An ultrasonic echo signal optimization method, device, medium and terminal device

Abstract

The present invention discloses an ultrasonic echo signal optimization method, device, medium and terminal device. In this application, the preprocessed echo signal received by the ultrasonic probe is obtained, and this signal is presented in the form of envelope line data in the corresponding rectangular coordinate system. Then, with these envelope line data as the center, multiple neighborhood blocks are divided in the rectangular coordinate system, and the similarity weights between these neighborhood blocks are calculated to assign corresponding weight values to each neighborhood block. Finally, by combining the adaptive filtering algorithm, the minimized energy function, the distance from the envelope line data to the preset focus, and the weights of each neighborhood block, the size of the filtering window is adjusted to optimize the ultrasonic echo signal, so as to solve the problem that accurate ultrasonic echo signals cannot be obtained in the prior art.

Description

Technical Field

[0001] The present invention relates to the field of ultrasonic echo signal optimization, and particularly to an ultrasonic echo signal optimization method, device, medium and terminal device. Background Art

[0002] In ultrasonic imaging (B-ultrasound), the image in the focal region is usually relatively clear, while the region far from the focus often shows defocus phenomenon, resulting in blurred images. This problem affects the observation and diagnosis of pathological tissues by medical personnel. Traditional methods such as deconvolution and super-resolution reconstruction technologies have certain effects, but often cannot achieve ideal results in complex tissue structures. For example, the super-resolution reconstruction technology enhances image details by increasing the image resolution, but when dealing with ultrasonic imaging, it may encounter the problem of over-smoothing, resulting in a deviation between the generated image and the real scene effect. In addition, the review of research progress in ultrasonic imaging detection technology points out that although modern ultrasonic imaging methods such as phased array methods have advantages such as high resolution and fast detection speed, the imaging resolution is not high at positions far from the focal point, and the variability of sound beam propagation in complex grain structures limits their scope of application. These problems lead to the inability to obtain accurate ultrasonic echo signals in the prior art. Summary of the Invention

[0003] The present invention provides an ultrasonic echo signal optimization method, device, medium and terminal device to solve the problem that accurate ultrasonic echo signals cannot be obtained in the prior art.

[0004] In a first aspect, the present application provides an ultrasonic echo signal optimization method, including:

[0005] Obtain the echo signal received by the preprocessed ultrasonic probe; wherein, the echo signal is the envelope data in the rectangular coordinate system;

[0006] Determine each neighborhood block in the rectangular coordinate system with the envelope data as the center;

[0007] Calculate each similarity weight between each neighborhood block to obtain each weight of each neighborhood block;

[0008] Adjust and optimize the filter window size according to the adaptive filtering algorithm, the minimum energy function, the distances from the envelope data to the preset focus, and the weights of each neighborhood block to obtain the optimized ultrasonic echo signal.

[0009] By acquiring the echo signal received by the ultrasonic probe and converting it into envelope data in the corresponding rectangular coordinate system, the present application can define a neighborhood block around each pixel point of the image. By calculating the similarity weights between these neighborhood blocks, the present application can identify similar pixel blocks in the image, providing important context information for subsequent image processing. Combining the adaptive filtering algorithm and the minimized energy function, and considering both the distance from the envelope data to the preset focus and the weights of each neighborhood block, the present application can dynamically adjust the size of the filtering window. This data-driven method makes the filtering process more accurate and can optimize according to the characteristics of different regions in the image. Therefore, the optimized envelope data can improve the focusing quality of ultrasonic imaging, especially in the regions far from the focus, thereby enhancing the clarity and contrast of the envelope data and providing more accurate and clear diagnostic information for medical personnel. The present application improves the accuracy and reliability of clinical diagnosis to solve the problem in the prior art that accurate ultrasonic echo signals cannot be obtained.

[0010] As a preferred embodiment of the first aspect, acquire the echo signal received by the preprocessed ultrasonic probe; wherein the echo signal is envelope data in the rectangular coordinate system, specifically:

[0011] Acquire the original echo signal of ultrasonic imaging;

[0012] Extract the initial envelope data according to the original echo signal; wherein the initial envelope data is represented in polar coordinates;

[0013] Convert the initial envelope data in the polar coordinate system into envelope data in the rectangular coordinate system according to the dynamic compression and scan conversion technology.

[0014] In this preferred embodiment, the present application acquires the original echo signal of ultrasonic imaging and extracts the initial envelope data therefrom, which represents the signal intensity and depth information received by the ultrasonic probe in polar coordinates. Subsequently, using the dynamic compression and scan conversion technology, the initial envelope data in the polar coordinate system is converted into envelope data in the corresponding rectangular coordinate system. This conversion process not only retains all the information of the original signal, but also makes the data processing more intuitive and convenient through the rectangular coordinate representation, facilitating subsequent image processing and analysis. Since the envelope data in the corresponding rectangular coordinate system is closer to the final image representation, it can be more directly applied to image processing algorithms such as adaptive filtering and minimized energy function to optimize the image quality. Therefore, the method of the present application not only improves the diagnostic value of ultrasonic imaging, but also may reduce misdiagnosis and missed diagnosis caused by poor image quality, enhancing the accuracy and reliability of clinical diagnosis.

[0015] As a preferred embodiment of the first aspect, after converting the initial envelope line data in the polar coordinate system into envelope line data in the rectangular coordinate system according to the dynamic compression and scan conversion technology, the following steps are further included:

[0016] Perform denoising processing on the envelope line data in the rectangular coordinate system according to the median filtering and Gaussian filtering technologies.

