Vascular ultrasound image processing method, device, equipment and medium

Abstract

Embodiments of the present invention disclose a method, apparatus, device, and medium for processing vascular ultrasound images. The method comprises: acquiring an ultrasound signal of a target vascular object and obtaining an initial vascular ultrasound image based on the ultrasound signal; determining a target brightness mapping parameter that matches the ultrasound signal; and adjusting the brightness of the initial vascular ultrasound image based on the target brightness mapping parameter to obtain a target vascular ultrasound image. The target brightness mapping parameter is determined by analyzing multiple groups of sample ultrasound signals acquired under the same sampling conditions as the ultrasound signal in a preset hyperspace. The technical solution of this embodiment achieves efficient adjustment of the dynamic range of vascular ultrasound images and improves vascular image quality during highly real-time intravascular ultrasound imaging.

Description

Technical Field

[0001] The embodiments of the present invention relate to the technical field of medical image processing, and in particular to a method, apparatus, device and medium for processing vascular ultrasound images. Background Art

[0002] Intravascular ultrasound (IVUS) is an intravascular imaging modality. Because IVUS images are affected by the ultrasound system's signal-to-noise ratio and the complex imaging environment, the resolution of different tissue components within the vessel, such as blood, fibrous plaques, and calcified plaques, is poor. Furthermore, the dynamic range of images displayed by current display devices cannot match the dynamic range of the transducer, which also increases the loss of dynamic range of IVUS images.

[0003] Typically, in the brightness mode of intravascular ultrasound imaging, time gain compensation and logarithmic compression methods are used to control image brightness and contrast to improve vascular ultrasound image quality. However, these methods rely heavily on experience and subjective assessment, resulting in unstable image processing results. Summary of the Invention

[0004] The embodiments of the present invention provide a method, apparatus, device and medium for processing vascular ultrasound images, which can improve the brightness contrast of different components in the blood vessels and enhance the effect of vascular ultrasound images.

[0005] In a first aspect, an embodiment of the present invention provides a method for processing a vascular ultrasound image, the method comprising:

[0006] Acquiring an ultrasonic signal of a target blood vessel object, and obtaining an initial blood vessel ultrasonic image based on the ultrasonic signal;

[0007] determining target brightness mapping parameters that match the ultrasound signal;

[0008] adjusting the brightness of the initial vascular ultrasound image based on the target brightness mapping parameter to obtain a target vascular ultrasound image;

[0009] The target brightness mapping parameters are parameters determined by analyzing multiple groups of sample ultrasonic signals collected under the same sampling conditions as the ultrasonic signal in a preset hyperspace.

[0010] In a second aspect, an embodiment of the present invention further provides a vascular ultrasound image processing device, the device comprising:

[0011] an image generation module, configured to acquire an ultrasonic signal of a target blood vessel object and obtain an initial blood vessel ultrasonic image based on the ultrasonic signal;

[0012] an image processing parameter acquisition module, configured to determine target brightness mapping parameters that match the ultrasound signal;

[0013] an image processing module, configured to adjust the brightness of the initial vascular ultrasound image based on the target brightness mapping parameter to obtain a target vascular ultrasound image;

[0014] The target brightness mapping parameters are parameters determined by analyzing multiple groups of sample ultrasonic signals collected under the same sampling conditions as the ultrasonic signal in a preset hyperspace.

[0015] In a third aspect, an embodiment of the present invention further provides a computer device, comprising:

[0016] one or more processors;

[0017] a memory for storing one or more programs;

[0018] When the one or more programs are executed by the one or more processors, the one or more processors implement the vascular ultrasound image processing method provided by any embodiment of the present invention.

[0019] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the vascular ultrasound image processing method provided by any embodiment of the present invention.

