Medical image processing method, medical imaging equipment and terminal equipment

Abstract

The invention discloses a medical image processing method, medical imaging equipment and terminal equipment, and the method comprises the steps: when a focus region in a medical image is extracted, firstly, a user manually identifies an initial focus region in the medical image, then generates a first image only containing the initial focus region, and carries out the automatic focus extraction of the first image, so as to obtain a focus result; therefore, the lesion area in the medical image is extracted in a semi-automatic mode combining manual operation and automatic operation, and high lesion extraction accuracy is kept while the efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of medical image processing, and in particular to a medical image processing method, a medical imaging device and a terminal device. Background Art

[0002] Medical images, such as magnetic resonance images, are images that reflect tissue structure or internal functions of tissue structure, and are usually presented in two or three dimensions. In some clinical applications, medical images can be used to extract lesions in tissue structure so that doctors can determine the corresponding conditions of the lesions. At present, the extraction of lesions in tissue structure through medical images mostly adopts the fully automatic extraction method of machine learning. Although this method is highly efficient, the classification model trained by machine learning using a batch of medical image data often has a high accuracy and performance that is greatly reduced in actual clinical applications.

[0003] In summary, there is currently a certain demand for high-efficiency and high-precision extraction of lesion areas in medical images. Summary of the invention

[0004] In view of the above problems, the present application provides a medical image processing method, a medical imaging device and a terminal device, which are described in detail below.

[0005] According to the first aspect, an embodiment provides a method for processing a medical image, comprising:

[0006] Acquire a medical image sequence of a target object, wherein the medical image sequence includes one or more frames of medical images;

[0007] Displaying the medical image sequence on a display interface;

[0008] In response to a user's operation on at least one frame of the displayed medical image sequence, determining a preliminary lesion area in the medical image sequence;

[0009] Based on the preliminary lesion region in the medical image sequence, generating one or more first images containing only the preliminary lesion region;

[0010] Perform lesion extraction on one or more frames of the first image to obtain lesion results.

[0011] In the above embodiment, the user first manually performs a preliminary calibration of the lesions in the medical image sequence to form a preliminary lesion area, and then performs subsequent automatic lesion extraction based on the preliminary lesion area in the medical image sequence to obtain the lesion result. The lesion area in the medical image is extracted in a semi-automatic manner that combines manual and automatic methods, which improves efficiency while maintaining a high lesion extraction accuracy.

[0012] In one embodiment, in response to a user's operation on at least one frame of the displayed medical image sequence, determining a preliminary lesion area in the medical image sequence includes:

[0013] In response to a marking operation performed by a user on a currently displayed frame of medical image, a region corresponding to the marking operation is determined, and the region corresponding to the marking operation is used as the preliminary lesion region.

[0014] In one embodiment, the lesion result includes: the largest lesion area and the size corresponding to the largest lesion area.

[0015] In one embodiment, when the first image is two-dimensional and consists of multiple frames, performing lesion extraction on one or more frames of the first image to obtain lesion results includes:

[0016] Performing three-dimensional interpolation reconstruction on the multiple frames of first images to obtain a reconstructed three-dimensional image;

[0017] Extracting one or more lesion areas in the three-dimensional image;

[0018] A lesion area that meets the preset conditions and a size corresponding to the lesion area that meets the preset conditions in one or more of the lesion areas are determined to obtain a lesion result.

[0019] In the above embodiment, multiple two-dimensional first images can be interpolated and reconstructed in three dimensions to obtain a three-dimensional image, and lesion extraction can be performed on the three-dimensional image to obtain a three-dimensional lesion result. Compared with directly extracting lesions in two-dimensional images, the obtained lesion results are more accurate.

[0020] In one embodiment, extracting one or more lesion areas in the three-dimensional image includes:

[0021] Pixel regions whose grayscale values ​​are greater than or equal to a preset threshold are extracted from the three-dimensional image to obtain one or more lesion regions.

[0022] In the above embodiment, the grayscale threshold method is used to extract the lesion, which does not require a complex algorithm, reduces the amount of calculation, and is easier to implement.

[0023] In one embodiment, acquiring a sequence of medical images of the target object includes:

[0024] importing a sequence of medical images of the target object;

[0025] Alternatively, one or more frames of medical images of the target object are collected in real time to obtain the medical image sequence.

