Method and apparatus for processing ultrasonic images

Abstract

The present application provides a method and device for processing ultrasonic images, which are used to solve the problem of how to improve the clarity and contrast of the edges of ultrasonic images at the image level. In the present application, the ultrasonic image is decomposed at multiple scales, the edges of the image at at least one scale are extracted, the eigenvalues and eigenvectors of the image at this scale are obtained based on the structure tensor matrix, and then a diffusion tensor is constructed based on the eigenvalues and eigenvectors to improve the image quality. Thus, an optimization process of the ultrasonic image is realized by using a non-linear diffusion method combined with an edge detection algorithm, and the edges and contrast of the ultrasonic image at this scale are optimized. Then, the ultrasonic images at multiple scales are reconstructed to obtain high-quality ultrasonic images.

Description

Technical Field

[0001] This application relates to the technical field of ultrasonic image processing, and in particular, to a method and device for processing ultrasonic images. Background Art

[0002] Medical ultrasonic imaging can be used to view the internal structure of diseased tissues in real time and does not cause radiation damage like CT, etc. It is currently widely used in clinical examinations.

[0003] However, compared with other medical images such as CT, ultrasonic images have disadvantages such as unclear edges and low contrast. When there is a high demand for the edge sharpness of the diseased area during the diagnosis process, the defects of ultrasonic images may affect the diagnosis results of clinicians.

[0004] To address the problems of insufficient edge sharpness and low contrast in ultrasonic images, optimization can be carried out at the signal level and the image level. Optimizing at the signal level is mainly to reduce components such as sidelobe components, grating lobe components, and noise components in the received signal and improve the accuracy of beam synthesis; optimizing at the image level is mainly to use image post-processing techniques to improve image contrast, edge sharpness, etc., thereby improving the quality of the image.

[0005] Based on the processing solution at the signal level, the optimization process of the source signal easily destroys the structure of the original signal, resulting in the loss of useful signals. Moreover, due to the large data volume of the original signal, the computational complexity introduced during the optimization process poses a challenge to the real-time performance of system imaging.

[0006] In the processing solution based on the image level, since the data volume of the image is significantly lower than that of the original signal, and the image post-processing technology has developed for decades, proper processing can improve the contrast, edge sharpness, and overall quality of ultrasonic images.

[0007] Based on this, how to improve the edge sharpness and contrast of ultrasonic images at the image level still needs to be improved. Summary of the Invention

[0008] Embodiments of this application provide a method and device for processing ultrasonic images, which are used to solve the problem of how to improve the edge sharpness and contrast of ultrasonic images at the image level.

[0009] In a first aspect, this application provides a method for processing ultrasonic images, the method comprising:

[0010] Obtain an ultrasonic image to be processed;

[0011] Perform multi-scale decomposition on the ultrasonic image to be processed to obtain sub-images of multiple scales; the sub-images of multiple scales include first-class sub-images and second-class sub-images; the first-class sub-images are sub-images of the target scale selected based on a preset rule;

[0012] Adjust the contrast of the first-class sub-images;

[0013] Perform edge extraction on the first-class sub-images to obtain the edge significance parameters of each pixel point in the first-class sub-images; and,

[0014] Obtain the eigenvalues and eigenvectors of the first-class sub-images based on the structure tensor matrix, and construct a diffusion tensor based on the eigenvalues and eigenvectors;

[0015] Perform diffusion processing on the first-class sub-images using the diffusion tensor and the edge significance parameters;

[0016] Perform a reconstruction operation on the first-class sub-images and the second-class sub-images to obtain a target ultrasonic image with the same size as the ultrasonic image to be processed.

[0017] Optionally, the performing edge extraction on the first-class sub-images to obtain the edge significance parameters of each pixel point in the first-class sub-images specifically includes:

[0018] Convert the first-class sub-images to the frequency domain to obtain frequency domain information;

[0019] Obtain the even-symmetric filtering responses and odd-symmetric filtering responses of each direction scale of the band-pass filter at each pixel point based on the frequency domain information;

[0020] For each pixel point, determine the edge significance parameter of the pixel point based on the even-symmetric filtering response and the odd-symmetric filtering response of each direction scale of the pixel point.

[0021] Optionally, the determining the edge significance parameter of the pixel point based on the even-symmetric filtering response and the odd-symmetric filtering response of each direction scale of the pixel point specifically includes:

[0022] Determine the edge significance parameter of the pixel point based on the following formula:

[0023]

[0024] where PAS is the edge significance parameter, odd s is the odd-symmetric filtering response of the pixel point at the direction scale s, even s is the even-symmetric filtering response at the direction scale s, T s is the noise estimate at the direction scale s, and ε is a constant greater than 0.

