CN110610147A - Blood vessel image extraction method, related device and storage device - Google Patents

Abstract

The invention is suitable for the technical field of optical imaging, and provides a blood vessel image extraction method, a related device and storage equipment, wherein the method comprises the following steps: acquiring a three-dimensional blood vessel image; denoising the three-dimensional blood vessel image in an opening operation and closing operation mode to obtain a denoised three-dimensional blood vessel image; extracting a blood vessel characteristic map of the denoised three-dimensional blood vessel image through a Gaussian scale space; carrying out image enhancement on the blood vessel characteristic diagram through a Hessian matrix to obtain a blood vessel enhanced image; and carrying out region growing on the blood vessel enhanced image to obtain a blood vessel extraction image.

Description

Blood vessel image extraction method, related device and storage device

technical field

The invention belongs to the technical field of optical imaging, and in particular relates to a blood vessel image extraction method, a related device and a storage device.

Background technique

Photoacoustic imaging is the latest development in the world, a breakthrough new non-invasive biomedical imaging technology with both optical and ultrasonic imaging advantages. Its principle is to introduce pulsed laser light into biological tissue, and the tissue will generate ultrasonic signals due to instantaneous thermal expansion. The light absorption information of the tissue is obtained by detecting this signal. Hemoglobin in biological tissue absorbs different wavelengths of light in different degrees, and the intensity of the excited photoacoustic signal is also different. Choose a high-absorption single wavelength to scan blood vessels, which can be directly imaged without injecting any contrast agent; while choosing multiple wavelengths to scan, you can obtain blood oxygen saturation, oxygen metabolism rate and other parameters through calculation, which is very important for the study of the anterior segment. Vascular morphology and function are of great significance.

Vessel extraction algorithms have always been the prerequisite for efficient disease diagnosis and have been widely used in imaging systems such as magnetic resonance and optical coherence tomography. However, there is no suitable three-dimensional vessel extraction algorithm for photoacoustic imaging. Although the two-dimensional blood vessel data extraction method is easy to implement, the useful information contained in the projected two-dimensional data is far less than that of the three-dimensional data, and cannot be efficiently used in the assessment of vascular lesions.

Contents of the invention

In view of this, the embodiments of the present invention provide a blood vessel image extraction method, a related device and a storage device, so as to solve the problem of poor imaging quality of scanned images in the prior art.

The first aspect of the embodiments of the present invention provides a blood vessel image extraction method, including:

Use the first parameter group to perform three-dimensional scanning on the target object to obtain three-dimensional scanning data. The first parameter group includes scanning distance parameters in the three directions of X, Y and Z axes, wherein the Z-axis scanning of the first parameter group distance is zero;

Determine the Z-axis dynamic scanning parameters of the target object according to the three-dimensional scanning data, and the Z-axis dynamic scanning parameters include N Z-axis distances of N points on the surface arc of the target object respectively projected to the focal plane, the said N is an integer greater than 1;

Adjusting the Z-axis dynamic scanning distance in the first parameter group according to the Z-axis dynamic scanning parameters to obtain a second parameter group;

performing arc scanning on the target object by using the second parameter set to obtain arc scanning image data of the target object.

In an implementation manner of an embodiment of the present application, the denoising processing is performed on the three-dimensional blood vessel image by means of opening operation and closing operation to obtain a denoised three-dimensional blood vessel image, including:

performing an erosion operation on the three-dimensional blood vessel image, and performing an opening operation on the etched three-dimensional blood vessel image to obtain an opening operation diagram;

performing an expansion operation on the three-dimensional blood vessel image, and performing a closing operation on the expanded three-dimensional blood vessel image to obtain a closing operation diagram;

Fourier transform is performed on the opening operation graph and the closing operation graph to obtain a denoised three-dimensional blood vessel image.

In one implementation manner of the embodiment of the present application,

Extracting the blood vessel feature map of the denoised three-dimensional blood vessel image through Gaussian scale space, including:

Convolving the preset Gaussian kernel function with the denoised three-dimensional blood vessel image to construct a Gaussian pyramid of the three-dimensional blood vessel image;

Obtaining the Gaussian scale space of the three-dimensional blood vessel image by subtracting images of adjacent scales in the Gaussian pyramid;

Extracting local extremum points in the Gaussian scale space to form a blood vessel feature map of the three-dimensional blood vessel image.