[0020] The technical solution of this embodiment obtains an initial vascular ultrasound image based on an ultrasound signal of a target vascular object after acquiring the ultrasound signal; then determines a target brightness mapping parameter that matches the ultrasound signal; and adjusts the brightness of the initial vascular ultrasound image based on the target brightness mapping parameter to obtain a target vascular ultrasound image to improve image quality. The target brightness mapping parameter is determined by analyzing multiple groups of sample ultrasound signals acquired under the same sampling conditions as the ultrasound signal in a preset hyperspace. The technical solution of this embodiment solves the problem of unstable image processing effects when adjusting the dynamic display range of intravascular ultrasound images in the prior art. This solution can achieve efficient and stable adjustment of the dynamic range of vascular ultrasound images during high-real-time intravascular ultrasound imaging, thereby improving vascular image quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A flowchart of a method for processing vascular ultrasound images provided by an embodiment of the present invention;

[0022] Figure 2 A flowchart of a method for processing vascular ultrasound images provided by an embodiment of the present invention;

[0023] Figure 3A schematic structural diagram of a vascular ultrasound image processing device provided by an embodiment of the present invention;

[0024] Figure 4 A schematic structural diagram of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0025] The present invention will be further described in detail below with reference to the accompanying drawings and examples. It will be understood that the specific embodiments described herein are intended only to illustrate the present invention and are not intended to limit the present invention. It should also be noted that, for ease of description, the accompanying drawings only illustrate portions relevant to the present invention, not all structures.

[0026] Figure 1 This is a flowchart of a vascular ultrasound image processing method provided in an embodiment of the present invention. This embodiment is applicable to scenarios where vascular ultrasound images are obtained by processing intravascular ultrasound signals. This method can be performed by a vascular ultrasound image processing device, which can be implemented using software and / or hardware and integrated into a computer device with application development capabilities.

[0027] like Figure 1 As shown, the blood vessel ultrasound image processing method of this embodiment includes the following steps:

[0028] S110 , acquiring an ultrasonic signal of a target blood vessel object, and obtaining an initial blood vessel ultrasonic image based on the ultrasonic signal.

[0029] The target blood vessel object may be a blood vessel object that can be imaged by intravascular ultrasound imaging equipment to collect signals and reflect the condition of the inner wall of the blood vessel.

[0030] After acquiring the ultrasound signal of the target vascular object, the ultrasound signal can be processed to obtain an initial vascular ultrasound image, converting the data signal into an image for representation. Each vascular ultrasound image has a corresponding brightness mapping curve, which reflects the mapping relationship between the pixel grayscale value in the vascular ultrasound image and the vascular ultrasound signal intensity. Correspondingly, the initial vascular ultrasound image corresponds to an initial brightness mapping curve.

[0031] In an optional manner, the acquired ultrasound signals can be input into a plurality of preset bandpass filters of different filtering frequency bands respectively to obtain ultrasound filter signals of corresponding frequency bands; then, time gain compensation is performed on each of the ultrasound filter signals respectively, and the compensated signals are superimposed to obtain processed ultrasound signals; and further, an initial vascular ultrasound image is obtained based on the processed ultrasound signals.

[0032] The gain value of the time gain compensation can be calculated by the formula T(r)=1-e -βr, where r is the distance from the ultrasonic transducer, β = ln10 αf / 20 , α is the attenuation parameter, and its unit is f is the transducer frequency in MHz.

[0033] It is understandable that during intravascular ultrasound signal imaging, the ultrasound signal emitted by the ultrasound transducer usually penetrates the blood first and then the tissue; its actual attenuation rate is an unknown variable, and different attenuation rates can be applied at different wavelengths (frequencies). The attenuation rate can be adjusted based on the experience of technicians in the field, or the gain value of the time gain compensation can be adjusted directly based on the empirical value.

[0034] S120: Determine target brightness mapping parameters that match the ultrasound signal.

[0035] In this embodiment, in order to enable the final vascular ultrasound image to have a larger dynamic display range and present a better image visual effect, the grayscale value display effect of the initial vascular ultrasound image is further adjusted, that is, the initial brightness mapping curve is changed.