[0025] Optionally, obtaining the eigenvalues and eigenvectors of the first type of subgraph based on the structure tensor matrix, and constructing a diffusion tensor based on the eigenvalues and the eigenvectors specifically includes:

[0026] Determining an edge judgment factor based on the difference between two eigenvalues of the first type of subgraph, where the edge judgment factor has a positive correlation with the difference;

[0027] Obtaining a target eigenvalue corresponding to the value range where the edge judgment factor is located as the first eigenvalue, and determining a preset constant value as the second eigenvalue;

[0028] Constructing the diffusion tensor using the first eigenvalue, the second eigenvalue, and the eigenvector.

[0029] Optionally, obtaining a target eigenvalue corresponding to the value range where the edge judgment factor is located as the first eigenvalue, and determining a preset constant value as the second eigenvalue specifically includes:

[0030] Determining the first eigenvalue and the second eigenvalue based on the following formula:

[0031]

[0032] λ1 = beta, if K≥K tissue 2 ,

[0033] λ2 = alpha

[0034] where λ1 is the first eigenvalue, K is the edge judgment factor, K tissue is the first preset threshold, beta and alpha are both fixed values, PAS is the edge significance parameter, and PAS_thresh is the second preset threshold.

[0035] Optionally, the method further includes:

[0036] If K < K tissue 2 , and the edge significance parameter of the pixel point is greater than the preset parameter threshold, then update the first eigenvalue to the product of beta and the PAS of the pixel point.

[0037] Optionally, obtaining the eigenvalues and eigenvectors of the first type of subgraph based on the structure tensor matrix, and constructing a diffusion tensor based on the eigenvalues and the eigenvectors specifically includes:

[0038] Constructing the diffusion tensor based on the following formula:

[0039]

[0040] Wherein, D is the diffusion tensor, ω1 and ω2 are the eigenvectors of the first type of sub - graph obtained based on the structure tensor matrix, λ1 is the first eigenvalue, and λ2 is the second eigenvalue.

[0041] Optionally, adjusting the contrast of the first type of sub - graph specifically includes:

[0042] For any pixel point, adjusting the contrast of the first type of sub - graph based on the following formula:

[0043]

[0044] Wherein, I′(x,y) is the updated gray - scale value of the pixel point, I(x,y) is the original gray - scale value of the pixel point, ω is a preset control coefficient, and mean(I) is the average gray - scale value of the first type of sub - graph.

[0045] Optionally, performing multi - scale decomposition on the to - be - processed ultrasonic image specifically includes:

[0046] Performing multi - scale decomposition on the to - be - processed ultrasonic image by using a Gaussian pyramid method or a wavelet pyramid method.

[0047] In a second aspect, the present application further provides an ultrasonic device, which is characterized by comprising: a processor, a memory, a display unit, and a probe;

[0048] The probe is used to emit ultrasonic signals;

[0049] The display unit is used to display ultrasonic images;

[0050] The processor is respectively connected to the probe and the display unit, and is configured to execute any one of the methods in the first aspect.

[0051] In a third aspect, an embodiment of the present application further provides a computer - readable storage medium. When the instructions in the computer - readable storage medium are executed by a processor of an electronic device, the electronic device can execute any one of the methods provided in the first aspect of the present application.

[0052] In a fourth aspect, an embodiment of the present application provides a computer program product, including a computer program. When the computer program is executed by a processor, it implements any one of the methods provided in the first aspect of the present application.

[0053] The technical solutions provided by the embodiments of the present application at least bring the following beneficial effects: In the present application, the ultrasonic image is decomposed at multiple scales, the edges of the image at at least one scale are extracted, and the eigenvalues and eigenvectors of the image at this scale are obtained based on the structure tensor matrix. Then, a diffusion tensor is constructed based on the eigenvalues and eigenvectors to improve the image quality. Thus, an optimization process of the ultrasonic image is realized by using a non-linear diffusion method combined with an edge detection algorithm, and the edges and contrast of the ultrasonic image at this scale are optimized. Then, the ultrasonic images at multiple scales are reconstructed to obtain high-quality ultrasonic images.

[0054] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present disclosure. Brief Description of the Drawings

[0055] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings required for use in the embodiments of the present application. Obviously, the following introduced drawings are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0056] Figure 1 It is a schematic structural diagram of the ultrasonic device provided by the embodiment of the present application;

[0057] Figure 2 It is a schematic diagram of the principle of the ultrasonic device provided by an embodiment of the present application for realizing an ultrasonic image;

[0058] Figure 3 It is one of the schematic flowcharts of the ultrasonic image processing method provided by an embodiment of the present application;

[0059] Figure 4 It is the second of the schematic flowcharts of the ultrasonic image processing method provided by an embodiment of the present application;

[0060] Figure 5A and Figure 5B It is a schematic diagram of the direction and eigenvector provided by an embodiment of the present application;

[0061] Figure 6 It is the third of the schematic flowcharts of the ultrasonic image processing method provided by an embodiment of the present application. Detailed Embodiments

[0062] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of this application. Among them, the described embodiments are some but not all of the embodiments of this application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in this application without creative efforts belong to the scope of protection of this application.

