CN110610147A - Blood vessel image extraction method, related device and storage equipment - Google Patents
Blood vessel image extraction method, related device and storage equipment Download PDFInfo
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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
Technical Field
The invention belongs to the technical field of optical imaging, and particularly relates to a blood vessel image extraction method, a related device and storage equipment.
Background
Photoacoustic imaging is a novel breakthrough noninvasive biomedical imaging technology which is newly developed internationally and has the advantages of optical imaging and ultrasonic imaging, and the principle is that pulse laser is guided into biological tissues, the tissues generate ultrasonic signals due to instantaneous thermal expansion, and light absorption information of the tissues is obtained by detecting the signals. The hemoglobin in the biological tissue has different absorption degrees to different wavelengths of light, and the excited photoacoustic signals have different strength degrees. The blood vessel is scanned by selecting the high-absorption single wavelength, so that direct imaging can be realized without injecting any contrast agent; and the parameters such as oxygen saturation, oxygen metabolic rate and the like in blood can be obtained through calculation by selecting multiple wavelengths for scanning, which has great significance for researching the form and the function of the anterior segment blood vessel.
The blood vessel extraction algorithm is always the premise of ensuring efficient disease diagnosis and is widely applied to imaging systems such as magnetic resonance imaging, optical coherence tomography and the like, and a proper three-dimensional blood vessel extraction algorithm does not exist in photoacoustic imaging. Although the two-dimensional blood vessel data extraction method is convenient to implement, useful information contained in the projected two-dimensional data is far smaller than that of the three-dimensional data, and the two-dimensional blood vessel data extraction method cannot be effectively used for evaluating blood vessel lesions.
Disclosure of Invention
In view of this, embodiments of the present invention provide a blood vessel image extraction method, a related apparatus, and a storage device, so as to solve the problem in the prior art that the imaging quality of a scanned image is not good.
A first aspect of an embodiment of the present invention provides a blood vessel image extraction method, including:
three-dimensionally scanning a target object by using a first parameter group to obtain three-dimensional scanning data, wherein the first parameter group comprises X, Y and scanning distance parameters in three directions of a Z axis, and the Z axis scanning distance of the first parameter group is zero;
determining Z-axis dynamic scanning parameters of the target object according to the three-dimensional scanning data, wherein the Z-axis dynamic scanning parameters comprise N Z-axis distances of N points on a surface arc line of the target object projected to a focal plane respectively, and 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;
and performing radian scanning on the target object by using the second parameter set to obtain radian scanning image data of the target object.
In an implementation manner of the embodiment of the present application, the denoising processing on the three-dimensional blood vessel image in an open operation and a closed operation manner to obtain a denoised three-dimensional blood vessel image includes:
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.
In one implementation of an embodiment of the present application,
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.
In one implementation of an embodiment of the present application,
the three-dimensional blood vessel image includes: the three-dimensional data reconstructs a maximum amplitude projection of the image.
A second aspect of an embodiment of the present invention provides a blood vessel image extraction device, including:
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.
In an implementation manner of the embodiment of the present application, 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.
In an implementation manner of the embodiment of the present application, 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.
In one implementation of the embodiment of the present application, the three-dimensional blood vessel image includes: the three-dimensional data reconstructs a maximum amplitude projection of the image.
A third aspect of the embodiments of the present application provides another electronic apparatus, including: the blood vessel image extraction method comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the computer program to realize the blood vessel image extraction method provided by the first aspect of the embodiment of the present application.
A fourth aspect of the embodiments of the present application provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the blood vessel image extraction method provided in the first aspect of the embodiments of the present application.
Compared with the prior art, the embodiment of the invention has the following beneficial effects:
in the embodiment of the application, the three-dimensional blood vessel image is denoised in an opening operation and closing operation mode, a blood vessel characteristic diagram of the denoised three-dimensional blood vessel image is extracted through a Gaussian scale space, the blood vessel characteristic diagram is subjected to image enhancement through a Hessian matrix to obtain a blood vessel enhanced image, and finally the blood vessel enhanced image is subjected to region growing to obtain a blood vessel extraction image. The method and the device can directly extract the three-dimensional blood vessel data without extracting the blood vessel data after two-dimensional projection is carried out on the data, and can be more efficiently applied to disease diagnosis.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed for the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a schematic flow chart of a blood vessel image extraction method provided by an embodiment of the invention;
FIG. 2 is a schematic diagram of an example provided by an embodiment of the invention;
FIG. 3 is a schematic diagram of another example provided by an embodiment of the present invention;
FIG. 4 is a schematic diagram of another example provided by an embodiment of the present invention;
fig. 5 is a diagram illustrating a configuration of a blood vessel image extraction device according to an embodiment of the present invention;
fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood by those skilled in the art, the technical solutions in the embodiments of the present invention will be clearly described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terms "comprises" and "comprising," and any variations thereof, in the description and claims of this invention and the above-described drawings are intended to cover non-exclusive inclusions. For example, a process, method, or system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus. Furthermore, the terms "first," "second," and "third," etc. are used to distinguish between different objects and are not used to describe a particular order.
