Vascular medical image processing method and apparatus therefor, medical image processing device

Abstract

The application relates to a blood vessel medical image processing method and device and a medical image processing equipment. The method takes at least part of marking points on a blood vessel in a blood vessel medical image as target marking points, determines a scaling coefficient corresponding to each target marking point based on a gray value corresponding to the target marking point, wherein the gray value is positively correlated with the scaling coefficient, adjusts a pipe diameter parameter corresponding to the target marking point based on the scaling coefficient, and obtains a processed blood vessel medical image. The pipe diameter difference of thick and thin blood vessels in the processed medical image data is larger than that before processing, the difference between blood vessels with different pipe diameters is differentiated, the shielding condition between blood vessel branches is improved, and the display effect is improved.

Description

Technical Field

[0001] This application relates to the field of image processing technology, and in particular to a method and apparatus for vascular medical image processing, a medical image processing device, and a computer storage medium. Background Technology

[0002] Blood vessels are characterized by their numerous and complex branches. Traditional methods of visualizing blood vessels often result in visually thicker branches in medical images, leading to poor display quality. Summary of the Invention

[0003] Therefore, it is necessary to provide a vascular medical image processing method and apparatus, medical image processing equipment, and computer storage medium that improve image display effect by optimizing pipe diameter parameters in order to address the above-mentioned technical problems.

[0004] In a first aspect, this application provides a method for processing vascular medical images, the method comprising:

[0005] Based on the gray values ​​corresponding to target markers on blood vessels in vascular medical images, a scaling factor corresponding to the target markers is determined; wherein, the target markers are at least some of the markers on blood vessels, and the gray values ​​are positively correlated with the scaling factor;

[0006] Based on the scaling factor, the tube diameter parameters corresponding to the target marker points are adjusted to obtain the processed vascular medical image.

[0007] In one embodiment, determining the scaling factor corresponding to the target marker point based on the grayscale value of the target marker point includes:

[0008] Obtain the grayscale value corresponding to each marker point on the blood vessel;

[0009] Select marker points whose grayscale values ​​are less than or equal to the target threshold as target marker points;

[0010] Based on the grayscale value corresponding to the target marker point, determine the scaling factor corresponding to the target marker point. The scaling factor is less than or equal to 1.

[0011] In one embodiment, the step of determining the target threshold includes:

[0012] Based on the gray values ​​corresponding to each marker point on all blood vessels in vascular medical images, determine the minimum and maximum gray values;

[0013] A first threshold is determined based on the minimum and maximum gray values; wherein the first threshold is greater than the minimum gray value and less than the maximum gray value.

[0014] Use the first threshold as the target threshold.

[0015] In one embodiment, the step of determining the target threshold includes:

[0016] Based on the gray values ​​corresponding to each marker point on all blood vessels in vascular medical images, determine the minimum and maximum gray values;

[0017] The first threshold is determined based on the minimum and maximum gray values;

[0018] The minimum of the first threshold and the second threshold is determined as the target threshold.

[0019] In one embodiment, determining a first threshold based on the minimum and maximum grayscale values ​​includes:

[0020] The first threshold is obtained by weighted summing of the minimum and maximum gray values.

[0021] In one embodiment, determining the scaling factor corresponding to the target marker point based on the grayscale value of the target marker point includes:

[0022] Based on the gray values ​​corresponding to the target markers and the nonlinear model, the scaling factor corresponding to the target markers is determined; whereby the nonlinear model is used to characterize the positive correlation between the gray values ​​and the scaling factor.

[0023] In one embodiment, the above-described vascular medical image processing method further includes:

[0024] Extracting the centerline of blood vessels from vascular medical images;

[0025] The markers on the center line of the blood vessel are identified as the markers on the blood vessel.

[0026] In one embodiment, the distance between any two adjacent markers on the blood vessel is less than or equal to a first preset distance value.

[0027] In one embodiment, the diameter parameters corresponding to the target marker points are adjusted based on a scaling factor to obtain a processed vascular medical image, including:

[0028] Determine the sub-segments corresponding to the target markers on the blood vessels;

[0029] Based on the scaling factor, the diameter parameters of the sub-segments corresponding to the target marker points are adjusted to obtain the processed vascular medical image.

[0030] Secondly, a vascular medical image processing device is also provided, the device comprising:

[0031] The scaling factor determination module is used to determine the scaling factor corresponding to the target marker point based on the gray value corresponding to the target marker point on the blood vessel in the vascular medical image; wherein the target marker point is at least a portion of the marker points on the blood vessel, and the gray value is positively correlated with the scaling factor;

[0032] The pipe diameter optimization module is used to adjust the pipe diameter parameters corresponding to the target marker points based on the scaling factor to obtain the processed vascular medical image.

[0033] Thirdly, this application also provides a medical image processing device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described vascular medical image processing method.

[0034] Fourthly, a computer-readable storage medium is also provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the above-described vascular medical image processing method.

[0035] The above-mentioned vascular medical image processing method and apparatus, medical image processing equipment, and computer storage medium have at least the following beneficial effects:

[0036] This vascular medical image processing method uses at least some marker points on blood vessels in a vascular medical image as target marker points. Due to the partial volume effect, the thicker the blood vessel, the higher the contrast agent concentration and the greater the intensity value, resulting in a higher grayscale value for that blood vessel in the vascular medical image. Conversely, the thinner the blood vessel, the lower the contrast agent concentration, resulting in a lower grayscale value in the vascular medical image. Furthermore, for two target marker points on a blood vessel with a small difference in diameter, the difference in grayscale value is more significant than the difference in diameter parameters. Based on this characteristic, the grayscale value can be used to determine the scaling factor. A scaling factor positively correlated with the grayscale value of the target marker points on the blood vessel is determined; the smaller the grayscale value, the smaller the scaling factor; conversely, the larger the grayscale value, the larger the scaling factor. This makes the difference in the diameter of blood vessels between thick and thin vessels in the scaled medical image greater than before processing, achieving differentiated display between blood vessels of different diameters, improving the occlusion between blood vessels, and enhancing the display effect.