[0063] Moreover, in the description of the embodiments of this application, unless otherwise specified, " / " means "or". For example, A / B may mean A or B; "and / or" in the text is only a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B may mean: A exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of this application, "a plurality of" means two or more than two.

[0064] Hereinafter, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as implying or suggesting relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the embodiments of this application, unless otherwise specified, the meaning of "a plurality of" is two or more than two.

[0065] In response to problems such as insufficient clarity of some edges and low contrast of ultrasonic images, this application provides a solution for improving the quality of ultrasonic images at the image level.

[0066] The inventive concept of this application can be summarized as follows: In this application, the ultrasonic image is decomposed at multiple scales, the edges of the image at at least one scale are extracted, and the eigenvalues and eigenvectors of the image at this scale are obtained based on the structure tensor matrix. Then, a diffusion tensor is constructed based on the eigenvalues and eigenvectors to improve the image quality. Thus, an optimization process of the ultrasonic image is realized by using a non-linear diffusion method combined with an edge detection algorithm, optimizing the edges and contrast of the ultrasonic image at this scale. Then, the ultrasonic images at multiple scales are reconstructed to obtain high-quality ultrasonic images.

[0067] The following will describe the method for processing ultrasonic images provided by this application with reference to the accompanying drawings.

[0068] The following will describe the ultrasonic device and the interference suppression method for ultrasonic images provided by the embodiments of this application with reference to the accompanying drawings.

[0069] See Figure 1 As shown, it is the structural block diagram of the ultrasonic device provided by the embodiments of this application.

[0070] It should be understood that Figure 1The illustrated ultrasonic device 100 is merely an example, and the ultrasonic device 100 may have more or fewer components than those Figure 1 shown in, may combine two or more components, or may have a different component configuration. The various components shown in the figures may be implemented in hardware, software, or a combination of hardware and software including one or more signal processing and / or application specific integrated circuits.

[0071] Figure 1 An exemplary block diagram of the hardware configuration of the ultrasonic device 100 according to an exemplary embodiment is shown in.

[0072] As Figure 1 shown, the ultrasonic device 100 may include, for example: a processor 110, a memory 120, a display unit 130, and a probe 140; wherein,

[0073] The probe 140 is configured to transmit ultrasonic signals;

[0074] The display unit 130 is configured to display ultrasonic images;

[0075] The memory 120 is configured to store data required for ultrasonic imaging, and may include software programs, application interface data, etc.;

[0076] The processor 110 is respectively connected to the probe 140, the display unit 130, and the memory 120, and is configured to execute:

[0077] Obtain an ultrasonic image to be processed;

[0078] Perform multi-scale decomposition on the ultrasonic image to be processed to obtain sub-images of multiple scales; the sub-images of multiple scales include first-class sub-images and second-class sub-images; the first-class sub-images are sub-images of a target scale selected based on a preset rule;

[0079] Adjust the contrast of the first-class sub-images;

[0080] Perform edge extraction on the first-class sub-images to obtain edge saliency parameters of each pixel point in the first-class sub-images; and,

[0081] Based on the structure tensor matrix, obtain the eigenvalues and eigenvectors of the first-class sub-images, and construct a diffusion tensor based on the eigenvalues and the eigenvectors;

[0082] Perform diffusion processing on the first-class sub-images using the diffusion tensor and the edge saliency parameters;

[0083] Perform a reconstruction operation on the first-class sub-images and the second-class sub-images to obtain a target ultrasonic image having the same size as the ultrasonic image to be processed.

[0084] Optionally, perform edge extraction on the first type of subgraph to obtain the edge saliency parameters of each pixel point in the first type of subgraph. The processor is specifically configured to perform:

[0085] Convert the first type of subgraph to the frequency domain to obtain frequency domain information;

[0086] Based on the frequency domain information, obtain the even-symmetric filtering responses and odd-symmetric filtering responses of the band-pass filter at each direction scale at each pixel point;

[0087] For each pixel point, determine the edge saliency parameter of the pixel point based on the even-symmetric filtering response and the odd-symmetric filtering response of the pixel point at each direction scale.

[0088] Optionally, perform the determination of the edge saliency parameter of the pixel point based on the even-symmetric filtering response and the odd-symmetric filtering response of the pixel point at each direction scale. The processor is specifically configured to perform:

[0089] Determine the edge saliency parameter of the pixel point based on the following formula:

[0090]

[0091] where PAS is the edge saliency parameter, odd s is the odd-symmetric filtering response of the pixel point at direction scale s, even s is the even-symmetric filtering response at direction scale s, T s is the noise estimate at direction scale s, and ε is a constant greater than 0.

