An ultrasonic processing device, an ultrasonic image processing method, and a medium

Abstract

This application provides an ultrasound processing device, an ultrasound image processing method, and a medium, relating to the field of image processing technology. The ultrasound processing device includes a memory and a processor. The processor is used to: acquire an initial ultrasound image and convert the initial ultrasound image from the spatial domain to the frequency domain to obtain a spectral image; suppress the frequency values ​​of the spectral image according to the position and / or magnitude of each frequency value in the spectral image to obtain a suppressed spectral image; convert the suppressed spectral image from the frequency domain to the spatial domain to obtain a spatial image; and compensate for the target region in the spatial image based on the initial ultrasound image to obtain a target ultrasound image, effectively removing noise artifacts from normal tissues and improving the uniformity of brightness display in different regions of the image.

Description

Technical Field

[0001] This application relates to the field of image processing technology, and in particular to an ultrasonic processing device, an ultrasonic image processing method, and a medium. Background Technology

[0002] Medical ultrasound imaging technology can view lesion information in real time and has no radiation damage to the human body. It is currently widely used in clinical examinations in hospitals.

[0003] However, because ultrasound imaging is an acoustic-based imaging method, it is subject to interference from many factors during the imaging process, such as sound wave interference, scattering, and electronic noise. Furthermore, the probe technology used during the examination directly affects the image quality. Therefore, compared to other medical images, ultrasound images typically exhibit more significant artifact noise interference and noticeable uneven brightness display. These artifacts, noise, and unevenness all interfere with the imaging of normal tissues, resulting in poor image structural uniformity and consequently affecting the accuracy of clinical diagnosis.

[0004] How to effectively remove artifact noise interference in ultrasound images and improve the uniformity of brightness display in different areas of the image has become an urgent problem to be solved. Summary of the Invention

[0005] This application provides an ultrasonic processing device, an ultrasonic image processing method, and a medium that can improve the uniformity of brightness display in different areas of an image.

[0006] This application provides an ultrasonic processing device, which includes a memory and a processor; the memory is used to store a computer program; when the computer program is executed on the processor, the processor is used to:

[0007] Acquire an initial ultrasound image and transform it from the spatial domain to the frequency domain to obtain a spectral image;

[0008] Based on the position of each frequency value in the spectrum image and / or the magnitude of each frequency value, frequency value suppression is performed on the spectrum image to obtain a suppressed spectrum image;

[0009] The suppressed spectrum image is transformed from the frequency domain to the spatial domain to obtain the spatial domain image;

[0010] Based on the initial ultrasound image, the target region in the spatial domain image is compensated to obtain the target ultrasound image.

[0011] One optional implementation is that the processor is specifically used for:

[0012] Acquire an initial ultrasound image and divide it into multiple sub-images, where each sub-image contains overlapping regions that overlap with other sub-images;

[0013] Perform Fourier transform on each of the multiple sub-images to obtain the spectral images of the multiple sub-images;

[0014] The spectral images of multiple sub-images are processed separately to obtain the suppressed spectral images corresponding to the multiple sub-images.

[0015] One optional implementation is that the processor is specifically used for:

[0016] Perform inverse Fourier transform on the suppressed spectrum images corresponding to multiple sub-images to obtain the spatial domain images of multiple sub-images;

[0017] Images belonging to the same overlapping region from multiple spatial images are weighted and fused. Based on the weighted and fused overlapping region image and the images of non-overlapping regions other than the overlapping region from multiple spatial images, a spatial image corresponding to the initial ultrasound image is obtained.

[0018] One optional implementation is that the processor is specifically used for:

[0019] For each spectral image in a plurality of sub-images, based on a preset suppression coefficient, the lateral frequency values ​​in the spectral image, excluding the central region of the spectrum, are suppressed to obtain a suppressed spectral image; or,

[0020] For each of the spectrum images of multiple sub-images, the horizontal and / or vertical frequency values ​​in the spectrum image that are within a preset frequency range are suppressed to obtain a suppressed spectrum image.

[0021] One optional implementation is that the processor is specifically used for:

[0022] For each spectral image in the multiple sub-images, perform the following operations:

[0023] Based on a preset suppression coefficient, the horizontal frequency values ​​in the spectrum image, except for the central region of the spectrum, are suppressed to obtain an intermediate image;

[0024] Suppress the horizontal and / or vertical frequency values ​​in the intermediate image that fall within a preset frequency range to obtain a suppressed spectrum image.

[0025] One optional implementation method is to determine the preset frequency range in the following way:

[0026] For each of the multiple sub-images in the spectrum image, determine the median frequency value in the spectrum image;

[0027] Determine the preset frequency range based on the median frequency value.

[0028] One optional implementation is that the processor is specifically used for:

[0029] For each spectrum image in the spectrum images of multiple sub-images, based on a preset filter, frequency values ​​in the spectrum image that are lower than a first threshold and / or higher than a second threshold are suppressed to obtain a suppressed spectrum image.

[0030] One optional implementation is that the processor is specifically used for:

[0031] The first region with an average gray value greater than a set threshold is selected from the initial ultrasound image, and the target region located at a specified position outside the first region is determined from the initial ultrasound image.

[0032] The target ultrasound image is obtained by replacing the image of the target region in the spatial domain image with the image of the target region in the initial ultrasound image.

[0033] This application also provides an ultrasound image processing method, which includes:

[0034] Acquire an initial ultrasound image and transform it from the spatial domain to the frequency domain to obtain a spectral image;

[0035] Based on the position of each frequency value in the spectrum image and / or the magnitude of each frequency value, frequency value suppression is performed on the spectrum image to obtain a suppressed spectrum image;

[0036] The suppressed spectrum image is transformed from the frequency domain to the spatial domain to obtain the spatial domain image;

[0037] Based on the initial ultrasound image, the target region in the spatial domain image is compensated to obtain the target ultrasound image.

[0038] This application embodiment also provides an ultrasound image processing device, which includes:

[0039] The acquisition unit is used to acquire an initial ultrasound image and convert the initial ultrasound image from the spatial domain to the frequency domain to obtain a spectral image.

