Ultrasonic echo signal optimization method and device, medium and terminal equipment

Abstract

The invention discloses an ultrasonic echo signal optimization method and device, a medium and terminal equipment. According to the invention, the preprocessed echo signal received by the ultrasonic probe is obtained, and the signal is presented in the form of envelope data under the corresponding rectangular coordinate system. Then, taking the envelope line data as a center, dividing a plurality of neighborhood blocks in a rectangular coordinate system, calculating similarity weights among the neighborhood blocks, and distributing a corresponding weight value for each neighborhood block; and finally, adjusting the size of a filtering window and optimizing the ultrasonic echo signal in combination with an adaptive filtering algorithm, a minimum energy function, the distance from envelope data to a preset focus and the weight of each neighborhood block, so as to solve the problem that the accurate ultrasonic echo signal cannot be obtained in the prior art.

Description

Technical Field

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

[0002] In ultrasound imaging (B-ultrasound), the image in the focal area is usually clearer, while the area far from the focal point often appears out of focus, resulting in blurred images. This problem affects medical professionals' observation and diagnosis of diseased tissues. Although traditional methods such as deconvolution and super-resolution reconstruction have certain effects, they often fail to achieve ideal results in complex tissue structures. For example, super-resolution reconstruction technology enhances image details by improving the resolution of the image, but when processing ultrasound imaging, it may encounter the problem of over-smoothing, resulting in a deviation between the generated image and the real scene effect. In addition, a review of the research progress of ultrasound imaging detection technology points out that although modern ultrasound imaging methods such as phased array methods have the advantages of high resolution and fast detection speed, the imaging resolution is not high away from the focal point, and the variability of sound beam propagation in complex grain structures limits its scope of application. These problems make it impossible to obtain accurate ultrasonic echo signals in existing technologies. Summary of the invention

[0003] The present invention provides an ultrasonic echo signal optimization method, device, medium and terminal equipment 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, comprising:

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

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

[0007] Calculating the similarity weights between the neighborhood blocks to obtain the weights of the neighborhood blocks;

[0008] According to the adaptive filtering algorithm, the minimization energy function, the distances from the envelope data to the preset focus and the weights of the neighborhood blocks, the filter window size is adjusted and optimized to obtain an optimized ultrasonic echo signal.

[0009] The present application can define neighborhood blocks around each pixel point in the image by acquiring 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 and provide important context information for subsequent image processing. Combining the adaptive filtering algorithm and the minimization energy function, while considering 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 filter window. This data-driven method makes the filtering process more accurate and can be optimized for the characteristics of different areas in the image. Therefore, the optimized envelope data can improve the focusing quality of ultrasonic imaging, especially in areas away from the focus, thereby enhancing the clarity and contrast of the envelope data, and providing medical personnel with more accurate and clearer diagnostic information. The present application improves the accuracy and reliability of clinical diagnosis to solve the problem that accurate ultrasonic echo signals cannot be obtained in the prior art.

[0010] As a preferred embodiment of the first aspect, the step of acquiring the echo signal received by the ultrasound probe after preprocessing, wherein the echo signal is envelope data in a rectangular coordinate system, is specifically:

[0011] Acquiring original echo signals of ultrasonic imaging;

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

[0013] According to the dynamic compression and scan conversion technology, the initial envelope data in the polar coordinate system is converted into envelope data in the rectangular coordinate system.

[0014] In this preferred embodiment, the present application obtains the original echo signal of ultrasonic imaging and extracts the initial envelope data therefrom, which represent the signal strength and depth information received by the ultrasonic probe in polar coordinates. Subsequently, the initial envelope data in the polar coordinate system is converted into envelope data in the corresponding rectangular coordinate system using dynamic compression and scan conversion technology. This conversion process not only retains all the information of the original signal, but also makes data processing more intuitive and convenient through the representation of the rectangular coordinate system, which is convenient for 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 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, and enhance the accuracy and reliability of clinical diagnosis.