[0017] In this preferred embodiment, the present application converts the initial envelope line data in the polar coordinate system into the corresponding envelope line data in the rectangular coordinate system by using the dynamic compression and scan conversion technology, realizing the spatial standardization of ultrasonic imaging data, which is convenient for further processing. Subsequently, the median filtering and Gaussian filtering technologies are applied to perform denoising processing on the corresponding envelope line data in the rectangular coordinate system, which can effectively reduce the random noise and salt-and-pepper noise in the image. While removing noise, the median filtering can also better retain the edge information of the image, and the Gaussian filtering reduces the influence of noise through smoothing processing. The combined use of these two filtering technologies not only improves the quality of the ultrasonic image but also enhances the clarity and contrast of the image. Therefore, the method of the present application can provide more accurate and clearer diagnostic information, reduce the risk of misdiagnosis and missed diagnosis, and thus improve the accuracy and reliability of clinical diagnosis.

[0018] As a preferred embodiment of the first aspect, calculating the similarity weights between each neighborhood block to obtain the weights of each neighborhood block specifically includes:

[0019] Calculate the similarity weights between each neighborhood block according to the Euclidean distance formula;

[0020] The formula for the Euclidean distance is:

[0021]

[0022] In the formula, (|N i -N j |) represents the Euclidean distance between each neighborhood block, h represents a preset parameter for controlling the adaptive filtering intensity, and w(i,j) represents the similarity weight between neighborhood blocks;

[0023] Obtain the weights of each neighborhood block according to each similarity weight.

[0024] In this preferred embodiment, the present application calculates the similarity weights between each neighborhood block in the ultrasonic imaging data, and uses the Euclidean distance to measure the degree of difference between these neighborhood blocks, so as to assign a weight reflecting their similarity degree to each pair of neighborhood blocks. This method enables the present application to accurately identify similar regions in the image, provides important context information for the adaptive filtering algorithm, and thus optimizes the filtering process. Especially when processing regions far from the focus, this method can significantly improve the clarity and contrast of the image, reduce misdiagnosis and missed diagnosis caused by poor image quality, and enhance the accuracy and reliability of clinical diagnosis. Through this method, the present application improves the overall quality of ultrasonic images and provides clearer and more reliable diagnostic data support for medical personnel.

[0025] As a preferred embodiment of the first aspect, obtaining the respective weights of each neighborhood block according to each similarity weight specifically includes:

[0026] Normalize the similarity weights between each neighborhood block so that the sum of the similarity weights between each neighborhood block is equal to one, and obtain the respective weights of each neighborhood block;

[0027] The process of the normalization process is:

[0028]

[0029] In the formula, w1(i,j) represents the weight of the neighborhood block, w(i,j) represents the similarity weight between the neighborhood blocks, and ∑ k w(i,k) represents the sum of the similarity weights between the nearby neighborhood blocks.

[0030] In this preferred embodiment, the present application calculates the similarity weights between each neighborhood block in the ultrasonic imaging data, uses the Euclidean distance to measure the degree of difference between these neighborhood blocks, and then identifies the similar regions in the image. Subsequently, these similarity weights are normalized to ensure that the sum of all weights is equal to one. This step is crucial because it ensures that in the subsequent filtering process, the weights of each neighborhood block can be considered on the same proportional scale. Through normalization, the present application can balance the influence of different neighborhood blocks on the target pixel, making the filtering algorithm more accurate and effective. This method is particularly suitable for processing regions far from the focus because it can adjust the filtering intensity according to the similarity of the neighborhood blocks, thereby significantly improving the image clarity and contrast of these regions.

[0031] As a preferred embodiment of the first aspect, adjusting and optimizing the filtering window size according to the adaptive filtering algorithm, the minimum energy function, the respective distances from the envelope data to the preset focus, and the respective weights of each neighborhood block to obtain the optimized ultrasonic echo signal specifically includes:

[0032] Adjust the initial filter window size according to the adaptive filtering algorithm, the distances from the envelope data to the preset focus, and the preset window size to obtain the first filter window size;

[0033] The process of adjusting the initial filter window size is as follows:

[0034] W(d(i)) = W0 + k·d(i);

[0035] In the formula, k is the proportionality coefficient, W0 is the preset window size, d(i) is the distance from the envelope data to the preset focus, and W(d(i)) is the first filter window size;

[0036] Optimize the first filter window size according to the minimum energy function and the weights of the respective neighborhood blocks to obtain the optimized ultrasonic echo signal.

[0037] In this preferred embodiment, the present application dynamically adjusts the size of the filter window by means of an adaptive filtering algorithm in combination with the distance between the envelope data and the preset focus and the preset window size. During the adjustment process, the proportionality coefficient and the distance from the envelope data to the focus jointly determine the size of the initial filter window. This adjustment method enables the filter window to adaptively change its size according to the specific position of each pixel point in the image, and more precisely process the local features in the image. In this way, the present application can effectively improve the image quality in the area far from the focus in ultrasonic imaging, improve the clarity and contrast of the image, thereby providing more accurate and clearer diagnostic information for medical personnel. This not only improves the diagnostic value of ultrasonic imaging, but also may reduce misdiagnosis and missed diagnosis caused by poor image quality, and enhances the accuracy and reliability of clinical diagnosis.