[0037] Furthermore, in methods that optimize blood vessel diameter based on obtaining diameter parameters from images, algorithmic issues during the generation of vascular medical images can lead to situations where blood vessels with small diameter differences in reality appear to have the same diameter parameter in the vascular medical image, thus affecting the optimization effect. The method provided in this application, however, uses grayscale values ​​to reflect the actual diameter of the blood vessel, thereby improving the accuracy of diameter parameter adjustment; and the grayscale value differences corresponding to small diameter differences are larger, allowing for better differentiated diameter parameter adjustments even at points on the blood vessel with small diameter parameter differences.

[0038] In addition, by selecting appropriate target markers, both the diameter characteristics of large blood vessels and the length characteristics of small blood vessels can be preserved, matching the user's image viewing needs. Attached Figure Description

[0039] Figure 1 This is an application environment diagram of a vascular medical image processing method in one embodiment;

[0040] Figure 2 This is a flowchart illustrating a vascular medical image processing method in one embodiment;

[0041] Figure 3 This is a flowchart illustrating a vascular medical image processing method in one embodiment;

[0042] Figure 4 This is a flowchart illustrating a vascular medical image processing method in one embodiment;

[0043] Figure 5 This is a flowchart illustrating a vascular medical image processing method in one embodiment;

[0044] Figure 6 This is a flowchart illustrating a vascular medical image processing method in one embodiment;

[0045] Figure 7 This is a flowchart illustrating a vascular medical image processing method in one embodiment;

[0046] Figure 8 This is a schematic diagram illustrating the positive correlation between vascular grayscale value and scaling factor in a vascular medical image with a maximum grayscale value of 300 and a minimum grayscale value of 50, as shown in one embodiment.

[0047] Figure 9a This is a schematic diagram of a vascular medical image before processing in one embodiment;

[0048] Figure 9b This is a schematic diagram of a vascular medical image after adjusting the diameter parameters of the vascular medical image in 9a in one embodiment;

[0049] Figure 10 This is a flowchart illustrating a vascular medical image processing method in one embodiment;

[0050] Figure 11 This is a structural block diagram of a vascular medical image processing device in one embodiment;

[0051] Figure 12 Here is a block diagram of the scaling factor determination module in one embodiment;

[0052] Figure 13 This is an internal structural diagram of a medical image processing device in one embodiment. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0054] The vascular medical image processing method provided in this application embodiment can be applied to, for example... Figure 1 In the application environment shown, the medical image processing device 102 communicates with the server 104 via a network. A data storage system can store the data that the server 104 needs to process. The data storage system can be integrated onto the server 104 or placed on a cloud or other network server. The medical image processing device 102 can acquire vascular medical images from the medical scanning device 106 based on the server 104, and process the input medical images by executing the steps of the following vascular medical image processing method to obtain optimized vascular medical images. The medical image processing device 102 can be, but is not limited to, various medical devices with image processing functions, such as vascular visualization system devices. The server 104 can be implemented using a standalone server or a server cluster composed of multiple servers. It should be noted that if the medical image processing device 102 itself integrates scanning and image processing, both the initial vascular medical image and the processed vascular medical image can be generated by the medical image processing device 102.

[0055] In practical applications, it has been found that abundant vascular branches can make it less intuitive for doctors to view the overall vascular structure, and too many vascular branches can affect the interpretation of images.

[0056] In one exemplary technique, there are two main methods for visualizing the vascular system. The first is to directly render the vascular structure obtained after image segmentation to obtain a visualized vascular image. The second is to skeletonize the segmented vascular system after image segmentation, extract the centerline, calculate the vessel diameter parameters, and then render the image based on the centerline and vessel diameter parameters to obtain a visualized vascular image. However, the vessels rendered by these two methods appear visually coarse, especially the thinner vessels near the ends, which does not match actual clinical needs. Furthermore, the coarse and dense representation of vascular branches leads to low image reading quality and efficiency.

[0057] To address the aforementioned technical problems, in one embodiment of this application, as follows: Figure 2 As shown, a first aspect is provided: this application provides a method for processing vascular medical images, the method comprising:

[0058] S800: Based on the grayscale values ​​corresponding to target marker points on blood vessels in a vascular medical image, determine the scaling factor corresponding to the target marker points; wherein, the target marker points are at least a portion of the marker points on the blood vessels, and the grayscale values ​​are positively correlated with the scaling factor. The target marker points on the blood vessels are at least a portion of the marker points on the blood vessels, and the marker points on the blood vessels can be points on the centerline of the blood vessels in the vascular medical image. That is, the determination of the target marker points can be achieved based on methods such as centerline extraction, or based on other image processing methods.

[0059] For example, a target line can be selected along the extension direction of the blood vessel, and multiple marker points can be determined on the target line at equal intervals. Alternatively, marker points can be determined at different locations on the blood vessel according to other rules. In one embodiment, the target line is the blood vessel centerline. For each blood vessel in a vascular medical image, multiple marker points can be extracted and determined based on the blood vessel centerline. In this case, multiple marker points can be determined at equal intervals along the extension direction of the blood vessel centerline in the vascular medical image. Alternatively, marker points can be determined at different locations on the blood vessel according to other rules. When using marker points on the blood vessel centerline as marker points for adjusting the vessel diameter parameters, since the distance from each point on the centerline to each point on the edge of the blood vessel cross-section at that point tends to be consistent, the determined vessel diameter parameters are more accurate in reflecting the true thickness of the blood vessel. That is, the scheme of determining the scaling factor based on the vessel diameter parameters corresponding to the marker points on the centerline fully considers that the cross-sections of the blood vessel are not necessarily circular but may be irregular in shape.

