Ultrasound image processing method and ultrasound image processing device

Abstract

The invention relates to the technical field of image processing, and particularly discloses an ultrasonic image processing method and an ultrasonic image processing device, and the method comprises the steps: constructing an ultrasonic image scattering model; performing guide filtering processing on the ultrasonic image, and determining a scattering attenuation function based on the filtered ultrasonic image; determining an attenuation coefficient distribution function according to the ultrasonic image; and obtaining a denoised ultrasonic image according to the scattering attenuation function, the attenuation coefficient distribution function and the ultrasonic image scattering model. According to the ultrasonic image processing method, the ultrasonic image scattering model is constructed, the scattering attenuation function is determined according to the ultrasonic image subjected to guide filtering, and the attenuation coefficient distribution function is determined according to the ultrasonic image, so that the denoised ultrasonic image can be obtained, and enhancement of the ultrasonic image is realized. When the ultrasonic image processing method is used for processing the ultrasonic image, the image definition of the ultrasonic image can be effectively enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of image processing, and in particular to an ultrasonic image processing method and an ultrasonic image processing device. Background Art

[0002] Medical ultrasound imaging systems use ultrasound to transmit anatomical information of the imaged human tissues. By processing echo data through software or hardware methods, the structural information of the imaged tissues can be extracted and displayed in real time on the display interface of the ultrasound imaging system. Currently, medical ultrasound imaging systems are widely used in the field of medical imaging diagnosis around the world, and can assist doctors in the exploration of human tissues such as internal organs, muscles, glands, skin, and fetuses.

[0003] Since the electronic noise signals in the ultrasound imaging system hardware itself and the random scattering signals that may appear when ultrasound waves propagate in human tissues will be superimposed on the echo signals of the actual tissues, the software or hardware calculation methods used in the ultrasound imaging system need to eliminate the above interference signals as much as possible to reduce their interference with the effective information of the image. In addition, the performance of restoring the tissue structure information in the ultrasound image determines the performance level of the ultrasound imaging system itself. Therefore, how to ensure that the ultrasound imaging system exhibits excellent image clarity has become a research hotspot in the industry. Summary of the invention

[0004] Based on this, it is necessary to provide an ultrasonic image processing method and an ultrasonic image processing device to solve the problem of how to improve the image clarity of an ultrasonic imaging system.

[0005] An ultrasonic image processing method comprises constructing an ultrasonic image scattering model; performing guided filtering on the ultrasonic image, and determining a scattering attenuation function based on the ultrasonic image after filtering; determining an attenuation coefficient distribution function according to the ultrasonic image; and obtaining a denoised ultrasonic image according to the scattering attenuation function, the attenuation coefficient distribution function and the ultrasonic image scattering model.

[0006] In one embodiment, the ultrasound image scattering model includes:

[0007] I(x,z)=J(x,z)*t(x,z)+V(x,z);

[0008] Among them, x represents the horizontal pixel coordinate, z represents the vertical pixel coordinate, I(x,z) represents the ultrasonic image, J(x,z) represents the denoised ultrasonic image, t(x,z) represents the attenuation coefficient distribution function, and V(x,z) represents the scattering attenuation function.

[0009] In one of the embodiments, the guided filtering processing of the ultrasound image and determining the scattering attenuation function based on the filtered ultrasound image include performing edge-preserving filtering processing on the ultrasound image based on a reference image and the guided filtering function to obtain a first scattering noise intensity; performing edge-preserving filtering processing on the local standard deviation based on the reference image and the guided filtering function to obtain a second scattering noise intensity; obtaining the filtered ultrasound image based on the first scattering noise intensity and the second scattering noise intensity; and determining the scattering attenuation function based on the ultrasound image and the filtered ultrasound image.

[0010] In one of the embodiments, before performing guided filtering on the ultrasound image to be processed and determining the scattering attenuation function based on the filtered ultrasound image, the method also includes performing ultrasound imaging on the target imaging area under different ultrasound emission conditions to obtain the reference image; the target imaging area is the imaging area corresponding to the ultrasound image.

[0011] In one embodiment, the method for calculating the first scattered noise intensity includes:

[0012] noise_smooth(x,z)=GIF(ref_im(x,z),noise_est(x,z),patch_size,eps);

[0013] Among them, noise_est(x,z)=input_im(x,z), noise_smooth(x,z) represents the first scattering noise intensity, GIF() represents the guide filter function, ref_im(x,z) represents the reference image, noise_est(x,z) represents the ultrasound grayscale image, input_im(x,z) represents the ultrasound image, patch_size represents the local filter window size of the filter, and eps represents the regularization parameter.

[0014] In one embodiment, the method for calculating the second scattered noise intensity includes:

[0015] noise_sm(x,z)=GIF(ref_im(x,z),abs(noise_smooth(x,z)-input_im(x,z)),patch_siz

[0016] e,eps);

[0017] Among them, noise_sm(x,z) represents the second scattered noise intensity, GIF() represents the guide filter function, ref_im(x,z) represents the reference image, abs() represents the modulus function, noise_smooth(x,z) represents the first scattered noise intensity, input_im(x,z) represents the ultrasound image, patch_size represents the local filter window size of the filter, and eps represents the regularization parameter.