In one implementation manner of the embodiment of the present application,

The three-dimensional blood vessel image includes: a maximum-magnitude projection image of the three-dimensional data reconstructed image.

The second aspect of the embodiments of the present invention provides a blood vessel image extraction device, including:

an acquisition unit, configured to acquire a three-dimensional blood vessel image;

A denoising unit, configured to perform denoising processing on the three-dimensional blood vessel image by means of an opening operation and a closing operation, to obtain a denoised three-dimensional blood vessel image;

A feature extraction unit, configured to extract a blood vessel feature map of the denoised three-dimensional blood vessel image through a Gaussian scale space;

An enhancement unit, configured to perform image enhancement on the blood vessel feature map through a Hessian matrix to obtain a blood vessel enhanced image;

The image extraction unit is configured to perform region growth on the enhanced blood vessel image to obtain an extracted blood vessel image.

In an implementation manner of the embodiment of the present application, the denoising unit is specifically used for:

performing an erosion operation on the three-dimensional blood vessel image, and performing an opening operation on the etched three-dimensional blood vessel image to obtain an opening operation diagram;

performing an expansion operation on the three-dimensional blood vessel image, and performing a closing operation on the expanded three-dimensional blood vessel image to obtain a closing operation diagram;

Fourier transform is performed on the opening operation graph and the closing operation graph to obtain a denoised three-dimensional blood vessel image.

In an implementation manner of the embodiment of the present application, the feature extraction unit is specifically used for:

Extracting the blood vessel feature map of the denoised three-dimensional blood vessel image through Gaussian scale space, including:

Convolving the preset Gaussian kernel function with the denoised three-dimensional blood vessel image to construct a Gaussian pyramid of the three-dimensional blood vessel image;

Obtaining the Gaussian scale space of the three-dimensional blood vessel image by subtracting images of adjacent scales in the Gaussian pyramid;

Extracting local extremum points in the Gaussian scale space to form a blood vessel feature map of the three-dimensional blood vessel image.

In an implementation manner of the embodiment of the present application, the three-dimensional blood vessel image includes: a maximum-magnitude projection image of a reconstructed image from three-dimensional data.

The third aspect of the embodiment of the present application provides another electronic device, including: a memory, a processor, and a computer program stored on the memory and operable on the processor. When the processor executes the computer program, , to implement the blood vessel image extraction method provided in the first aspect of the embodiment of the present application.

The fourth aspect of the embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the blood vessel image extraction method provided in the first aspect of the above-mentioned embodiment of the present application is implemented.

The beneficial effect that the embodiment of the present invention exists compared with prior art is:

In the embodiment of the present application, the 3D blood vessel image is denoised by means of opening operation and closing operation, the blood vessel feature map of the denoised 3D blood vessel image is extracted through the Gaussian scale space, and then the Hessian matrix Image enhancement is performed on the blood vessel feature map to obtain a blood vessel enhanced image, and finally region growth is performed on the blood vessel enhanced image to obtain a blood vessel extraction image. The application can directly extract the three-dimensional blood vessel data without performing two-dimensional projection on the data and then extract the blood vessel data, which can be more efficiently used in disease diagnosis.

Description of drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the accompanying drawings that need to be used in the descriptions of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only of the present invention. For some embodiments, those of ordinary skill in the art can also obtain other drawings based on these drawings without any creative effort.

FIG. 1 is a schematic flowchart of a blood vessel image extraction method provided by an embodiment of the present invention;

Fig. 2 is a schematic diagram of an example provided by an embodiment of the present invention;

Fig. 3 is a schematic diagram of another example provided by the embodiment of the present invention;

Fig. 4 is a schematic diagram of another example provided by the embodiment of the present invention;

Fig. 5 is a structural example diagram of a blood vessel image extraction device provided by an embodiment of the present invention;

FIG. 6 is a schematic structural diagram of an electronic device provided by an embodiment of the present invention.