[0036] The target brightness mapping parameters are parameters determined by analyzing multiple groups of sample ultrasonic signals collected under the same sampling conditions as the ultrasonic signals in a preset hyperspace. The same sampling conditions may be the situation where the blood vessels are sampled by the same signal acquisition device. Each signal acquisition device with different models or equipment parameters will have a different signal dynamic range, and the corresponding target brightness mapping parameters are different. The signal acquisition device may be an ultrasonic transducer capable of converting, transmitting and receiving ultrasonic signals. The target brightness mapping parameters are analyzed and verified for the corresponding signal acquisition device, and are better parameters for processing ultrasonic images.

[0037] Furthermore, the preset signal analysis dimensions may include parameters such as ultrasound signal intensity (brightness), signal intensity mean, signal intensity variance, and signal texture characteristics, which can be used to represent different intravascular tissue components, thereby effectively distinguishing different vascular tissue components. The preset hyperspace is a multidimensional space established based on the signals from these multiple dimensions. Furthermore, the analysis results based on each dimension can be analyzed within the preset hyperspace to obtain a set of brightness mapping parameters that effectively distinguish different vascular tissue components.

[0038] In an optional embodiment, ultrasound signal sample data can be pre-collected for different signal acquisition devices. Based on extensive data analysis, a universal, optimal brightness mapping parameter can be determined that matches the corresponding signal acquisition device. When the corresponding brightness mapping parameter is needed, the device parameters of the signal acquisition device corresponding to the initial ultrasound image to be processed can be used to query a preset brightness mapping parameter table to obtain the target brightness mapping parameter.

[0039] S130 : Adjust the brightness of the initial vascular ultrasound image based on the target brightness mapping parameter to obtain a target vascular ultrasound image.

[0040] Among them, the adjusting the brightness of the initial vascular ultrasound image based on the target brightness mapping parameters can be a process of updating the initial brightness mapping curve according to the target brightness mapping parameters, and obtaining the target brightness mapping curve through the target brightness mapping parameters, thereby obtaining the target vascular ultrasound image.

[0041] Based on the adjustment of this step, the different vascular tissue components in the vascular ultrasound image can be more distinguished and the visual effect can be better.

[0042] The technical solution of this embodiment obtains an initial vascular ultrasound image based on an ultrasound signal of a target vascular object after acquiring the ultrasound signal; then determines a target brightness mapping parameter that matches the ultrasound signal; and adjusts the brightness of the initial vascular ultrasound image based on the target brightness mapping parameter to obtain a target vascular ultrasound image to improve image quality. The target brightness mapping parameter is determined by analyzing multiple groups of sample ultrasound signals acquired under the same sampling conditions as the ultrasound signal in a preset hyperspace. The technical solution of this embodiment solves the problem of unstable image processing effects when adjusting the dynamic display range of intravascular ultrasound images in the prior art. This solution can achieve efficient and stable adjustment of the dynamic range of vascular ultrasound images during high-real-time intravascular ultrasound imaging, thereby improving vascular image quality.

[0043] Figure 2 This flowchart illustrates a method for processing vascular ultrasound images according to an embodiment of the present invention. This embodiment shares the same inventive concept as the method for processing vascular ultrasound images in the preceding embodiment and further describes the process for determining target brightness mapping parameters. This method can be performed by a vascular ultrasound image processing device, which can be implemented using software and / or hardware and integrated into a computer device with application development capabilities.

[0044] like Figure 2 As shown, the blood vessel ultrasound image processing method of this embodiment includes the following steps:

[0045] S210 , acquiring multiple groups of sample ultrasonic signals collected by the target signal collection device, and performing signal preprocessing on the multiple groups of sample ultrasonic signals respectively to obtain corresponding spatial frequency signal graphs.

[0046] The target signal acquisition device can be any type of device used for intravascular ultrasound imaging and requiring optimal brightness mapping parameter exploration, such as an ultrasonic transducer capable of converting, transmitting, and receiving ultrasonic signals. This is because different devices have different ultrasonic signal conversion and signal acquisition capabilities, so it is necessary to determine a brightness mapping parameter that is more suitable for each different type of signal acquisition device. This process can be achieved by analyzing a large number of sample ultrasound signals of the target vascular object collected by each signal acquisition device.