[0038] As a preferred embodiment of the first aspect, the optimizing the first filter window size according to the minimum energy function and the weights of the respective neighborhood blocks to obtain the optimized ultrasonic echo signal is specifically:

[0039] Optimize the first filter window size according to the minimum energy function and the weights of the respective neighborhood blocks to obtain the optimized envelope data;

[0040] The formula of the minimum energy function is:

[0041] E"(i) = Σ i (E′(i) - E(i)) 2 + λΣ i Σ j∈W(d(i)) w1(i, j)(E′(i) - E′(j)) 2 ;

[0042] Wherein, E”(i) is the pixel value of the envelope data after iterative optimization, E'(i) and E'(j) are the pixel values of the optimized envelope data, E(i) and E(j) are the i-th and j-th pixel values in the envelope data, λ is the regularization parameter, w1(i,j) represents the weight of the neighborhood block, and W(d(i)) is the size of the first filtering window;

[0043] An optimized ultrasonic echo signal is obtained according to the optimized envelope data.

[0044] In this preferred embodiment, the present application optimizes the size of the first filtering window by minimizing the energy function and combining the weights of each neighborhood block, thereby further improving the quality of ultrasonic imaging. The purpose of minimizing the energy function is to find an optimal filtering effect. It measures the difference between the filtered envelope data and the original data and adjusts the filtering parameters by combining the neighborhood block weights. In this process, the regularization parameter helps control the intensity of the filtering, preventing over-smoothing or noise amplification. In this way, the size and shape of the filtering window are further optimized to better adapt to local image features, improving the clarity and contrast of the image. The optimized envelope data can thus more accurately reflect the information of the original ultrasonic signal, providing a clearer and more reliable diagnostic image for medical personnel, reducing the risk of misdiagnosis and missed diagnosis, and enhancing the accuracy and reliability of clinical diagnosis.

[0045] In a second aspect, the present application provides an ultrasonic echo signal optimization device. The ultrasonic echo signal optimization device includes an acquisition module, a calculation module, and an optimization module;

[0046] The acquisition module is used to acquire the echo signal received by the preprocessed ultrasonic probe; wherein, the echo signal is the envelope data in the rectangular coordinate system;

[0047] The calculation module is used to determine each neighborhood block in the rectangular coordinate system with the envelope data as the center;

[0048] Calculate the similarity weights between each neighborhood block to obtain the weights of each neighborhood block;

[0049] The optimization module is used to adjust and optimize the filtering window size according to the adaptive filtering algorithm, the minimized energy function, the distances from the envelope data to the preset focus, and the weights of each neighborhood block to obtain an optimized ultrasonic echo signal.

[0050] This device uses three modules to divide the work and coordinate with each other, which can better optimize the ultrasonic echo signal. By acquiring the echo signal received by the ultrasonic probe and converting it into envelope data in the corresponding rectangular coordinate system, this application can define neighborhood blocks around each pixel point of the image. By calculating the similarity weights between these neighborhood blocks, this application can identify similar pixel blocks in the image, providing important context information for subsequent image processing. Combining the adaptive filtering algorithm and the minimum energy function, and considering both the distance from the envelope data to the preset focus and the weights of each neighborhood block, this application can dynamically adjust the size of the filtering window. This data-driven method makes the filtering process more accurate and can optimize according to the characteristics of different regions in the image. Therefore, the optimized envelope data can improve the focusing quality of ultrasonic imaging, especially in the region far from the focus, thereby enhancing the clarity and contrast of the envelope data and providing more accurate and clear diagnostic information for medical personnel. This application improves the accuracy and reliability of clinical diagnosis to solve the problem of unable to obtain accurate ultrasonic echo signals in the prior art.

[0051] In a third aspect, this application provides a computer-readable storage medium, which includes a stored computer program. When the computer program runs, it controls the device where the computer-readable storage medium is located to execute an ultrasonic echo signal optimization method as described above. Its beneficial effects are the same as those of the ultrasonic echo signal optimization method provided in the first aspect of this application.

[0052] In a fourth aspect, this application provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements any ultrasonic echo signal optimization method as described in the first aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 : It is a schematic flowchart of an embodiment of the ultrasonic echo signal optimization method provided by this application;

[0054] Figure 2 : It is a schematic structural diagram of an embodiment of the ultrasonic echo signal optimization device provided by this application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0055] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present invention.

[0056] Embodiment 1

[0057] Please refer to Figure 1 , which is an ultrasonic echo signal optimization method provided by an embodiment of the present invention.

[0058] In this embodiment, the process of optimizing the ultrasonic echo signal in the present application is described in detail through steps S01 - S03.

[0059] S01: Obtain the echo signal received by the pre - processed ultrasonic probe; wherein, the echo signal is the envelope data in the rectangular coordinate system.

[0060] The obtaining of the echo signal received by the pre - processed ultrasonic probe; wherein the echo signal is the envelope data in the rectangular coordinate system, specifically:

[0061] Obtain the original echo signal of ultrasonic imaging;

[0062] Extract the initial envelope data according to the original echo signal; wherein, the initial envelope data is represented in polar coordinates;

[0063] According to the dynamic compression and scan conversion technology, convert the initial envelope data in the polar coordinate system into the envelope data in the rectangular coordinate system. Assume that the size of the envelope in the polar coordinate is 512 (depth) x 128 (number of lines); the converted envelope data in the corresponding rectangular coordinate system is of size 512 (depth) x 512 (number of lines).

[0064] In this preferred embodiment, the present application obtains the original echo signal of ultrasonic imaging and extracts the initial envelope data therefrom, which represents the signal intensity and depth information received by the ultrasonic probe in polar coordinates. Subsequently, using the dynamic compression and scan conversion technology, the initial envelope data in the polar coordinate system is converted into the envelope data in the corresponding rectangular coordinate system. This conversion process not only retains all the information of the original signal, but also makes the data processing more intuitive and convenient through the representation in the rectangular coordinate system, facilitating subsequent image processing and analysis. Since the envelope data in the corresponding rectangular coordinate system is closer to the final image representation, it can be more directly applied to image processing algorithms, such as adaptive filtering and minimizing the energy function, etc., to optimize the image quality. Therefore, the method of the present application not only improves the diagnostic value of ultrasonic imaging, but also may reduce misdiagnosis and missed diagnosis caused by poor image quality, enhancing the accuracy and reliability of clinical diagnosis.

