obtain the imaging significance coefficient, thereby improving the significance of the subsequent antagonistic significance space and ensuring that the quick switching imaging meets the preset significance requirements.

[0144] In step 106, the medical display is quickly imaged using the imaging significance coefficient and the antagonism estimate.

[0145] In some embodiments, the quick imaging of the medical display using the imaging significance coefficient and the antagonism estimation value may be implemented by the following steps:

[0146] Convert all color-gray switching channel components into antagonistic significant space through the imaging verification sequence and the imaging significant coefficient;

[0147] Color or grayscale imaging of a medical display is switched according to the antagonism estimation value and the antagonism significance space.

[0148] In specific implementation, if the imaging significance coefficient is lower than the preset significance, the product of the preset significance coefficient and the preset tolerance threshold is used as the new preset tolerance threshold, and steps 102-104 are repeated according to the imaging verification sequence and the new preset tolerance threshold until the imaging significance coefficient is higher than the preset significance. If the imaging significance coefficient is higher than the preset significance, the antagonistic distribution space composed of all color-gray switching channel components is used as the antagonistic significance space; the antagonistic estimation value and the preset antagonistic threshold are compared. If the brightness antagonistic value of the antagonistic estimation value is greater than the preset antagonistic threshold, the antagonistic significance space is initialized to the grayscale image of the color-gray switching medical image through the existing technology. If the brightness antagonistic value of the antagonistic estimation value is less than the preset antagonistic threshold, the antagonistic significance space is initialized to the color image of the color-gray switching medical image through the existing technology. After initialization, the antagonistic significance space is used to quickly switch color or grayscale imaging through the existing technology.

[0149] It should be noted that in the present application, the antagonistic salient space is obtained according to the imaging verification sequence and the imaging saliency coefficient, and then the imaging is quickly switched, so that the antagonistic saliency space is more in line with the visual recognition habits, and the visual saliency of the quick switching imaging is effectively improved.

[0150] In the present application, first, the antagonistic typical value of the object medical image is obtained, and the antagonistic typical value is used to describe the most representative antagonistic value of the object medical image. Secondly, the antagonistic estimated tolerance used to describe the representativeness of the antagonistic typical value to the object medical image is obtained through an imaging verification sequence. The imaging verification sequence is a sequence used to verify the imaging effect. The use of the antagonistic estimated tolerance can effectively improve the imaging effect and speed during subsequent imaging switching. Then, the antagonistic estimated tolerance is used to combine the antagonistic typical value and the color-gray switching distribution array respectively to obtain the antagonistic estimated value used to describe the antagonistic contrast difference of the focal color value and the imaging significance system used to describe the significance of the focal color value. The color-gray switching distribution array is used to describe the focal color value comparison results in the historical images of the same type of medical images. The antagonistic estimation value can be used to improve the imaging speed during imaging switching, and the imaging significance coefficient can be used to ensure the imaging effect during imaging switching. Finally, the antagonistic estimation value and the imaging significance coefficient are used to perform quick imaging on the medical display, which reduces the interference of noise in the color-gray switching medical image on the quick switching imaging and improves the significance of the focal pixel in the quick switching imaging. In summary, the medical display can be used to classify and extract the color-gray antagonism values ​​of the significant part of the medical image, and realize the switching of color or grayscale imaging for different medical images using personalized antagonism values.

[0151] In addition, in another aspect of the present application, in some embodiments, the present application provides an obstetric abdominal color ultrasound image display system, the obstetric abdominal color ultrasound image display system also includes a control unit, referring to Figure 4 , which is a schematic diagram of exemplary hardware and / or software of a control unit according to some embodiments of the present application, the control unit includes: an acquisition module 201, an extraction module 202, a processing module 203 and an imaging module 204, which are described as follows:

[0152] Acquisition module 201, in this application, acquisition module 201 is mainly used to acquire the object medical image of the target medical display to obtain the color-gray switching medical image;

[0153] Extraction module 202, in the present application, the extraction module 202 is mainly used to determine the antagonistic typical value of the color-gray switching medical image, and extract the imaging verification sequence and the color-gray switching distribution array in the color-gray switching medical image through the antagonistic typical value;

[0154] Processing module 203, in the present application, the processing module 203 is mainly used to determine the antagonism estimation tolerance of the antagonism typical value according to the imaging verification sequence;

[0155] In addition, the processing module 203 in the present application is also used to convert the antagonism typical value into a corresponding antagonism estimation value through the antagonism estimation tolerance;

[0156] In addition, the processing module 203 in the present application is also used to determine all color-gray switching channel components of the color-gray switching distribution array, and obtain the imaging significance coefficient of the color-gray switching distribution array from the antagonistic estimation tolerance and all color-gray switching channel components;

[0157] The imaging module 204 in the present application is mainly used to perform quick imaging on the medical display using the imaging significance coefficient and the antagonism estimation value.