[0047] Specifically, the process of performing signal preprocessing on multiple groups of sample ultrasonic signals to obtain corresponding spatial frequency signal graphs may include the following steps:

[0048] First, synchronous compression wavelet transform is performed on the signals at each sampling angle in the multiple groups of sample ultrasonic signals to obtain a signal time-frequency diagram corresponding to each sampling angle in the multiple groups of sample ultrasonic signals.

[0049] It is understandable that, based on the form and characteristics of vascular ultrasound signal acquisition, ultrasound signals are typically represented using polar coordinate data. Therefore, in each set of sample ultrasound signals, each angle in the polar coordinate system corresponds to a time signal sequence. In this embodiment, using angle as the dimension, a synchronous compressed wavelet transform is performed on the sample ultrasound signals at each angle to construct channels corresponding to multiple ultrasound signals of different frequencies. The number of channels is typically set to 3-5, thereby obtaining a signal time-frequency diagram corresponding to each sampling angle of the sample ultrasound signal. This is a three-dimensional diagram determined by time-frequency-signal intensity values.

[0050] Then, the signal time-frequency diagrams corresponding to each sampling angle are combined to obtain a three-dimensional grid-like spatial frequency signal diagram. That is, the signal time-frequency diagrams at different angles are superimposed and displayed in one space, forming a four-dimensional data diagram.

[0051] S220 , identifying different vascular tissues in the spatial frequency signal image, and collecting data of multiple preset signal analysis dimensions for each of the vascular tissues.

[0052] Identifying different vascular tissues within spatial frequency signal images can be accomplished through neural network image recognition, identifying different vascular components, such as blood, tissue, fibrous plaques, calcified plaques, and stents. The different vascular tissues within multiple spatial frequency signal images are pre-labeled, and a neural network model capable of identifying these components is trained. The spatial frequency signal images are then input into this neural network model to generate corresponding identification results for the different vascular tissue components. Manual labeling or other methods for identifying vascular tissue components can also be used to identify different vascular tissues within spatial frequency signal images.

[0053] Based on the identification results of different vascular tissues, data corresponding to multiple preset signal analysis dimensions can be further analyzed, such as the signal intensity value, signal intensity mean, signal intensity variance, and texture description parameters of the region where each vascular tissue is located, corresponding to the data points of different vascular tissue components identified in the spatial frequency signal graph. The texture description parameters can be at least one of a gray level co-occurrence matrix, a homogeneous texture descriptor, and an edge histogram descriptor.

[0054] S230 : Based on the data of the multiple preset signal analysis dimensions of each of the vascular tissues, analyze and determine the target brightness mapping parameters in a hyperspace formed by the multiple preset signal analysis dimensions.

[0055] First, a hyperspace can be established using the multiple preset signal analysis dimensions. Then, the distribution area of ​​each vascular tissue in the hyperspace can be determined based on the data from the multiple preset signal analysis dimensions for each vascular tissue. A vascular tissue component in a spatial frequency signal graph can correspond to a point in the hyperspace. Vascular tissue components of the same category in multiple spatial frequency signal graphs obtained after processing multiple sample ultrasound signals can correspond to multiple points in the hyperspace, forming a point cloud.

[0056] Then, the brightness mapping curve value corresponding to the initial vascular ultrasound image (ie, the initial brightness mapping curve value) is adjusted. Each time the brightness mapping curve value is adjusted, the distance between the centers of the point clouds composed of different types of vascular tissue components is calculated.

[0057] Ultimately, the target brightness mapping parameters are determined based on the brightness mapping curve value that maximizes the distance between the center points of the vascular tissue distribution areas. This can be understood as finding a set of target brightness curve mapping parameters that maximizes the distance between the centers of the point clouds composed of different types of vascular tissue components, thereby achieving better differentiation between the different tissue components.

[0058] The distance may be calculated using Euclidean distance or other distance calculation rules, or may be calculated and determined using artificial intelligence image recognition.