[0026] In one embodiment, the medical image is a magnetic resonance image, a CT image or an ultrasound image.

[0027] According to a second aspect, an embodiment provides a medical imaging device, comprising:

[0028] An imaging module, used to acquire a medical image sequence of a target object, wherein the medical image sequence includes one or more frames of medical images;

[0029] A display module, used for displaying the medical image sequence on a display interface;

[0030] The first processor is used to execute the method described in any one of the above embodiments.

[0031] According to the third aspect, an embodiment provides a terminal device, including:

[0032] A display component, used for displaying the medical image sequence;

[0033] Memory for storing computer programs;

[0034] The second processor is configured to implement the method described in any one of the above embodiments by executing the computer program stored in the memory.

[0035] According to a fourth aspect, an embodiment provides a computer-readable storage medium, on which a computer program is stored, and the computer program can be executed by a processor to implement the method described in any one of the above embodiments.

[0036] According to the medical image processing method and medical imaging device of the above-mentioned embodiment, when extracting the lesion area in the medical image, the user first manually identifies the preliminary lesion area in the medical image, and then generates a first image containing only the preliminary lesion area, and automatically extracts the lesion from the first image to obtain the lesion result; thus, the present invention extracts the lesion area in the medical image by a semi-automatic method combining manual and automatic methods, thereby improving the efficiency while maintaining a high lesion extraction accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 is a schematic structural diagram of a medical imaging device according to an embodiment;

[0038] Figure 2 A schematic diagram of the structure of a terminal device according to an embodiment;

[0039] Figure 3 is a flow chart of a method for processing a medical image according to an embodiment;

[0040] Figure 4The figure is a specific flow chart of a method for processing medical images according to an embodiment. DETAILED DESCRIPTION

[0041] The present invention is further described in detail below by specific embodiments in conjunction with the accompanying drawings. Wherein similar elements in different embodiments adopt associated similar element numbers. In the following embodiments, many detailed descriptions are for making the present application better understood. However, those skilled in the art can easily recognize that some features can be omitted in different situations, or can be replaced by other elements, materials, methods. In some cases, some operations related to the present application are not shown or described in the specification, this is to avoid the core part of the present application being overwhelmed by too much description, and for those skilled in the art, it is not necessary to describe these related operations in detail, and they can fully understand the related operations according to the description in the specification and the general technical knowledge in the art.

[0042] In addition, the features, operations or characteristics described in the specification can be combined in any appropriate manner to form various implementations. At the same time, the steps or actions in the method description can also be interchanged or adjusted in a manner that is obvious to those skilled in the art. Therefore, the various sequences in the specification and the drawings are only for the purpose of clearly describing a certain embodiment and are not meant to be a required sequence, unless otherwise specified that a certain sequence must be followed.

[0043] The serial numbers of the components in this document, such as "first", "second", etc., are only used to distinguish the objects described and do not have any order or technical meaning. The "connection" and "coupling" mentioned in this application, unless otherwise specified, include direct and indirect connections (couplings).

[0044] Medical images may include magnetic resonance images, ultrasound images, and CT images, etc. The following takes the extraction of stroke-related lesions in magnetic resonance images as an example for explanation.

[0045] Stroke, also known as infarction, causes insufficient blood flow to the brain, which in turn causes brain cell death and damage to the brain. Stroke is mainly divided into ischemic stroke (also known as cerebral infarction) and hemorrhagic stroke (also known as cerebral hemorrhage), both of which can cause abnormal brain function.

[0046] Magnetic resonance imaging (MRI) is another medical imaging technology after CT. Diffusion-weighted imaging (DWI) sequence is a commonly used MRI sequence in clinical and scientific research. It is a sequence that can reflect the diffusion movement and restriction degree of water molecules in tissues and lesions. It can reflect the changes in the microstructure of tissues and thus determine whether there are lesions. Compared with CT, MRI does not have electrical radiation. On the other hand, MRI has advantages in the diagnosis of craniocerebral lesions. According to the consensus of multidisciplinary experts in stroke monitoring in China, MRI has higher sensitivity than CT in diagnosing cerebral infarction. It also has obvious advantages over CT in the detection of transient ischemic attacks and small craniocerebral stroke lesions. In the process of MRI image data processing, in order to locate the brain area of ​​the stroke lesion of interest and calculate the volume of the corresponding lesion, it is necessary to first segment and extract the lesion from the three-dimensional MRI image. The traditional method uses some software to manually circle the lesion area, which is time-consuming and labor-intensive. Recently, with the development of machine learning in the field of medical imaging, some models have emerged that use machine learning to extract lesion areas. However, the classification models trained by machine learning using a batch of MRI data often have high accuracy and performance that are greatly reduced in actual clinical applications.