Example one
Referring to fig. 1, an embodiment of a blood vessel image extraction method in the embodiment of the present application includes:
101. acquiring a three-dimensional blood vessel image;
illustratively, 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. Such as the anterior segment of the eye. Specifically, the anterior segment includes: anterior chamber, posterior chamber, zonules of the lens, angle of the chamber, portion of the lens, peripheral vitreous body, retinal and extraocular muscle attachment sites, conjunctiva, and the like.
Illustratively, the three-dimensional scan data in the embodiment of the present application includes: and reconstructing a maximum amplitude projection graph of the image by using the three-dimensional data and the signal intensity of the corresponding pixel point in the maximum amplitude projection graph.
102. 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 opening operation generally smoothes the contour of the object, breaks narrower necks, and eliminates thin protrusions. The close operation also smoothes a portion of the contour, but in contrast to the open operation, it typically closes narrow discontinuities and elongated ravines, eliminates small holes, and fills breaks in the contour.
Exemplarily, performing corrosion operation processing on the three-dimensional blood vessel image, and performing opening operation on the corroded three-dimensional blood vessel image to obtain an opening operation graph; 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.
103. Extracting a blood vessel characteristic map of the denoised three-dimensional blood vessel image through a Gaussian scale space;
the Gaussian scale space is a theory for performing multi-scale analysis on an image, and the basic idea is as follows: assuming that f (x) is a Fourier spectrum of an original signal defined in a [0, pi ] range, x is frequency, t is a scale parameter and e is a natural constant in a Gaussian kernel function formula introducing scale parameters, and the Gaussian kernel function formula is convolved with f (x), and a spatial representation sequence of f (x) under different scales can be obtained by changing parameters of the kernel function.
Exemplarily, 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.
104. Performing image enhancement on the blood vessel characteristic map through a Hessian matrix;
and carrying out image enhancement on the blood vessel characteristic diagram through a Hessian matrix to obtain a blood vessel enhanced image.
The Hessian matrix finds gradient information of each pixel point by solving a second derivative of each pixel point in an image, and for the special form of the blood vessel, the gradient along the central line direction of the blood vessel is basically unchanged, and the gradient along the section direction of the blood vessel is greatly changed, so that blood vessel signals and other signals are distinguished, and the blood vessel signals are enhanced.
105. And carrying out region growing on the blood vessel enhanced image to obtain a blood vessel extraction image.
Region growing is the process of grouping pixels or sub-regions into larger regions according to a predefined criterion. The basic idea is to extract all blood vessel signals by starting with a set of growing points, merging adjacent pixels or regions with similar properties to the growing points with the growing points to form new growing points, and repeating the process until the growing points cannot grow. When a blood vessel signal is extracted, a point with a very strong signal is set as a seed point (because the intensity of a photoacoustic signal of blood is stronger than that of other signals), and a certain threshold is set for growth until the growth is impossible.
In the embodiment of the application, the three-dimensional blood vessel image is denoised in an opening operation and closing operation mode, a blood vessel characteristic diagram of the denoised three-dimensional blood vessel image is extracted through a Gaussian scale space, the blood vessel characteristic diagram is subjected to image enhancement through a Hessian matrix to obtain a blood vessel enhanced image, and finally the blood vessel enhanced image is subjected to region growing to obtain a blood vessel extraction image. The method and the device can directly extract the three-dimensional blood vessel data without extracting the blood vessel data after two-dimensional projection is carried out on the data, and can be more efficiently applied to disease diagnosis.
Example two
The embodiment of the present application describes, by taking a specific experiment as an example, a blood vessel image extraction method in the embodiment of the present application, including:
the target object of the embodiment of the application is the root of the iris on the surface of the top of the eyeball, and the blood vessel image of the root of the iris is scanned by a three-dimensional scanning device to obtain the blood vessel image of the iris as shown in fig. 2.
In the embodiment of the present application, the blood vessel image framed in the rectangular frame in fig. 2 is processed, and the two rectangular frames in the figure are respectively marked as region 1 and region 2). As shown, the two marker regions have two different vessel characteristics. Region 1 represents the unordered iris portion of the distributed vasculature, while region 2 represents the radially distributed blood vessels of the iris portion.