[0065] As a preferred embodiment of Embodiment 1, after converting the initial envelope data in the polar coordinate system into the envelope data in the corresponding rectangular coordinate system according to the dynamic compression and scan conversion technology, it further includes:

[0066] Denoise the envelope data in the corresponding rectangular coordinate system according to median filtering and Gaussian filtering techniques.

[0067] In this preferred embodiment, the present application converts the initial envelope data in the polar coordinate system into envelope data in the corresponding rectangular coordinate system by using dynamic compression and scan conversion techniques, realizing the spatial standardization of ultrasonic imaging data, thus facilitating further processing. Subsequently, median filtering and Gaussian filtering techniques are applied to denoise the envelope data in the corresponding rectangular coordinate system, which can effectively reduce random noise and salt-and-pepper noise in the image. While removing noise, median filtering can also better retain the edge information of the image, and Gaussian filtering reduces the influence of noise through smoothing processing. The combined use of these two filtering techniques not only improves the quality of ultrasonic images but also enhances the clarity and contrast of the images. Therefore, the method of the present application can provide more accurate and clear diagnostic information, reduce the risk of misdiagnosis and missed diagnosis, and thus improve the accuracy and reliability of clinical diagnosis.

[0068] S02: Determine each neighborhood block in the rectangular coordinate system with the envelope data as the center.

[0069] S03: Calculate each similarity weight between the neighborhood blocks to obtain each weight of the neighborhood blocks.

[0070] As a preferred embodiment of Embodiment 1, the calculating each similarity weight between the neighborhood blocks to obtain each weight of the neighborhood blocks is specifically as follows:

[0071] Calculate each similarity weight between the neighborhood blocks according to the Euclidean distance formula;

[0072] The formula for the Euclidean distance is:

[0073]

[0074] In the formula, (|N i -N j |) represents the Euclidean distance between the neighborhood blocks, h represents a preset parameter for controlling the adaptive filtering intensity, which is used to control the weight of the similarity metric, and w(i,j) represents the similarity weight between the neighborhood blocks;

[0075] Obtain each weight of the neighborhood blocks according to each similarity weight.

[0076] In this preferred embodiment, the present application calculates the similarity weights between each neighborhood block in the ultrasonic imaging data, uses the Euclidean distance to measure the degree of difference between these neighborhood blocks, and thus assigns a weight reflecting their similarity degree to each pair of neighborhood blocks. This method enables the present application to accurately identify similar regions in the image, provides important context information for the adaptive filtering algorithm, and thereby optimizes the filtering process. Especially when dealing with regions far from the focus, this method can significantly improve the clarity and contrast of the image, reduce misdiagnosis and missed diagnosis caused by poor image quality, and enhance the accuracy and reliability of clinical diagnosis. Through this method, the present application improves the overall quality of the ultrasonic image and provides clearer and more reliable diagnostic data support for medical professionals.

[0077] S04: According to the adaptive filtering algorithm, the minimum energy function, the respective distances from the envelope data to the preset focus, and the respective weights of the respective neighborhood blocks, adjust and optimize the filtering window size to obtain an optimized ultrasonic echo signal.

[0078] As a preferred embodiment of Embodiment 1, obtaining the respective weights of the respective neighborhood blocks according to the respective similarity weights specifically includes:

[0079] Normalize the similarity weights between the respective neighborhood blocks so that the sum of the similarity weights between the respective neighborhood blocks is equal to one, and obtain the respective weights of the respective neighborhood blocks;

[0080] The process of the normalization process is as follows:

[0081]

[0082] In the formula, w1(i,j) represents the weight of the neighborhood block, w(i,j) represents the similarity weight between the neighborhood blocks, and ∑ k w(i,k) represents the sum of the similarity weights between the nearby neighborhood blocks.

[0083] In this preferred embodiment, the present application calculates the similarity weights between each neighborhood block in the ultrasonic imaging data, uses the Euclidean distance to measure the degree of difference between these neighborhood blocks, and then identifies similar regions in the image. Subsequently, normalize these similarity weights to ensure that the sum of all weights is equal to one. This step is crucial because it ensures that in the subsequent filtering process, the weights of each neighborhood block can be considered on the same proportional scale. Through normalization, the present application can balance the influence of different neighborhood blocks on the target pixel, making the filtering algorithm more accurate and effective. This method is particularly suitable for dealing with regions far from the focus because it can adjust the filtering intensity according to the similarity of the neighborhood blocks, thereby significantly improving the image clarity and contrast of these regions.

[0084] As a preferred embodiment of Embodiment 1, adjusting and optimizing the filter window size according to the adaptive filtering algorithm, the minimized energy function, the distances from the envelope data to the preset focus, and the weights of the respective neighborhood blocks to obtain the optimized ultrasonic echo signal specifically includes:

[0085] Adjusting the initial filter window size according to the adaptive filtering algorithm, the distances from the envelope data to the preset focus, and the preset window size to obtain the first filter window size;

[0086] The process of adjusting the initial filter window size is as follows:

[0087] W(d(i)) = W0 + k·d(i);

[0088] In the formula, k is the proportional coefficient, W0 is the preset window size, d(i) is the distance from the envelope data to the preset focus, and W(d(i)) is the first filter window size;

[0089] Optimizing the first filter window size according to the minimized energy function and the weights of the respective neighborhood blocks to obtain the optimized ultrasonic echo signal.