[0015] As a preferred embodiment of the first aspect, after converting 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, the method further comprises:

[0016] The envelope data in the rectangular coordinate system is denoised according to median filtering and Gaussian filtering techniques.

[0017] In this preferred embodiment, the present application realizes the spatial standardization of ultrasonic imaging data by converting the initial envelope data in the polar coordinate system into the envelope data in the corresponding rectangular coordinate system using dynamic compression and scan conversion technology, thereby facilitating further processing. Subsequently, the envelope data in the corresponding rectangular coordinate system is denoised by applying median filtering and Gaussian filtering technology, which can effectively reduce the random noise and salt and pepper noise in the image. While removing noise, the median filter can also better retain the edge information of the image, while the Gaussian filter reduces the influence of noise by smoothing. 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, the calculating of the similarity weights between the neighborhood blocks to obtain the weights of the neighborhood blocks is specifically:

[0019] Calculate the similarity weights between the neighborhood blocks 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 the preset parameter for controlling the strength of adaptive filtering, and w(i,j) represents the similarity weight between neighborhood blocks;

[0023] According to each similarity weight, each weight of each neighborhood block is obtained.

[0024] In this preferred embodiment, the present application calculates the similarity weights between each neighborhood block in the ultrasound imaging data, and uses the Euclidean distance to measure the degree of difference between these neighborhood blocks, thereby assigning a weight reflecting the degree of similarity to each pair of neighborhood blocks. This method enables the present application to accurately identify similar areas in the image, provide important contextual information for the adaptive filtering algorithm, and thus optimize the filtering process. Especially when processing areas 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 ultrasound images and provides medical personnel with clearer and more reliable diagnostic data support.

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

[0026] Normalizing the similarity weights between the neighborhood blocks so that the sum of the similarity weights between the neighborhood blocks is equal to one, thereby obtaining the weights of the neighborhood blocks;

[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 neighborhood blocks, ∑ 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, and uses the Euclidean distance to measure the degree of difference between these neighborhood blocks, thereby identifying similar areas 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 the weight of each neighborhood block can be considered on the same scale in the subsequent filtering process. Through normalization, the present application can balance the impact of different neighborhood blocks on the target pixel, making the filtering algorithm more accurate and effective. This method is particularly suitable for processing areas away from the focus because it can adjust the filtering strength according to the similarity of the neighborhood blocks, thereby significantly improving the image clarity and contrast in these areas.

[0031] As a preferred embodiment of the first aspect, the filter window size is adjusted and optimized 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 neighborhood blocks to obtain the optimized ultrasonic echo signal, specifically:

[0032] According to the adaptive filtering algorithm, the distances from the envelope data to the preset focus and the preset window size, the initial filtering window size is adjusted to obtain a first filtering window size;

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

[0034] W(d(i))=W0+k·d(i);

[0035] Wherein, 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;

[0036] According to the minimization of the energy function and the weights of the neighborhood blocks, the first filter window size is optimized to obtain an optimized ultrasonic echo signal.

[0037] In this preferred embodiment, the present application dynamically adjusts the size of the filter window through an adaptive filtering algorithm, combined with the distance between the envelope data and the preset focus and the preset window size. During the adjustment process, the scale factor 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 size according to the specific position of each pixel in the image, and more accurately process local features in the image. In this way, the present application can effectively improve the image quality of areas away from the focus in ultrasonic imaging, improve the clarity and contrast of the image, and thus provide medical personnel with more accurate and clearer diagnostic information. This not only improves the diagnostic value of ultrasonic imaging, but also may reduce misdiagnosis and missed diagnosis caused by poor image quality, and enhance the accuracy and reliability of clinical diagnosis.