[0040] The suppression unit is used to suppress the frequency values ​​of the spectrum image based on the position of each frequency value in the spectrum image and / or the magnitude of each frequency value, so as to obtain a suppressed spectrum image.

[0041] The conversion unit is used to convert the suppressed spectrum image from the frequency domain to the spatial domain to obtain a spatial domain image;

[0042] The compensation unit is used to compensate the target region in the spatial domain image based on the initial ultrasound image to obtain the target ultrasound image.

[0043] This application also provides a computer-readable storage medium storing a computer program for causing a computer to perform the steps in the above-described ultrasound image processing method.

[0044] This application also provides a computer program product, including a computer program stored in a computer-readable storage medium; when the processor of the ultrasonic processing device reads the computer program from the computer-readable storage medium, the processor executes the computer program, causing the ultrasonic processing device to perform the steps in the above-described ultrasonic image processing method.

[0045] In this embodiment, an initial ultrasound image is acquired and converted from the spatial domain to the frequency domain to obtain a spectral image. Frequency suppression is applied to the spectral image based on the position and / or magnitude of each frequency value in the spectral image, resulting in a suppressed spectral image. The suppressed spectral image is then converted from the frequency domain to the spatial domain to obtain a spatial image. Based on the initial ultrasound image, compensation is applied to the target region in the spatial image to obtain the target ultrasound image. Based on the ultrasound image processing method provided in this embodiment, by suppressing frequency values ​​in the spectral image in the frequency domain according to their position and / or magnitude, interference from noise artifacts on normal tissues can be effectively identified and removed, and the uniformity of brightness display in different regions of the image can be improved. Furthermore, by compensating for the target region in the spatial image, the loss of effective information in the target ultrasound image is avoided. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 This is a schematic diagram of the structure of an ultrasonic processing device provided in an embodiment of this application;

[0048] Figure 2 This is a schematic diagram illustrating the principle of ultrasonic image processing using an ultrasonic processing device provided in an embodiment of this application.

[0049] Figure 3 This is a schematic diagram of another ultrasonic processing device provided in an embodiment of this application;

[0050] Figure 4 This is an application scenario diagram of an ultrasound image processing method provided in an embodiment of this application;

[0051] Figure 5A schematic flowchart of an ultrasound image processing method provided in an embodiment of this application;

[0052] Figure 6 A schematic diagram of an initial ultrasound image provided in an embodiment of this application;

[0053] Figure 7 A schematic diagram of a spectrum image provided in an embodiment of this application;

[0054] Figure 8 A schematic diagram of a target ultrasound image provided in an embodiment of this application;

[0055] Figure 9 A flowchart illustrating a process for determining a suppressed spectral image, as provided in an embodiment of this application;

[0056] Figure 10 A schematic diagram illustrating a sub-image segmentation method provided in an embodiment of this application;

[0057] Figure 11 A schematic diagram illustrating another sub-image segmentation method provided in an embodiment of this application;

[0058] Figure 12 A schematic flowchart illustrating a spatial image determination process provided in an embodiment of this application;

[0059] Figure 13 A flowchart illustrating a method for determining a preset frequency range provided in an embodiment of this application;

[0060] Figure 14 A flowchart illustrating another spectral image suppression method provided in this application embodiment;

[0061] Figure 15 A schematic flowchart of an image compensation method provided in an embodiment of this application;

[0062] Figure 16 A schematic diagram of a candidate region provided in an embodiment of this application;

[0063] Figure 17 A schematic diagram of another candidate region provided in an embodiment of this application;

[0064] Figure 18 This is a schematic diagram of an ultrasound image processing device provided in an embodiment of this application. Detailed Implementation

[0065] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. Although the embodiments of this application provide method operation steps as shown in the following embodiments or drawings, the method may include more or fewer operation steps based on conventional or non-creative labor. In steps where there is no logically necessary causal relationship, the execution order of these steps is not limited to the execution order provided in the embodiments of this application. In actual processing or when the device executes the method, it may be executed in the order shown in the embodiments or drawings, or in combination. Obviously, the embodiments described in this application are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of this application.

[0066] In the embodiments of this application, the term "and / or" describes the relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. The character " / " generally indicates that the preceding and following associated objects have an "or" relationship.

[0067] Medical ultrasound imaging technology allows for real-time visualization of lesions and offers advantages such as being radiation-free, non-invasive, and relatively inexpensive, leading to its widespread use in clinical diagnosis. However, because medical ultrasound imaging is based on acoustic principles, it is susceptible to interference from various factors during the imaging process, including sound wave interference, scattering, and electronic noise. Furthermore, external factors such as the probe's fit against the patient can also cause interference. Compared to other imaging methods like CT and MRI, it suffers from lower contrast, poorer detail resolution, and less uniform image brightness. Specifically, this manifests as significant artifact noise and uneven brightness display in adjacent areas. These artifacts, noise, and unevenness interfere with the imaging of normal tissues, resulting in poor image structural uniformity and consequently affecting the accuracy of clinical diagnosis.

[0068] Currently, ultrasonic processing equipment commonly uses spatial compounding and speckle noise suppression algorithms to reduce image noise. While these methods can improve the readability of ultrasound images to some extent through noise reduction, they cannot effectively eliminate artifact noise or improve the uniformity of brightness display across different areas of the image. Therefore, effectively removing artifact noise interference from ultrasound images and improving the uniformity of brightness display across different areas of the image has become an urgent problem to be solved.

[0069] To address the aforementioned issues, this application provides an ultrasound processing device, ultrasound image processing method, and medium that can effectively solve the problems of artifact noise and uneven image brightness. By converting the acquired ultrasound image to the frequency domain and suppressing specified frequency values ​​in the frequency domain according to their location and magnitude, and then converting the suppressed spectral image to the spatial domain and compensating based on the initial ultrasound image, the interference of noise artifacts on normal tissues can be effectively identified and removed, and the uniformity of brightness display in different areas of the image can be improved, significantly enhancing the overall quality of the ultrasound image.