[0090] In this preferred embodiment, the present application dynamically adjusts the size of the filter window through the adaptive filtering algorithm, in combination with the distance between the envelope data and the preset focus and the preset window size. During the adjustment process, the proportional coefficient and the distance from the envelope data to the focus jointly determine the size of the initial filter window. This adjustment method enables the filter window to adaptively change its size according to the specific position of each pixel point in the image, and more precisely process the local features in the image. In this way, the present application can effectively improve the image quality in the area far from the focus in ultrasonic imaging, improve the clarity and contrast of the image, thereby providing more accurate and clear diagnostic information for medical personnel. This not only improves the diagnostic value of ultrasonic imaging, but also may reduce misdiagnosis and missed diagnosis caused by poor image quality, enhancing the accuracy and reliability of clinical diagnosis.

[0091] As a preferred embodiment of Embodiment 1, optimizing the first filter window size according to the minimized energy function and the weights of the respective neighborhood blocks to obtain the optimized ultrasonic echo signal specifically includes:

[0092] Optimizing the first filter window size according to the minimized energy function and the weights of the respective neighborhood blocks to obtain the optimized envelope data;

[0093] The specific method according to the minimized energy function and the weights of the respective neighborhood blocks is as follows:

[0094] The envelope data is updated iteratively by minimizing the energy function until convergence or reaching a predetermined number of iterations, at which point the iteration stops.

[0095] The formula for the minimized energy function is as follows:

[0096] E''(i) = Σ i (E'(i) - E(i)) 2 + λΣ i Σ j∈W(d(i)) w1(i, j)(E'(i) - E'(j)) 2 ;

[0097] In the formula, E''(i) is the pixel value of the envelope data after iterative optimization, E'(i) and E'(j) are the pixel values of the envelope data after optimization, E(i) and E(j) are the i-th and j-th pixel values in the envelope data, λ is the regularization parameter, w1(i, j) represents the weight of the neighborhood block, and W(d(i)) is the size of the first filtering window;

[0098] Replace the envelope data with the weighted average of all similar blocks:

[0099]

[0100] E'(i) is the pixel value of the optimized envelope data, E(j) is the j-th pixel value in the envelope data, and w1(i, j) represents the weight of the neighborhood block;

[0101] Based on the optimized envelope data, an optimized ultrasonic echo signal is obtained.

[0102] In this preferred embodiment, the present application optimizes the size of the first filtering window by minimizing the energy function in combination with the weights of each neighborhood block, thereby further improving the quality of ultrasonic imaging. The purpose of minimizing the energy function is to find an optimal filtering effect, which measures the difference between the filtered envelope data and the original data and adjusts the filtering parameters in combination with the neighborhood block weights. In this process, the regularization parameter helps control the intensity of the filtering, preventing over-smoothing or noise amplification. In this way, the size and shape of the filtering window are further optimized to better adapt to local image features, improving the clarity and contrast of the image. The optimized envelope data can thus more accurately reflect the information of the original ultrasonic signal, providing clearer and more reliable diagnostic images for medical personnel, reducing the risk of misdiagnosis and missed diagnosis, and enhancing the accuracy and reliability of clinical diagnosis.

[0103] By acquiring the echo signal received by the ultrasonic probe and converting it into envelope data in the corresponding rectangular coordinate system, this application can define neighborhood blocks around each pixel point of the image. By calculating the similarity weights between these neighborhood blocks, this application can identify similar pixel blocks in the image, providing important context information for subsequent image processing. Combining an adaptive filtering algorithm and a minimized energy function, and considering both the distance from the envelope data to the preset focus and the weights of each neighborhood block, this application can dynamically adjust the size of the filtering window. This data-driven method makes the filtering process more precise and can optimize according to the characteristics of different regions in the image. Therefore, the optimized envelope data can improve the focusing quality of ultrasonic imaging, especially in regions far from the focus, thereby enhancing the clarity and contrast of the envelope data and providing more accurate and clear diagnostic information for medical personnel. This application improves the accuracy and reliability of clinical diagnosis to solve the problem in the prior art that accurate ultrasonic echo signals cannot be obtained.

[0104] Embodiment 2

[0105] Please refer to Figure 2 , which is the ultrasonic echo signal optimization device provided by the embodiment of this application.

[0106] In this embodiment, the ultrasonic echo signal optimization device includes an acquisition module 10, a calculation module 20, and an optimization module 30.

[0107] The acquisition module 10 is used to acquire the echo signal received by the preprocessed ultrasonic probe; wherein, the echo signal is envelope data in the rectangular coordinate system.

[0108] The acquisition of the echo signal received by the preprocessed ultrasonic probe; wherein the echo signal is envelope data in the rectangular coordinate system, specifically:

[0109] Acquire the original echo signal of ultrasonic imaging;

[0110] According to the original echo signal, extract the initial envelope data; wherein, the initial envelope data is represented in polar coordinates;

[0111] According to the dynamic compression and scan conversion technology, convert the initial envelope data in the polar coordinate system into the corresponding envelope data in the rectangular coordinate system. Assume that the size of the envelope in the polar coordinate is 512 (depth) x 128 (number of lines); the converted envelope data in the corresponding rectangular coordinate system is 512 (depth) x 512 (number of lines).