[0059] Once the target brightness mapping parameters are determined, they can be applied in the process of processing relevant intravascular ultrasound images.

[0060] S240 . When the ultrasonic signal to be processed of the target blood vessel object acquired by the target signal acquisition device is acquired, an initial blood vessel ultrasonic image is obtained based on the ultrasonic signal to be processed.

[0061] S250 : Adjust the brightness of the initial vascular ultrasound image based on the target brightness mapping parameter to obtain a target vascular ultrasound image.

[0062] The technical solution of this embodiment is to first use a target signal acquisition device to sample a large number of ultrasonic signals to obtain multiple groups of sample ultrasonic signals, and then perform signal preprocessing on each of the multiple groups of sample ultrasonic signals to obtain corresponding spatial frequency signal maps; then identify different vascular tissues in the spatial frequency signal maps, and collect data from multiple preset signal analysis dimensions for each of the vascular tissues; then analyze and determine the target brightness mapping parameters in a hyperspace formed by the multiple preset signal analysis dimensions based on the data from the multiple preset signal analysis dimensions for each of the vascular tissues. After obtaining the ultrasonic signal of the target vascular object imaged by the target signal acquisition device, an initial vascular ultrasonic image is obtained based on the ultrasonic signal; then, a target brightness mapping parameter matching the signal acquisition device that acquired the ultrasonic signal is determined; and the brightness of the initial vascular ultrasonic image is adjusted based on the target brightness mapping parameter to obtain a target vascular ultrasonic image to improve image quality; wherein the target brightness mapping parameter is a parameter determined based on the analysis of the multiple groups of sample ultrasonic signals acquired by the signal acquisition device in the preset hyperspace. The technical solution of this embodiment solves the problem of unstable image processing effect when adjusting the dynamic display range of intravascular ultrasound images in the prior art. It can achieve efficient and stable adjustment of the dynamic range of vascular ultrasound images during high real-time intravascular ultrasound imaging, thereby improving the quality of vascular images.

[0063] Figure 3 This is a schematic structural diagram of a vascular ultrasound image processing device provided in an embodiment of the present invention. This embodiment is applicable to scenarios where vascular ultrasound images are obtained by signal processing based on intravascular ultrasound signals. The device can be implemented by software and / or hardware and integrated into a computer terminal device with application development capabilities.

[0064] like Figure 3 As shown, the vascular ultrasound image processing device includes: an image generating module 310 , an image processing parameter acquiring module 320 and an image processing module 330 .

[0065] Among them, the image generation module 310 is used to obtain the ultrasonic signal of the target vascular object and obtain an initial vascular ultrasonic image based on the ultrasonic signal; the image processing parameter acquisition module 320 is used to determine the target brightness mapping parameters that match the ultrasonic signal; the image processing module 330 is used to adjust the brightness of the initial vascular ultrasonic image based on the target brightness mapping parameters to obtain the target vascular ultrasonic image; wherein, the target brightness mapping parameters are parameters determined by analyzing multiple groups of sample ultrasonic signals collected under the same sampling conditions as the ultrasonic signal in a preset hyperspace.

[0066] The technical solution of this embodiment obtains an initial vascular ultrasound image based on an ultrasound signal of a target vascular object after acquiring the ultrasound signal; then determines a target brightness mapping parameter that matches the ultrasound signal; and adjusts the brightness of the initial vascular ultrasound image based on the target brightness mapping parameter to obtain a target vascular ultrasound image to improve image quality. The target brightness mapping parameter is determined by analyzing multiple groups of sample ultrasound signals acquired under the same sampling conditions as the ultrasound signal in a preset hyperspace. The technical solution of this embodiment solves the problem of unstable image processing effects when adjusting the dynamic display range of intravascular ultrasound images in the prior art. This solution can achieve efficient and stable adjustment of the dynamic range of vascular ultrasound images during high-real-time intravascular ultrasound imaging, thereby improving vascular image quality.