[0060] Grayscale value refers to a parameter that reflects the thickness of blood vessels. For example, in vascular medical images obtained from CT (Computed Tomography) scans, due to the partial volume effect, the value of each voxel in the vascular medical image represents the average CT value of the corresponding unit tissue (a unit of measurement for the density of a local tissue or organ in the human body, also known as Henle units). In voxels of thicker parts of blood vessels, the proportion of blood is higher, and the contrast agent concentration is higher, resulting in a higher intensity value in the vascular medical image. The higher the intensity value, the larger the grayscale value displayed in the vascular medical image. Conversely, in voxels of thinner parts of blood vessels, the proportion of blood is lower, and the contrast agent concentration is lower, resulting in a lower intensity value and a lower grayscale value. Therefore, the grayscale value changes from thick to thin, and the grayscale value is positively correlated with the vessel diameter. Furthermore, for two marker points on a blood vessel with similar diameters, the corresponding grayscale values ​​can differ significantly. Therefore, using a scaling factor based on grayscale values ​​provides higher accuracy. Furthermore, grayscale values ​​can reflect the actual thickness of blood vessels, and their reliability is not affected by the imaging algorithms used in the vascular medical image process. However, when extracting diameter parameters from vascular medical images and determining scaling factors based on these parameters, the imaging algorithm can cause blood vessels with small diameter differences in reality to appear with equal diameter parameters in the vascular medical image. If this is placed on the same blood vessel, it will appear as if the branches of the blood vessel are of uniform thickness, which differs from the actual branching of the blood vessel and further affects the effectiveness of diameter parameter adjustment.

[0061] There are many ways to determine grayscale values. For example, some devices can directly obtain grayscale images of blood vessels after scanning, such as the vascular medical images obtained from CT scans mentioned above. For this image, to obtain the grayscale values ​​at the marked points, the vascular medical image can be segmented, the vessel centerline extracted, and the marked points on the centerline determined. Further, the grayscale value of the marked points on the centerline is determined to be the grayscale value of the corresponding pixel in the original vascular medical image before image segmentation. This ensures that the grayscale value is the same as the original image before processing, improving the accuracy and reliability of adjusting pipe diameter parameters based on this grayscale value.

[0062] Target markers refer to the markers on which the tube diameter will be adjusted. The principle for their selection is to ensure that after adjusting the tube diameter of these target markers, the vascular network in the vascular medical image will be clearer and the visual effect will be better.

[0063] S900, based on the scaling factor, adjusts the pipe diameter parameters corresponding to the target marker points to obtain the processed vascular medical image. Based on the selected marker points, the pipe diameter parameters at each target marker point are scaled, using the marker point as the adjustment unit. This scaling refers to scaling the pipe diameter presented in the vascular medical image. The process of adjusting based on the scaling factor can be understood as multiplying the vascular pipe diameter parameters in the vascular medical image by the scaling factor to obtain the adjusted vascular pipe diameter parameters, which are then displayed.

[0064] Specifically, the vascular medical image processing method provided in this application optimizes the vessel diameter by targeting marker points on blood vessels in vascular medical images. Firstly, the marker points can be blood vessels within a region of interest in the vascular medical image, such as branches of blood vessels in the heart region of a thoracic vascular image. This means that the steps of the vascular medical image processing method provided in this application can be performed only on a portion of the vascular medical image. In this case, the selection range of the target marker points can be constrained by marking only the blood vessels within the region of interest. Alternatively, the steps of the vascular medical image processing method in this application can be performed on the entire vascular medical image to adjust the vessel diameter parameters of the entire image. By selecting at least some marker points as target marker points, a scaling factor is determined for each target marker point that is positively correlated with its grayscale value. The smaller the grayscale value, the smaller the scaling factor; conversely, the larger the grayscale value, the larger the scaling factor. Marker points with larger grayscale values ​​have larger tube diameter parameters, while marker points with smaller grayscale values ​​have smaller tube diameter parameters. Based on this, the difference in tube diameter between thick and thin blood vessels in the vascular medical image after tube diameter parameter scaling is greater than that before processing, realizing the differentiated display between blood vessels of different diameters, improving the occlusion between blood vessel branches, and enhancing the display effect.

[0065] When adjusting the diameter of at least some marker points on a blood vessel, these target marker points to be adjusted still generally follow the positive correlation between grayscale value and scaling factor. That is, in the same vascular medical image, the larger the blood vessel diameter parameter corresponding to the target marker point, the larger the grayscale value and the larger the scaling factor; the smaller the blood vessel diameter parameter, the smaller the grayscale value and the smaller the scaling factor. Based on the scaling factor adjustment, the difference between thick and thin blood vessels in the original image is increased, thus optimizing the visual display effect.

[0066] In one embodiment, adjusting the tube diameter parameters corresponding to the target marker points based on the scaling factor can be achieved by traversing the marker points along the vessel centerline. If the branches of the blood vessels in the vascular medical image are already known, the tube diameter parameters at each target marker point can also be adjusted by traversing the vessel branches. The determination of the vessel branches can be based on the extraction of the vessel centerline, and the number of marker points on the vessel branches can be the same as the number of marker points on the centerline, which will not be elaborated here.

[0067] In one embodiment, the determination of vascular branches may include the following steps:

[0068] Extract the centerline of blood vessels from vascular medical images; the extraction of the centerline can be implemented based on the description in the above embodiments, and will not be repeated here.

[0069] The degree of each marker point on the vascular centerline is determined; the selection of marker points can also be based on the description in other embodiments. The marking process can involve assigning a value of 1 to points on the centerline and a value of 0 to points outside the centerline for each point in the vascular medical image. The degree of a marker point can be determined based on whether it has adjacent marker points and the number of adjacent marker points. The number of adjacent marker points of the current marker point is the degree of the current marker point. Based on the degree of the marker points, they can be classified into endpoints (markers with a degree of 1 or greater than or equal to 3) and points between the two endpoints on the centerline (points with a degree of 2, i.e., only two adjacent marker points).