[0047] In response to the above problems, an embodiment of the present invention provides a medical image processing method, which combines manual identification and automatic extraction for the extraction of lesion areas in medical images such as magnetic resonance images, thereby improving the accuracy of lesion area extraction while ensuring efficiency, thereby providing strong support for the positioning of lesion areas such as stroke and other lesion-related research.

[0048] Please refer to Figure 1 , Figure 1 A schematic diagram of the structure of a medical imaging device is shown, and the medical imaging device includes: an imaging module 101, a display module 102 and a first processor 103. The imaging module 101 is used to obtain a medical image sequence of a target object, and the medical image sequence includes one or more frames of medical images. The display module 102 is used to display content, for example, displaying the medical image sequence, etc., and the display module 102 may include a touch display screen.

[0049] In some embodiments, the imaging module 101 may be a component of a medical imaging device for collecting medical images. For example, for magnetic resonance imaging, the imaging module 101 may be a module composed of a radio frequency system, a gradient system, and a computer system in the magnetic resonance imaging device, and is used to perform magnetic resonance imaging on the target object. For another example, for ultrasonic imaging, the imaging module 101 may be a module composed of an ultrasonic transmitting and receiving system, and is used to perform ultrasonic imaging on the target object.

[0050] In some embodiments, the first processor 103 may use at least one of a circuit, a single or multiple application specific integrated circuits (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field programmable gate array (FPGA), a central processing unit (CPU), a controller, a microcontroller, and a microprocessor, so that the processor 103 can execute some or all of the steps in the medical image processing method in each embodiment of the present application, or any combination of the steps.

[0051] In some embodiments, the display module 102 may be a display screen or a touch screen screen for displaying medical images, interactive interfaces and other contents.

[0052] In some embodiments, the medical imaging device may further include an interactive component, which may be a mouse, a keyboard, or the like, for a user to operate.

[0053] Please refer to Figure 2 , Figure 2 A schematic diagram of the structure of a terminal device is shown. The terminal device includes: a display component 201, a memory 202 and a second processor 203.

[0054] In this embodiment, the display component 201, the memory 202 and the second processor 203 are the main components of the terminal device. Of course, the terminal device may also include some other components, which will not be described in detail here.

[0055] The display component 201 is used to display data, and the display component may be a display screen or a touch display screen.

[0056] The memory 202 can be used as a computer-readable storage medium, and is used here to store a program, which may be a program code corresponding to the medical image processing method provided in the present application.

[0057] The second processor 203 is connected to the memory 202 and is used to execute the program stored in the memory 202 to implement the method disclosed in the embodiment of the present invention.

[0058] The following describes the method flow executed in the medical imaging device or terminal device.

[0059] Please refer to Figure 3 In some embodiments, a medical image processing method is provided. The medical image processing method is used to be executed in the first processor 103 or the second processor 203, and includes the following steps:

[0060] Step 301: Acquire a medical image sequence of a target object. The medical image sequence includes one or more frames of medical images. In some embodiments, the medical image sequence may be a multi-layer two-dimensional magnetic resonance image sequence, and the medical image is a two-dimensional magnetic resonance image; in other embodiments, the medical image sequence may also be other two-dimensional medical image sequences, such as an ultrasound image sequence, a CT image sequence, etc. This embodiment does not specifically limit the type of medical image. Multiple frames of medical images are two or more frames of medical images.

[0061] In some embodiments, the acquired medical image sequence of the target object may be image data collected in advance and stored in an internal or external memory, which may be imported through some communication media (eg, a communication interface, a USB flash drive, etc.) to acquire the medical image sequence.

[0062] In some other embodiments, the acquired medical image sequence of the target object may be a medical image sequence formed by one or more frames of medical images acquired in real time by a medical imaging device.