[0018] In one embodiment, the method for calculating the filtered ultrasound image includes:

[0019] noise_filtered(x,z)=noise_smooth(x,z)-noise_sm(x,z);

[0020] Wherein, noise_filtered(x,z) represents the filtered ultrasound image, noise_smooth(x,z) represents the first scattered noise intensity, and noise_sm(x,z) represents the second scattered noise intensity.

[0021] In one embodiment, the method for calculating the scattering attenuation function includes:

[0022] V(x,z)=max(min(input_im(x,z),p*noise_filtered(x,z)),0);

[0023] Among them, V(x,z) represents the scattering attenuation function, max() represents the maximum value comparison function, min() represents the minimum value comparison function, p represents the display clarity restoration parameter, the value range of p is (0,1), input_im(x,z) represents the ultrasound image, and noise_filtered(x,z) represents the filtered ultrasound image.

[0024] In one of the embodiments, determining the attenuation coefficient distribution function based on the ultrasound image includes acquiring a local minimum image and a local median image of the ultrasound image, and determining a proportional relationship between the local minimum image and the local median image; determining pixel point parameters of the ultrasound image based on the proportional relationship and a preset comparison value; and determining the attenuation coefficient distribution function based on the pixel point parameters.

[0025] In one embodiment, the method for calculating the local minimum image includes:

[0026] dark_I(x,z)=min_local(input_im(x,z),patch_size);

[0027] Among them, dark_I(x,z) represents the local minimum image, min_local() represents the minimum value capture function, input_im(x,z) represents the ultrasound image, and patch_size represents the local filter window size of the filter.

[0028] In one embodiment, the method for calculating the local median image includes:

[0029] mid_I(x,z)=mid_local(input_im(x,z),patch_size);

[0030] Among them, mid_I(x,z) represents the local median image, mid_local() represents the median grabbing function, input_im(x,z) represents the ultrasound image, and patch_size represents the local filtering window size of the filter.

[0031] In one embodiment, the method for calculating the proportional relationship includes:

[0032] dark_ratio(x,z)=dark_I(x,z) / mid_I(x,z);

[0033] Among them, dark_ratio(x,z) represents the proportional relationship, dark_I(x,z) represents the local minimum image, and mid_I(x,z) represents the local median image.

[0034] In one embodiment, the method for calculating the pixel parameters of the ultrasound image includes:

[0035] A(x,z)=dark_I(x,z)*dark_ratio(x,z);

[0036] Wherein, A(x,z) represents the pixel point parameters of the ultrasound image, dark_I(x,z) represents the local minimum image, and dark_ratio(x,z) represents the proportional relationship.

[0037] In one embodiment, the method for calculating the attenuation coefficient distribution function includes:

[0038] t(x,z)=1-w*A(x,z);

[0039] Among them, t(x,z) represents the attenuation coefficient distribution function, w represents the filtering effect adjustment coefficient, the value range of w is (0,1), and A(x,z) represents the pixel point parameter of the ultrasound image.

[0040] An ultrasonic image processing device includes a model building module for building an ultrasonic image scattering model; a first calculation module for performing a guide filter process on the ultrasonic image and determining a scattering attenuation function based on the filtered ultrasonic image; a second calculation module for determining an attenuation coefficient distribution function according to the ultrasonic image; and a denoising processing module for acquiring a denoised ultrasonic image according to the scattering attenuation function, the attenuation coefficient distribution function and the ultrasonic image scattering model.

[0041] The ultrasonic image processing method can obtain a denoised ultrasonic image and enhance the ultrasonic image by constructing an ultrasonic image scattering model, determining a scattering attenuation function according to the ultrasonic image after the guide filter, and determining an attenuation coefficient distribution function according to the ultrasonic image. The ultrasonic image processing method can be used to process the ultrasonic image to effectively enhance the image clarity of the ultrasonic image. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the implementation methods of this specification or the technical solutions in the prior art, the drawings required for use in the implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some implementation methods recorded in this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0043] Figure 1 This is a schematic diagram of a method flow of an ultrasound image processing method in one of the embodiments of the present application;

[0044] Figure 2 This is a schematic diagram of natural environment observation in one of the embodiments of this application;

[0045] Figure 3 This is a schematic diagram of ultrasound imaging observation in one of the embodiments of the present application;

[0046] Figure 4 This is a comparison diagram of ultrasound images before and after processing in one of the embodiments of the present application, wherein (a) is the ultrasound image to be processed, and (b) is the ultrasound image after denoising;

[0047] Figure 5 This is a schematic flow chart of a method for determining a scattering attenuation function in one embodiment of the present application;

[0048] Figure 6 This is a schematic flow chart of a method for determining a scattering attenuation function in one embodiment of the present application;

[0049] Figure 7 This is a schematic diagram of the structure of an ultrasonic image processing device in one of the embodiments of the present application;

[0050] Figure 8 This is a schematic diagram of the structure of a device for implementing an ultrasonic image processing method in one of the embodiments of the present application;

[0051] Fig. 9 This is a diagram of the internal structure of a computer device in one of the embodiments of the present application. DETAILED DESCRIPTION

[0052] In order to facilitate the understanding of the present invention, the present invention will be described more fully below with reference to the relevant drawings. The preferred embodiments of the present invention are given in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to understand the disclosure of the present invention more thoroughly and comprehensively.