[0112] In this preferred embodiment, the present application obtains the original echo signal of ultrasonic imaging and extracts the initial envelope data therefrom. These data represent the signal intensity and depth information received by the ultrasonic probe in the polar coordinate system. Subsequently, using dynamic compression and scan conversion techniques, the initial envelope data in the polar coordinate system is converted into envelope data in the corresponding rectangular coordinate system. This conversion process not only retains all the information of the original signal, but also makes the data processing more intuitive and convenient through the representation in the rectangular coordinate system, facilitating subsequent image processing and analysis. Since the envelope data in the corresponding rectangular coordinate system is closer to the final image representation, it can be more directly applied to image processing algorithms such as adaptive filtering and minimizing energy functions to optimize the image quality. Therefore, the method of the present application not only improves the diagnostic value of ultrasonic imaging, but also may reduce misdiagnosis and missed diagnosis caused by poor image quality, enhancing the accuracy and reliability of clinical diagnosis.

[0113] As a preferred embodiment of the second embodiment, after converting the initial envelope data in the polar coordinate system into envelope data in the corresponding rectangular coordinate system according to the dynamic compression and scan conversion technique, it further includes:

[0114] Performing denoising processing on the envelope data in the corresponding rectangular coordinate system according to median filtering and Gaussian filtering techniques.

[0115] In this preferred embodiment, the present application converts the initial envelope data in the polar coordinate system into envelope data in the corresponding rectangular coordinate system by using dynamic compression and scan conversion techniques, realizing the spatial standardization of ultrasonic imaging data, thus facilitating further processing. Subsequently, applying median filtering and Gaussian filtering techniques to perform denoising processing on the envelope data in the corresponding rectangular coordinate system can effectively reduce random noise and salt-and-pepper noise in the image. Median filtering can better retain the edge information of the image while removing noise, while Gaussian filtering reduces the influence of noise through smoothing processing. The combined use of these two filtering techniques not only improves the quality of ultrasonic images, but also enhances the clarity and contrast of the images. Therefore, the method of the present application can provide more accurate and clear diagnostic information, reducing the risk of misdiagnosis and missed diagnosis, thereby improving the accuracy and reliability of clinical diagnosis.

[0116] The calculation module 20 is used to determine each neighborhood block in the rectangular coordinate system with the envelope data as the center, calculate each similarity weight between the neighborhood blocks, and obtain each weight of each neighborhood block.

[0117] As a preferred embodiment of the first embodiment, calculating each similarity weight between the neighborhood blocks and obtaining each weight of each neighborhood block specifically includes:

[0118] Calculate the similarity weights between the respective neighborhood blocks according to the Euclidean distance formula;

[0119] The formula for the Euclidean distance is:

[0120]

[0121] In the formula, (|N i -N j |) represents the Euclidean distance between the respective neighborhood blocks, h represents a preset parameter for controlling the adaptive filtering strength, which is used to control the weight of the similarity metric, and w(i, j) represents the similarity weight between the neighborhood blocks;

[0122] Obtain the weights of the respective neighborhood blocks according to the respective similarity weights.

[0123] In this preferred embodiment, the present application calculates the similarity weights between the respective neighborhood blocks in the ultrasonic imaging data, uses the Euclidean distance to measure the degree of difference between these neighborhood blocks, and thus assigns a weight reflecting their similarity degree to each pair of neighborhood blocks. This method enables the present application to accurately identify similar regions in the image, provides important context information for the adaptive filtering algorithm, and thus optimizes the filtering process. Especially when processing regions far from the focus, this method can significantly improve the clarity and contrast of the image, reduce misdiagnosis and missed diagnosis caused by poor image quality, and enhance the accuracy and reliability of clinical diagnosis. Through this method, the present application improves the overall quality of the ultrasonic image and provides clearer and more reliable diagnostic data support for medical personnel.

[0124] The optimization module 30 is used to adjust and optimize the filtering window size according to the adaptive filtering algorithm, the minimum energy function, the respective distances from the envelope data to the preset focus, and the respective weights of the respective neighborhood blocks, and obtain an optimized ultrasonic echo signal.

[0125] As a preferred embodiment of the second embodiment, the obtaining of the weights of the respective neighborhood blocks according to the respective similarity weights is specifically:

[0126] Perform normalization processing on the similarity weights between the respective neighborhood blocks so that the sum of the similarity weights between the respective neighborhood blocks is equal to one, and obtain the weights of the respective neighborhood blocks;

[0127] The process of the normalization processing is:

[0128]

[0129] In the formula, w1(i, j) represents the weight of the neighborhood block, w(i, j) represents the similarity weight between the neighborhood blocks, and ∑ k w(i, k) represents the sum of the similarity weights between the nearby neighborhood blocks.

[0130] In this preferred embodiment, the present application calculates the similarity weights between each neighborhood block in the ultrasonic imaging data, uses the Euclidean distance to measure the degree of difference between these neighborhood blocks, and then identifies the similar regions in the image. Subsequently, these similarity weights are normalized to ensure that the sum of all weights is equal to one. This step is crucial because it ensures that in the subsequent filtering process, the weights of each neighborhood block can be considered on the same proportional scale. Through normalization, the present application can balance the influence of different neighborhood blocks on the target pixel, making the filtering algorithm more accurate and effective. This method is particularly suitable for processing regions far from the focus because it can adjust the filtering intensity according to the similarity of the neighborhood blocks, thereby significantly improving the image clarity and contrast of these regions.