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

[0039] According to the minimized energy function and the weights of the respective neighborhood blocks, the first filter window size is optimized to obtain optimized envelope data;

[0040] The formula for minimizing the energy function is:

[0041] E″(i)=∑ 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 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;

[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 filter window by minimizing the energy function combined 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 adjusts the filtering parameters by measuring the difference between the filtered envelope data and the original data and combining the neighborhood block weights. In this process, the regularization parameter helps control the intensity of the filter to prevent excessive smoothing or noise amplification. In this way, the size and shape of the filter window are further optimized to better adapt to local image features and improve image clarity and contrast. The optimized envelope data can therefore more accurately reflect the information of the original ultrasonic signal, provide medical personnel with clearer and more reliable diagnostic images, reduce the risk of misdiagnosis and missed diagnosis, and enhance 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 ultrasonic probe after preprocessing; wherein the echo signal is envelope data in a rectangular coordinate system;

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

[0048] Calculating the similarity weights between the neighborhood blocks to obtain the weights of the neighborhood blocks;

[0049] The optimization module is used to adjust and optimize 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 neighborhood blocks to obtain an optimized ultrasonic echo signal.

[0050] The device uses three modules to divide the work and coordinate the work to better optimize the ultrasonic echo signal. The present application can define a neighborhood block around each pixel point in the image by acquiring 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 and provide important context information for subsequent image processing. Combining the adaptive filtering algorithm and the minimization energy function, while considering the distance from the envelope data to the preset focus and the weight of each neighborhood block, the present application can dynamically adjust the size of the filter window. This data-driven method makes the filtering process more accurate and can be optimized for the characteristics of different areas in the image. Therefore, the optimized envelope data can improve the focusing quality of ultrasonic imaging, especially in areas far away from the focus, thereby enhancing the clarity and contrast of the envelope data, and providing medical personnel with more accurate and clearer diagnostic information. The present application improves the accuracy and reliability of clinical diagnosis to solve the problem that accurate ultrasonic echo signals cannot be obtained in the prior art.

[0051] In a third aspect, the present application provides a computer-readable storage medium, the computer-readable storage medium including a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute an ultrasonic echo signal optimization method as described above. Its beneficial effects are the same as those of an ultrasonic echo signal optimization method provided in the first aspect of the present application.

[0052] In a fourth aspect, the present application provides a terminal device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements any one of the ultrasonic echo signal optimization methods described in the first aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 : A schematic diagram of a flow chart of an embodiment of an ultrasonic echo signal optimization method provided in the present application;

[0054] Figure 2 : A structural schematic diagram of an embodiment of the ultrasonic echo signal optimization device provided in the present application. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[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 this application is described in detail through steps S01-S03.

[0059] S01: Acquire the echo signal received by the ultrasound probe after preprocessing; wherein the echo signal is envelope data in a rectangular coordinate system.

[0060] The acquisition of the echo signal received by the preprocessed ultrasonic probe, wherein the echo signal is envelope data in a rectangular coordinate system, is specifically:

[0061] Acquiring original echo signals of ultrasonic imaging;

[0062] Extracting 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, the initial envelope data in the polar coordinate system is converted into envelope data in the rectangular coordinate system, assuming that the size of the envelope in the polar coordinate system is 512 (depth) x 128 (number of lines); the envelope data converted into the corresponding rectangular coordinate system has a size of 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 represent the signal strength and depth information received by the ultrasonic probe in polar coordinates. Subsequently, the initial envelope data in the polar coordinate system is converted into envelope data in the corresponding rectangular coordinate system using dynamic compression and scan conversion technology. This conversion process not only retains all the information of the original signal, but also makes data processing more intuitive and convenient through the representation of the rectangular coordinate system, which is convenient for 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 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, and enhance the accuracy and reliability of clinical diagnosis.

[0065] As a preferred embodiment of the first 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 technology, the method further includes:

[0066] According to the median filtering and Gaussian filtering techniques, the envelope data in the corresponding rectangular coordinate system is subjected to denoising processing.

[0067] In this preferred embodiment, the present application realizes the spatial standardization of ultrasonic imaging data by converting the initial envelope data in the polar coordinate system into the envelope data in the corresponding rectangular coordinate system using dynamic compression and scan conversion technology, thereby facilitating further processing. Subsequently, the envelope data in the corresponding rectangular coordinate system is denoised by applying median filtering and Gaussian filtering technology, which can effectively reduce the random noise and salt and pepper noise in the image. While removing noise, the median filter can also better retain the edge information of the image, while the Gaussian filter reduces the influence of noise by smoothing. 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.