[0053] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0054] At present, the image denoising methods widely studied in the field of medical ultrasound imaging technology include Gaussian filtering, mean filtering, diffusion filtering, etc. Although these methods are simple to implement, it is common that while smoothing the noise, the effective structure is also smoothed, thereby destroying the original information. In addition, the above filtering methods can only smooth the noise, but cannot eliminate the noise or reduce the noise level. Therefore, the above filtering methods cannot effectively improve the image clarity. Moreover, although the wavelet filtering method is a research hotspot, its calculation and processing are complex. Therefore, this method has high requirements for hardware computing power, which increases the equipment cost. At the same time, the problem of computing efficiency needs to be considered when applying it.

[0055] The present application provides an ultrasound image processing method that can effectively enhance the image clarity of ultrasound images. In one embodiment, Figure 1 This is a schematic diagram of a method flow of an ultrasonic image processing method in one embodiment of the present application. The ultrasonic image processing method may include the following steps S100 to S400.

[0056] Step S100: constructing an ultrasound image scattering model.

[0057] In geographic image processing research, how to solve the impact of fog in environmental images on image clarity and contrast has always been a research hotspot. Fog in the natural environment is caused by the scattering of light by impurities and particles in the air. These scattered lights are superimposed on the emitted light of various natural scenes and captured and imaged, showing problems such as whitening of the image, low contrast, and unclear scenery. The above problems also exist in ultrasound images obtained by medical ultrasound imaging systems, which seriously affect doctors' diagnosis.

[0058] Whether it is natural objects for light or the targets detected by the ultrasonic imaging system for sound waves, there are corresponding reflection intensity coefficients, and the reflection intensity coefficient of the detected target is different from that of the surrounding targets. Therefore, the color performance of different detected targets can be observed through the difference in reflection characteristics between different objects. If there are no other interference signals affecting the reflected signal of the target, then the color and boundaries of both natural scenery and the targets detected by the ultrasonic transducer can be clearly distinguished.

[0059] Figure 2 This is a schematic diagram of natural environment observation in one of the embodiments of this application, such as Figure 2 As shown in the figure, when there is atmospheric fog in the natural environment, not only will the fog continuously and randomly scatter the sunlight to form messy light observed by the observer, but it will also affect the reflected light of the natural observation scene, causing a certain energy attenuation and reducing the brightness of the natural observation scene. The result of the superposition of the scattering of fog and the reflection of the natural observation scene is that the observed scene is blurred.

[0060] Figure 3 This is a schematic diagram of ultrasound imaging observation in one of the embodiments of the present application. Figure 3 The circular object in the middle represents the target structure, and the rest of the area represents the scattering structure. The ultrasound imaging system uses an ultrasound imaging transducer to transmit incident sound waves to the target area, and the ultrasound imaging system constructs an ultrasound image based on the echo signal reflected from the target area. Figure 3 Similarly, when an ultrasonic imaging system detects a target, the incident sound waves are randomly scattered by various scattering structures in the area where the target is located, and these scattered sound waves are randomly coherently superimposed and returned to the imaging transducer. At the same time, the scattered sound waves are coherently superimposed with the reflected sound waves of the target, which causes the real echo signal of the target to be interfered and weakened, making the ultrasonic image unclear.

[0061] In one embodiment, specifically, the ultrasound image scattering model may be:

[0062] I(x,z)=J(x,z)*t(x,z)+V(x,z);

[0063] Wherein, in the above-mentioned ultrasound image scattering model, x can represent the horizontal pixel coordinate, and z can represent the vertical pixel coordinate. I(x,z) can be used to represent the ultrasound image to be processed, that is, the ultrasound image with noise actually obtained when the current ultrasound imaging system performs ultrasound imaging on the target object in the target area. J(x,z) can represent the denoised ultrasound image, that is, the real echo image of the target object that is not affected by the scattered sound waves when the ultrasound imaging system performs ultrasound imaging on the target object in the target area.

[0064] t(x,z) can represent the attenuation coefficient distribution function, that is, t(x,z) can be used to represent the distribution of attenuation coefficients inherent in the target area that will attenuate the reflected sound waves of the target object. Furthermore, J(x,z)*t(x,z) can be used to represent the attenuation process of the reflected sound waves of the target object in the target area. As the imaging depth increases, the attenuation of the sound waves also increases accordingly. V(x,z) can represent the scattering attenuation function, that is, V(x,z) can be used to represent the additional influence of the scattering structure in the target area on the reflected sound waves of the target object.

[0065] Step S200: performing guided filtering processing on the ultrasound image, and determining a scattering attenuation function based on the filtered ultrasound image.

[0066] Guide filtering is a filtering method that can quickly implement edge-preserving filtering of images. Compared with bilateral filtering, guide filtering has the good effect of edge-preserving smoothing, and also has the speed advantage of linear time. In this embodiment, by performing guide filtering on the ultrasonic image to be processed I(x,z), a filtered ultrasonic image can be obtained. The filtered ultrasonic image removes the scattered noise part, so that the scattering attenuation function V(x,z) can be estimated based on the initial ultrasonic image I(x,z) and the filtered ultrasonic image.

[0067] Step S300: determining an attenuation coefficient distribution function according to an ultrasound image.

[0068] When sound waves propagate in the human body, part of the energy will be absorbed and lost by the tissues, and the remaining energy will gradually decrease as the propagation distance (or time) increases. However, in practical applications, the target area of ​​ultrasound imaging has complex structures, so the attenuation degree at different spatial positions is different, and a single attenuation coefficient value cannot be used to represent it. Therefore, the attenuation of the sound wave is represented by the attenuation coefficient distribution function. Different pixels in the ultrasound image have different attenuation coefficients. According to the distribution of each spatial pixel point in the ultrasound image I (x, z) to be processed, the attenuation coefficient distribution function t (x, z) can be estimated.