[0070] Figure 1 This application provides a hardware configuration block diagram of an ultrasonic processing device according to an embodiment. In some embodiments, the ultrasonic processing device may be an ultrasonic device 100. It should be understood that... Figure 1 The ultrasonic device 100 shown is merely an example, and the ultrasonic device 100 may have more than Figure 1 The more or fewer components shown can be combined into two or more components, or they can have different component configurations. The various components shown in the figure can 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] like Figure 1 As shown, the ultrasound device 100 includes: a processor 110, a memory 120, a display unit 130, and a probe 140; wherein:

[0072] Probe 140 is used for acquiring ultrasound images based on ultrasound.

[0073] Display unit 130 is used to display ultrasound images;

[0074] The memory 120 is used to store data required for ultrasound imaging, and may include software programs, application interface data, etc.

[0075] Processor 110, connected to probe 140, display unit 130 and memory 120 respectively, is used for:

[0076] The initial ultrasound image acquired by the probe is obtained, and the initial ultrasound image is transformed from the spatial domain to the frequency domain to obtain the spectrum image;

[0077] Based on the position of each frequency value in the spectrum image and / or the magnitude of each frequency value, frequency value suppression is performed on the spectrum image to obtain a suppressed spectrum image;

[0078] The suppressed spectrum image is transformed from the frequency domain to the spatial domain to obtain the spatial domain image;

[0079] Based on the initial ultrasound image, the target region in the spatial domain image is compensated to obtain the target ultrasound image.

[0080] Figure 2 This is a schematic diagram illustrating the application principle according to an embodiment of this application. This portion can be derived from... Figure 1 The ultrasonic processing device shown is implemented in some modules or functional components. The following description will only focus on the main components, while other components, such as memory, controller, and control circuit, will not be elaborated here.

[0081] like 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.

[0082] The display unit 220 may include a display panel 221 and a backlight assembly 222. The display panel 221 is configured to display ultrasound images, 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 illuminate the display panel 221.

[0083] The processor 230 can be configured to control the backlight brightness of each backlight zone in the backlight assembly 222, and to control the ultrasonic probe to emit ultrasonic signals, receive ultrasonic echo signals and analyze them to obtain ultrasonic images.

[0084] The processor 230 can optimize ultrasound images, effectively identify and remove noise artifacts that interfere with normal tissues, improve the uniformity of brightness display in different areas of the image, and improve the overall quality of ultrasound images.

[0085] In addition, embodiments of this application may also perform image processing on the ultrasound image after the ultrasound probe acquires the ultrasound image to be processed by other ultrasound processing devices independent of the ultrasound probe.

[0086] In one embodiment, the aforementioned ultrasonic processing device can also be a server, such as... Figure 1 The server 300 is shown. In this embodiment, the structure of the ultrasonic processing device can be as follows: Figure 3 As shown, Figure 3 The ultrasonic processing device 300 is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0087] like Figure 3 As shown, the components of the ultrasonic processing device 300 may include, but are not limited to: at least one processor 301, at least one memory 302, and a bus 303 connecting different system components (including memory 302 and processor 301).

[0088] Bus 303 represents one or more of several bus structures, including a memory bus or memory controller, peripheral bus, processor, or local bus using any of the multiple bus structures.

[0089] The memory 302 may include a readable medium in the form of volatile memory, such as random access memory (RAM) 321 or cache memory 322, and may further include read-only memory (ROM) 323.

[0090] The memory 302 may also include a program / utility 325 having a set (at least one) of program modules 324, including but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0091] The ultrasound processing device 300 can also communicate with one or more external devices 304 (e.g., keyboard, pointing device, etc.), and with one or more devices that enable a user to interact with the image processing device 300, or with any device that enables the ultrasound processing device 300 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via the input / output (I / O) interface 305. Furthermore, the ultrasound processing device 300 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), or public network, such as the Internet) via a network adapter 306. Figure 3 As shown, network adapter 306 communicates with other modules used in the ultrasonic processing device 300 via bus 303. It should be understood that, although not shown in the figure, other hardware or software modules can be used in conjunction with the ultrasonic processing device 300, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0092] Processor 301 is specifically used to execute the following procedures:

[0093] Acquire an initial ultrasound image and transform it from the spatial domain to the frequency domain to obtain a spectral image;

[0094] Based on the position of each frequency value in the spectrum image and / or the magnitude of each frequency value, frequency value suppression is performed on the spectrum image to obtain a suppressed spectrum image;

[0095] The suppressed spectrum image is transformed from the frequency domain to the spatial domain to obtain the spatial domain image;

[0096] Based on the initial ultrasound image, the target region in the spatial domain image is compensated to obtain the target ultrasound image.

[0097] like Figure 4 The diagram shown illustrates an application scenario of another image processing method according to an embodiment of this application. The diagram includes: an ultrasound probe 10, a server 20, and a memory 30 within the server 20.

[0098] The ultrasound probe 10 is used to acquire ultrasound images and store the acquired ultrasound images in the memory 30.

[0099] When processing an ultrasound image, the initial ultrasound image to be processed is first retrieved from the memory 30. The server 20 retrieves the initial ultrasound image and converts it from the spatial domain to the frequency domain to obtain a spectrum image. Based on the position of each frequency value in the spectrum image and / or the magnitude of each frequency value, frequency suppression is performed on the spectrum image to obtain a suppressed spectrum image. The suppressed spectrum image is then converted from the frequency domain to the spatial domain to obtain a spatial image. Based on the initial ultrasound image, the target region in the spatial image is compensated to obtain the target ultrasound image.

[0100] The description in this application details only a single server and memory; however, those skilled in the art should understand that the illustrated ultrasound probe 10, server 20, and memory 30 are intended to illustrate that the technical solution of this application relates to the operation of an ultrasound processing device, server, and memory. The detailed description of a single server and memory is at least for ease of explanation and does not imply any limitation on the number, type, or location of servers and memories. It should be noted that adding additional modules to or removing individual modules from the illustrated environment does not change the underlying concept of the exemplary embodiments of this application.