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

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

[0070] As a preferred embodiment of the first embodiment, the similarity weights between the neighborhood blocks are calculated to obtain the weights of the neighborhood blocks, specifically:

[0071] Calculate the similarity weights 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 each neighborhood block, h represents the preset parameter for controlling the strength of adaptive filtering, which is used to control the weight of similarity measurement, and w(i,j) represents the similarity weight between neighborhood blocks;

[0075] According to each similarity weight, each weight of each neighborhood block is obtained.

[0076] In this preferred embodiment, the present application calculates the similarity weights between each neighborhood block in the ultrasound imaging data, and uses the Euclidean distance to measure the degree of difference between these neighborhood blocks, thereby assigning a weight reflecting the degree of similarity to each pair of neighborhood blocks. This method enables the present application to accurately identify similar areas in the image, provide important contextual information for the adaptive filtering algorithm, and thus optimize the filtering process. Especially when processing areas 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 ultrasound images and provides medical personnel with clearer and more reliable diagnostic data support.

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

[0078] As a preferred embodiment of the first embodiment, the weights of each neighborhood block are obtained according to each similarity weight, specifically:

[0079] Normalizing the similarity weights between the neighborhood blocks so that the sum of the similarity weights between the neighborhood blocks is equal to one, thereby obtaining the weights of the neighborhood blocks;

[0080] The process of the normalization process is:

[0081]

[0082] In the formula, w1(i,j) represents the weight of the neighborhood block, w(i,j) represents the similarity weight between neighborhood blocks, ∑ 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, and uses the Euclidean distance to measure the degree of difference between these neighborhood blocks, thereby identifying similar areas 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 the weight of each neighborhood block can be considered on the same scale in the subsequent filtering process. Through normalization, the present application can balance the impact of different neighborhood blocks on the target pixel, making the filtering algorithm more accurate and effective. This method is particularly suitable for processing areas away from the focus because it can adjust the filtering strength according to the similarity of the neighborhood blocks, thereby significantly improving the image clarity and contrast in these areas.

[0084] As a preferred embodiment of the first embodiment, the filter window size is adjusted and optimized according to the adaptive filtering algorithm, the minimization 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:

[0085] According to the adaptive filtering algorithm, the distances from the envelope data to the preset focus and the preset window size, the initial filtering window size is adjusted to obtain a first filtering window size;

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

[0087] W(d(i))=W0+k·d(i);

[0088] Wherein, 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] According to the minimization of the energy function and the weights of the neighborhood blocks, the first filter window size is optimized to obtain an optimized ultrasonic echo signal.

[0090] In this preferred embodiment, the present application dynamically adjusts the size of the filter window through an adaptive filtering algorithm, combined with the distance between the envelope data and the preset focus and the preset window size. During the adjustment process, the scale factor 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 size according to the specific position of each pixel in the image, and more accurately process local features in the image. In this way, the present application can effectively improve the image quality of areas away from the focus in ultrasonic imaging, improve the clarity and contrast of the image, and thus provide medical personnel with more accurate and clearer diagnostic information. This not only improves the diagnostic value of ultrasonic imaging, but also may reduce misdiagnosis and missed diagnosis caused by poor image quality, and enhance the accuracy and reliability of clinical diagnosis.

[0091] As a preferred embodiment of the first embodiment, the first filter window size is optimized according to the minimization of the energy function and the weights of each neighborhood block to obtain the optimized ultrasonic echo signal, specifically:

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

[0093] The minimization of the energy function and the weights of the neighborhood blocks is specifically as follows:

[0094] The envelope data is updated repeatedly by minimizing the energy function until convergence or the predetermined number of iterations is reached, and the iteration is stopped.

[0095] The formula for minimizing the energy function is:

[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] 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 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 a weighted average of all similar blocks:

[0099]

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

[0101] An optimized ultrasonic echo signal is obtained according to the optimized envelope data.