[0158] The above describes in detail an example of an obstetric abdominal color ultrasound image display system and a control method thereof provided by an embodiment of the present application. It can be understood that, in order to realize the above functions, the corresponding device includes a hardware structure and / or software module corresponding to each function. It should be easily appreciated by those skilled in the art that, in combination with the units and algorithm steps of each example described in the embodiments disclosed herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present application.

[0159] In some embodiments, the present application also provides a computer device, comprising a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the above-mentioned obstetric abdominal color ultrasound image display method.

[0160] In some embodiments, reference Figure 5 , the dotted line in the figure indicates that the unit or the module is optional, and the figure is a schematic diagram of the structure of a computer device for implementing an obstetric abdominal color Doppler ultrasound image display method according to an embodiment of the present application. The above-mentioned obstetric abdominal color Doppler ultrasound image display method in the above-mentioned embodiment can be Figure 5 The computer device shown in the figure is implemented, and the computer device includes at least one processor 301, a memory 302 and at least one communication unit 305. The computer device can be a terminal device, a server or a chip.

[0161] The processor 301 may be a general-purpose processor or a special-purpose processor. For example, the processor 301 may be a central processing unit (CPU), which may be used to control the computer device, execute software programs, and process data of the software programs. The computer device may also include a communication unit 305 to implement signal input (reception) and output (transmission).

[0162] For example, the computer device may be a chip, the communication unit 305 may be an input and / or output circuit of the chip, or the communication unit 305 may be a communication interface of the chip, and the chip may be a component of a terminal device, a network device, or other devices.

[0163] For another example, the computer device may be a terminal device or a server, and the communication unit 305 may be a transceiver of the terminal device or the server, or the communication unit 305 may be a transceiver circuit of the terminal device or the server.

[0164] The computer device may include one or more memories 302, on which a program 304 is stored. The program 304 can be executed by the processor 301 to generate instructions 303, so that the processor 301 performs the method described in the above method embodiment according to the instructions 303. Optionally, data (such as a target audit model) can also be stored in the memory 302. Optionally, the processor 301 can also read the data stored in the memory 302, and the data can be stored at the same storage address as the program 304, or the data can be stored at a different storage address from the program 304.

[0165] The processor 301 and the memory 302 may be provided separately or integrated together, for example, integrated on a system on chip (SOC) of the terminal device.

[0166] It should be understood that each step of the above method embodiment can be completed by a hardware-based logic circuit or software-based instructions in the processor 301. The processor 301 can be a CPU, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, such as discrete gates, transistor logic devices, or discrete hardware components.

[0167] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0168] For example, in some embodiments, the present application also provides a computer-readable storage medium, in which instructions or codes are stored. When the instructions or codes are run on a computer, the computer implements the above-mentioned obstetric abdominal color ultrasound image display method when executed.

[0169] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0170] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for displaying obstetric abdominal color ultrasound images, characterized in that: The steps include: Acquire the object medical image of the target medical display to obtain the color-gray switching medical image; Determine an antagonistic typical value of the color-gray switching medical image, the antagonistic typical value represents the most representative antagonistic value in the object medical image, and extract an imaging verification sequence and a color-gray switching distribution array in the color-gray switching medical image through the antagonistic typical value; Determining an antagonism estimation tolerance of the antagonism typical value according to the imaging verification sequence; Converting the antagonism typical value into a corresponding antagonism estimation value by using the antagonism estimation tolerance; Determine all color-gray switching channel components of the color-gray switching distribution array, and obtain the imaging significance coefficient of the color-gray switching distribution array from the antagonistic estimation tolerance and all color-gray switching channel components; The medical display is quickly imaged using the imaging significance coefficient and the antagonism estimate.