[0101] It should be noted that the memory 30 in the embodiments of this application can be, for example, a cache system, hard disk storage, memory storage, etc. Furthermore, the ultrasound image processing method proposed in this application is not only applicable to… Figure 4 The application scenarios shown are also applicable to any device that requires ultrasound image processing.

[0102] For ease of understanding, the ultrasound image processing method provided in this application will be described in detail below through specific embodiments.

[0103] In some embodiments, the ultrasound image processing method provided in this application can be as follows: Figure 1 Alternatively, the ultrasonic processing equipment shown in Figure 3 can be used to complete the process. Figure 5 A schematic flowchart of an ultrasound image processing method provided in an embodiment of this application is shown. Figure 5 As shown, the method specifically includes the following steps:

[0104] Step 501: Acquire an initial ultrasound image and convert the initial ultrasound image from the spatial domain to the frequency domain to obtain a spectral image;

[0105] In practice, after acquiring the initial ultrasound image, the Fourier transform method is used to transform the initial ultrasound image from the spatial domain to the frequency domain. Since the Fourier transform method is a classic method in digital image processing, its processing procedure can be found in existing technologies and will not be described in detail here. The initial ultrasound image is as follows: Figure 6 As shown.

[0106] By processing the initial ultrasound image, a frequency domain spectral image corresponding to the initial ultrasound image is obtained, such as... Figure 7 As shown (above) Figure 6 (A schematic diagram of the converted spectral image). The spectral image is used to reflect the frequency information of brightness changes in the initial ultrasound image. The spectral image is essentially a matrix composed of multiple frequency values.

[0107] Step 502: Based on the position of each frequency value in the spectrum image and / or the magnitude of each frequency value, suppress the frequency values ​​in the spectrum image to obtain a suppressed spectrum image.

[0108] The non-uniformity of ultrasound images is mainly caused by the interference of artifact noise and the uneven brightness between regions. Among them, artifact noise is mainly manifested as texture sound shadows with low or high brightness. This kind of artifact noise can be effectively removed by suppressing the lateral frequency values ​​of the spectrum image. However, since some of the artifact regions in the image are valid regions, that is, the detected lesion regions, it is necessary to distinguish the validity of the artifact information. In the embodiments of this application, frequency value suppression is performed on the spectrum image according to the position of each frequency value in the spectrum image, which can effectively avoid the influence on the valid artifact regions.

[0109] To address the issue of uneven brightness between regions, the overall brightness of the image can be made more uniform by suppressing specific frequency ranges based on the magnitude of each frequency value.

[0110] It should be noted that the two frequency suppression methods mentioned above in this application, namely, suppressing the frequency image based on the position of each frequency value in the spectrum image, and suppressing the frequency image based on the magnitude of each frequency value, can be freely selected by the user according to their needs, such as using any one of them or using both simultaneously.

[0111] Step 503: The suppressed spectrum image is converted from the frequency domain to the spatial domain to obtain a spatial domain image;

[0112] In practice, the inverse Fourier transform method can be used to transform the suppressed spectrum image from the frequency domain to the spatial domain, obtaining a spatial domain image corresponding to the suppressed spectrum image. Since the inverse Fourier transform method is also a classic method in digital image processing, its processing procedure can be found in existing technologies and will not be elaborated upon here.

[0113] Step 504: Based on the initial ultrasound image, compensate for the target region in the spatial image to obtain the target ultrasound image.

[0114] During implementation, the target region in the obtained spatial image is compensated based on the initial ultrasound image, and the desired target ultrasound image is finally obtained, such as... Figure 8 As shown (above) Figure 6 (A schematic diagram of the corresponding target ultrasound image), where the target region refers to the area in the spatial image that contains effective information, such as the lesion area.

[0115] After obtaining the target ultrasound image, other spatial domain noise reduction and enhancement methods can be applied to the target ultrasound image. For example, a speckle noise suppression algorithm can be used to suppress speckle noise in the target ultrasound image, thereby further improving the visual effect of the ultrasound image.

[0116] The aforementioned ultrasound image processing method, by converting the acquired ultrasound image to the frequency domain and suppressing specified frequency values ​​in the frequency domain according to the location and / or magnitude of the frequency values, can effectively identify and remove the interference of noise artifacts on normal tissues, improve the uniformity of brightness display in different regions of the image, and then convert the suppressed spectral image to the spatial domain and compensate according to the initial ultrasound image, thus avoiding the loss of effective information in the target ultrasound image and significantly improving the overall quality of the ultrasound image.

[0117] The following details the process of processing the initial ultrasound image to obtain the corresponding suppressed spectrum image after acquiring the initial ultrasound image. Figure 9 As shown, it specifically includes the following steps:

[0118] Step 901: Obtain an initial ultrasound image and divide the initial ultrasound image into multiple sub-images, wherein each sub-image contains an overlapping region that overlaps with other sub-images;

[0119] In this embodiment of the application, after acquiring the initial ultrasound image, the initial ultrasound image is divided into multiple sub-images. The specific division method can be selected by the user according to their needs, for example... Figure 10 The initial ultrasound image is divided into a predetermined number of sub-images along the horizontal direction, as shown, or as... Figure 11 The initial ultrasound image is divided into a predetermined number of sub-images along both the horizontal and vertical directions. This predetermined number can be set by the user or set to 1, meaning no sub-image division is performed. In an optional embodiment, relevant options can be displayed on the display unit of the ultrasound processing device for the user to select.

[0120] It should be noted that, to avoid unnatural appearances in the image when synthesizing a single image from the sub-images, in this embodiment, each sub-image contains overlapping regions that overlap with other sub-images (sub-images adjacent to the given sub-image), such as... Figure 10 As shown, when these overlapping regions are finally synthesized into a single image, a weighted fusion coefficient needs to be set to ensure that the overlapping regions have the same energy distribution as other regions.