Detailed ways

In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are the embodiment of the present invention. Some examples, but not all examples. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present invention.

The terms "comprising" and any variations thereof in the description and claims of the present invention and the above drawings are intended to cover non-exclusive inclusion. For example, a process, method or system, product or device comprising a series of steps or units is not limited to the listed steps or units, but optionally also includes steps or units not listed, or optionally further includes Other steps or units inherent in these processes, methods, products or apparatus. In addition, the terms "first", "second", and "third", etc. are used to distinguish different objects, not to describe a specific order.

Embodiment one

Please refer to Fig. 1, an embodiment of a blood vessel image extraction method in the embodiment of the present application, including:

101. Obtain a three-dimensional blood vessel image;

Exemplarily, the three-dimensional blood vessel image in the embodiment of the present application may be obtained by scanning the target object. In the embodiment of the present application, the target object may be an animal organ with a curved surface. For example, the anterior segment of the human body. Specifically, the anterior segment includes: the anterior chamber, the posterior chamber, the zonular ligament of the lens, the angle of the chamber, part of the lens, the peripheral vitreous, the retina, the attachment point of the extraocular muscles, and the conjunctiva.

Exemplarily, the three-dimensional scanning data in the embodiment of the present application includes: a maximum-magnitude projection map of a reconstructed image of the three-dimensional data, and a signal intensity of a corresponding pixel in the maximum-amplitude projection map.

102. Perform denoising processing on the three-dimensional blood vessel image by means of an opening operation and a closing operation, to obtain a denoised three-dimensional blood vessel image;

The opening operation generally smoothes the contours of objects, breaking off narrow necks and eliminating thin protrusions. The closing operation also smoothes part of the contour, but in contrast to the opening operation, it usually bridges narrower discontinuities and elongated gullies, eliminates small holes, and fills gaps in the contour line.

Exemplarily, an erosion operation is performed on the three-dimensional blood vessel image, and an opening operation is performed on the eroded three-dimensional blood vessel image to obtain an opening operation graph; an expansion operation is performed on the three-dimensional blood vessel image, and an expansion operation is performed on the expanded three-dimensional blood vessel image. Close operation to obtain a closed operation graph; performing Fourier transform on the open operation graph and the closed operation graph to obtain a denoised three-dimensional blood vessel image.

103. Extracting a blood vessel feature map of the denoised three-dimensional blood vessel image through a Gaussian scale space;

Gaussian scale space is a theory for multi-scale analysis of images. Its basic idea is: assuming that f(x) is the Fourier spectrum of the original signal defined in the range of [0, π], the Gaussian kernel function of the scale parameter is introduced into the formula x is the frequency, t is the scale parameter, e is the natural constant, and convolved with f(x), by changing the parameters of the kernel function, the spatial representation sequence of f(x) at different scales can be obtained.

Exemplarily, the preset Gaussian kernel function is convolved with the denoised three-dimensional blood vessel image to construct a Gaussian pyramid of the three-dimensional blood vessel image; by subtracting images of adjacent scales in the Gaussian pyramid, the A Gaussian scale space of the three-dimensional blood vessel image; extracting local extremum points in the Gaussian scale space to form a blood vessel feature map of the three-dimensional blood vessel image.

104. Perform image enhancement on the blood vessel feature map by using a Hessian matrix;

Image enhancement is performed on the blood vessel feature map through a Hessian matrix to obtain a blood vessel enhanced image.

The Hessian matrix finds the gradient information of each pixel by calculating the second-order derivative of each pixel in the image. For the special shape of blood vessels, the gradient along the centerline of the blood vessel is basically unchanged, and the gradient along the direction of the blood vessel section In order to distinguish the vascular signal from other signals, the vascular signal can be enhanced.

105. Perform region growing on the blood vessel enhanced image to obtain a blood vessel extraction image.

Region growing is the process of aggregating pixels or sub-regions into larger regions according to predefined criteria. The basic idea is to start from a group of growth points, merge adjacent pixels or regions with similar properties to the growth point to form a new growth point, and repeat this process until it cannot grow, so as to extract all blood vessel signals. When extracting blood vessel signals, first set the points with very strong signals as seed points (because the photoacoustic signal strength of blood will be stronger than other signals), and set a certain threshold to grow until it cannot grow.