[0070] The branches of blood vessels in a vascular medical image are determined based on the degree of each marker point. For example, this can be achieved by traversing the marker points and determining the branches of blood vessels in the medical image based on the degree of each marker point. An endpoint is always the endpoint of a blood vessel branch; a marker point with a degree greater than or equal to 3 indicates that the end of the blood vessel branch containing that marker point is connected to other blood vessel branches; a marker point with a degree of 1 indicates that the end of the blood vessel branch containing that marker point is not connected to other blood vessel branches, and is either the start or end point of the blood vessel. For example, a marker point can be used as a seed node to traverse the marker points. During the traversal, each blood vessel branch (the portion between two endpoints) can be determined, and the start and end points of the blood vessels can be identified.

[0071] The starting point here refers to the marker with the largest pipe diameter parameter among the markers with a degree of 1. The ending point refers to the other markers with a degree of 1.

[0072] The start and end points can also be determined based on the root node. This can be based on preset rules; for example, the point with the largest diameter parameter can be used as the root node, and the root node can be used as the seed node. Following the direction from the seed node to the next marker point, the degree of the next marker point is determined to determine its type (endpoint, point between two endpoints on the centerline). The next marker point is then updated as the seed node. This process of "following the direction from the seed node to the next marker point, determining the degree of the next marker point, and determining the type of the next marker point" is repeated until all marker points have been traversed. During this process, various parameters of the blood vessel can be obtained, including diameter parameters such as radius, average radius, and overall diameter.

[0073] In one embodiment, such as Figure 3As shown in Figure S800, based on the grayscale value at the target marker point, the scaling factor corresponding to the target marker point is determined, including:

[0074] S820, obtain the grayscale values ​​corresponding to each marker point on the blood vessel; here, each marker point on the blood vessel refers to the marker point on the blood vessel shown in the vascular medical image, for example, it can be a marker point on the center line of the blood vessel.

[0075] S840, Select marker points with gray values ​​less than or equal to the target threshold as target marker points;

[0076] S860 determines the scaling factor corresponding to the target marker point based on the grayscale value of the target marker point. The scaling factor is less than or equal to 1. That is, only marker points with grayscale values ​​less than or equal to the target threshold can be adjusted.

[0077] The selection of the target threshold can be based on user-configurable settings. For example, for vascular medical images of the heart, the target marker points can be determined based on the average vessel radius. For marker points with grayscale values ​​less than or equal to the target threshold, a scaling factor that is positively correlated with the grayscale value and less than or equal to 1 is determined. For instance, if the scaling factor for marker points with grayscale values ​​equal to the target threshold is determined to be 1, then following the positive correlation between grayscale values ​​and scaling factors, the larger the absolute value of the difference between the grayscale value and the target threshold at other target marker points, the smaller the corresponding scaling factor. Based on this, the scaling factor at each target marker point is determined to guide the adjustment of vessel diameter parameters in the vascular medical image, resulting in the processed vascular medical image.

[0078] In one embodiment, for markers with grayscale values ​​greater than or equal to a target threshold, their diameter parameters can be kept unchanged, i.e., the scaling factor is equal to 1. Based on this, combined with the scaling factor of less than or equal to 1 determined for markers with grayscale values ​​less than or equal to the target threshold in the previous embodiment, this guides the adjustment of the diameter parameters corresponding to the target markers in the vascular medical image. In the processed vascular medical image, the diameter parameters of markers with grayscale values ​​greater than the target threshold are not adjusted. For markers with grayscale values ​​less than or equal to the target threshold, the larger the absolute value of the difference between the grayscale value and the target threshold, the greater the reduction in the diameter parameters of the processed target markers. This effectively preserves the diameter characteristics of thicker blood vessels and the length characteristics of thinner blood vessels.

[0079] Of course, besides the examples mentioned above, in order to optimize the visual display effect by increasing the difference in blood vessel diameter, the diameter parameters of the markers with gray values ​​greater than the target threshold can also be adjusted. This can be achieved by increasing the diameter parameters at these markers to amplify the difference between blood vessels of different sizes. Therefore, in one embodiment, based on the gray value corresponding to the target marker, a scaling factor is determined for the target marker, such as... Figure 4 As shown, it includes:

[0080] S880, select marker points with gray values ​​greater than or equal to the target threshold as target marker points;

[0081] S890 determines the scaling factor corresponding to the target marker point based on the gray value corresponding to the target marker point. The scaling factor is greater than or equal to 1.

[0082] For markers with gray values ​​greater than the target threshold, the tube diameter parameters are adjusted. Following the positive correlation between gray values ​​and scaling factors, the scaling factor at each target marker is determined. This scaling factor is greater than or equal to 1. Based on the vascular medical image processing guided by this scaling factor, the tube diameter parameters at markers with gray values ​​greater than or equal to the target threshold in the vascular medical image can be increased. The larger the absolute value of the difference between the gray value of the target marker and the target threshold before adjustment, the greater the thickening of the tube diameter after adjustment. This increases the difference in tube diameter between large and small blood vessels and presents the tube diameter dimension of large blood vessels well, as well as the length of small blood vessels.

[0083] Of course, it should be noted that the embodiments of this application can adjust the tube diameter parameters not only for markers with gray values ​​less than or equal to the target threshold, or only for markers with gray values ​​greater than or equal to the target threshold, but also for all markers on the blood vessel. The selection principle is as described above; any scheme that can increase the visual difference in the diameter of blood vessels of different sizes falls under the above-mentioned target marker selection implementation scheme.

[0084] The determination of target marker points is inseparable from the target threshold. If the target threshold is chosen too small or too large, there will be too few marker points to adjust, resulting in a small difference in the diameter of blood vessels in the adjusted vascular medical image. Based on this, in one embodiment, such as Figure 5-7 As shown, the steps for determining the target threshold include:

[0085] S720, based on the gray values ​​corresponding to each marker point on all blood vessels in the vascular medical image, determines the minimum gray value and the maximum gray value;

[0086] S740, determine a first threshold based on the minimum gray value and the maximum gray value; wherein, the first threshold is greater than the minimum gray value and less than the maximum gray value;

[0087] S760, the first threshold is used as the target threshold.