[0121] Step 902: Perform Fourier transform on each of the multiple sub-images to obtain the spectral images of the multiple sub-images;

[0122] Since the Fourier transform method is a classic method in digital image processing, its processing procedure can be found in existing technologies and will not be described in detail here.

[0123] Step 903: Process the spectral images of multiple sub-images respectively to obtain the suppressed spectral images corresponding to the multiple sub-images.

[0124] The process of processing the spectrum image of each sub-image is similar to the process of processing the spectrum image in step 502 above, that is, frequency value suppression is performed on the spectrum image according to the position of each frequency value in the spectrum image and / or the magnitude of each frequency value, which will not be described in detail here.

[0125] In step 903 above, after obtaining the suppressed spectrum images corresponding to multiple sub-images, the suppressed spectrum images are processed to obtain the corresponding spatial domain images, such as... Figure 12 As shown, the specific steps include the following:

[0126] Step 1201: Perform inverse Fourier transform on the suppressed spectrum images corresponding to the multiple sub-images respectively to obtain the spatial domain images of the multiple sub-images;

[0127] Since the inverse Fourier transform method is also a classic method in digital image processing, its processing procedure can be found in existing technologies and will not be elaborated here.

[0128] Step 1202: Weighted fusion of images belonging to the same overlapping region in multiple spatial images, and obtaining a spatial image corresponding to the initial ultrasound image based on the weighted fused overlapping region image and the images of non-overlapping regions other than the overlapping region in multiple spatial images.

[0129] Since the initial ultrasound image is divided into multiple sub-images in step 901 above, an inverse Fourier transform is performed on the spectral image of each sub-image to obtain the spatial domain image of each sub-image. It is also necessary to combine the spatial domain images of each sub-image to form a spatial domain image corresponding to the initial ultrasound image.

[0130] Specifically, images belonging to the same overlapping region from multiple spatial domain images are weighted and fused to obtain the corresponding overlapping region image. For example, if both sub-image 1 and sub-image 2 contain overlapping region 1, during weighted fusion, the image corresponding to overlapping region 1 in the spatial domain image corresponding to sub-image 1 and the image corresponding to overlapping region 1 in the spatial domain image corresponding to sub-image 2 are weighted and fused to obtain the weighted fused overlapping region image corresponding to overlapping region 1. The weighting fusion coefficients can be set in various ways, which are not limited in this application, but they must all meet the basic condition that the cumulative weight at the same location is 1.

[0131] After obtaining the overlapping region image obtained by weighted fusion of the overlapping region images, the overlapping region image and the non-overlapping regions other than the overlapping regions in the multiple spatial images are combined according to the positions when divided in step 901 to obtain the spatial image corresponding to the initial ultrasound image.

[0132] The following details the specific steps in step 502 above, which involve suppressing the frequency values ​​of the spectrum image based on their positions and / or magnitudes to obtain a suppressed spectrum image.

[0133] It should be noted that the above-mentioned methods for suppressing frequency values ​​in spectrum images include a variety of methods, and users can select any one or more combinations of them according to their actual needs. Furthermore, the order in which the various methods for suppressing frequency values ​​in spectrum images are used in this application is not restricted.

[0134] In this embodiment, the presence of some artifact noise in ultrasound images can interfere with the display of target area information, i.e., lesion information. Some lesions, such as stones and malignant nodules, are determined by the acoustic imaging mechanism to have a distinct tail sign behind them, which appears as artifact noise in the ultrasound image. This artifact noise is helpful for doctors in diagnosing these types of lesions. Therefore, the artifact noise areas in ultrasound images cannot be generalized. Some artifact areas are caused by improper contact between the probe and the human body and need to be suppressed; while some artifacts are normal pathological features that help doctors in diagnosis and do not require treatment. Due to issues such as scattering, reflection, and sidelobe interference of ultrasound waves, ultrasound images may exhibit significant speckle noise and uneven brightness display in adjacent areas, thus affecting the doctor's diagnosis.

[0135] Based on the analysis of the mechanism and impact of the above-mentioned problems, this application proposes a spectral image suppression method, specifically including the following two approaches:

[0136] Method 1: For each of the spectrum images of the multiple sub-images, based on a preset suppression coefficient, the lateral frequency values ​​in the spectrum image other than the center region of the spectrum are suppressed to obtain a suppressed spectrum image.

[0137] Since artifact noise in ultrasound images is typically longitudinal, it manifests as transverse noise after the initial ultrasound image is converted into a spectral image. Furthermore, because most of the effective information in the spectral image is concentrated in the central region, this embodiment suppresses transverse frequency values ​​outside the central region of the spectral image. Transverse spectral values ​​refer to the spectral values ​​obtained by viewing them horizontally. The degree of suppression can be controlled by setting a suppression coefficient, which can be set by the user according to their needs; this application does not impose any restrictions on this setting.

[0138] Method 2: For each of the spectrum images of the multiple sub-images, suppress the horizontal and / or vertical frequency values ​​in the spectrum image that are within a preset frequency range to obtain a suppressed spectrum image.

[0139] To address the issue of uneven brightness display in ultrasound images, selective suppression of lateral and longitudinal frequency values ​​at specific frequency points in the spectral image can be achieved. Generally, frequency changes at points of uneven brightness display in adjacent areas of an ultrasound image are not abrupt; the corresponding frequency values ​​in the spectral image fall between the edge and homogeneous regions, i.e., within a specified range between the maximum and minimum frequency values. Therefore, specific frequency values ​​can be suppressed by setting a preset frequency range.

[0140] Users can choose to suppress either the horizontal or vertical frequency values, or both, as needed. They can also set the suppression parameters to control the uniformity of the image.

[0141] In one alternative implementation, such as Figure 13 As shown, the preset frequency range in Method 2 above can be determined in the following way:

[0142] Step 1301: For each of the spectrum images in the spectrum images of the multiple sub-images, determine the median frequency value in the spectrum image;

[0143] Specifically, for the spectrum image of any sub-image, the frequency values ​​in the spectrum image are sorted according to their numerical value (either from largest to smallest or from smallest to largest), and the frequency value in the middle position is determined as the median frequency value based on the sorting result.