[0069] Step S400: acquiring a denoised ultrasonic image according to the scattering attenuation function, the attenuation coefficient distribution function and the ultrasonic image scattering model.

[0070] After the scattering attenuation function V(x,z) and the attenuation coefficient distribution function t(x,z) are determined respectively, and the ultrasonic image I(x,z) to be processed is also known, the denoised ultrasonic image J(x,z) can be obtained according to the ultrasonic image scattering module constructed in step S100, thereby enhancing the clarity of the ultrasonic image. Specifically, according to the ultrasonic image scattering model, the calculation method of the denoised ultrasonic image J(x,z) can be:

[0071] J(x,z)=(I(x,z)-V(x,z)) / t(x,z);

[0072] The ultrasonic image processing method provided in the present application simulates the imaging process of the ultrasonic image by constructing an ultrasonic image scattering model. Among them, the scattering attenuation function V(x,z) simulates the additional influence of the scattering structure in the target area on the reflected sound wave of the target object, and the attenuation coefficient distribution function t(x,z) simulates the attenuation coefficient distribution at different positions in the target area. By performing a guide filter on the ultrasonic image I(x,z), the scattering attenuation function V(x,z) is determined according to the initial ultrasonic image I(x,z) and the ultrasonic image after the guide filter, and the attenuation coefficient distribution function t(x,z) is determined according to the initial ultrasonic image I(x,z), so that the denoised ultrasonic image J(x,z) can be obtained to enhance the ultrasonic image.

[0073] Figure 4 This is a comparison diagram of ultrasound images before and after processing in one of the embodiments of the present application, where (a) is the ultrasound image to be processed, and (b) is the ultrasound image after denoising. It can be seen that the ultrasound image processing method can effectively enhance the image clarity of the ultrasound image. In addition, the present application uses a guide filtering method with good edge-preserving filtering effect to process the ultrasound image, which will not destroy the original information in the ultrasound image. Figure 4 As shown in the figure, the denoised ultrasound image can effectively restore the tissue structure information in the image and output an ultrasound image with high definition and accurate structural information. At the same time, the wizard filter method has a fast calculation speed, which effectively improves the calculation efficiency of the system and has low requirements for hardware computing power, thereby reducing the hardware cost of the system.

[0074] Figure 5 This is a flow chart of a method for determining a scatter attenuation function in one embodiment of the present application. In one embodiment, a guide filter is performed on an ultrasound image, and determining the scatter attenuation function based on the filtered ultrasound image may include the following steps S210 to S240.

[0075] Step S210: performing edge-preserving filtering on the ultrasound image based on the reference image and the guide filter function to obtain a first scattered noise intensity.

[0076] Image guide filtering is a linear shift-variable filtering process, including a guide image, an input image, and an output image. The guide image can be set in advance according to the specific application, or it can be directly taken as the input image. With the help of the guide image, the output image can be made more structured and less smooth than the input image. In this embodiment, the reference image can be set as the guide image, the ultrasound image to be processed can be set as the input image, and the denoised ultrasound image can be set as the output image. Among them, the reference image can be an ultrasound image with less noise, and the imaging target area of ​​the reference image is the same as the imaging target area of ​​the ultrasound image to be processed.

[0077] In the present application, the function GIF() can be used to represent the guide filter function (guide filter). By inputting the ultrasound image and the reference image into the guide filter function, the ultrasound image can be subjected to edge-preserving filtering, thereby obtaining the first scattering noise intensity noise_smooth(x,z) corresponding to the ultrasound image after filtering.

[0078] In one embodiment, specifically, the calculation method of the first scattered noise intensity noise_smooth(x,z) may be:

[0079] noise_smooth(x,z)=GIF(ref_im(x,z),noise_est(x,z),patch_size,eps);

[0080] In the above formula, noise_est(x,z) can be used to represent the scattering intensity estimation value of the ultrasonic grayscale image, and the scattering intensity estimation value noise_est(x,z) is filtered using GIF(). Since the scattering structure of the target determines the scattering noise intensity of the ultrasonic image, the stronger the scattering of the target, the stronger the scattering interference to the surrounding area, while the echo-free area will not generate scattering interference to the surrounding area, but will only be interfered passively. Therefore, in this embodiment, the ultrasonic image to be processed can be regarded as the scattering intensity estimation value noise_est(x,z) of the ultrasonic grayscale image, that is, noise_est(x,z)=input_im(x,z).

[0081] In the calculation formula of the first scattered noise intensity noise_smooth(x,z), noise_smooth(x,z) can be used to represent the first scattered noise intensity corresponding to the ultrasonic image after filtering, and GIF() can be used to represent the guide filter function. ref_im(x,z) can be used to represent the reference image. input_im(x,z) can be used to represent the ultrasonic image to be processed, that is, the ultrasonic image I(x,z) corresponding to the ultrasonic imaging target area to be filtered. patch_size can be used to represent the local filter window size of the filter, and eps can be used to represent the regularization parameter. Among them, the local filter window size patch_size and the regularization parameter eps can be arbitrarily set according to actual application requirements. For example, the local filter window size patch_size can be set to an integer greater than or equal to 3, and the regularization parameter eps can be set to 0.001.