[0102] In this preferred embodiment, the present application optimizes the size of the first filter window by minimizing the energy function combined 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 adjusts the filtering parameters by measuring the difference between the filtered envelope data and the original data and combining the neighborhood block weights. In this process, the regularization parameter helps control the intensity of the filter to prevent excessive smoothing or noise amplification. In this way, the size and shape of the filter window are further optimized to better adapt to local image features and improve image clarity and contrast. The optimized envelope data can therefore more accurately reflect the information of the original ultrasonic signal, provide medical personnel with clearer and more reliable diagnostic images, reduce the risk of misdiagnosis and missed diagnosis, and enhance the accuracy and reliability of clinical diagnosis.

[0103] The present application can define neighborhood blocks around each pixel point in the image by acquiring 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 and provide important context information for subsequent image processing. Combining the adaptive filtering algorithm and the minimization energy function, while considering 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 filter window. This data-driven method makes the filtering process more accurate and can be optimized for the characteristics of different areas in the image. Therefore, the optimized envelope data can improve the focusing quality of ultrasonic imaging, especially in areas away from the focus, thereby enhancing the clarity and contrast of the envelope data, and providing medical personnel with more accurate and clearer diagnostic information. The present application improves the accuracy and reliability of clinical diagnosis to solve the problem that accurate ultrasonic echo signals cannot be obtained in the prior art.

[0104] Embodiment 2

[0105] Please refer to Figure 2 , is an ultrasonic echo signal optimization device provided in an embodiment of the present 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 ultrasound probe after preprocessing; wherein the echo signal is envelope data in a rectangular coordinate system.

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

[0109] Acquiring original echo signals of ultrasonic imaging;

[0110] Extracting initial envelope data according to the original echo signal; wherein the initial envelope data is represented in polar coordinates;

[0111] According to 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, assuming that the size of the envelope in the polar coordinate system is 512 (depth) x 128 (number of lines); the envelope data converted into the corresponding rectangular coordinate system has a size of 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, which represent the signal strength and depth information received by the ultrasonic probe in polar coordinates. Subsequently, the initial envelope data in the polar coordinate system is converted into envelope data in the corresponding rectangular coordinate system using dynamic compression and scan conversion technology. This conversion process not only retains all the information of the original signal, but also makes data processing more intuitive and convenient through the representation of the rectangular coordinate system, which is convenient for 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 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, and enhance 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 technology, the method further includes:

[0114] According to the median filtering and Gaussian filtering techniques, the envelope data in the corresponding rectangular coordinate system is subjected to denoising processing.

[0115] In this preferred embodiment, the present application realizes the spatial standardization of ultrasonic imaging data by converting the initial envelope data in the polar coordinate system into the envelope data in the corresponding rectangular coordinate system using dynamic compression and scan conversion technology, thereby facilitating further processing. Subsequently, the envelope data in the corresponding rectangular coordinate system is denoised by applying median filtering and Gaussian filtering technology, which can effectively reduce the random noise and salt and pepper noise in the image. While removing noise, the median filter can also better retain the edge information of the image, while the Gaussian filter reduces the influence of noise by smoothing. 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.

[0116] The calculation module 20 is used to determine each neighborhood block in a rectangular coordinate system based on 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, the similarity weights between the neighborhood blocks are calculated to obtain the weights of the neighborhood blocks, specifically:

[0118] Calculate the similarity weights between the 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 each neighborhood block, h represents the preset parameter for controlling the strength of adaptive filtering, which is used to control the weight of similarity measurement, and w(i,j) represents the similarity weight between neighborhood blocks;

[0122] According to each similarity weight, each weight of each neighborhood block is obtained.

[0123] In this preferred embodiment, the present application calculates the similarity weights between each neighborhood block in the ultrasound imaging data, and uses the Euclidean distance to measure the degree of difference between these neighborhood blocks, thereby assigning a weight reflecting the degree of similarity to each pair of neighborhood blocks. This method enables the present application to accurately identify similar areas in the image, provide important contextual information for the adaptive filtering algorithm, and thus optimize the filtering process. Especially when processing areas 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 ultrasound images and provides medical personnel with clearer and more reliable diagnostic data support.