[0131] As a preferred embodiment of the second embodiment, adjusting and optimizing the filtering window size according to the adaptive filtering algorithm, the minimized energy function, the respective distances from the envelope data to the preset focus, and the respective weights of the respective neighborhood blocks to obtain the optimized ultrasonic echo signal specifically includes:

[0132] Adjusting the initial filtering window size according to the adaptive filtering algorithm, the respective distances from the envelope data to the preset focus, and the preset window size to obtain the first filtering window size;

[0133] The process of adjusting the initial filtering window size is as follows:

[0134] W(d(i)) = W0 + k·d(i);

[0135] In the formula, k is the proportionality coefficient, W0 is the preset window size, d(i) is the distance from the envelope data to the preset focus, and W(d(i)) is the first filtering window size;

[0136] Optimizing the first filtering window size according to the minimized energy function and the respective weights of the respective neighborhood blocks to obtain the optimized ultrasonic echo signal.

[0137] In this preferred embodiment, the present application dynamically adjusts the size of the filtering window by means of an adaptive filtering algorithm, in combination with the distance between the envelope data and the preset focus and the preset window size. During the adjustment process, the proportionality coefficient and the distance from the envelope data to the focus jointly determine the size of the initial filtering window. This adjustment method enables the filtering window to adaptively change its size according to the specific position of each pixel point in the image, and more precisely process the local features in the image. In this way, the present application can effectively improve the image quality in the area far from the focus in ultrasonic imaging, enhance the clarity and contrast of the image, and thus provide more accurate and clear diagnostic information for medical personnel. This not only improves the diagnostic value of ultrasonic imaging, but also may reduce misdiagnosis and missed diagnosis caused by poor image quality, and enhances the accuracy and reliability of clinical diagnosis.

[0138] As a preferred embodiment of the second embodiment, optimizing the size of the first filtering window according to the minimized energy function and the respective weights of the respective neighborhood blocks to obtain an optimized ultrasonic echo signal specifically includes:

[0139] Optimizing the size of the first filtering window according to the minimized energy function and the respective weights of the respective neighborhood blocks to obtain optimized envelope data;

[0140] The minimizing the energy function and the respective weights of the respective neighborhood blocks specifically includes:

[0141] Repeatedly iteratively updating the envelope data through the minimized energy function until convergence or a predetermined number of iterations is reached, and then stopping the iteration.

[0142] The formula of the minimized energy function is:

[0143] E″(i) = Σ i (E′(i) - E(i)) 2 + λΣ i Σ j∈W(d(i)) w1(i, j)(E′(i) - E′(j)) 2 ;

[0144] In the formula, E″(i) is the pixel value of the envelope data after iterative optimization, E′(i) and E′(j) are the pixel values of the optimized envelope data, E(i) and E(j) are the i-th and j-th pixel values in the envelope data, λ is the regularization parameter, w1(i, j) represents the weight of the neighborhood block, and W(d(i)) is the size of the first filtering window;

[0145] Replacing the envelope data with the weighted average of all similar blocks:

[0146]

[0147] E'(i) is the pixel value of the optimized envelope data, E(j) is the j-th pixel value in the envelope data, and w1(i,j) represents the weight of the neighborhood block;

[0148] Based on the optimized envelope data, an optimized ultrasonic echo signal is obtained.

[0149] In this preferred embodiment, the present application optimizes the size of the first filtering window by minimizing the energy function in combination with the weights of each neighborhood block, thereby further improving the quality of ultrasonic imaging. The purpose of minimizing the energy function is to find an optimal filtering effect. It measures the difference between the filtered envelope data and the original data and adjusts the filtering parameters in combination with the neighborhood block weights. In this process, the regularization parameter helps control the intensity of filtering and prevents over-smoothing or noise amplification. In this way, the size and shape of the filtering window are further optimized to better adapt to local image features, improving the clarity and contrast of the image. The optimized envelope data can thus more accurately reflect the information of the original ultrasonic signal, providing clearer and more reliable diagnostic images for medical personnel, reducing the risk of misdiagnosis and missed diagnosis, and enhancing the accuracy and reliability of clinical diagnosis.

[0150] This device can better optimize the ultrasonic echo signal by using three modules to work separately and coordinately. The present application can define neighborhood blocks around each pixel point of the image by obtaining the echo signal received by the ultrasonic probe and converting it into envelope data in the corresponding rectangular coordinate system. By calculating the similarity weights between these neighborhood blocks, the present application can identify similar pixel blocks in the image, providing important context information for subsequent image processing. Combining the adaptive filtering algorithm and the minimized energy function, and considering both the distance from the envelope data to the preset focus and the weights of each neighborhood block, the present application can dynamically adjust the size of the filtering window. This data-driven method makes the filtering process more accurate and can optimize according to the characteristics of different regions in the image. Therefore, the optimized envelope data can improve the focusing quality of ultrasonic imaging, especially in the region far from the focus, thereby enhancing the clarity and contrast of the envelope data and providing more accurate and clearer diagnostic information for medical personnel. The present application improves the accuracy and reliability of clinical diagnosis to solve the problem that an accurate ultrasonic echo signal cannot be obtained in the prior art.

[0151] Embodiment 3:

[0152] The embodiment of the present application provides a computer-readable storage medium, which includes a stored computer program. When the computer program runs, it controls the device where the computer-readable storage medium is located to execute the ultrasonic echo signal optimization method described above;

[0153] Among them, for the above-mentioned method for optimizing ultrasonic echo signals, when it is implemented in the form of a software functional unit and used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, to implement all or part of the processes in the above-mentioned embodiment methods of the present invention, it can also be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-mentioned various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0154] Embodiment 4

[0155] The present application provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements any one of the ultrasonic echo signal optimization methods described in Embodiment 1.