[0144] It should be noted that if Method 2 above only suppresses the horizontal frequency values ​​in the spectrum image within the preset frequency range, the method for determining the median frequency value corresponding to the preset frequency range is as follows: sort the horizontal frequency values ​​according to their numerical values ​​(either from largest to smallest or from smallest to largest), and determine the frequency value in the middle position as the median frequency value based on the sorting result; when Method 2 above only suppresses the vertical frequency values ​​in the spectrum image within the preset frequency range, the method for determining the median frequency value threshold is similar and will not be repeated here.

[0145] When suppressing the longitudinal frequency values ​​and longitudinal spectrum values ​​in the spectrum image that are within a preset frequency range, the median of the corresponding frequency value can be determined based on all the spectrum values ​​in the spectrum image, or the median of the frequency value corresponding to the longitudinal frequency value and the longitudinal spectrum value can be determined separately.

[0146] Step 1302: Determine the preset frequency range based on the median frequency value.

[0147] Specifically, the range of frequency values ​​within the range of the median of the above frequency values ​​can be set as the preset frequency range. The above setting difference can be set by the user according to their needs, such as 300 Hz.

[0148] In an optional implementation, the above-mentioned method one and method two can be selected only one, i.e., an OR relationship. The above-mentioned processing of the spectral images of the plurality of sub-images to obtain the suppressed spectral images corresponding to the plurality of sub-images specifically includes any of the following steps:

[0149] For each spectral image in the spectral images of multiple sub-images, based on a preset suppression coefficient, the lateral frequency values ​​in the spectral image except for the spectral center region are suppressed to obtain a suppressed spectral image;

[0150] For each of the spectrum images of multiple sub-images, the horizontal and / or vertical frequency values ​​in the spectrum image that are within a preset frequency range are suppressed to obtain a suppressed spectrum image.

[0151] In one optional implementation, the above-mentioned method one and method two can be related as an additive method, that is, the two methods are executed simultaneously in a sequential order, such as... Figure 14 As shown, when processing the spectral images of the multiple sub-images to obtain the suppressed spectral images corresponding to the multiple sub-images, the following operations are specifically performed for each spectral image of the multiple sub-images:

[0152] Step 1401: Based on a preset suppression coefficient, suppress the lateral frequency values ​​in the spectrum image except for the central region of the spectrum to obtain an intermediate image;

[0153] Step 1402: Suppress the horizontal and / or vertical frequency values ​​in the intermediate image that are within a preset frequency range to obtain a suppressed spectrum image.

[0154] It should be noted that the above execution process can also be as follows: For each spectrum image in the spectrum images of multiple sub-images, firstly, suppress the horizontal frequency values ​​and / or vertical frequency values ​​in the intermediate image that are within the preset frequency range to obtain the intermediate image; then, based on the preset suppression coefficient, suppress the horizontal frequency values ​​in the intermediate image except for the central region of the spectrum to obtain the suppressed spectrum image.

[0155] In addition to the above-mentioned spectral image suppression method, this application also proposes another spectral image suppression method, namely, using frequency domain spatial filtering methods to optimize artifact noise and brightness unevenness between different regions. When the ultrasound image is converted to the frequency domain, the spatial domain filtering method can also be used to process the spectral results. Therefore, useless information in the spectral image can be suppressed by filtering methods.

[0156] Specifically, the above-mentioned processing of the spectral images of the plurality of sub-images to obtain the suppressed spectral images corresponding to the plurality of sub-images specifically includes:

[0157] For each of the spectrum images of the plurality of sub-images, based on a preset filter, frequency values ​​in the spectrum image that are lower than a first threshold and / or higher than a second threshold are suppressed to obtain a suppressed spectrum image.

[0158] Specifically, for artifact noise removal, low-pass filter-based methods can be used, such as Butworth low-pass filters or Gaussian low-pass filters, to suppress the spectral results in a specific region that exceed a second threshold. This suppresses the spectral results corresponding to artifact noise. For addressing brightness unevenness between different regions, high-pass filter-based methods can be used, such as Butworth high-pass filters, Gaussian high-pass filters, or ideal high-pass filters, to suppress the spectral results in a specific region that fall below a first threshold. This suppresses the spectral results corresponding to areas with uneven brightness.

[0159] The first threshold and the second threshold can be set by the user according to their needs, or the default values ​​configured by the system can be used. No restrictions are imposed in this embodiment.

[0160] The following details the process in step 504 above, where the target region in the spatial domain image is compensated based on the initial ultrasound image to obtain the target ultrasound image. Figure 15 As shown, this step specifically includes:

[0161] Step 1501: Select a first region with an average gray value greater than a set threshold from the initial ultrasound image, and determine a target region located at a specified position outside the first region from the initial ultrasound image.

[0162] In the initial ultrasound image, invalid artifact noise is caused by improper contact between the probe and the human body, generally appearing as an artifact region from top to bottom. In contrast, the effective acoustic shadow has a clear lesion area at its starting point, such as stones or malignant nodules, generally appearing as a relatively large and bright area above the acoustic shadow. Therefore, after processing the image in the frequency domain, it is necessary to combine the characteristics of the suppression region in the spatial domain (i.e., the characteristics in the initial ultrasound image) to determine whether to suppress the acoustic shadow region. That is, when there is a bright, clustered area at the beginning of the artifact, the gray value of the acoustic shadow region is based on the initial ultrasound image (i.e., compensation is performed); when there is no bright, clustered area at the beginning of the artifact, the gray value of the acoustic shadow region is based on the spatial domain image result.

[0163] In practice, the grayscale values ​​in the initial ultrasound image are detected, and a first region with an average grayscale value greater than a set threshold is selected. Specifically, the first grayscale value points in the initial ultrasound image with grayscale values ​​greater than a set value can be selected first, and a candidate region of a preset size corresponding to any of the selected first grayscale value points can be determined. The first grayscale value can be located in the center, upper left, lower left, etc. of the candidate region, which can be set by the user according to their needs. This application embodiment does not impose any restrictions.