[0124] The optimization module 30 is used to adjust and optimize the filter window size according to the adaptive filtering algorithm, the minimization 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.

[0125] As a preferred embodiment of the second embodiment, the weights of each neighborhood block are obtained according to each similarity weight, specifically:

[0126] Normalizing the similarity weights between the neighborhood blocks so that the sum of the similarity weights between the neighborhood blocks is equal to one, thereby obtaining the weights of the neighborhood blocks;

[0127] The process of the normalization process is:

[0128]

[0129] In the formula, w1(i,j) represents the weight of the neighborhood block, w(i,j) represents the similarity weight between neighborhood blocks, ∑ 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, and uses the Euclidean distance to measure the degree of difference between these neighborhood blocks, thereby identifying similar areas 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 the weight of each neighborhood block can be considered on the same scale in the subsequent filtering process. Through normalization, the present application can balance the impact of different neighborhood blocks on the target pixel, making the filtering algorithm more accurate and effective. This method is particularly suitable for processing areas away from the focus because it can adjust the filtering strength according to the similarity of the neighborhood blocks, thereby significantly improving the image clarity and contrast in these areas.

[0131] As a preferred embodiment of the second embodiment, the filter window size is adjusted and optimized according to the adaptive filtering algorithm, the minimization 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:

[0132] According to the adaptive filtering algorithm, the distances from the envelope data to the preset focus and the preset window size, the initial filtering window size is adjusted to obtain a first filtering window size;

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

[0134] W(d(i))=W0+k·d(i);

[0135] Wherein, 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;

[0136] According to the minimization of the energy function and the weights of the neighborhood blocks, the first filter window size is optimized to obtain an optimized ultrasonic echo signal.

[0137] In this preferred embodiment, the present application dynamically adjusts the size of the filter window through an adaptive filtering algorithm, combined with the distance between the envelope data and the preset focus and the preset window size. During the adjustment process, the scale factor 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 size according to the specific position of each pixel in the image, and more accurately process local features in the image. In this way, the present application can effectively improve the image quality of areas away from the focus in ultrasonic imaging, improve the clarity and contrast of the image, and thus provide medical personnel with more accurate and clearer diagnostic information. This not only improves the diagnostic value of ultrasonic imaging, but also may reduce misdiagnosis and missed diagnosis caused by poor image quality, and enhance the accuracy and reliability of clinical diagnosis.

[0138] As a preferred embodiment of the second embodiment, the first filter window size is optimized according to the minimization of the energy function and the weights of each neighborhood block to obtain the optimized ultrasonic echo signal, specifically:

[0139] According to the minimized energy function and the weights of the respective neighborhood blocks, the first filter window size is optimized to obtain optimized envelope data;

[0140] The minimization of the energy function and the weights of the neighborhood blocks is specifically as follows:

[0141] The envelope data is updated repeatedly by minimizing the energy function until convergence or the predetermined number of iterations is reached, and the iteration is stopped.

[0142] The formula for minimizing the energy function is:

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

[0144] 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 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;

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

[0146]

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

[0148] An optimized ultrasonic echo signal is obtained according to the optimized envelope data.

[0149] In this preferred embodiment, the present application optimizes the size of the first filter window by minimizing the energy function combined 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 adjusts the filtering parameters by measuring the difference between the filtered envelope data and the original data and combining the neighborhood block weights. In this process, the regularization parameter helps control the intensity of the filter to prevent excessive smoothing or noise amplification. In this way, the size and shape of the filter window are further optimized to better adapt to local image features and improve image clarity and contrast. The optimized envelope data can therefore more accurately reflect the information of the original ultrasonic signal, provide medical personnel with clearer and more reliable diagnostic images, reduce the risk of misdiagnosis and missed diagnosis, and enhance the accuracy and reliability of clinical diagnosis.