[0156] For the above-mentioned specific embodiments, the purpose, technical solution, and beneficial effects of the present invention have been further described in detail. It should be understood that the above-mentioned are only specific embodiments of the present invention and are not used to limit the protection scope of the present invention. In particular, it is pointed out that for those skilled in the art, any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An ultrasonic echo signal optimization method, characterized in that, Including: Obtaining the echo signal received by the preprocessed ultrasonic probe; wherein, the echo signal is envelope data in a rectangular coordinate system; Determining each neighborhood block in the rectangular coordinate system with the envelope data as the center; Calculating each similarity weight between the neighborhood blocks to obtain each weight of each neighborhood block; According to the adaptive filtering algorithm, the minimum energy function, the distances from the envelope data to the preset focus, and the weights of the neighborhood blocks, adjusting and optimizing the size of the filtering window to obtain the optimized ultrasonic echo signal.

2. The ultrasonic echo signal optimization method according to claim 1, wherein The obtaining the echo signal received by the preprocessed ultrasonic probe; wherein the echo signal is envelope data in a rectangular coordinate system, specifically: Obtaining the original echo signal of ultrasonic imaging; Extracting the initial envelope data according to the original echo signal; wherein, the initial envelope data is represented in polar coordinates; Converting the initial envelope data in polar coordinates to envelope data in a rectangular coordinate system according to the dynamic compression and scan conversion technology.

3. The ultrasonic echo signal optimization method according to claim 2, characterized in that After converting the initial envelope data in polar coordinates to envelope data in a rectangular coordinate system according to the dynamic compression and scan conversion technology, it further includes: Performing denoising processing on the envelope data in the rectangular coordinate system according to the median filtering and Gaussian filtering technologies.

4. The ultrasonic echo signal optimization method according to claim 1, characterized in that The calculating each similarity weight between the neighborhood blocks to obtain each weight of each neighborhood block, specifically: Calculating each similarity weight between the neighborhood blocks according to the Euclidean distance formula; The formula of the Euclidean distance is: where (|N i -N j |) represents the Euclidean distance between each neighborhood block, h represents a preset parameter for controlling the adaptive filtering strength, and w(i,j) represents the similarity weight between neighborhood blocks; Obtaining each weight of each neighborhood block according to each similarity weight.

5. The ultrasonic echo signal optimization method according to claim 4, characterized in that The obtaining each weight of each neighborhood block according to each similarity weight, specifically: Normalizing the similarity weights between the neighborhood blocks so that the sum of the similarity weights between the neighborhood blocks is equal to one to obtain each weight of each neighborhood block; The process of the normalization is: Wherein, w1(i,j) represents the weight of the neighborhood block, w(i,j) represents the similarity weight between neighborhood blocks, and ∑ k w(i, k) represents the sum of the similarity weights between the nearby neighborhood blocks.

6. The ultrasonic echo signal optimization method according to claim 1, characterized in that The adjusting and optimizing the size of the filtering window according to the adaptive filtering algorithm, the minimum energy function, the distances from the envelope data to the preset focus, and the weights of the neighborhood blocks to obtain the optimized ultrasonic echo signal, specifically: Adjusting the initial filtering window size according to the adaptive filtering algorithm, the distances from the envelope data to the preset focus, and the preset window size to obtain the first filtering window size; The process of adjusting the initial filtering window size is: W(d(i)) = W0 + k·d(i); In the formula, k is the proportionality coefficient, W0 is the preset window size, d(i) is the distance from the envelope data to the preset focus, and W(d(i)) is the first filtering window size; Optimizing the first filtering window size according to the minimum energy function and the weights of the neighborhood blocks to obtain the optimized ultrasonic echo signal.

7. The ultrasonic echo signal optimization method according to claim 6, characterized in that The optimizing the first filtering window size according to the minimum energy function and the weights of the neighborhood blocks to obtain the optimized ultrasonic echo signal, specifically: Optimizing the first filtering window size according to the minimum energy function and the weights of the neighborhood blocks to obtain the optimized envelope data; The formula of the minimized energy function is as follows: E″(i) = ∑ i (E′(i) - E(i)) 2 + λ∑ i ∑ j∈W(d(i)) w1(i, j)(E′(i) - E′(j)) 2 ; In the formula, E″(i) is the pixel value of the envelope data after iterative optimization, E′(i) and E′(j) are the pixel values of the optimized envelope data, E(i) and E(j) are the i-th and j-th pixel values in the envelope data, λ is the regularization parameter, w1(i, j) represents the weight of the neighborhood block, and W(d(i)) is the size of the first filtering window; Based on the optimized envelope data, an optimized ultrasonic echo signal is obtained.

8. An ultrasonic echo signal optimization device, characterized in that It includes an acquisition module, a calculation module, and an optimization module; The acquisition module is used to acquire the echo signal received by the preprocessed ultrasonic probe; wherein, the echo signal is the envelope data in the rectangular coordinate system; The calculation module is used to determine each neighborhood block in the rectangular coordinate system with the envelope data as the center; Calculate the similarity weights between the neighborhood blocks to obtain the weights of the neighborhood blocks; The optimization module is used to adjust and optimize the size of the filtering window according to the adaptive filtering algorithm, the minimized energy function, the distances from the envelope data to the preset focal points, and the weights of the neighborhood blocks, so as to obtain an optimized ultrasonic echo signal.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein when the computer program runs, it controls the device where the computer-readable storage medium is located to execute the ultrasonic echo signal optimization method according to any one of claims 1 to 7.

10. A terminal device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the ultrasonic echo signal optimization method according to any one of claims 1 to 7.

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