[0088] Considering the different thicknesses of blood vessels in different locations, the threshold can be selected based on the distribution of blood vessel thickness for each vascular medical image. Therefore, as mentioned above, the maximum and minimum gray values ​​can be determined based on the gray values ​​of all marked points on the blood vessels (it should be noted that if only the diameter parameters of the region of interest in the vascular medical image need to be adjusted, this can be achieved by marking points only on the blood vessels in the region of interest), and the threshold mentioned above can be determined based on these two values ​​to identify the target marked points.

[0089] In one embodiment, the determination of the maximum and minimum grayscale values ​​can be achieved by traversing the marker points. By traversing the marker points on the blood vessel, the maximum and minimum grayscale values ​​are updated based on their relationship with the existing maximum and minimum grayscale values ​​during the traversal. Initially, this comparison and update process can begin by initializing the maximum and minimum grayscale values. Traversing the marker points can be achieved by directly traversing the marker points on the centerline based on centerline extraction, or by traversing each marker point based on whether all blood vessel branches have been traversed. In one embodiment, the scaling factor can also be determined by traversing the blood vessel branches, which will not be elaborated upon here.

[0090] In one embodiment, S740, determining a first threshold based on the minimum grayscale value and the maximum grayscale value includes:

[0091] The first threshold is obtained by weighted summation of the minimum and maximum gray values.

[0092] A first threshold is determined by weighted summation, and a target threshold is further obtained. Taking into full account the minimum and maximum gray values, the target marker points can be adaptively determined based on the maximum difference in blood vessel diameter. This method of determining the target threshold and selecting target marker points ensures that the marker points with adjusted vessel diameters in the final processed vascular medical image match the user's needs, improving the visual effect of blood vessel display.

[0093] In one embodiment, the process of weighted summation based on the minimum and maximum grayscale values ​​can be implemented using the following expression:

[0094] threshold=a*GrayValue_Min+(1-a)*GrayValue_Max, a∈[0,1]

[0095] Where `threshold` is the first threshold, `GrayValue_Min` is the minimum grayscale value, `rGrayValue_Max` is the maximum grayscale value, and `a` is a linearly weighted scaling factor. `a` can be configured based on the user's viewing needs for vessel diameter, combined with the relationship between vessel diameter and grayscale values. For example, by properly configuring `a`, the target threshold can be determined as a grayscale value ranking in the 40% range from largest to smallest. Using this grayscale value as the target threshold, and marking points with grayscale values ​​less than or equal to this target threshold as target markers, the vessel diameter parameters at the target markers in the top 40% of grayscale values ​​can remain unchanged, preserving the diameter characteristics of larger vessels. Furthermore, by determining a scaling factor for the target markers in the bottom 60% of grayscale values ​​(this scaling factor is less than or equal to 1), the smaller the grayscale value of the target marker, the greater the reduction in vessel diameter parameter, thus optimizing the visual display effect of vessels in vascular medical images.

[0096] In one embodiment, S800, determining a scaling factor matching the grayscale value based on the grayscale value at the target marker point includes:

[0097] Based on the grayscale value at the target marker point and the nonlinear model, a scaling factor matching the grayscale value is determined; wherein, the nonlinear model is used to characterize the positive correlation mapping relationship between the grayscale value and the scaling factor.

[0098] Compared to linear models, nonlinear models use grayscale values ​​as input. Based on the scaling factor determined by the nonlinear model, the scaling factor increases more as the grayscale value increases. The scaling factor determined by this model guides the adjustment of the grayscale value at the target marker point. The difference in the thickness of blood vessels in the resulting vascular medical image is also greater, which can more significantly improve the appearance of blood vessels in the processed vascular medical image.

[0099] In one embodiment, the nonlinear model can be as follows:

[0100]

[0101] Where f(x) represents the scaling factor corresponding to the target marker point with a diameter parameter of x, x represents the gray value of a target marker point on a branch of a blood vessel; b represents the maximum gray value among all gray values ​​of all marker points on all blood vessels; c represents the minimum gray value among all gray values ​​of marker points. b and c can be obtained by statistical analysis of the gray values ​​of each marker point on the blood vessel in the vascular medical image.

[0102] The correspondence between the grayscale value of blood vessels and the scaling factor under this nonlinear model is as follows: Figure 8 As shown.

[0103] Based on testing, in a vascular medical image, statistical analysis showed that when b = 300 and c = 50, in a test case, for example... Figure 9a The original vascular medical image shown is processed to obtain the following vascular medical image: Figure 9b As shown in the diagram, the overall optimization effect of the blood vessel branches is better after diameter optimization, which meets the user's viewing needs. Due to the target threshold setting, thicker blood vessels with gray values ​​greater than or equal to the target threshold will not be reduced. For other markers with gray values ​​less than the target threshold, the diameter parameter scaling is smaller at the first few thicker markers, while thinner blood vessels will be reduced to varying degrees. The thinner the blood vessel, the greater the reduction in diameter parameter at the target marker. Furthermore, as the gray value of the blood vessel decreases, i.e., the blood vessel diameter parameter decreases, the reduction in diameter parameter at the target marker will become increasingly greater.

[0104] After adjusting the vessel diameter parameters, the vascular medical image can be colored to distinguish different types of blood vessels. For example, the hepatic vein, inferior vena cava, portal vein, hepatic artery, and bile duct are colored differently, improving the display effect of the processed vascular medical image. Coloring can be done on a unit basis, focusing on a single complete blood vessel branch. When adjusting the vessel diameter parameters, on the same blood vessel branch, only the diameter parameters at target markers with grayscale values ​​less than or equal to the target threshold can be adjusted. In other words, the diameter parameters can be adjusted only at a portion of the blood vessel branch.