[0150] The device uses three modules to divide the work and coordinate the work to better optimize the ultrasonic echo signal. The present application can define a neighborhood block around each pixel point in the image by acquiring 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 and provide important context information for subsequent image processing. Combining the adaptive filtering algorithm and the minimization energy function, while considering the distance from the envelope data to the preset focus and the weight of each neighborhood block, the present application can dynamically adjust the size of the filter window. This data-driven method makes the filtering process more accurate and can be optimized for the characteristics of different areas in the image. Therefore, the optimized envelope data can improve the focusing quality of ultrasonic imaging, especially in areas far away from the focus, thereby enhancing the clarity and contrast of the envelope data, and providing medical personnel with more accurate and clearer diagnostic information. The present application improves the accuracy and reliability of clinical diagnosis to solve the problem that accurate ultrasonic echo signals cannot be obtained in the prior art.

[0151] Embodiment three:

[0152] An embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the ultrasonic echo signal optimization method;

[0153] Wherein, the ultrasonic echo signal optimization method, if implemented in the form of a software functional unit and used as an independent product, can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, 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, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), 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, and when the processor executes the computer program, any one of the ultrasonic echo signal optimization methods described in Example 1 is implemented.

[0156] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for optimizing ultrasonic echo signals, characterized in that: include: Acquire the echo signal received by the preprocessed ultrasonic probe; wherein the echo signal is envelope data in a rectangular coordinate system; Determine each neighborhood block in a rectangular coordinate system based on the envelope data as the center; Calculating the similarity weights between the neighborhood blocks to obtain the weights of the neighborhood blocks; According to the adaptive filtering algorithm, the minimization energy function, the distances from the envelope data to the preset focus and the weights of the neighborhood blocks, the filter window size is adjusted and optimized to obtain an optimized ultrasonic echo signal.

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

3. The ultrasonic echo signal optimization method according to claim 1, characterized in that: After converting 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, the method further includes: The envelope data in the rectangular coordinate system is denoised according to median filtering and Gaussian filtering techniques.

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

5. The ultrasonic echo signal optimization method according to claim 4, characterized in that: The weights of each neighborhood block are obtained 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, thereby obtaining the weights of the neighborhood blocks; The process of normalization is as follows: In the formula, w1(i,j) represents the weight of the neighborhood block, w(i,j) represents the similarity weight between neighborhood blocks, ∑ 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 filter window size is adjusted and optimized 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 neighborhood blocks to obtain the optimized ultrasonic echo signal, specifically: According to the adaptive filtering algorithm, the distances from the envelope data to the preset focus and the preset window size, the initial filtering window size is adjusted to obtain a first filtering window size; The process of adjusting the initial filter window size is as follows: W(d(i))=W0+k·d(i); Wherein, 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; According to the minimization of the energy function and the weights of the neighborhood blocks, the first filter window size is optimized to obtain an optimized ultrasonic echo signal.

7. The ultrasonic echo signal optimization method according to claim 6, characterized in that: The first filter window size is optimized according to the minimization energy function and the weights of each neighborhood block to obtain the optimized ultrasonic echo signal, specifically: According to the minimized energy function and the weights of the respective neighborhood blocks, the first filter window size is optimized to obtain optimized envelope data; The formula for minimizing the energy function is: E″(i)=∑ i (E′(i)-E(i)) 2 +λΣ i Σ j∈W (d(i)W1(i,j)(E′(i)-E′(i)) 2 ; 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 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; An optimized ultrasonic echo signal is obtained according to the optimized envelope data.

8. An ultrasonic echo signal optimization device, characterized in that: It includes acquisition module, calculation module and optimization module; The acquisition module is used to acquire the echo signal received by the ultrasonic probe after preprocessing; wherein the echo signal is envelope data in a rectangular coordinate system; The calculation module is used to determine each neighborhood block in a rectangular coordinate system based on the envelope data as the center; Calculating 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 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 neighborhood blocks 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 is executed, the device where the computer-readable storage medium is located is controlled to execute the ultrasonic echo signal optimization method according to any one of claims 1 to 7.

10. A terminal device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements the ultrasonic echo signal optimization method according to any one of claims 1 to 7 when executing the computer program.

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