Referring to fig. 3, fig. 3 shows processing results obtained through each key step of the blood vessel image extraction method in the embodiment of the present application. In fig. 3, (a) to (d) represent the process of extracting blood vessels from the region 1, and (e) to (h) represent the process of extracting blood vessels from the region two. (d) And (h) final vessel extraction maps, respectively. In fig. 3, (a) and (e) are the original unprocessed images of the area 1 and the area 2, respectively.
As can be seen by comparing the vessel signals in the figures, in (b) and (f) of fig. 3, we can clearly see that the Hessian matrix-based method enhances the entire image of the vessel signals. However, the microvascular signal is still relatively weak and therefore cannot be diagnosed. But then the microvascular signal is further enhanced by intensity conversion, resulting in (c) and (g) of fig. 3; further, after completing the region growing of the blood vessel enhanced image, as shown in (d) and (h) of fig. 3, the background noise is completely removed, resulting in a clear blood vessel image.
For the accuracy of the algorithm in the embodiment of the present application, the comparison between the original image (fig. 3(a) and (e)) and the final image "fig. 3 (d) and (h)" is performed by making a dotted line in fig. 3, as shown in fig. 4, and the irregular wavy line and the impulse line in fig. 4 (a) represent the signal intensity at the dotted line in fig. 3(a) and (d), respectively; the irregular wavy line and the pulse line in (b) represent the signal intensity at the yellow dotted line in fig. 3(e) and (h), respectively. Observation revealed that vascular signals were accurately extracted.
EXAMPLE III
An embodiment of the present application further provides a blood vessel image extraction device for implementing the blood vessel image extraction method, please refer to fig. 5, including:
an obtaining unit 501, configured to obtain a three-dimensional blood vessel image;
the denoising unit 502 is configured to perform denoising processing on the three-dimensional blood vessel image through an open operation and a close 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 enhancing unit 504, configured to perform image enhancement on the blood vessel feature map through a hessian matrix to obtain a blood vessel enhanced image;
and an image extraction unit 505, configured to perform region growing on the blood vessel enhanced image to obtain a blood vessel extraction image.
In an implementation manner of the embodiment of the present application, the denoising unit 502 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.
In an implementation manner of the embodiment of the present application, the feature extraction unit 503 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.
In one implementation of the embodiment of the present application, the three-dimensional blood vessel image includes: the three-dimensional data reconstructs a maximum amplitude projection of the image.
For a specific process of each function module in the electronic device provided in this embodiment to implement each function, please refer to the specific content described in the embodiment shown in fig. 1, which is not described herein again.
Example four
An embodiment of the present application provides an electronic device, please refer to fig. 6, which includes:
a memory 601, a processor 602, and a computer program stored on the memory 601 and executable on the processor 602, when the processor 602 executes the computer program, the blood vessel image extraction method described in the embodiment shown in fig. 1 is implemented.
Further, the electronic device further includes:
at least one input device 603 and at least one output device 604.
The memory 601, the processor 602, the input device 603, and the output device 604 are connected by a bus 605.
The input device 603 may be a camera, a touch panel, a physical button, a mouse, or the like. The output device 604 may be embodied as a display screen.
The Memory 601 may be a high-speed Random Access Memory (RAM) Memory, or a non-volatile Memory (non-volatile Memory), such as a disk Memory. The memory 601 is used for storing a set of executable program code, and the processor 602 is coupled to the memory 601.
Further, an embodiment of the present application also provides a computer-readable storage medium, where the computer-readable storage medium may be provided in an electronic device in the foregoing embodiments, and the computer-readable storage medium may be the memory in the foregoing embodiment shown in fig. 3. The computer-readable storage medium has stored thereon a computer program which, when executed by a processor, implements the blood vessel image extraction method described in the foregoing embodiment shown in fig. 1. Further, the computer-readable storage medium may be various media that can store program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a RAM, a magnetic disk, or an optical disk.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical division, and in actual implementation, there may be other divisions, for example, multiple modules or components may be combined or integrated into another system, or some features may be omitted, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or modules, and may be in an electrical, mechanical or other form.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical modules, may be located in one place, or may be distributed on a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present application may be integrated into one processing module, or each of the modules may exist alone physically, or two or more modules are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode.
The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a readable storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned readable storage medium includes: various media capable of storing program codes, such as a U disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk.
It should be noted that, for the sake of simplicity, the above-mentioned method embodiments are described as a series of acts or combinations, but those skilled in the art should understand that the present application is not limited by the described order of acts, as some steps may be performed in other orders or simultaneously according to the present application. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required in this application.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
The above description is provided for the blood vessel image extraction method, the electronic device and the computer readable storage medium, and for those skilled in the art, there are variations in the specific implementation and application scope according to the ideas of the embodiments of the present 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.
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