[0164] In implementation, a candidate region of a preset size can be determined using any first grayscale value point as the center point, such as... Figure 16 As shown, the candidate region can be a rectangular region with a length of 1 and a width of 2, or a region of other shapes (such as a square); alternatively, any first grayscale value can be used as the top-left point to determine the corresponding candidate region, such as... Figure 17 As shown, a square region with a side length of 3 is defined with the first gray value point as the top left point.

[0165] After determining the candidate region for each first gray value point, the average gray value of each candidate region is determined. If the average gray value of a candidate region is greater than a set threshold, the region outside the candidate region at a specified location (as shown below) is set as the target region.

[0166] Step 1502: Use the image of the target region in the initial ultrasound image to replace the image in the spatial domain image corresponding to the target region, and obtain the target ultrasound image.

[0167] After determining the target region, the image of the target region in the spatial domain image is replaced with the image of the corresponding target region in the initial ultrasound image. That is, the target region in the spatial domain image is compensated to obtain the target ultrasound image.

[0168] Based on the same inventive concept, embodiments of this application also provide an ultrasonic image processing device. For example... Figure 18 As shown, it is a schematic diagram of the structure of an ultrasound image processing device, which may include:

[0169] The acquisition unit 1801 is used to acquire the initial ultrasound image collected by the ultrasound probe and convert the initial ultrasound image from the spatial domain to the frequency domain to obtain a spectrum image;

[0170] The suppression unit 1802 is used to suppress the frequency values ​​of the spectrum image according to the position of each frequency value in the spectrum image and / or the magnitude of each frequency value, so as to obtain a suppressed spectrum image.

[0171] The conversion unit 1803 is used to convert the suppressed spectrum image from the frequency domain to the spatial domain to obtain a spatial domain image;

[0172] The compensation unit 1804 is used to compensate the target region in the spatial image based on the initial ultrasound image to obtain the target ultrasound image.

[0173] Optionally, the aforementioned acquisition unit 1801 is specifically used for:

[0174] Acquire an initial ultrasound image and divide it into multiple sub-images, where each sub-image contains overlapping regions that overlap with other sub-images;

[0175] Perform Fourier transform on each of the multiple sub-images to obtain the spectral images of the multiple sub-images;

[0176] The suppression unit 1802 is specifically used to process the spectral images of multiple sub-images respectively to obtain the suppressed spectral images corresponding to the multiple sub-images.

[0177] Optionally, the aforementioned conversion unit 1803 is specifically used for:

[0178] Perform inverse Fourier transform on the suppressed spectrum images corresponding to multiple sub-images to obtain the spatial domain images of multiple sub-images;

[0179] Images belonging to the same overlapping region from multiple spatial images are weighted and fused. Based on the weighted and fused overlapping region image and the images of non-overlapping regions other than the overlapping region from multiple spatial images, a spatial image corresponding to the initial ultrasound image is obtained.

[0180] Optionally, the suppression unit 1802 described above is specifically used for:

[0181] For each spectral image in a plurality of sub-images, based on a preset suppression coefficient, the lateral frequency values ​​in the spectral image, excluding the central region of the spectrum, are suppressed to obtain a suppressed spectral image; or,

[0182] For each of the spectrum images of multiple sub-images, the horizontal and / or vertical frequency values ​​in the spectrum image that are within a preset frequency range are suppressed to obtain a suppressed spectrum image.

[0183] Optionally, the suppression unit 1802 described above is specifically used for:

[0184] For each spectral image in the multiple sub-images, perform the following operations:

[0185] Based on a preset suppression coefficient, the horizontal frequency values ​​in the spectrum image, except for the central region of the spectrum, are suppressed to obtain an intermediate image;

[0186] Suppress the horizontal and / or vertical frequency values ​​in the intermediate image that fall within a preset frequency range to obtain a suppressed spectrum image.

[0187] Optionally, the preset frequency range is determined in the following way:

[0188] For each of the multiple sub-images in the spectrum image, determine the median frequency value in the spectrum image;

[0189] Determine the preset frequency range based on the median frequency value.

[0190] Optionally, the suppression unit 1802 described above is specifically used for:

[0191] For each spectrum image in the spectrum images of multiple sub-images, based on a preset filter, frequency values ​​in the spectrum image that are lower than a first threshold and / or higher than a second threshold are suppressed to obtain a suppressed spectrum image.

[0192] Optionally, the aforementioned compensation unit 1804 is specifically used for:

[0193] The first region with an average gray value greater than a set threshold is selected from the initial ultrasound image, and the target region located at a specified position outside the first region is determined from the initial ultrasound image.

[0194] The target ultrasound image is obtained by replacing the image of the target region in the spatial domain image with the image of the target region in the initial ultrasound image.

[0195] In some possible implementations, various aspects of the ultrasound image processing method provided in this application can also be implemented as a program product, comprising a computer program that, when run on an electronic device, causes the electronic device to perform the steps of the ultrasound image processing method according to the various exemplary embodiments of this application described above. For example, the electronic device can perform actions such as... Figure 5 The steps are shown in the figure.

[0196] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A 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 thereof. More specific examples (a non-exhaustive list) of readable storage media include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0197] The program product of the embodiments of this application may employ a portable compact disc read-only memory (CD-ROM) and include a computer program, and may run on an electronic device. However, the program product of this application is not limited thereto. In this document, the readable storage medium may be any tangible medium that contains or stores a program that may be used by or in conjunction with a command execution system, apparatus, or device.

[0198] A readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying a readable computer program. This propagated data signal may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting a program for use by or in conjunction with a command execution system, apparatus, or device.

[0199] Computer programs contained on readable media may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0200] Computer programs for performing the operations of this application can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The computer program can execute entirely on the user's electronic device, partially on the user's device, as a standalone 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 remote electronic devices, the remote electronic device can be connected to the user's electronic device via 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., via the Internet using an Internet service provider).