[0105] Of course, it should be noted that, in addition to the examples mentioned above, nonlinear models can also employ other polynomial models such as exponential transformation, logarithmic transformation, and polynomial transformation.

[0106] For schemes that reduce blood vessel diameter parameters with grayscale values ​​less than or equal to a target threshold, to avoid an excessively large threshold determined based on the maximum and minimum grayscale values ​​due to an excessively large maximum grayscale value of blood vessels in vascular medical images, the blood vessel diameter, which is not intended to be reduced, is reduced. Therefore, in one embodiment, such as... Figure 7 As shown, the steps for determining the target threshold include:

[0107] S720, based on the gray values ​​corresponding to each marker point of all blood vessel branches in the vascular medical image, determine the minimum and maximum gray values;

[0108] S740, determine a first threshold based on the minimum gray value and the maximum gray value; the first threshold may be the result obtained by weighted summation of the minimum gray value and the maximum gray value in the above embodiment.

[0109] S780, determine the minimum value between the first threshold and the preset second threshold as the target threshold. The second threshold can be configured based on the user's viewing needs for the visual representation of blood vessel branches.

[0110] In one embodiment, determining the target marker point may further include:

[0111] The target markers are selected from the top N percent of grayscale values ​​in the vascular medical image. The top N percent refers to the number of markers whose grayscale values ​​are sorted from smallest to largest. Then, the diameter parameters of markers with smaller grayscale values ​​are adjusted. For example, when N is 60, the diameter parameters of the top 40% of markers with the largest grayscale values ​​are not adjusted. This preserves the diameter characteristics of larger blood vessels and allows for the reduction of the diameter of smaller blood vessels, thereby optimizing the display effect of the vascular medical image.

[0112] In one embodiment, the above-described vascular medical image processing method further includes:

[0113] Extracting the centerline of blood vessels from vascular medical images;

[0114] The markers on the center line of the blood vessel are identified as the markers on the blood vessel.

[0115] In one embodiment, the distance between any two adjacent markers on the blood vessel is less than or equal to a first preset distance value.

[0116] There are several ways to extract the centerline. For example, one method is to acquire medical images of black and bright blood vessels; perform vessel identification processing on both images to obtain black and bright blood vessel identification images; register and fuse these images to obtain a fused vessel identification image; and extract the segmental centerline of each vessel segment in the fused vessel identification image, then determine the centerline of the vessel in the medical image (which can be either a black or bright blood image) based on the segmental centerline of each vessel segment. Alternatively, based on the medical image, the distance from any point on a cross-section to points on the edge of the cross-section can be calculated, and the point with the smallest difference between the distances from the cross-section to the edge can be taken as the center point. Fitting the center points on each interface can yield the vessel centerline. The aforementioned center point can be directly used as a marker point on the vessel. For example, when the parallel distance between two adjacent cross-sections is less than or equal to the first preset distance in this embodiment, the center point can be directly used as a marker point on the vessel. Of course, based on the obtained vessel centerline, the points can be remarked to obtain the marker points on the vessel. For example, when extracting the centerline from medical images of dark and bright blood vessels, the marker points can be selected on the centerline according to preset rules. For example, the marker points can be selected at equal intervals.

[0117] To avoid excessively large intervals between marker points on the blood vessel, which could lead to unadjusted blood vessels between adjacent marker points during diameter parameter adjustment and affect the adjustment effect, in one embodiment, the distance between any two adjacent marker points on the blood vessel is less than or equal to a first preset distance value. This first preset distance value can be set based on the required adjustment precision; for example, it can be a value approaching infinity. Alternatively, it can be based on the pixels displayed in the image; for example, the first preset distance is equal to the physical spatial size of a single pixel.

[0118] When the marker on the blood vessel is a marker on the center line of the blood vessel, the marker can be determined based on the extraction of the center line, determining one endpoint on the center line as a marker, and determining each marker on the center line based on the extension direction of the center line along the endpoint, with a first preset distance as the interval between two adjacent markers.

[0119] If the selected markers on blood vessels are spaced far apart, the result of adjusting the blood vessel diameter parameters based on the markers will be that a long section of blood vessel between the markers will not be adjusted. This will lead to an abnormal difference in the diameter parameters of the blood vessel segment and the adjacent target markers in the adjusted blood vessel medical image.

[0120] To address this situation, in one embodiment, the diameter parameters corresponding to the target marker points are adjusted based on a scaling factor to obtain a processed vascular medical image, including:

[0121] The sub-segments corresponding to the target markers on the blood vessel are determined; a sub-segment refers to a section of the blood vessel determined based on the target markers and segmentation rules. Segmentation rules may include, but are not limited to, the rules exemplified in the embodiments of this application.

[0122] Based on the scaling factor, the diameter parameters of the sub-segments corresponding to the target markers are adjusted to obtain the processed vascular medical image; for example, the sub-segment can be the part between two adjacent target markers on the center line of the blood vessel, which can be a straight line segment or a curve.

[0123] In this configuration, the distance between the two closest endpoints of two adjacent sub-segments is less than or equal to a second preset distance. This second preset distance can be zero, thus enabling full adjustment of the vascular portion between two adjacent target markers. Alternatively, depending on the required precision of the tube diameter adjustment, a second preset distance that approaches infinity but is not zero can be set.

[0124] When the distance between adjacent target markers is greater than a third preset distance, a sub-segment corresponding to such target markers is determined. The sub-segmentation rule can be based on the current target marker as the starting point, extending along the vessel's extension direction, with the next target marker as the ending point, defining the segment containing the vessel between the two target markers as the sub-segment corresponding to the current target marker. Alternatively, it can be based on the current target marker A as the center, using the distance D1 between A and its left-adjacent target marker B, and the distance D2 between A and its right-adjacent target marker C, defining the vessel segment within the range from D1 / 2 to D2 / 2 to the right of A as the aforementioned sub-segment. Of course, it can also be based on the current target marker A as the center, using a preset radius along the vessel's extension direction, defining the vessel segment within that radius as the sub-segment corresponding to A. In summary, there can be various sub-segmentation rules, but the general principle is that the interval between two adjacent sub-segments cannot exceed a second preset distance, to ensure that nearby vessels still exhibit smooth changes after adjusting the vessel diameter parameters.