[0092] Optionally, perform obtaining the eigenvalues and eigenvectors of the first type of subgraph based on the structure tensor matrix, and constructing a diffusion tensor based on the eigenvalues and the eigenvectors. The processor is specifically configured to perform:

[0093] Determine an edge judgment factor based on the difference between the two eigenvalues of the first type of subgraph. The edge judgment factor has a positive correlation with the difference;

[0094] Obtain the target eigenvalue corresponding to the value range where the edge judgment factor is located as the first eigenvalue, and determine a preset constant value as the second eigenvalue;

[0095] Construct the diffusion tensor using the first eigenvalue, the second eigenvalue, and the eigenvector.

[0096] Optionally, perform obtaining the target eigenvalue corresponding to the value range where the edge judgment factor is located as the first eigenvalue, and determining a preset constant value as the second eigenvalue. The processor is specifically configured to perform:

[0097] The first eigenvalue and the second eigenvalue are determined based on the following formula:

[0098]

[0099] λ1 = beta, if K ≥ K tissue 2 ,

[0100] λ2 = alpha

[0101] where λ1 is the first eigenvalue, K is the edge judgment factor, K tissue is the first preset threshold, beta and alpha are both fixed values, PAS is the edge significance parameter, and PAS_thresh is the second preset threshold.

[0102] Optionally, the processor is further configured to execute:

[0103] If K < K tissue 2 , and the edge significance parameter of the pixel is greater than the preset parameter threshold, then update the first eigenvalue to the product of beta and the PAS of the pixel.

[0104] Optionally, when executing obtaining the eigenvalue and eigenvector of the first type of sub - graph based on the structure tensor matrix and constructing a diffusion tensor based on the eigenvalue and the eigenvector, the processor is specifically configured to execute:

[0105] Construct the diffusion tensor based on the following formula:

[0106]

[0107] where D is the diffusion tensor, ω1 and ω2 are both eigenvectors of the first type of sub - graph obtained based on the structure tensor matrix, λ1 is the first eigenvalue, and λ2 is the second eigenvalue.

[0108] Optionally, when executing adjusting the contrast of the first type of sub - graph, the processor is specifically configured to execute:

[0109] For any pixel, adjust the contrast of the first type of sub - graph based on the following formula:

[0110]

[0111] where I′(x, y) is the updated gray - level value of the pixel, I(x, y) is the original gray - level value of the pixel, ω is the preset control coefficient, and mean(I) is the average gray - level value of the first type of sub - graph.

[0112] Optionally, perform multi-scale decomposition on the to-be-processed ultrasound image, and the processor is specifically configured to perform:

[0113] Perform multi-scale decomposition on the to-be-processed ultrasound image in a Gaussian pyramid manner or a wavelet pyramid manner.

[0114] Figure 2 It is a schematic diagram of the application principle according to an embodiment of the present application. Among them, this part can be implemented by Figure 1 Some modules or functional components of the shown ultrasound device. Only the main components will be described below, and other components, such as memories, controllers, control circuits, etc., will not be elaborated here.

[0115] Such as Figure 2 As shown, the application environment may include a user interface 210, a display unit 220 for displaying the user interface, and a processor 230.

[0116] The display unit 220 may include a display panel 221 and a backlight assembly 222. Among them, the display panel 321 is configured to display the ultrasound image, and the backlight assembly 222 is located on the back of the display panel 221. The backlight assembly 222 may include multiple backlight zones (not shown in the figure), and each backlight zone can emit light to light up the display panel 221.

[0117] The processor 230 may be configured to control the brightness of the backlight sources of each backlight zone in the backlight assembly 222, and control the probe to emit an ultrasonic signal, and receive the ultrasonic echo signal and perform analysis to obtain an ultrasound image.

[0118] Among them, the processor 230 may perform optimization processing on the ultrasound image to improve the edge sharpness and contrast of the ultrasound image.

[0119] Such as Figure 3 As shown, it is a schematic flowchart of the method for processing an ultrasound image in an embodiment of the present application. After obtaining the original ultrasound image using the original ultrasound signal, the original ultrasound image can be used as the to-be-processed ultrasound image, and the following steps are performed on this image:

[0120] In step 301, obtain the to-be-processed ultrasound image;

[0121] Thus, the to-be-processed ultrasound image in the present application is generated based on the original ultrasound echo signal, so that more original signal information is retained in the to-be-processed ultrasound image.

[0122] In step 302, perform multi-scale decomposition on the to-be-processed ultrasound image to obtain sub-images of multiple scales; the sub-images of multiple scales include the first type of sub-images and the second type of sub-images; the first type of sub-images are sub-images of the target scale selected based on a preset rule.

[0123] In the embodiments of the present application, multi-scale decomposition can be performed in the way of Gaussian pyramid or wavelet pyramid. The image is decomposed into multiple different scales, where the length and width of the image in the next layer are 1 / 2 of those in the previous layer. For example, if the size of the original ultrasound image is M*N, after Gaussian pyramid decomposition, images of multiple scales with gradually decreasing sizes such as M*N, 1 / 2M*1 / 2*N, 1 / 4*M*1 / 4*N... can be obtained. Since the more layers there are, the smaller the image of the smallest scale becomes, and the detailed information will become very little, the significance of improving the detailed resolution needs to be considered. Therefore, in implementation, 3-5 layers can be divided.