[0063] It should be noted that the multiple frames of medical images in the above-mentioned medical image sequence may be scanned medical images of different layers of the target area of ​​the target object, for example, a magnetic resonance image sequence includes multiple layers of two-dimensional magnetic resonance images, or may be different cross-sectional images of the target area of ​​the target object, for example, an ultrasound image sequence includes multiple frames of two-dimensional ultrasound images of different cross-sections. The medical image sequence may also include other forms of multiple frames of medical images, which are not specifically limited in this embodiment.

[0064] In some embodiments, after acquiring the medical image sequence of the target object, the medical image sequence needs to be preprocessed for subsequent processing, and the preprocessing at least includes: converting the format of the medical images in the medical image sequence. For example, for magnetic resonance images, the magnetic resonance images need to be converted from DICOM format to a 19×159×159 matrix, where the pixels of each magnetic resonance image are 159×159 mm 2 ; and / or, normalizing the medical images in the medical image sequence, for example, normalizing the medical images to 0-255 according to the maximum grayscale value, so that the images are standardized and are not affected by units and weighted values.

[0065] Step 302: Displaying the medical image sequence in a display interface.

[0066] In some embodiments, if a medical image sequence contains multiple frames of medical images, when displaying, the multiple frames of medical images can be displayed simultaneously in the same screen by arranging the multiple frames of images in a tiled manner. When a certain frame of image needs to be viewed separately, the image can be selected for separate display by clicking on the image. In other embodiments, when displaying, only one frame of medical image can be displayed separately, and each frame of medical image in the medical image sequence can be displayed frame by frame through operations such as the user sliding the image, clicking on the next frame, or setting the display time of each frame of medical image.

[0067] Step 303: In response to a user's operation on at least one frame of a medical image in the displayed medical image sequence, a preliminary lesion region in the medical image sequence is determined.

[0068] When the doctor is viewing the medical images in the displayed medical image sequence, he can roughly mark the lesion area in the medical image through input components such as a keyboard, a mouse or a touch display screen. For example, he can circle the preliminary lesion area in the currently displayed medical image. It should be noted that the doctor can only mark the preliminary lesion area for some medical images in the medical image sequence. For some medical images that do not show the lesion area, when these medical images are displayed, the doctor does not need to perform any marking operation and continues to view the next frame of the medical image.

[0069] In one embodiment, in step 302 and step 303, the bbox_select class can be used in Python to create an interactive interface, take the medical image sequence as input, traverse each frame image in the medical image sequence, and perform preliminary lesion area identification on the medical image with lesions.

[0070] Step 304: Based on the preliminary lesion area in the medical image sequence, generate one or more frames of first images containing only the preliminary lesion area. In one embodiment, for the range of the preliminary lesion area in the medical image sequence, a mask of the preliminary lesion area is generated, and the portion outside the preliminary lesion area is removed to obtain one or more frames of two-dimensional first images containing only the preliminary lesion area. Among them, for the range of the preliminary lesion area in the medical image sequence, a mask of the preliminary lesion area is generated, and the corresponding mask can be generated by using the cv toolkit cv.2fillPoly function according to the boundary information of the circled range corresponding to the preliminary lesion area in the medical image sequence, and the first image containing only the circled range of the preliminary lesion area is generated by using the numpy toolkit np.multiply function. It should be noted that the first image containing only the preliminary lesion area means that only the original pixel information of the preliminary lesion area is retained, and the rest of the image is processed as black.

[0071] In some embodiments, for magnetic resonance images, the medical image sequence usually includes multiple layers of two-dimensional magnetic resonance images, and then multiple frames of two-dimensional first images can be obtained. Therefore, it is also necessary to perform three-dimensional reconstruction on the multiple frames of two-dimensional first images to obtain corresponding three-dimensional images. The specific three-dimensional reconstruction method can be: use the RegularGridInterpolator function under scipy.interpolate to interpolate and reconstruct the multiple frames of first images to ensure that the voxel ratio is 1×1×1 and there is no distortion when displayed in three dimensions. The final voxel size is 1.4375×1.4375×1.4375mm 3 .

[0072] Step 305: extract lesions from one or more frames of the first image to obtain lesion results.