[0125] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0126] Based on the same inventive concept, this application also provides a vascular medical image processing apparatus for implementing the aforementioned vascular medical image processing method. The solution provided by this apparatus is similar to the implementation described in the above method; therefore, the specific limitations in one or more embodiments of the vascular medical image processing apparatus provided below can be found in the limitations of the vascular medical image processing method described above, and will not be repeated here.

[0127] In one embodiment, such as Figure 10 As shown, a vascular medical image processing device is provided, the device comprising:

[0128] The scaling factor determination module 800 is used to determine the scaling factor corresponding to the target marker point based on the gray value corresponding to the target marker point on the blood vessel in the vascular medical image; wherein, the target marker point is at least a portion of the marker points on the blood vessel, and the gray value is positively correlated with the scaling factor;

[0129] The pipe diameter optimization module 900 is used to adjust the pipe diameter parameters corresponding to the target marker points based on the scaling factor to obtain the processed vascular medical image.

[0130] The definitions of terms can be found in the above method embodiments and will not be repeated here. Specifically, for each blood vessel branch in at least a portion of the blood vessel branches in a vascular medical image, the scaling factor determination module 800 takes each of the marker points in at least a portion of the marker points of the blood vessel branch as a target marker point, determines the scaling factor corresponding to the target marker point based on the gray value of the target marker point, and sends it to the pipe diameter optimization module 900. Finally, the pipe diameter optimization module 900 adjusts the pipe diameter parameters corresponding to the target marker point in the vascular medical image based on the scaling factor to obtain the processed vascular medical image. The gray value is positively correlated with the scaling factor.

[0131] In one embodiment, such as Figure 11 As shown, the scaling factor determination module 800 includes:

[0132] The grayscale value acquisition unit 820 is used to acquire the grayscale value corresponding to each marker point on the blood vessel.

[0133] The first target blood vessel selection unit 840 is used to select marker points with gray values ​​less than or equal to the target threshold as target marker points;

[0134] The first scaling factor determination unit 860 is used to determine the scaling factor corresponding to the target marker point based on the gray value corresponding to the target marker point, wherein the scaling factor is less than or equal to 1.

[0135] In one embodiment, the scaling factor determination module 800 includes:

[0136] The second target blood vessel selection unit 880 is used to select marker points with gray values ​​greater than the target threshold as target marker points;

[0137] The second scaling factor determination unit 890 is used to determine the scaling factor corresponding to the target marker point based on the gray value corresponding to the target marker point, and the scaling factor is greater than 1.

[0138] In one embodiment, the vascular medical image processing device, such as Figure 12 As shown, it also includes:

[0139] The grayscale extreme value determination module 720 is used to determine the minimum and maximum grayscale values ​​based on the grayscale values ​​corresponding to each marker point on all blood vessels in the vascular medical image;

[0140] The first threshold determination module 740 is used to determine a first threshold based on the minimum gray value and the maximum gray value; wherein the first threshold is greater than the minimum gray value and less than the maximum gray value;

[0141] The first target threshold determination module 760 is used to use the first threshold as the target threshold.

[0142] In one embodiment, the first threshold determination module 740 includes:

[0143] The weighted summation unit 742 is used to perform weighted summation based on the minimum gray value and the maximum gray value to obtain the first threshold.

[0144] In one embodiment, such as Figure 12 As shown, the device also includes:

[0145] The second target threshold determination module 780 is used to determine the minimum value between the first threshold and the preset second threshold as the target threshold.

[0146] In one embodiment, the scaling factor determination module 800 includes:

[0147] The third scaling factor determination unit is used to determine the scaling factor that matches the gray value based on the gray value at the target marker point and the nonlinear model; wherein, the nonlinear model is used to characterize the positive correlation mapping relationship between the gray value and the scaling factor.

[0148] In one embodiment, the above-mentioned vascular medical image processing device further includes:

[0149] The vascular centerline extraction module is used to extract the vascular centerline from vascular medical images;

[0150] The blood vessel marker determination module is used to determine the markers on the center line of the blood vessel as the markers on the blood vessel.

[0151] In one embodiment, the distance between any two adjacent markers on the blood vessel is less than or equal to a first preset distance value.

[0152] In one embodiment, the pipe diameter optimization module 900 includes:

[0153] The sub-segment determination unit is used to determine the sub-segments corresponding to the target marker points on the blood vessel;

[0154] The tube diameter smoothing optimization unit is used to adjust the tube diameter parameters of the sub-segments corresponding to the target marker points based on the scaling factor, so as to obtain the processed vascular medical image.

[0155] Each module in the aforementioned vascular medical image processing device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of the medical image processing device in hardware form or independent of it, or stored in the memory of the medical image processing device in software form, so that the processor can call and execute the corresponding operations of each module.

[0156] In one embodiment, a medical image processing device is provided. This medical image processing device can be a terminal, and its internal structure diagram can be as follows: Figure 13As shown, the medical image processing device includes a processor, memory, communication interface, display screen, and input device connected via a system bus. The processor provides computational and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage medium. The communication interface allows for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a vascular medical image processing method. The display screen can be an LCD screen or an e-ink screen. The input device can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the device's casing, or an external keyboard, touchpad, or mouse.

[0157] Those skilled in the art will understand that Figure 13 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the medical image processing device to which the present application is applied. A specific medical image processing device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0158] Thirdly, this application also provides a medical image processing device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement, for example... Figure 2 The following steps are shown:

[0159] S800, based on the gray values ​​corresponding to the target markers on the blood vessels in the vascular medical image, determines the scaling factor corresponding to the target markers; wherein, the target markers are at least some of the markers on the blood vessels, and the gray values ​​are positively correlated with the scaling factor;

[0160] S900 adjusts the tube diameter parameters corresponding to the target marker points based on the scaling factor to obtain the processed vascular medical image.