In the embodiment of the present application, the 3D blood vessel image is denoised by means of opening operation and closing operation, the blood vessel feature map of the denoised 3D blood vessel image is extracted through the Gaussian scale space, and then the Hessian matrix Image enhancement is performed on the blood vessel feature map to obtain a blood vessel enhanced image, and finally region growth is performed on the blood vessel enhanced image to obtain a blood vessel extraction image. The application can directly extract the three-dimensional blood vessel data without performing two-dimensional projection on the data and then extract the blood vessel data, which can be more efficiently used in disease diagnosis.

Embodiment 2

The embodiment of the present application takes a specific experiment as an example to illustrate the blood vessel image extraction method in the embodiment of the present application, including:

The target object of the embodiment of the present application is the root of the iris on the top surface of the eyeball. The image of blood vessels at the root of the iris is scanned by a three-dimensional scanning device to obtain the image of blood vessels in the iris as shown in FIG. 2 .

Wherein, the embodiment of the present application processes the blood vessel image framed in the rectangular frame in FIG. 2 , and the two rectangular frames in the figure are respectively marked as area 1 and area 2). As shown, the two labeled regions have two different vessel characteristics. Region 1 represents the disordered iris-partially distributed vasculature, while region 2 represents iris-partially distributed blood vessels.

Please refer to FIG. 3 . FIG. 3 shows the processing results obtained through each key step of the blood vessel image extraction method in the embodiment of the present application. Among them, (a) to (d) in FIG. 3 are the process of blood vessel extraction in area 1, and (e) to (h) are the process of blood vessel extraction in area 2. (d) and (h) are the final blood vessel extraction images, respectively. (a) and (e) in Fig. 3 are the original unprocessed images of region 1 and region 2, respectively.

By comparing the blood vessel signals in the figure, in (b) and (f) of Figure 3, we can clearly see that the method based on the Hessian matrix enhances the whole image of the blood vessel signals. However, the microvascular signal is still relatively weak and thus not diagnostic. However, after intensity conversion, the microvascular signal is further enhanced, and (c) and (g) in Figure 3 are obtained; further, after the region growth of the enhanced blood vessel image is completed, as shown in (d) and (h) in Figure 3, the Background noise is completely removed, resulting in clear images of blood vessels.

For the accuracy of the algorithm in the embodiment of the present application, make dotted line in Fig. 3 and carry out the contrast of original image (Fig. 3 (a) and (e)) and final image " Fig. 3 (d) and (h) ", As shown in Figure 4, the irregular wavy line and pulse line in Figure 4 (a) represent the signal strength at the dotted line in Figure 3 (a) and (d) respectively; the irregular wavy line and pulse line in (b) Represent the signal intensity at the yellow dotted line in Figure 3(e) and (h), respectively. Observation shows that blood vessel signals are accurately extracted.

Embodiment 3

The embodiment of the present application also provides a blood vessel image extraction device that implements the above blood vessel image extraction method, please refer to FIG. 5 , including:

An acquisition unit 501, configured to acquire a three-dimensional blood vessel image;

A denoising unit 502, configured to perform denoising processing on the three-dimensional blood vessel image by means of opening operation and closing operation, to obtain a denoised three-dimensional blood vessel image;

A feature extraction unit 503, configured to extract a blood vessel feature map of the denoised three-dimensional blood vessel image through a Gaussian scale space;

An enhancement unit 504, configured to perform image enhancement on the blood vessel feature map through a Hessian matrix to obtain a blood vessel enhanced image;

The image extraction unit 505 is configured to perform region growing on the enhanced blood vessel image to obtain an extracted blood vessel image.

In an implementation manner of the embodiment of the present application, the denoising unit 502 is specifically configured to:

performing an erosion operation on the three-dimensional blood vessel image, and performing an opening operation on the etched three-dimensional blood vessel image to obtain an opening operation diagram;

performing an expansion operation on the three-dimensional blood vessel image, and performing a closing operation on the expanded three-dimensional blood vessel image to obtain a closing operation diagram;

Fourier transform is performed on the opening operation graph and the closing operation graph to obtain a denoised three-dimensional blood vessel image.