[0073] In some embodiments, after reconstructing the reconstructed three-dimensional image by interpolating multiple frames of the first two-dimensional image in three dimensions, a pixel area whose grayscale value is greater than or equal to a preset threshold is extracted from the three-dimensional image to obtain one or more lesion areas, and then, the lesion area that meets the preset conditions and the size corresponding to the lesion area that meets the preset conditions are determined in the one or more lesion areas to obtain the lesion result. In one embodiment, the preset condition may be a lesion area with the largest size (volume) or a size (volume) greater than a certain threshold. For example, the lesion result may include the extracted lesion area with the largest size and its corresponding size. Wherein, if it is a three-dimensional image, the size of the lesion area is the volume of the lesion area; if it is a two-dimensional image, the size of the lesion area is the area of ​​the lesion area. In one embodiment, for a three-dimensional image, the cc3d.largest_k function under the cc3d toolkit can be used to extract the lesion area with the largest volume, and the voxel number is calculated using cc3d.statistics, and the volume of the lesion area is calculated using the voxel number × voxel volume. In one embodiment, the lesion result can be displayed on a display interface.

[0074] Please refer to Figure 4 Taking magnetic resonance images as an example, a specific implementation of the medical image processing method of an embodiment of the present invention is provided. Taking magnetic resonance images as an example, it is specifically described below.

[0075] Step 401: Acquire a magnetic resonance image sequence, wherein the magnetic resonance image sequence includes 19 layers of magnetic resonance images.

[0076] Step 402: preprocessing the 19 layers of magnetic resonance images in the magnetic resonance image sequence, which can be specifically divided into two parts.

[0077] Step 402-1: Convert the 19-layer magnetic resonance images from DICOM format to a 19×159×159 matrix, where the pixels of each magnetic resonance image are 159×159 mm2 .

[0078] Step 402 - 2 : performing normalization processing on the grayscale values ​​of the 19-layer magnetic resonance image, and normalizing the grayscale values ​​of the image to 0-255, so that the image is standardized and is not affected by the unit and weight value.

[0079] The preprocessing process of the above two steps can be executed sequentially or one step can be executed selectively. In addition, the preprocessing process can also be other processing methods, which are not specifically limited in this embodiment.

[0080] Step 403: Display the 19 layers of magnetic resonance images after the above preprocessing through an interactive interface or other means, and the user reviews the 19 layers of magnetic resonance images one by one by operating the interactive interface, and marks the approximate lesion area on a layer of magnetic resonance image where the lesion exists, that is, marks the preliminary lesion area. The interactive interface can be created by using the bbox_select class in python, taking the medical image sequence as input, traversing each frame image in the medical image sequence, and performing preliminary lesion area marking on the medical image where the lesion exists.

[0081] Step 404: Generate a 19-layer first image containing only the preliminary lesion area through the 19-layer magnetic resonance image with the preliminary lesion area marked above, that is, generate a mask of the preliminary lesion area. Among them, for one or more layers of magnetic resonance images without the preliminary lesion area marked, by adjusting the pixel values, all of these magnetic resonance images are displayed in black, and for other one or more layers of magnetic resonance images indicating the preliminary lesion area, the pixel values ​​of the parts other than the preliminary lesion area are also adjusted, and the parts other than the preliminary lesion area are also displayed in black, so as to obtain a 19-layer first image. Among them, for generating a mask of the preliminary lesion area, the cv.2fillPoly function of the cv toolkit can be used to generate a corresponding mask according to the boundary information of the marked range corresponding to the preliminary lesion area, and the np.multiply function of the numpy toolkit can be used to generate a first image containing only the circled range of the preliminary lesion area.

[0082] Step 405: Interpolate the 19 layers of the first image to reconstruct a three-dimensional image. In one embodiment, the specific three-dimensional reconstruction method may be: use the RegularGridInterpolator function under scipy.interpolate to interpolate and reconstruct multiple frames of the first image to ensure that the voxel ratio is 1×1×1 and there is no distortion when displayed in three dimensions. The final voxel size is 1.4375×1.4375×1.4375mm 3 .

[0083] Step 406: Setting a threshold to automatically extract one or more final lesion regions from the three-dimensional image. The region in the three-dimensional image with a pixel value greater than the threshold can be used as the final lesion region, and the lesion region obtained in this way may be one or more.