[0161] In one embodiment, when the processor executes the computer program, it also implements other steps of the above-described vascular medical image processing method and achieves corresponding beneficial effects, which will not be elaborated here.

[0162] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0163] S800, based on the gray values ​​corresponding to the target markers on the blood vessels in the vascular medical image, determines the scaling factor corresponding to the target markers; wherein, the target markers are at least some of the markers on the blood vessels, and the gray values ​​are positively correlated with the scaling factor;

[0164] S900 adjusts the tube diameter parameters corresponding to the target marker points based on the scaling factor to obtain the processed vascular medical image.

[0165] In one embodiment, when the computer program is executed by the processor, it also implements other steps of the above-described vascular medical image processing method and achieves corresponding beneficial effects, which will not be elaborated here.

[0166] In one embodiment, a computer program product is provided, comprising a computer program that, when executed by a processor, implements some or all of the method steps of the above-described vascular medical image processing method and achieves the corresponding beneficial effects.

[0167] It should be noted that the data involved in this application (including but not limited to user data used for analysis, stored data, and displayed data) are all information and data authorized by the user or fully authorized by all parties (such as vascular medical images of the user).

[0168] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0169] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0170] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for processing vascular medical images, characterized in that, The method includes: Based on the gray values ​​corresponding to target markers on blood vessels in vascular medical images, a scaling factor corresponding to the target markers is determined; wherein, the target markers are at least some markers on the blood vessels, and the gray values ​​are positively correlated with the scaling factor; Based on the scaling factor, the diameter parameters corresponding to the target marker points are adjusted to obtain the processed vascular medical image, so that the difference in diameter between the thick and thin blood vessel branches in the processed vascular medical image is greater than the difference in diameter between the thick and thin blood vessel branches in the unprocessed vascular medical image, thereby reducing the occlusion between blood vessels. Wherein, determining the scaling factor corresponding to the target marker point based on the grayscale value corresponding to the target marker point includes: Obtain the grayscale value corresponding to each marker point on the blood vessel; The marker points whose grayscale values ​​are less than or equal to the target threshold are selected as the target marker points; Based on the gray value corresponding to the target marker point, a scaling factor corresponding to the target marker point is determined, wherein the scaling factor is less than or equal to 1; or, Determining the scaling factor corresponding to the target marker point based on the grayscale value of the target marker point includes: Based on the grayscale value corresponding to the target marker point and the nonlinear model, a scaling factor corresponding to the target marker point is determined; wherein, the nonlinear model is used to characterize the positive correlation mapping relationship between the grayscale value and the scaling factor; as the grayscale value increases, the scaling factor increases to a greater extent.

2. The method according to claim 1, characterized in that, The steps for determining the target threshold include: Based on the gray values ​​corresponding to each marker point on all blood vessels in the vascular medical image, the minimum gray value and the maximum gray value are determined; A first threshold is determined based on the minimum gray value and the maximum gray value; wherein the first threshold is greater than the minimum gray value and less than the maximum gray value; The first threshold is used as the target threshold.

3. The method according to claim 1, characterized in that, The steps for determining the target threshold include: Based on the gray values ​​corresponding to each marker point on all blood vessels in the vascular medical image, the minimum gray value and the maximum gray value are determined; A first threshold is determined based on the minimum gray value and the maximum gray value; The minimum value between the first threshold and the second threshold is determined to be the target threshold.

4. The method according to claim 2 or 3, characterized in that, Determining a first threshold based on the minimum grayscale value and the maximum grayscale value includes: The first threshold is obtained by weighted summation of the minimum gray value and the maximum gray value.

5. The method according to claim 1, characterized in that, The method further includes: Extract the centerline of the blood vessels from the vascular medical image; The marker point on the center line of the blood vessel is determined as the marker point on the blood vessel.

6. The method according to any one of claims 1-3 and 5, characterized in that, The distance between any two adjacent markers on the blood vessel is less than or equal to a first preset distance value.

7. The method according to any one of claims 1-3 and 5, characterized in that, The process of adjusting the tube diameter parameters corresponding to the target marker points based on the scaling factor to obtain the processed vascular medical image includes: Determine the sub-segment corresponding to the target marker point on the blood vessel; Based on the scaling factor, the diameter parameters of the sub-segments corresponding to the target marker points are adjusted to obtain the processed vascular medical image.

8. A vascular medical image processing device, characterized in that, The device includes: A scaling factor determination module is used to determine a scaling factor corresponding to a target marker point based on the gray value corresponding to the target marker point on the blood vessel in a vascular medical image; wherein the target marker point is at least a portion of the marker points on the blood vessel, and the gray value is positively correlated with the scaling factor; The pipe diameter optimization module is used to adjust the pipe diameter parameters corresponding to the target marker point based on the scaling factor to obtain the processed vascular medical image, so that the difference in pipe diameter between thick and thin blood vessel branches in the processed vascular medical image is greater than the difference in pipe diameter between thick and thin blood vessel branches in the unprocessed vascular medical image, thereby reducing the occlusion between blood vessels. The scaling factor determination module includes: A grayscale value acquisition unit is used to acquire the grayscale value corresponding to each marker point on the blood vessel; The first target blood vessel selection unit is used to select the marker point whose gray value is less than or equal to the target threshold as the target marker point; The first scaling factor determining unit is used to determine a scaling factor corresponding to the target marker point based on the gray value corresponding to the target marker point, wherein the scaling factor is less than or equal to 1; or, The scaling factor determination module includes: The third scaling factor determination unit is used to determine the scaling factor corresponding to the target marker point based on the gray value corresponding to the target marker point and the nonlinear model; wherein, the nonlinear model is used to characterize the positive correlation mapping relationship between the gray value and the scaling factor; as the gray value increases, the scaling factor increases to a greater extent.

9. A medical image processing device, comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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