[0082] Step S220: performing edge-preserving filtering on the local standard deviation based on the reference image and the guide filter function to obtain a second scattered noise intensity.

[0083] In one embodiment, specifically, the calculation method of the second scattered noise intensity may be:

[0084] noise_sm(x,z)=GIF(ref_im(x,z),abs(noise_smooth(x,z)-input_im(x,z)),patch_siz

[0085] e,eps);

[0086] Among them, noise_sm(x,z) can represent the second scattered noise intensity, GIF() can represent the guide filter function, ref_im(x,z) can represent the reference image, abs() can represent the modulus function, noise_smooth(x,z) can represent the first scattered noise intensity, input_im(x,z) can represent the ultrasound image, patch_size can represent the local filter window size of the filter, and eps can represent the regularization parameter.

[0087] In this embodiment, the local standard deviation may refer to the modulus of the difference between the first scattered noise intensity noise_smooth(x,z) and the ultrasonic image input_im(x,z) before processing. There will still be some textures in the ultrasonic image after filtering, but these textures are not scattered noise, and there is no need to perform denoising on these textures, so the local standard deviation of the ultrasonic image input_im(x,z) before processing is subtracted from the first scattered noise intensity noise_smooth(x,z) to ensure that what remains is scattered noise. GIF filtering is used again to estimate the scattered noise intensity of the difference. By inputting the reference image and the local standard deviation into the wizard filter function, the second scattered noise intensity noise_sm(x,z) corresponding to the local standard deviation can be obtained.

[0088] Step S230: acquiring a filtered ultrasound image according to the first scattered noise intensity and the second scattered noise intensity.

[0089] In one embodiment, specifically, the calculation method of the filtered ultrasound image may be:

[0090] noise_filtered(x,z)=noise_smooth(x,z)-noise_sm(x,z);

[0091] Here, noise_filtered(x,z) may represent the filtered ultrasound image, noise_smooth(x,z) may represent the first scattered noise intensity, and noise_sm(x,z) may represent the second scattered noise intensity.

[0092] The filtered ultrasound image noise_filtered(x,z) can be obtained by subtracting the first scattered noise intensity noise_smooth(x,z) after noise edge-preserving smoothing from the second scattered noise intensity noise_sm(x,z) corresponding to the local standard deviation.

[0093] Step S240: determining a scattering attenuation function according to the ultrasonic image and the filtered ultrasonic image.

[0094] In one embodiment, specifically, the calculation method of the scattering attenuation function may be:

[0095] V(x,z)=max(min(input_im(x,z),p*noise_filtered(x,z)),0);

[0096] Among them, V(x,z) can represent the scattering attenuation function, max() can represent the maximum value comparison function, min() can represent the minimum value comparison function, p can represent the display clarity restoration parameter, the value range of p is (0,1), and noise_filtered(x,z) can represent the filtered ultrasound image.

[0097] In this embodiment, the estimated value of the scatter attenuation function V(x,z) can be determined by comparing the ultrasonic image to be processed input_im(x,z) and the filtered ultrasonic image noise_filtered(x,z). At the same time, the restoration effect of the display clarity of the target object can be controlled by adjusting the value of the parameter p, wherein the value range of p can be (0,1). Specifically, the minimum value is compared between input_im(x,z) and p*noise_filtered(x,z); then min(input_im(x,z),p*noise_filtered(x,z)) is compared with 0, and the maximum value is compared between the two. The comparison result is the estimated value of the scatter attenuation function V(x,z).

[0098] In one of the embodiments, before performing guided filtering on the ultrasound image and determining the scatter attenuation function based on the filtered ultrasound image, the ultrasound image processing method may further include step S211.

[0099] Step S211: performing ultrasonic imaging on the target imaging area under different ultrasonic emission conditions to obtain a reference image; the target imaging area is an imaging area corresponding to the ultrasonic image.

[0100] The reference image may refer to an ultrasound image with less input noise, and the imaging target area of ​​the reference image may be the same as the imaging area of ​​the filtered image. In this embodiment, the ultrasound imaging system may be made to perform imaging processing on the target area under different ultrasound emission conditions to obtain ultrasound images of the target area under different ultrasound emission conditions. Furthermore, an ultrasound image with less input noise may be selected from ultrasound images under different ultrasound emission conditions as a reference image. In some other embodiments, an image with less noise may be obtained using other ultrasound scanning or reconstruction methods, or the ultrasound image to be processed may be subjected to noise reduction processing to obtain an ultrasound image with different noise performance as a reference image.

[0101] Figure 6 This is a flow chart of a method for determining a scattering attenuation function in one embodiment of the present application. In one embodiment, determining the attenuation coefficient distribution function according to an ultrasound image may include the following steps S310 to S330.

[0102] Step S310: Acquire a local minimum image and a local median image of the ultrasound image, and determine a proportional relationship between the local minimum image and the local median image.

[0103] When sound waves propagate in the human body, part of their energy will be absorbed and lost by tissues, and the remaining energy will gradually decrease with the increase of propagation distance (or propagation time). In addition, the structure in the actual ultrasound imaging target area is complex, so the attenuation degree is different at different spatial positions and cannot be simply represented by one attenuation coefficient value. Therefore, the attenuation coefficient t(x,z) is estimated for different spatial pixel points in the ultrasound image.