[0124] In implementation, subgraphs of one scale can be selected as the first type of subgraph, or subgraphs of multiple scales can be selected as the first type of subgraph, which can be set according to actual needs, and all are applicable to the embodiments of the present application. The operations of step 303-step 306 are performed on the first type of subgraph of each scale.

[0125] In step 303, the contrast of the first type of subgraph is adjusted.

[0126] In a possible embodiment, the gray value of the area with a lower gray value in the image is adjusted. After this step, the contrast of the image will be greatly improved compared with the image before processing. For example, it can be implemented as: for any pixel point, the contrast of the first type of subgraph is adjusted based on the following formula (1):

[0127]

[0128] In formula (1), I′(x,y) is the updated gray value of the pixel point, I(x,y) is the original gray value of the pixel point, ω is a preset control coefficient, and mean(I) is the average gray value of the first type of subgraph.

[0129] Increasing the contrast of the image, especially enhancing the clarity of diseased areas such as cysts and nodules, can provide strong support for medical diagnosis.

[0130] In step 304, edge extraction is performed on the first type of subgraph to obtain the edge saliency parameters of each pixel point in the first type of subgraph.

[0131] In the embodiments of the present application, the edge saliency parameter can be understood as the probability of describing the corresponding pixel point as an edge point, or the confidence of the edge point. In implementation, when performing edge detection on the first type of subgraph, an edge extraction method based on pixel gradient can be used, such as edge detection methods like sobel and Prewitt operators. However, the edge detection accuracy of these methods needs to be improved, and the edge extraction effect for low-contrast regions needs to be improved. If the edge extraction effect for low-contrast regions is not good, it is easy to cause the unrecognized edges to become blurred after smoothing processing.

[0132] In order to improve the accuracy of edge extraction, a phase-based edge extraction method is provided in this application. The theoretical basis in this method is as follows: After expanding a square wave into a Fourier series, all Fourier components are sine waves. When the phase of the square wave is at 0° and 180°, they correspond to the rising edge and the falling edge respectively; after expanding a triangular wave into a Fourier series, all Fourier components are cosine waves. When the phase of the triangular wave is at 90° and 270°, they correspond to the peak and the trough respectively. If the rising edge, falling edge, peak and trough of the signal are regarded as the edges of the signal, then the position with the maximum phase consistency after Fourier transform corresponds to the position of the signal edge. Based on this principle, in the embodiments of this application, edge saliency parameters can be extracted based on a band-pass filter, such as Figure 4 shown, which may include the following steps:

[0133] In step 401, the first type of sub-graph is transformed into the frequency domain to obtain frequency domain information;

[0134] In step 402, based on the frequency domain information, the even-symmetric filtering responses and odd-symmetric filtering responses of the band-pass filter at each pixel point in each direction scale are obtained;

[0135] The band-pass filter can adopt Riesz filter, log-gabor, gabor filter, etc.

[0136] In step 403, for each pixel point, the edge saliency parameter of the pixel point is determined based on the even-symmetric filtering response and odd-symmetric filtering response of the pixel point in each direction scale.

[0137] Taking the signal f M as an example, the result after filtering the frequency domain signal of the first type of sub-graph by the Riesz filter is:

[0138] f M =(f, f R )=(f, r1*f, r2*f)=(even, odd) (2)

[0139] In formula (2), where f R is the Rieze transform of the 2D signal f (i.e., the first type of sub-graph), r1 and r2 respectively represent the odd part and the even part of the Riesz filter, and even and odd respectively represent the even-symmetric filtering response and the odd-symmetric filtering response.

[0140] The spatial domain expressions of the Riesz filters r1 and r2 are as shown in formula (3):

[0141]

[0142] In Equation (3), x1 and x2 are the signal values of the first type of sub-graph at the (x1, x2) positions in the frequency domain. After obtaining the spatial domain expressions of r1 and r2 at each position, the edge saliency parameter of each pixel can be obtained.

[0143] In some embodiments, in order to

[0144] Equation (2) can be used to calculate (even, odd). Then Equation (4) is used to determine the edge saliency parameter of each pixel.

[0145]

[0146] In Equation (4), PAS is the edge saliency parameter, odd s is the odd-symmetric filtering response of the pixel at the direction scale s, even s is the even-symmetric filtering response at the direction scale s, T s is the noise estimate at the direction scale s, and ε is a constant greater than 0.

[0147] Each pixel in the frequency domain can include multiple direction scales, such as 8 or 12, which can be configured according to actual requirements.

[0148] In step 305, the eigenvalues and eigenvectors of the first type of sub-graph are obtained based on the structure tensor matrix, and a diffusion tensor is constructed based on the eigenvalues and eigenvectors.