In an implementation manner of the embodiment of the present application, the feature extraction unit 503 is specifically configured to:

Extracting the blood vessel feature map of the denoised three-dimensional blood vessel image through Gaussian scale space, including:

Convolving the preset Gaussian kernel function with the denoised three-dimensional blood vessel image to construct a Gaussian pyramid of the three-dimensional blood vessel image;

Obtaining the Gaussian scale space of the three-dimensional blood vessel image by subtracting images of adjacent scales in the Gaussian pyramid;

Extracting local extremum points in the Gaussian scale space to form a blood vessel feature map of the three-dimensional blood vessel image.

In an implementation manner of the embodiment of the present application, the three-dimensional blood vessel image includes: a maximum-magnitude projection image of a reconstructed image from three-dimensional data.

For the specific process of each functional module in the electronic device provided in this embodiment to realize their respective functions, please refer to the specific content described in the embodiment shown in FIG. 1 above, and details will not be repeated here.

Embodiment 4

An embodiment of the present application provides an electronic device, please refer to Figure 6, the electronic device includes:

The memory 601, the processor 602, and the computer program stored in the memory 601 and operable on the processor 602, when the processor 602 executes the computer program, implements the blood vessel image extraction method described in the embodiment shown in FIG. 1 above.

Further, the electronic device also includes:

At least one input device 603 and at least one output device 604 .

The aforementioned memory 601 , processor 602 , input device 603 and output device 604 are connected through a bus 605 .

Wherein, the input device 603 may specifically be a camera, a touch panel, a physical button or a mouse, and the like. The output device 604 may specifically be a display screen.

The memory 601 may be a high-speed random access memory (RAM, Random Access Memory) memory, or a non-volatile memory (non-volatile memory), such as a disk memory. The memory 601 is used to store a set of executable program codes, and the processor 602 is coupled to the memory 601 .

Further, the embodiment of the present application also provides a computer-readable storage medium, which can be set in the electronic device in each of the above-mentioned embodiments, and the computer-readable storage medium can be the memory in the example embodiment. A computer program is stored on the computer-readable storage medium, and when the program is executed by a processor, the blood vessel image extraction method described in the foregoing embodiment shown in FIG. 1 is implemented. Further, the computer storage medium can also be various media that can store program codes such as U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), RAM, magnetic disk or optical disk.

In the several embodiments provided in this application, it should be understood that the disclosed devices and methods may be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the division of the modules is only a logical function division. In actual implementation, there may be other division methods. For example, multiple modules or components can be combined or May be integrated into another system, or some features may be ignored, or not implemented. In another point, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces, and the indirect coupling or communication connection of devices or modules may be in electrical, mechanical or other forms.

The modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, that is, they may be located in one place, or may be distributed to multiple network modules. Part or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional module in each embodiment of the present application may be integrated into one processing module, each module may exist separately physically, or two or more modules may be integrated into one module. The above-mentioned integrated modules can be implemented in the form of hardware or in the form of software function modules.

If the integrated modules are realized in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of software products, and the computer software products are stored in a readable memory The medium contains several instructions to enable a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The above-mentioned readable storage medium includes: various media capable of storing program codes such as U disk, mobile hard disk, ROM, RAM, magnetic disk or optical disk.

It should be noted that, for the sake of simplicity of description, the aforementioned method embodiments are expressed as a series of action combinations, but those skilled in the art should know that the present application is not limited by the described action sequence. Depending on the application, certain steps may be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by this application.

In the foregoing embodiments, the descriptions of each embodiment have their own emphases, and for parts not described in detail in a certain embodiment, reference may be made to relevant descriptions of other embodiments.

The above is the description of the blood vessel image extraction method, electronic device and computer-readable storage medium provided by this application. For those skilled in the art, according to the ideas of the embodiments of this application, there will be changes in the specific implementation and application scope In summary, the contents of this specification should not be construed as limiting the application.