[0104] According to the ultrasound image input_im(x,z), a local minimum image and a local median image corresponding to the initial ultrasound image input_im(x,z) are calculated.

[0105] In one embodiment, the calculation method of the local minimum image may be:

[0106] dark_I(x,z)=min_local(input_im(x,z),patch_size);

[0107] Among them, dark_I(x,z) can represent the local minimum image, min_local() can represent the minimum value capture function, input_im(x,z) can represent the ultrasound image, and patch_size can represent the local filter window size of the filter.

[0108] Specifically, min_local(input_im(x,z),patch_size) can be calculated by taking the minimum value of the local image area of ​​patch_size centered on each pixel and its surrounding pixels in the ultrasound image input_im(x,z), and filling it with the center pixel of the patch in dark_I(x,z).

[0109] In one embodiment, the local median image may be calculated by:

[0110] mid_I(x,z)=mid_local(input_im(x,z),patch_size);

[0111] Among them, mid_I(x,z) can represent the local median image, mid_local() can represent the median grab function, input_im(x,z) can represent the ultrasound image, and patch_size can represent the local filter window size of the filter.

[0112] Specifically, mid_local(input_im(x,z),patch_size) can be calculated by taking the median value of a local image area of ​​patch_size centered on each pixel and its surrounding pixels in the ultrasound image input_im(x,z), and filling it into the center pixel of the patch in mid_I(x,z).

[0113] The local minimum image dark_I(x,z) is ratioed to the local median image mid_I(x,z) to determine the proportional relationship between the local minimum image and the local median image.

[0114] In one embodiment, specifically, the proportional relationship may be calculated as follows:

[0115] dark_ratio(x,z)=dark_I(x,z) / mid_I(x,z);

[0116] Among them, dark_ratio(x,z) can represent the proportional relationship between the local minimum image and the local median image, dark_I(x,z) can represent the local minimum image, and mid_I(x,z) can represent the local median image.

[0117] Step S320: Determine the pixel parameters of the ultrasound image according to the proportional relationship and the preset comparison value.

[0118] In this embodiment, a preset comparison value ratio_thresh can also be set to be used as a reference value for comparison with the ratio relationship dark_ratio(x,z) between the local minimum image and the local median image. In practical applications, the preset comparison value ratio thresh is a number that does not exceed the maximum value of dark_ratio(x,z). By comparing the ratio relationship dark_ratio(x,z) between the local minimum image and the local median image with the preset comparison value ratio_thresh, and setting the value of the ratio relationship dark_ratio(x,z) that is less than the preset comparison value ratio_thresh to 0, a more accurate ratio relationship dark_ratio(x,z) is obtained. At the same time, by setting the value that is smaller than dark_ratio(x,z) and ratio_thresh to 0, the minimum value of the pixel in the pixel point parameter A(x,z) can be limited, thereby limiting the maximum value of the attenuation coefficient distribution function t(x,z).

[0119] The pixel point parameter A(x,z) of the ultrasound image is determined according to the updated ratio relation dark_ratio(x,z) and the local minimum image dark_I(x,z).

[0120] In one embodiment, specifically, the method for calculating the pixel point parameters of the ultrasound image may be:

[0121] A(x,z)=dark_I(x,z)*dark_ratio(x,z);

[0122] Among them, A(x,z) can represent the pixel parameters of the ultrasound image, dark_I(x,z) can represent the local minimum image, and dark_ratio(x,z) can represent the proportional relationship.

[0123] Step S330: Determine the attenuation coefficient distribution function according to the pixel parameters.

[0124] After determining the pixel parameter A(x,z) of the ultrasound image, the attenuation coefficient distribution function t(x,z) can be estimated.

[0125] In one embodiment, specifically, the calculation method of the attenuation coefficient distribution function may be:

[0126] t(x,z)=1-w*A(x,z);

[0127] Among them, t(x,z) can represent the attenuation coefficient distribution function, w can represent the filtering effect adjustment coefficient, the value of w can be adjusted arbitrarily according to the actual filtering effect, the value range of w can be (0,1), and A(x,z) can represent the pixel point parameters of the ultrasound image.

[0128] It should be understood that, although the steps in the flowcharts of the accompanying drawings of the specification are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts of the accompanying drawings of the specification may include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0129] Based on the description of the above-mentioned ultrasonic image processing method embodiment, the present application also provides an ultrasonic image processing device. The device may include a device (including a distributed system), software (application), module, component, server, client, etc. using the method described in the embodiment of this specification and a device combined with necessary implementation hardware. Based on the same innovative concept, the device in one or more embodiments provided by the embodiment of the present disclosure is as described in the following embodiments. Since the implementation scheme of the device to solve the problem is similar to the method, the implementation of the specific device in the embodiment of this specification can refer to the implementation of the aforementioned method, and the repetitions will not be repeated. As used below, the term "unit" or "module" can implement a combination of software and / or hardware for a predetermined function. Although the device described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceived.

[0130] Figure 7 This is a structural schematic diagram of an ultrasonic image processing device in one of the embodiments of the present application. In one of the embodiments, the ultrasonic image processing device may include a model building module 100, a first calculation module 200, a second calculation module 300 and a denoising processing module 400.

[0131] The model building module 100 can be used to build an ultrasonic image scattering model. In the embodiment of the present disclosure, the model building module 100 can build an ultrasonic image scattering model with reference to the atmospheric scattering model used in the natural scene image defogging algorithm, and use the ultrasonic image scattering model to simulate the process of ultrasonic imaging system detecting the target object.