[0149] Among them, the eigenvectors and eigenvalues are obtained according to the structure tensor matrix, as shown in Equation (5):

[0150]

[0151] In Equation (5), J represents the structure tensor matrix, I x represents the horizontal direction gradient, I y represents the vertical direction gradient, represents the square value of the horizontal direction gradient, G σ represents the Gaussian kernel with a standard deviation of σ, ω1 and ω2 respectively represent the eigenvectors, and these two eigenvectors represent the two directions with the largest and smallest neighborhood gray-scale differences. μ1 and μ2 respectively represent the eigenvalues, and these two eigenvalues represent the largest and smallest intensities of the neighborhood gray-scale differences. The edge direction is as Figure 5A shown, and the eigenvector is as Figure 5B shown. Figure 5B Among them, T1 and T2 respectively represent the eigenvectors in the tangent direction, and N1 and N2 respectively represent the eigenvectors in the normal direction. The above eigenvectors can all be calculated through mathematical formulas and will not be elaborated here.

[0152] In a possible implementation, the edge judgment factor can be determined based on the difference between two eigenvalues of the first type of sub-graph, and the edge judgment factor has a positive correlation with the difference.

[0153] In a possible implementation, it can be based on the following formula (6):

[0154] K = (λ1 - λ2) 2 (6)

[0155] In formula (6), K represents the edge judgment factor, and both λ1 and λ2 are the extracted eigenvalues, that is, μ1 and μ2 in formula (5).

[0156] After determining the edge judgment factor using formula (6), the two eigenvalues are reconstructed. For ease of calculation, different intervals can be divided for the edge judgment factor, and different intervals correspond to different eigenvalues. For example, it can be implemented as obtaining the target eigenvalue corresponding to the value range where the edge judgment factor is located as the first eigenvalue, and determining the preset constant value as the second eigenvalue. When different intervals correspond to different eigenvalues, edge smoothing can be achieved. Selecting a fixed value for λ2 can...

[0157] As shown in formula (7) is an example of determining the first eigenvalue and the second eigenvalue:

[0158]

[0159] In formula (7), λ1 is the first eigenvalue, K is the edge judgment factor, K tissue is the first preset threshold, beta and alpha are both fixed values, PAS is the edge significance parameter, and PAS_thresh is the second preset threshold. Based on formula (7), when K < K tissue 2 it indicates that non-edge features can perform a smoothing operation on the eigenvalues, and when K ≥ K tissue 2 it indicates edge features, so the edge features are enhanced based on formula (7). Thus, the purpose of smoothing non-edges and enhancing edges can be achieved.

[0160] In order to further enhance the edges, in the embodiments of the present application, formula (7) is optimized, and the optimized formula is as shown in formula (8):

[0161]

[0162] In formula (8), λ1 is the first eigenvalue, K is the edge judgment factor, K tissue is the first preset threshold, beta and alpha are both fixed values, PAS is the edge significance parameter, and PAS_thresh is the second preset threshold.

[0163] The meaning of formula (8) is that if K < K tissue 2 , and the edge saliency parameter of the pixel point is greater than the preset parameter threshold, then the first eigenvalue is the product of beta and the PAS of the pixel point. That is, when K < K tissue 2 but PAS > PAS_thresh, it indicates that this point is closer to the edge and needs to be enhanced. Therefore, λ1 = beta * PAS is used to enhance this point to achieve the purpose of enhancing the edge.

[0164] After obtaining the first eigenvalue, the second eigenvalue, and the two vectors ω1 and ω2, formula (9) can be used to construct the diffusion vector:

[0165]

[0166] In formula (9), D is the diffusion tensor, ω1 and ω2 are the eigenvectors of the first type of subgraph obtained based on the structure tensor matrix, λ1 is the first eigenvalue, and λ2 is the second eigenvalue.

[0167] In step 306, based on the constructed diffusion vector, the first type of subgraph is diffusively processed using the diffusion tensor and the edge saliency parameter;

[0168] Based on the diffusion vector constructed by formula (9), since formula (8) enhances the edge, after substituting the eigenvalue constructed by formula (8) into formula (9) for diffusion processing, it can achieve the effect of smoothing non-edges and enhancing edges.

[0169] In step 307, a reconstruction operation is performed on the first type of subgraph and the second type of subgraph to obtain a target ultrasound image with the same size as the ultrasound image to be processed.

[0170] When performing the pyramid reconstruction operation, the processing results at different scales are reconstructed respectively, and finally an ultrasound image processing result with the same size as the original image is obtained. For example, the image sizes of L1, L2, L3... Ld layers are M * N, 1 / 2M * 1 / 2N, 1 / 4M * 1 / 4N... 1 / 2*(d - 1)*M * 1 / 2*(d - 1)*N respectively. The results of steps 2 to 3 at L1, L2, L3... Ld layers are: O1, O2, O3,... Od. The reconstruction process is from bottom to top. Od,... O3, O2 are reconstructed in sequence to expand their sizes to the size of the upper layer, and then reconstructed with the results of the upper layer. The above reconstruction and the reconstruction process continue until the initial layer, and finally an optimized ultrasound image is obtained.