Claims (10)

1. A blood vessel image extraction method is characterized by comprising the following steps:

acquiring a three-dimensional blood vessel image;

denoising the three-dimensional blood vessel image in an opening operation and closing operation mode to obtain a denoised three-dimensional blood vessel image;

extracting a blood vessel characteristic map of the denoised three-dimensional blood vessel image through a Gaussian scale space;

carrying out image enhancement on the blood vessel characteristic diagram through a Hessian matrix to obtain a blood vessel enhanced image;

and carrying out region growing on the blood vessel enhanced image to obtain a blood vessel extraction image.

2. The method of claim 1,

the denoising processing is performed on the three-dimensional blood vessel image in an opening operation and closing operation mode to obtain a denoised three-dimensional blood vessel image, and the denoising processing comprises the following steps:

carrying out corrosion operation processing on the three-dimensional blood vessel image, and carrying out opening operation on the corroded three-dimensional blood vessel image to obtain an opening operation diagram;

performing expansion operation processing on the three-dimensional blood vessel image, and performing closing operation on the expanded three-dimensional blood vessel image to obtain a closing operation diagram;

and carrying out Fourier transform on the open operation graph and the closed operation graph to obtain a denoised three-dimensional blood vessel image.

3. The method of claim 1,

extracting a blood vessel characteristic map of the denoised three-dimensional blood vessel image through a Gaussian scale space, comprising the following steps of:

convolving a preset Gaussian kernel function with the denoised three-dimensional blood vessel image to construct a Gaussian pyramid of the three-dimensional blood vessel image;

subtracting images of adjacent scales in the Gaussian pyramid to obtain a Gaussian scale space of the three-dimensional blood vessel image;

and extracting local extreme points in the Gaussian scale space to form a blood vessel characteristic map of the three-dimensional blood vessel image.

4. The method of claim 1,

the three-dimensional blood vessel image includes: the three-dimensional data reconstructs a maximum amplitude projection of the image.

5. A blood vessel image extraction device characterized by comprising:

an acquisition unit for acquiring a three-dimensional blood vessel image;

the denoising unit is used for denoising the three-dimensional blood vessel image in an opening operation and closing operation mode to obtain a denoised three-dimensional blood vessel image;

the feature extraction unit is used for extracting a blood vessel feature map of the denoised three-dimensional blood vessel image through a Gaussian scale space;

the enhancement unit is used for carrying out image enhancement on the blood vessel characteristic map through a Hessian matrix to obtain a blood vessel enhanced image;

and the image extraction unit is used for carrying out region growing on the blood vessel enhanced image to obtain a blood vessel extraction image.

6. The apparatus of claim 5,

the denoising unit is specifically configured to:

carrying out corrosion operation processing on the three-dimensional blood vessel image, and carrying out opening operation on the corroded three-dimensional blood vessel image to obtain an opening operation diagram;

performing expansion operation processing on the three-dimensional blood vessel image, and performing closing operation on the expanded three-dimensional blood vessel image to obtain a closing operation diagram;

and carrying out Fourier transform on the open operation graph and the closed operation graph to obtain a denoised three-dimensional blood vessel image.

7. The apparatus of claim 5,

the feature extraction unit is specifically configured to:

extracting a blood vessel characteristic map of the denoised three-dimensional blood vessel image through a Gaussian scale space, comprising the following steps of:

convolving a preset Gaussian kernel function with the denoised three-dimensional blood vessel image to construct a Gaussian pyramid of the three-dimensional blood vessel image;

subtracting images of adjacent scales in the Gaussian pyramid to obtain a Gaussian scale space of the three-dimensional blood vessel image;

and extracting local extreme points in the Gaussian scale space to form a blood vessel characteristic map of the three-dimensional blood vessel image.

8. The apparatus of claim 5,

the three-dimensional blood vessel image includes: the three-dimensional data reconstructs a maximum amplitude projection of the image.

9. An electronic device, comprising: memory, processor and computer program stored on the memory and executable on the processor, characterized in that the processor implements the method of any one of claims 1 to 4 when executing the computer program.

10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the method of any one of claims 1 to 4.