[0067] Optionally, the vascular ultrasound image processing device further includes a parameter analysis module, configured to analyze and determine the target brightness mapping parameters in a preset hyperspace based on a plurality of groups of sample ultrasound signals acquired under the same sampling conditions as the ultrasound signal;

[0068] The parameter analysis module is specifically used for:

[0069] performing signal preprocessing on the plurality of groups of sample ultrasonic signals respectively to obtain corresponding spatial frequency signal graphs;

[0070] Identifying different vascular tissues in the spatial frequency signal image, and collecting statistics of data of multiple preset signal analysis dimensions for each of the vascular tissues;

[0071] Based on the data of the multiple preset signal analysis dimensions of each of the vascular tissues, the target brightness mapping parameters are analyzed and determined in a hyperspace formed by the multiple preset signal analysis dimensions.

[0072] Optionally, the parameter analysis module is specifically used to:

[0073] Performing synchronous compression wavelet transform on the signals at each sampling angle in the multiple groups of sample ultrasonic signals to obtain a signal time-frequency diagram corresponding to each sampling angle in the multiple groups of sample ultrasonic signals;

[0074] The signal time-frequency diagrams corresponding to each sampling angle are combined to obtain a spatial frequency signal diagram in the form of a three-dimensional grid.

[0075] Optionally, the parameter analysis module is specifically used to:

[0076] The signal strength value, signal strength mean, signal strength variance corresponding to the vascular tissue and the texture description parameters of the region where each vascular tissue is located are respectively counted.

[0077] Optionally, the parameter analysis module is specifically used to:

[0078] Determining the distribution area of ​​each vascular tissue in the hyperspace based on the data of the multiple preset signal analysis dimensions of each vascular tissue;

[0079] The brightness mapping curve value corresponding to the initial vascular ultrasound image is adjusted so that the brightness mapping curve value that makes the distance between the center points of the distribution areas of the vascular tissues the maximum distance value is used as the target brightness mapping parameter.

[0080] Optionally, the image generation module 310 is specifically configured to:

[0081] Inputting the ultrasonic signal into a plurality of preset bandpass filters of different filtering frequency bands respectively to obtain ultrasonic filtering signals of corresponding frequency bands;

[0082] performing time gain compensation on each of the ultrasonic filter signals respectively, and superimposing the compensated signals to obtain a processed ultrasonic signal;

[0083] The initial blood vessel ultrasound image is obtained according to the processed ultrasound signal.

[0084] Optionally, the ultrasonic signal is acquired by a signal acquisition device, and the image processing parameter acquisition module 320 is specifically used to:

[0085] A preset brightness mapping parameter table is queried according to the device parameters of the signal acquisition device to obtain the target brightness mapping parameters.

[0086] The vascular ultrasonic image processing device provided in the embodiment of the present invention can execute the vascular ultrasonic image processing method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0087] Figure 4 A schematic structural diagram of a computer device provided in an embodiment of the present invention. Figure 4 A block diagram of an exemplary computer device 12 suitable for use in implementing embodiments of the present invention is shown. Figure 4The computer device 12 shown is only an example and should not limit the functionality and scope of use of the embodiments of the present invention. The computer device 12 can be any terminal device with computing capabilities, such as an intelligent controller, a server, a mobile phone, or other terminal devices.

[0088] like Figure 4 As shown, computer device 12 is implemented as a general-purpose computing device. Components of computer device 12 may include, but are not limited to, one or more processors or processing units 16, system memory 28, and a bus 18 that connects various system components (including system memory 28 and processing unit 16).

[0089] Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures. Examples of these architectures include, but are not limited to, an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MAC) bus, an Enhanced ISA bus, a Video Electronics Standards Association (VESA) local bus, and a Peripheral Component Interconnect (PCI) bus.

[0090] The computer device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the computer device 12, including volatile and non-volatile media, removable and non-removable media.

[0091] System memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. Computer device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 34 may be configured to read and write non-removable, non-volatile magnetic media ( Figure 4 Not shown, often called a "hard drive"). Although Figure 4 Not shown, a magnetic disk drive for reading and writing to a removable non-volatile magnetic disk (e.g., a "floppy disk"), and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 via one or more data media interfaces. System memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of various embodiments of the present invention.