[0171] To facilitate a systematic understanding of the embodiments of the present application, the following is combined with Figure 6 to illustrate this:

[0172] In step 601, an ultrasonic image to be processed is obtained.

[0173] In step 602, the ultrasonic image to be processed is subjected to multi-scale decomposition to obtain sub-images of multiple scales.

[0174] In step 603, the contrast of at least one scale of the sub-images of multiple scales is enhanced.

[0175] In step 604, at least one scale of the sub-images of multiple scales is transformed into the frequency domain to obtain frequency domain information, and the even-symmetric filtering responses and odd-symmetric filtering responses of each direction scale of the band-pass filter at each pixel point are obtained based on the frequency domain information.

[0176] In step 605, for each pixel point, an edge saliency parameter of the pixel point is determined based on the even-symmetric filtering responses and odd-symmetric filtering responses of each direction scale of the pixel point.

[0177] In step 606, for at least one scale of the sub-images of multiple scales, the difference between the two eigenvalues of the sub-image determines an edge judgment factor, and the target eigenvalue corresponding to the value range where the edge judgment factor is located is obtained as the first eigenvalue, and a preset constant value is determined as the second eigenvalue.

[0178] In step 607, a diffusion tensor is constructed using the first eigenvalue, the second eigenvalue, and an eigenvector.

[0179] In step 608, diffusion processing is performed on the corresponding sub-image using the diffusion tensor and the edge saliency parameter;

[0180] In step 609, a reconstruction operation is performed on the sub-images of all scales to obtain a target ultrasonic image having the same size as the ultrasonic image to be processed.

[0181] In an exemplary embodiment, the present application also provides a computer-readable storage medium including instructions, such as a memory 120 including instructions, and the above instructions can be executed by a processor 110 of an ultrasonic device 100 to complete the above ultrasonic image processing method. Optionally, the storage medium may be a non-transitory computer-readable storage medium. For example, the non-transitory computer-readable storage medium may be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, and an optical data storage device, etc.

[0182] In an exemplary embodiment, a computer program product is also provided, including a computer program, and when the computer program is executed by a processor 110, it implements the ultrasonic image processing method provided by the present application.

[0183] In an exemplary embodiment, various aspects of a method for processing an ultrasonic image provided by the present application can also be implemented in the form of a program product, which includes program code. When the program product runs on a computer device, the program code is used to cause the computer device to execute the steps in the method for processing an ultrasonic image according to various exemplary embodiments described above in this specification.

[0184] The program product may adopt any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0185] The program product for the method for processing an ultrasonic image according to the embodiments of the present application may adopt a portable compact disk read-only memory (CD-ROM) and include program code, and may run on an electronic device. However, the program product of the present application is not limited thereto. In this document, the readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0186] The readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which the readable program code is carried. Such a propagated data signal may take various forms, including - but not limited to - an electromagnetic signal, an optical signal, or any suitable combination of the above. The readable signal medium may also be any readable medium other than the readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0187] The program code contained on the readable medium may be transmitted by any appropriate medium, including - but not limited to - wireless, wired, optical fiber cable, RF, etc., or any suitable combination of the above.

[0188] The program code for performing the operations of the present application can be written in any combination of one or more programming languages. The programming languages include object-oriented programming languages such as Java, C++, etc., and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's electronic device, partially on the user's device, executed as a stand-alone software package, partially on the user's electronic device and partially on a remote electronic device, or entirely on a remote electronic device or server. In cases involving a remote electronic device, the remote electronic device can be connected to the user's electronic device through any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external electronic device (e.g., by using an Internet service provider to connect through the Internet).

[0189] It should be noted that although several units or subunits of the device are mentioned in the above detailed description, this division is merely exemplary and not mandatory. In fact, according to the embodiments of the present application, the features and functions of the two or more units described above can be embodied in one unit. Conversely, the features and functions of one unit described above can be further divided and embodied by multiple units.

[0190] In addition, although the operations of the method of the present application are described in a specific order in the drawings, this does not require or imply that these operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. Additionally or alternatively, some steps can be omitted, multiple steps can be combined into one step for execution, and / or one step can be decomposed into multiple steps for execution.

[0191] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0192] This application is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded processors, or other programmable image scaling devices to produce a machine, such that the instructions executed by the processors of the computer or other programmable image scaling devices generate means for implementing the functions specified in the Figure 1 one or more flows and / or blocks Figure 1 one or more blocks.

[0193] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable image scaling device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including instruction means that implement the functions specified in the Figure 1 one or more flows and / or blocks Figure 1 one or more blocks.

[0194] These computer program instructions can also be loaded onto a computer or other programmable image scaling device, such that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in the Figure 1 one or more flows and / or blocks Figure 1 one or more blocks.