2. The method according to claim 1, characterized in that Extracting the imaging verification sequence and the color-gray switching distribution array in the color-gray switching medical image by using the antagonistic typical value specifically includes: Obtaining a historical color-gray antagonism value group corresponding to the color-gray switching medical image; Decoupling, verifying and separating the historical color-gray antagonism value group by using the antagonism typical value to obtain an imaging verification sequence; Pixel difference comparison is performed on the color-gray switching medical image to obtain a color-gray switching distribution array.

3. The method according to claim 1, characterized in that Determining the antagonism estimation tolerance of the antagonism typical value according to the imaging verification sequence specifically includes: Determining an imaging stabilization factor and a decoupling verification approach source according to the imaging verification sequence; The decoupling verification approach source is converted into a decoupling fluctuation degree by the imaging stability factor; The antagonism estimation tolerance of the antagonism typical value is obtained by using the decoupling fluctuation and the imaging stability factor.

4. The method according to claim 1, characterized in that Converting the antagonism typical value into a corresponding antagonism estimation value through the antagonism estimation tolerance is to use the antagonism typical value corresponding to the antagonism estimation tolerance exceeding a preset tolerance threshold as the antagonism estimation value.

5. The method according to claim 1, characterized in that Determining all color-gray switching channel components of the color-gray switching distribution array specifically includes: For each pixel point in the color-gray switching distribution array, obtain the red-green antagonism value and the blue-yellow antagonism value of each pixel point in the color-gray switching distribution array; Performing antagonistic correction on the red-green antagonistic value and the blue-yellow antagonistic value of each pixel point according to the antagonistic estimation value, determining the antagonistic correction value of each pixel point in the color-gray switching distribution array, and then determining the antagonistic distribution space; Channel separation is performed on the antagonistic distribution space to obtain all color-gray switching channel components.

6. The method according to claim 1, characterized in that The imaging significant coefficient of the color-gray switching distribution array is obtained from the antagonistic estimation tolerance and all color-gray switching channel components, specifically including: Extracting single channel features of the color-gray switching channel component at different scales to obtain antagonistic channel layers at different scales; Linearly interpolating the antagonistic channel layers of different scales to obtain an antagonistic channel pyramid; Performing edge difference comparison on the antagonistic channel pyramid through a center-periphery difference operation to obtain a multi-scale channel layer feature map; The imaging saliency coefficient is obtained through the multi-scale channel layer feature map.

7. The method according to claim 1, characterized in that Performing quick imaging on the medical display using the imaging significance coefficient and the antagonism estimation value specifically includes: Convert all color-gray switching channel components into antagonistic significant space through the imaging verification sequence and the imaging significant coefficient; Color or grayscale imaging of a medical display is switched according to the antagonism estimation value and the antagonism significance space.

8. A medical display capable of quickly switching between color and grayscale imaging, characterized in that: A control unit is included, and the control unit includes: An acquisition module, used for acquiring an object medical image of a target medical display to obtain a color-gray switching medical image; An extraction module, used to determine the antagonistic typical value of the color-gray switching medical image, the antagonistic typical value represents the most representative antagonistic value in the object medical image, and extract the imaging verification sequence and the color-gray switching distribution array in the color-gray switching medical image through the antagonistic typical value; A processing module, configured to determine an antagonism estimation tolerance of the antagonism typical value according to the imaging verification sequence; The processing module is further used to convert the antagonism typical value into a corresponding antagonism estimation value through the antagonism estimation tolerance; The processing module is further used to determine all color-gray switching channel components of the color-gray switching distribution array, and obtain the imaging significance coefficient of the color-gray switching distribution array from the antagonistic estimation tolerance and all color-gray switching channel components; An imaging module is used to perform quick imaging on the medical display using the imaging significance coefficient and the antagonism estimation value.

9. A computer device, characterized in that: The computer device includes a memory and a processor, the memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the obstetric abdominal color ultrasound image display method described in any one of claims 1 to 7.

10. A computer-readable storage medium, wherein instructions or codes are stored in the computer-readable storage medium. When the instructions or codes are executed on a computer, the computer implements the obstetric abdominal color ultrasound image display method according to any one of claims 1 to 7.

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