[0092] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in system memory 28. Such program modules 42 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may include an implementation of a network environment. Program modules 42 generally perform the functions and / or methods of the embodiments described herein.

[0093] The computer device 12 may also communicate with one or more external devices 14 (e.g., a keyboard, a pointing device, a display 24, etc.), one or more devices that enable a user to interact with the computer device 12, and / or any device that enables the computer device 12 to communicate with one or more other computing devices (e.g., a network card, a modem, etc.). Such communication may be performed via an input / output (I / O) interface 22. Furthermore, the computer device 12 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 20. As shown, the network adapter 20 communicates with the other modules of the computer device 12 via the bus 18. It should be understood that although Figure 4 Not shown, other hardware and / or software modules may be used in conjunction with computer device 12, including but not limited to microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0094] The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, such as implementing the vascular ultrasound image processing method provided in the embodiment of the present invention, which includes:

[0095] Acquiring an ultrasonic signal of a target blood vessel object, and obtaining an initial blood vessel ultrasonic image based on the ultrasonic signal;

[0096] determining target brightness mapping parameters that match the ultrasound signal;

[0097] adjusting the brightness of the initial vascular ultrasound image based on the target brightness mapping parameter to obtain a target vascular ultrasound image;

[0098] The target brightness mapping parameters are parameters determined by analyzing multiple groups of sample ultrasonic signals collected under the same sampling conditions as the ultrasonic signal in a preset hyperspace.

[0099] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the method for processing vascular ultrasound images provided in any embodiment of the present invention is implemented. The method includes:

[0100] Acquiring an ultrasonic signal of a target blood vessel object, and obtaining an initial blood vessel ultrasonic image based on the ultrasonic signal;

[0101] determining target brightness mapping parameters that match the ultrasound signal;

[0102] adjusting the brightness of the initial vascular ultrasound image based on the target brightness mapping parameter to obtain a target vascular ultrasound image;

[0103] The target brightness mapping parameters are parameters determined by analyzing multiple groups of sample ultrasonic signals collected under the same sampling conditions as the ultrasonic signal in a preset hyperspace.

[0104] The computer storage medium of the embodiment of the present invention can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to: an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or component, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer 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. In this document, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device.

[0105] A computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0106] Program code embodied on a computer-readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0107] The computer program code for performing the operations of the present invention can be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0108] Those skilled in the art will appreciate that the modules or steps of the present invention described above can be implemented using a general-purpose computing device. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Alternatively, they can be implemented using program code executable by a computer device, which can then be stored in a storage device and executed by the computing device. Alternatively, they can be fabricated into separate integrated circuit modules, or multiple modules or steps can be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.

[0109] Note that the above are only preferred embodiments of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and that various obvious changes, readjustments, and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments and may include many other equivalent embodiments without departing from the concept of the present invention. The scope of the present invention is determined by the scope of the appended claims.

Claims

1. A method for processing vascular ultrasound images, characterized in that: include: Acquiring an ultrasonic signal of a target blood vessel object, and obtaining an initial blood vessel ultrasonic image based on the ultrasonic signal; determining target brightness mapping parameters that match the ultrasound signal; adjusting the brightness of the initial vascular ultrasound image based on the target brightness mapping parameter to obtain a target vascular ultrasound image; The target brightness mapping parameter is a parameter determined by analyzing multiple groups of sample ultrasonic signals acquired under the same sampling conditions as the ultrasonic signal in a preset hyperspace; The process of analyzing and determining the target brightness mapping parameters in a preset hyperspace based on multiple groups of sample ultrasonic signals acquired under the same sampling conditions as the ultrasonic signal includes: performing signal preprocessing on the plurality of groups of sample ultrasonic signals respectively to obtain corresponding spatial frequency signal graphs; Identifying different vascular tissues in the spatial frequency signal image, and collecting statistics of data of multiple preset signal analysis dimensions for each of the vascular tissues; Based on the data of the multiple preset signal analysis dimensions of each of the vascular tissues, the target brightness mapping parameters are analyzed and determined in the hyperspace formed by the multiple preset signal analysis dimensions, wherein the same sampling condition is to collect signals from the blood vessels through the same signal acquisition device, and the target brightness mapping parameters corresponding to the same signal acquisition device are the same.