[0195] Although the preferred embodiments of the present application have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concepts. Therefore, the appended claims are intended to be construed to include the preferred embodiments and all changes and modifications that fall within the scope of the present application.

[0196] Obviously, those skilled in the art can make various changes and variations to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalent technologies, the present application is also intended to include these modifications and variations.

Claims

1. A method for processing ultrasonic images, characterized in that, The method includes: Obtaining an ultrasonic image to be processed; Performing multi-scale decomposition on the ultrasonic image to be processed to obtain sub-images of multiple scales; the sub-images of multiple scales include first-type sub-images and second-type sub-images; the first-type sub-images are sub-images of a target scale selected based on a preset rule; Enhancing the contrast of the first-type sub-images; Performing edge extraction on the first-type sub-images to obtain edge significance parameters of each pixel point in the first-type sub-images; and Obtaining eigenvalues and eigenvectors of the first-type sub-images based on a structure tensor matrix, determining an edge judgment factor based on the difference between two eigenvalues of the first-type sub-images, where the edge judgment factor has a positive correlation with the difference; obtaining a target eigenvalue corresponding to the value range where the edge judgment factor is located as the first eigenvalue, and determining a preset constant value as the second eigenvalue; wherein, for each pixel point, if the edge judgment factor is less than the square of a first preset threshold and the edge significance parameter of the pixel point is greater than a second preset threshold, then updating the first eigenvalue to the product of the edge significance parameter of the pixel point and a fixed value; constructing a diffusion tensor using the first eigenvalue, the second eigenvalue, and the eigenvectors; Performing diffusion processing on the first-type sub-images using the diffusion tensor and the edge significance parameters; Performing a reconstruction operation on the first-type sub-images and the second-type sub-images to obtain a target ultrasonic image having the same size as the ultrasonic image to be processed.

2. The method according to claim 1, characterized in that, The performing edge extraction on the first-type sub-images to obtain edge significance parameters of each pixel point in the first-type sub-images specifically includes: Converting the first-type sub-images to the frequency domain to obtain frequency domain information; Obtaining even-symmetric filtering responses and odd-symmetric filtering responses of each direction scale of a band-pass filter at each pixel point based on the frequency domain information; For each pixel point, determining the edge significance parameter of the pixel point based on the even-symmetric filtering response and the odd-symmetric filtering response of each direction scale of the pixel point.

3. The method according to claim 2, wherein The determining the edge significance parameter of the pixel point based on the even-symmetric filtering response and the odd-symmetric filtering response of each direction scale of the pixel point specifically includes: Determining the edge significance parameter of the pixel point based on the following formula: where PAS is the edge saliency parameter, odd s is the odd-symmetric filtering response of the pixel point at the direction scale s, even s is the even-symmetric filtering response at the direction scale s, T s is the noise estimate at the direction scale s, and ε is a constant greater than 0.

4. The method according to claim 1, wherein The obtaining a target eigenvalue corresponding to the value range where the edge judgment factor is located as the first eigenvalue, and determining a preset constant value as the second eigenvalue specifically includes: Determining the first eigenvalue and the second eigenvalue based on the following formula: and PAS ≤ PAS_thresh λ1 = beta, if K ≥ K tissue 2 , λ1 = beta * PAS, if K < K tissue 2 and PAS > PAS_thresh λ2 = alpha Among them, λ1 is the first eigenvalue, K is the edge judgment factor, K tissue is the first preset threshold, beta and alpha are both fixed values, PAS is the edge significance parameter, and PAS_thresh is the second preset threshold.

5. The method according to claim 1, wherein The obtaining eigenvalues and eigenvectors of the first-type sub-images based on a structure tensor matrix, and constructing a diffusion tensor based on the eigenvalues and the eigenvectors specifically includes: Constructing the diffusion tensor based on the following formula: where D is the diffusion tensor, ω1 and ω2 are both eigenvectors of the first-type sub-images obtained based on the structure tensor matrix, λ1 is the first eigenvalue, and λ2 is the second eigenvalue.

6. The method according to claim 1, characterized in that The adjusting the contrast of the first-type sub-images specifically includes: For any pixel point, adjusting the contrast of the first-type sub-images based on the following formula: Among them, I′(x,y) is the updated gray value of the pixel, I(x,y) is the original gray value of the pixel, ω is a preset control coefficient, and mean(I) is the average gray value of the first type of sub-image.

7. The method according to claim 1, characterized in that, The multi-scale decomposition of the to-be-processed ultrasonic image specifically includes: Performing multi-scale decomposition on the to-be-processed ultrasonic image by using a Gaussian pyramid method or a wavelet pyramid method.

8. An ultrasonic device, characterized in that, It includes: A processor, a memory, a display unit, and a probe; The probe is used to transmit ultrasonic signals; The display unit is used to display ultrasonic images; The processor is respectively connected to the probe and the display unit, and is configured to execute the method according to any one of claims 1-7.

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