[0132] The first calculation module 200 can be used to perform a guided filtering process on the ultrasonic image and determine the scattering attenuation function based on the filtered ultrasonic image. The first calculation module 200 can obtain a filtered ultrasonic image by performing a guided filtering on the ultrasonic image to be processed I(x,z). The filtered ultrasonic image removes the scattered noise part, so that the scattering attenuation function V(x,z) can be estimated based on the initial ultrasonic image I(x,z) and the filtered ultrasonic image.

[0133] The second calculation module 300 can be used to determine the attenuation coefficient distribution function according to the ultrasound image. Considering that the attenuation coefficients of different pixels in the ultrasound image are different, the second calculation module 300 can analyze the distribution of each spatial pixel point in the ultrasound image I (x, z) to be processed, so as to estimate the attenuation coefficient distribution function t (x, z).

[0134] The denoising processing module 400 can be used to obtain a denoised ultrasonic image J(x,z) according to the scattering attenuation function V(x,z), the attenuation coefficient distribution function t(x,z) and the ultrasonic image scattering model. After the scattering attenuation function V(x,z) and the attenuation coefficient distribution function t(x,z) are determined respectively, and the ultrasonic image I(x,z) to be processed is also known, the ultrasonic image scattering module constructed according to the model construction module 100 can obtain the denoised ultrasonic image J(x,z), thereby enhancing the clarity of the ultrasonic image.

[0135] The ultrasonic image processing device provided by the present application constructs an ultrasonic image scattering model through a model building module 100 to simulate the imaging process of the ultrasonic image. Among them, the scattering attenuation function V(x,z) simulates the additional influence of the scattering structure in the target area on the reflected sound wave of the target object, and the attenuation coefficient distribution function t(x,z) simulates the attenuation coefficient distribution at different positions in the target area. The ultrasonic image I(x,z) is guided filtered by the first calculation module 200, and the scattering attenuation function V(x,z) is determined according to the initial ultrasonic image I(x,z) and the ultrasonic image after the guide filtering. The attenuation coefficient distribution function t(x,z) is determined according to the initial ultrasonic image I(x,z) by the second calculation module 300, so that the denoising processing module 400 can obtain the denoised ultrasonic image J(x,z) to enhance the ultrasonic image I(x,z).

[0136] It is understandable that the various embodiments of the above methods, devices, etc. in this specification are described in a progressive manner, and the same / similar parts between the various embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. For related parts, refer to the description of other method embodiments.

[0137] Figure 8 FIG. 1 is a schematic diagram of a device structure for implementing an ultrasonic image processing method in one embodiment of the present application. Figure 8 The ultrasonic image processing device S00 may include a processing component S20, which further includes one or more processors, and a memory resource represented by a memory S22 for storing instructions executable by the processing component S20, such as an application. The application stored in the memory S22 may include one or more modules, each corresponding to a set of instructions. In addition, the processing component S20 is configured to execute the instructions to perform the ultrasonic image processing method described above.

[0138] The ultrasonic image processing device S00 may further include: a power supply component S24 configured to perform power management of the ultrasonic image processing device S00, a wired or wireless network interface S26 configured to connect the ultrasonic image processing device S00 to a network, and an input / output (I / O) interface S28. The ultrasonic image processing device S00 may operate based on an operating system stored in the memory S22, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD or the like.

[0139] In an exemplary embodiment, a computer-readable storage medium including instructions is also provided, such as a memory S22 including instructions, and the instructions can be executed by a processor of the ultrasonic image processing device S00 to complete the above method. The storage medium can be a computer-readable storage medium, for example, the computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0140] In an exemplary embodiment, a computer program product is further provided. The computer program product includes instructions. The instructions can be executed by a processor of the ultrasound image processing device S00 to complete the above method.

[0141] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Fig. 9 As shown, Fig. 9 This is an internal structure diagram of a computer device in one of the embodiments of the present application. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data related to users and tasks used in the above-mentioned ultrasonic image processing method. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, an ultrasonic image processing method is implemented.

[0142] Those skilled in the art will understand that Fig. 9 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0143] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0144] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the hardware + program embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0145] It should be noted that the above-mentioned devices, electronic devices, servers, etc. may also include other implementation methods according to the description of the method embodiments, and the specific implementation methods may refer to the description of the relevant method embodiments. At the same time, the new embodiments composed of the mutual combination of the features between the various methods and device, equipment, and server embodiments still fall within the scope of implementation covered by the present disclosure, and will not be described one by one here.

[0146] In the description of this specification, the description with reference to the terms "some embodiments", "other embodiments", "ideal embodiments", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic descriptions of the above terms do not necessarily refer to the same embodiment or example.

[0147] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0148] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the invention patent. It should be pointed out that for ordinary technicians in this field, several modifications and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be based on the attached claims.

Claims

1. An ultrasonic image processing method, characterized in that: include: Constructing ultrasound image scattering model; performing guided filtering on the ultrasound image, and determining a scattering attenuation function based on the filtered ultrasound image; determining an attenuation coefficient distribution function according to the ultrasound image; A denoised ultrasonic image is acquired according to the scattering attenuation function, the attenuation coefficient distribution function and the ultrasonic image scattering model.