2. The method according to claim 1, characterized in that The signal preprocessing is performed on each of the plurality of groups of sample ultrasonic signals to obtain corresponding spatial frequency signal graphs, including: Performing synchronous compression wavelet transform on the signals at each sampling angle in the multiple groups of sample ultrasonic signals to obtain a signal time-frequency diagram corresponding to each sampling angle in the multiple groups of sample ultrasonic signals; The signal time-frequency diagrams corresponding to each sampling angle are combined to obtain a spatial frequency signal diagram in the form of a three-dimensional grid.

3. The method according to claim 1, characterized in that Counting data of multiple preset signal analysis dimensions of each of the vascular tissues includes: The signal strength value, signal strength mean, signal strength variance corresponding to the vascular tissue and the texture description parameters of the region where each vascular tissue is located are respectively counted.

4. The method according to claim 1, wherein The step of analyzing and determining the target brightness mapping parameters in a hyperspace formed by the multiple preset signal analysis dimensions based on the data of the multiple preset signal analysis dimensions of each of the vascular tissues includes: Determining the distribution area of ​​each vascular tissue in the hyperspace based on the data of the multiple preset signal analysis dimensions of each vascular tissue; The brightness mapping curve value corresponding to the initial vascular ultrasound image is adjusted so that the brightness mapping curve value that makes the distance between the center points of the distribution areas of the vascular tissues the maximum distance value is used as the target brightness mapping parameter.

5. The method according to claim 1, wherein The obtaining of an initial blood vessel ultrasound image based on the ultrasound signal includes: Inputting the ultrasonic signal into a plurality of preset bandpass filters of different filtering frequency bands respectively to obtain ultrasonic filtering signals of corresponding frequency bands; performing time gain compensation on each of the ultrasonic filter signals respectively, and superimposing the compensated signals to obtain a processed ultrasonic signal; The initial blood vessel ultrasound image is obtained according to the processed ultrasound signal.

6. The method according to claim 1, characterized in that The ultrasonic signal is acquired by a signal acquisition device, and a target brightness mapping parameter matching the ultrasonic signal is determined, including: A preset brightness mapping parameter table is queried according to the device parameters of the signal acquisition device to obtain the target brightness mapping parameters.

7. A blood vessel ultrasonic image processing device, characterized in that: include: an image generation module, configured to acquire an ultrasonic signal of a target blood vessel object and obtain an initial blood vessel ultrasonic image based on the ultrasonic signal; an image processing parameter acquisition module, configured to determine target brightness mapping parameters that match the ultrasound signal; an image processing module, configured to adjust the brightness of the initial vascular ultrasound image based on the target brightness mapping parameter to obtain a target vascular ultrasound image, wherein the target brightness mapping parameter is a parameter determined by analyzing in a preset hyperspace a plurality of groups of sample ultrasound signals acquired under the same sampling conditions as the ultrasound signal; a parameter analysis module, configured to perform signal preprocessing on each of the plurality of groups of sample ultrasonic signals to obtain corresponding spatial frequency signal graphs; Identifying different vascular tissues in the spatial frequency signal image, and collecting statistics of data of multiple preset signal analysis dimensions for each of the vascular tissues; Based on the data of the multiple preset signal analysis dimensions of each of the vascular tissues, the target brightness mapping parameters are analyzed and determined in the hyperspace formed by the multiple preset signal analysis dimensions, wherein the same sampling conditions are to collect signals from the blood vessels through the same signal acquisition device, and the target brightness mapping parameters corresponding to the same signal acquisition device are the same.

8. A computer device, characterized in that: The computer device comprises: one or more processors; a memory for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the vascular ultrasound image processing method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the vascular ultrasound image processing method according to any one of claims 1 to 6 is implemented.

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