[0201] Furthermore, although the operations of the method of this application are described in a specific order in the accompanying 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, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0202] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing a computer-usable computer program.

[0203] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, produce a machine for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0204] These computer program commands may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the commands stored in the computer-readable storage medium produce an article of manufacture including command means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0205] These computer program commands can also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing the commands executed on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0206] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0207] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. An ultrasonic treatment device, characterized by, The memory is configured to store a computer program, and the processor is configured to: acquire an initial ultrasound image, and convert the initial ultrasound image from a spatial domain to a frequency domain to obtain a spectrum image; perform frequency value suppression on the spectrum image according to positions of the frequency values in the spectrum image and / or magnitudes of the frequency values, to obtain a suppressed spectrum image; convert the suppressed spectrum image from the frequency domain to the spatial domain to obtain a spatial domain image; perform compensation on a target region in the spatial domain image according to the initial ultrasound image, to obtain a target ultrasound image; The processor is specifically configured to: acquire the initial ultrasound image, and divide the initial ultrasound image into a plurality of sub-images, wherein each sub-image has an overlapping region that overlaps with other sub-images; perform Fourier transform on the plurality of sub-images respectively to obtain spectrum images of the plurality of sub-images; perform processing on the spectrum images of the plurality of sub-images respectively to obtain suppressed spectrum images corresponding to the plurality of sub-images; The processor is specifically configured to: perform inverse Fourier transform on the suppressed spectrum images corresponding to the plurality of sub-images respectively to obtain spatial domain images of the plurality of sub-images; perform weighted fusion on images belonging to the same overlapping region in the plurality of spatial domain images, and obtain a spatial domain image corresponding to the initial ultrasound image according to the weighted fusion images of the overlapping regions and images of non-overlapping regions in the plurality of spatial domain images except the overlapping regions; the weight in the weighted fusion process is pre-set, and the weight cumulative value at the same position is 1; the weight is used to ensure that the images of the overlapping regions and the images of the non-overlapping regions except the overlapping regions have the same energy distribution; The processor is specifically configured to: for each spectrum image in the spectrum images of the plurality of sub-images, perform suppression on transverse frequency values in the spectrum image except a spectrum center region based on a preset suppression coefficient, to obtain a suppressed spectrum image; or for each spectrum image in the spectrum images of the plurality of sub-images, perform suppression on transverse frequency values and / or longitudinal frequency values in the spectrum image within a preset frequency range, to obtain a suppressed spectrum image; the preset suppression coefficient is pre-set and is used to control uniformity of the plurality of sub-images.

2. The apparatus of claim 1, wherein, The processor is specifically configured to: for each spectrum image in the spectrum images of the plurality of sub-images, perform the following operations respectively: perform suppression on transverse frequency values in the spectrum image except a spectrum center region based on a preset suppression coefficient, to obtain an intermediate image; perform suppression on transverse frequency values and / or longitudinal frequency values in the intermediate image within a preset frequency range, to obtain a suppressed spectrum image.

3. The apparatus of claim 1 or 2, wherein, The preset frequency range is determined by the following method: for each spectrum image in the spectrum images of the plurality of sub-images, determine a median value of the frequency values in the spectrum image; determine the preset frequency range according to the median value of the frequency values.

4. The apparatus of claim 1, wherein, The processor is specifically configured to: For each of the spectrum images of the plurality of sub-images, based on a preset filter, frequency values lower than a first threshold and / or higher than a second threshold in the spectrum image are suppressed to obtain a suppressed spectrum image.

5. The apparatus of claim 1, wherein, The processor is specifically configured to: filtering out, from the initial ultrasound image, a first region with an average gray value greater than a set threshold, and determining a target region located at a specified position outside the first region from the initial ultrasound image; replacing, with an image of the target region in the initial ultrasound image, an image corresponding to the target region in the spatial domain image to obtain the target ultrasound image.

6. An ultrasound image processing method, characterized by, The method comprises: obtaining an initial ultrasound image, and converting the initial ultrasound image from a spatial domain to a frequency domain to obtain a spectrum image; suppressing frequency values of the spectrum image according to positions of the frequency values in the spectrum image and / or sizes of the frequency values to obtain a suppressed spectrum image; converting the suppressed spectrum image from the frequency domain to the spatial domain to obtain a spatial domain image; compensating a target region in the spatial domain image according to the initial ultrasound image to obtain a target ultrasound image; The method comprises: obtaining the initial ultrasound image, and dividing the initial ultrasound image into a plurality of sub-images, wherein each sub-image has an overlapping region overlapping with other sub-images; performing Fourier transform on the plurality of sub-images respectively to obtain spectrum images of the plurality of sub-images; processing the spectrum images of the plurality of sub-images respectively to obtain suppressed spectrum images corresponding to the plurality of sub-images; The method comprises: performing inverse Fourier transform on the suppressed spectrum images corresponding to the plurality of sub-images respectively to obtain spatial domain images of the plurality of sub-images; performing weighted fusion on images belonging to the same overlapping region in the plurality of spatial domain images, and obtaining a spatial domain image corresponding to the initial ultrasound image according to the weighted fusion images of the overlapping regions and images of non-overlapping regions in the plurality of spatial domain images except the overlapping regions; the weight in the weighted fusion process is preset, and the weight cumulative value at the same position is 1; the weight is used to ensure that the images of the overlapping regions and the images of the non-overlapping regions except the overlapping regions have the same energy distribution; The method comprises: For each of the spectrum images of the plurality of sub-images, based on a preset suppression coefficient, suppressing transverse frequency values in the spectrum image except a spectrum center region to obtain a suppressed spectrum image; or, For each of the plurality of spectral images of the plurality of sub-images, a horizontal frequency value and / or a longitudinal frequency value in a preset frequency range in the spectral image is suppressed to obtain a suppressed spectral image; the preset suppression coefficient is preset and used to control uniformity of the plurality of sub-images.

7. A computer readable storage medium characterized in that, The computer readable storage medium stores a computer program, and the computer program is used to make a computer execute steps of the method in claim 6.

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