[0084] Step 407: Find the largest lesion region from the final lesion regions extracted in step 406, and calculate the volume value of the largest lesion region. In one embodiment, for a three-dimensional image, the cc3d.largest_k function in the cc3d toolkit can be used to extract the lesion region with the largest volume, and the voxel number can be calculated using cc3d.statistics, and the volume of the lesion region can be calculated using the voxel number×voxel volume.

[0085] Therefore, the embodiment of the present invention improves the extraction efficiency of the lesion region and also improves the extraction accuracy of the lesion region through the above-mentioned semi-automatic lesion region extraction method.

[0086] Those skilled in the art will appreciate that all or part of the functions of the various methods in the above-mentioned embodiments can be implemented by hardware or by computer programs. When all or part of the functions in the above-mentioned embodiments are implemented by computer programs, the program can be stored in a computer-readable storage medium, and the storage medium can include: read-only memory, random access memory, disk, optical disk, hard disk, etc., and the program is executed by a computer to implement the above-mentioned functions. For example, the program is stored in the memory of the device, and when the program in the memory is executed by the processor, all or part of the above-mentioned functions can be implemented. In addition, when all or part of the functions in the above-mentioned embodiments are implemented by computer programs, the program can also be stored in a storage medium such as a server, another computer, disk, optical disk, flash disk or mobile hard disk, and can be downloaded or copied and saved in the memory of the local device, or the system of the local device is updated, and when the program in the memory is executed by the processor, all or part of the functions in the above-mentioned embodiments can be implemented.

[0087] The above specific examples are used to illustrate the present invention, which is only used to help understand the present invention and is not intended to limit the present invention. For those skilled in the art, according to the concept of the present invention, some simple deductions, modifications or substitutions can be made.

Claims

1. A method for processing medical images, characterized in that: include: Acquire a medical image sequence of a target object, wherein the medical image sequence includes one or more frames of medical images; Displaying the medical image sequence on a display interface; In response to a user's operation on at least one frame of the displayed medical image sequence, determining a preliminary lesion area in the medical image sequence; Based on the preliminary lesion region in the medical image sequence, generating one or more first images containing only the preliminary lesion region; Perform lesion extraction on one or more frames of the first image to obtain lesion results.

2. The method according to claim 1, characterized in that The determining of a preliminary lesion region in the medical image sequence in response to a user's operation on at least one frame of the displayed medical image sequence comprises: In response to a marking operation performed by a user on a currently displayed frame of medical image, a region corresponding to the marking operation is determined, and the region corresponding to the marking operation is used as the preliminary lesion region.

3. The method according to claim 1, characterized in that The lesion results include: the largest lesion area and the size corresponding to the largest lesion area.

4. The method according to claim 1, characterized in that In the case where the first image is two-dimensional and consists of multiple frames, performing lesion extraction on one or more frames of the first image to obtain lesion results includes: Performing three-dimensional interpolation reconstruction on the multiple frames of first images to obtain a reconstructed three-dimensional image; Extracting one or more lesion areas in the three-dimensional image; A lesion area that meets the preset conditions and a size corresponding to the lesion area that meets the preset conditions in one or more of the lesion areas are determined to obtain a lesion result.

5. The method according to claim 4, characterized in that The extracting one or more lesion areas in the three-dimensional image comprises: Pixel regions whose grayscale values ​​are greater than or equal to a preset threshold are extracted from the three-dimensional image to obtain one or more lesion regions.

6. The method according to claim 1, characterized in that The obtaining of a medical image sequence of the target object comprises: importing a sequence of medical images of the target object; Alternatively, one or more frames of medical images of the target object are collected in real time to obtain the medical image sequence.

7. The method according to any one of claims 1 to 6, characterized in that The medical image is a magnetic resonance image, a CT image or an ultrasound image.

8. A medical imaging device, characterized in that: include: An imaging module, used to acquire a medical image sequence of a target object, wherein the medical image sequence includes one or more frames of medical images; A display module, used for displaying the medical image sequence on a display interface; The first processor is configured to execute the method according to any one of claims 1 to 7.

9. A terminal device, characterized in that: include: A display component, used for displaying the medical image sequence; Memory for storing computer programs; A second processor is configured to implement the method according to any one of claims 1 to 7 by executing the computer program stored in the memory.

10. A computer-readable storage medium, characterized in that: A computer program is stored on the medium, and the computer program can be executed by a processor to implement the method according to any one of claims 1 to 7.

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