2. The ultrasonic image processing method according to claim 1, characterized in that: The ultrasound image scattering model includes: I(x,z)=J(x,z)*t(x,z)+V(x,z); Among them, x represents the horizontal pixel coordinate, z represents the vertical pixel coordinate, I(x,z) represents the ultrasonic image, J(x,z) represents the denoised ultrasonic image, t(x,z) represents the attenuation coefficient distribution function, and V(x,z) represents the scattering attenuation function.

3. The ultrasonic image processing method according to claim 1 or 2, characterized in that: The step of performing guided filtering on the ultrasonic image and determining the scattering attenuation function based on the filtered ultrasonic image comprises: Performing edge-preserving filtering on the ultrasound image based on a reference image and a guide filter function to obtain a first scattered noise intensity; Performing edge-preserving filtering on the local standard deviation based on the reference image and the guide filter function to obtain a second scattered noise intensity; acquiring the filtered ultrasound image according to the first scattered noise intensity and the second scattered noise intensity; The scatter attenuation function is determined based on the ultrasound image and the filtered ultrasound image.

4. The ultrasonic image processing method according to claim 3, characterized in that: Before performing guided filtering on the ultrasonic image to be processed and determining the scatter attenuation function based on the filtered ultrasonic image, the method further includes: Ultrasonic imaging is performed on a target imaging area under different ultrasonic emission conditions to obtain the reference image; the target imaging area is an imaging area corresponding to the ultrasonic image.

5. The ultrasonic image processing method according to claim 3, characterized in that: The calculation method of the first scattered noise intensity includes: noise_smooth(x,z)=GIF(ref_im(x,z),noise_est(x,z),patch_size,eps); Among them, noise_est(x,z)=input_im(x,z), noise_smooth(x,z) represents the first scattering noise intensity, GIF() represents the guide filter function, ref_im(x,z) represents the reference image, noise_est(x,z) represents the ultrasound grayscale image, input_im(x,z) represents the ultrasound image, patch_size represents the local filter window size of the filter, and eps represents the regularization parameter.

6. The ultrasonic image processing method according to claim 3, characterized in that: The calculation method of the second scattered noise intensity includes: noise_sm(x,z)=GIF(ref_im(x,z),abs(noise_smooth(x,z)-input_im(x,z)),patch_size,eps); Among them, noise_sm(x,z) represents the second scattered noise intensity, GIF() represents the guide filter function, ref_im(x,z) represents the reference image, abs() represents the modulus function, noise_smooth(x,z) represents the first scattered noise intensity, input_im(x,z) represents the ultrasound image, patch_size represents the local filter window size of the filter, and eps represents the regularization parameter.

7. The ultrasonic image processing method according to claim 3, characterized in that: The method for calculating the filtered ultrasonic image includes: noise_filtered(x,z)=noise_smooth(x,z)-noise_sm(x,z); Wherein, noise_filtered(x,z) represents the filtered ultrasound image, noise_smooth(x,z) represents the first scattered noise intensity, and noise_sm(x,z) represents the second scattered noise intensity.

8. The ultrasonic image processing method according to claim 3, characterized in that: The calculation method of the scattering attenuation function includes: V(x,z)=max(min(input_im(x,z),p*noise_filtered(x,z)),0); Among them, V(x,z) represents the scattering attenuation function, max() represents the maximum value comparison function, min() represents the minimum value comparison function, p represents the display clarity restoration parameter, the value range of p is (0,1), input_im(x,z) represents the ultrasound image, and noise_filtered(x,z) represents the filtered ultrasound image.

9. The ultrasonic image processing method according to claim 1 or 2, characterized in that: Determining the attenuation coefficient distribution function according to the ultrasound image comprises: Acquire a local minimum image and a local median image of the ultrasound image, and determine a proportional relationship between the local minimum image and the local median image; Determining pixel parameters of the ultrasound image according to the proportional relationship and a preset comparison value; The attenuation coefficient distribution function is determined according to the pixel point parameters.

10. The ultrasonic image processing method according to claim 9, characterized in that: The method for calculating the local minimum image includes: dark_I(x,z)=min_local(input_im(x,z),patch_size) Among them, dark_I(x,z) represents the local minimum image, min_local() represents the minimum value capture function, input_im(x,z) represents the ultrasound image, and patch_size represents the local filter window size of the filter.

11. The ultrasonic image processing method according to claim 9, characterized in that: The method for calculating the local median image includes: mid_I(x,z)=mid_local(input_im(x,z),patch_size); Among them, mid_I(x,z) represents the local median image, mid_local() represents the median grabbing function, input_im(x,z) represents the ultrasound image, and patch_size represents the local filtering window size of the filter.

12. The ultrasonic image processing method according to claim 9, characterized in that: The calculation method of the proportional relationship includes: dark_ratio(x,z)=dark_I(x,z) / mid_I(x,z); Among them, dark_ratio(x,z) represents the proportional relationship, dark_I(x,z) represents the local minimum image, and mid_I(x,z) represents the local median image.

13. An ultrasonic image processing device, characterized in that: include: A model building module, used for building an ultrasound image scattering model; A first calculation module, configured to perform a guided filtering process on the ultrasound image and determine a scattering attenuation function based on the filtered ultrasound image; A second calculation module, used to determine an attenuation coefficient distribution function according to the ultrasound image; The denoising processing module is used to obtain a denoised ultrasonic image according to the scattering attenuation function, the attenuation coefficient distribution function and the ultrasonic image scattering model.

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