Color correction method and device for eye fundus image, and storage medium

Abstract

The invention provides a color correction method and device for an eye fundus image and a storage medium, and the method employs a nonlinear highlight suppression algorithm to carry out smooth compression processing on a highlight region after basic brightness correction, thereby effectively avoiding the problems of overexposure and detail loss of highlight regions such as an optic disc caused by a conventional linear transformation method. The texture detail information in the highlight area is reserved, and the visibility of the details of the optic disk area in the eye fundus image is improved. According to the method, two adjustable parameters including the highlight brightness threshold and the suppression intensity control coefficient are set, the highlight suppression degree can be adaptively adjusted according to the features of different eye fundus images, and the method has good robustness and generalization performance for the eye fundus images with different color deviation degrees.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of medical image processing, in particular to a color correction method and device for fundus images, and a storage medium. BACKGROUND

[0002] Fundus images are important evidence for diagnosing and screening various eye diseases (such as glaucoma, age-related macular degeneration, retinal diseases) and systemic diseases (such as diabetic complications, hypertension) in ophthalmic clinics. The quality of fundus images, especially their color fidelity, directly affects whether doctors can accurately identify early lesions and assess disease progression. Therefore, image correction techniques are particularly important in the field of fundus imaging.

[0003] However, in current clinical practice, there are many challenges in obtaining high-quality and standardized fundus images. First, the internal optical systems, sensors and built-in image processing algorithms of fundus imaging systems with different technical routes differ, resulting in systematic color deviations in images captured by different devices even under the same shooting conditions. Second, even if the same device is used, factors such as the intensity of the lighting, the dilation state of the pupil, and the clarity of the patient's ocular refractive medium can also cause color deviations between batches of images.

[0004] The color correction methods in the prior art mainly include: (1) Methods based on white balance: such as gray world method, perfect reflection method, etc. These methods assume that the average value of all colors in the image should be gray, and adjust the gain of the color channel to eliminate color deviation. However, the color distribution of fundus images has special characteristics (such as high brightness of the optic disc, dark red of blood vessels, etc.), which do not meet the gray world assumption, resulting in unsatisfactory correction results.

[0005] (2) Methods based on color transfer: by calculating the mean and standard deviation of each channel in a certain color space between the source image and the target image, the color distribution of the source image is adjusted to be consistent with that of the target image. A representative method is the Reinhard color transfer algorithm. This method can better unify the overall color style of the image, but has the following shortcomings: Linear transformation is used for the brightness channel, which can easily cause overexposure in high-light areas (such as the optic disc), resulting in the loss of important texture detail information; The particularity of high-light areas in fundus images is not considered, and there is no targeted high-light suppression mechanism.

[0006] (3) Methods based on deep learning: in recent years, color correction methods based on convolutional neural networks have appeared, but such methods require a large amount of labeled data for training, have high computational complexity, and the generalization ability of the model is limited by the distribution of the training data, making it difficult to adapt to the diversity of devices and shooting conditions in clinical environments.

[0007] Therefore, there is an urgent need for a color correction method that can unify the overall color style of fundus images while effectively suppressing overexposure in bright areas and preserving important details. Summary of the Invention

[0008] To achieve the above-mentioned objectives and other advantages of the present invention, a first objective of the present invention is to provide a color correction method for fundus images, comprising the following steps: Obtain a reference color fundus image and a color fundus image to be corrected; The reference color fundus image and the color fundus image to be corrected are converted to different color spaces and their parameters are statistically analyzed. A linear intensity transformation is performed on the brightness channel of the color fundus image to be corrected to correct the base brightness; The base brightness value of the brightness channel of the color fundus image to be corrected is subjected to nonlinear specular suppression processing to correct the final brightness; Chromaticity correction is performed on the chromaticity channels of the color fundus image to be corrected using mean shift transformation.

[0009] Furthermore, it also includes the following steps: The luminance and chromaticity values ​​of the final corrected color fundus image are processed by channel merging and converted back to the original color space to obtain the fundus image after color correction.

[0010] Further, the step of converting the color space of the reference color fundus image and the color fundus image to be corrected and statistically analyzing the parameters includes: The color spaces of the reference color fundus image and the color fundus image to be corrected are converted to the CIELAB color space, and the mean values ​​of the L*, a*, and b* channels and the standard deviation of the L* channel are calculated respectively.

[0011] Furthermore, after the step of converting the color space of the reference color fundus image and the color fundus image to be corrected and statistically analyzing the parameters, the method further includes: The effective fundus region in the color fundus image to be corrected is selected to exclude the interference of the black background region at the image edge on the calculation of statistical features.

[0012] Furthermore, the effective fundus area is determined using a circular mask method, specifically including the following steps: Extract the height h and width w of the color fundus image to be corrected; Calculate the center coordinates, with the x-coordinate being w / 2 and the y-coordinate being h / 2; Calculate the radius of the circular mask, which is equal to half the smaller of the height h and the width w; Create two-dimensional grid coordinate matrices X and Y, where the elements of matrix X represent the horizontal coordinates of pixels and the elements of matrix Y represent the vertical coordinates of pixels; Calculate the distance from each pixel to the center based on the equation of a circle, and determine whether the distance is less than or equal to the radius. If so, the pixel belongs to the effective fundus region, and the value at the corresponding position in the mask matrix is ​​set to true. If not, then the pixel does not belong to the effective fundus region, and the value at the corresponding position in the mask matrix is ​​set to false.

[0013] Further, the step of performing linear intensity transformation on the luminance channel of the color fundus image to be corrected to correct the base luminance includes: The L* channel of the color fundus image to be corrected is subjected to linear intensity transformation to obtain the basic luminance value of the L* channel. The formula for calculating the linear intensity transformation is as follows: in, p represents the original luminance value of the L* channel of the color fundus image to be corrected, where p denotes any pixel. and These are the mean and standard deviation of the L* channel of the color fundus image to be corrected, respectively. and These are the mean and standard deviation of the L* channel of the reference color fundus image, respectively.

[0014] Furthermore, the step of performing nonlinear highlight suppression processing on the base brightness value of the brightness channel of the color fundus image to be corrected to correct the final brightness includes: The baseline brightness value of the L* channel of the color fundus image to be corrected Nonlinear specular suppression processing is performed to obtain the final corrected luminance value of the color fundus image to be corrected. The calculation formula for the nonlinear specular highlight suppression processing is as follows: in, T is the set high brightness threshold, and T is the set suppression intensity control coefficient.

[0015] Furthermore, the step of performing chromaticity correction on the chromaticity channels of the color fundus image to be corrected through mean shift transformation includes: Chromaticity correction is performed on the a* and b* channels of the color fundus image to be corrected using a mean shift transformation. The original chromaticity values ​​of the pixels in the a* and b* channels are added to the mean values ​​of the a* and b* channels of the reference color fundus image and the mean values ​​of the a* and b* channels of the color fundus image to be corrected, respectively, to obtain the corrected chromaticity values ​​of the a* and b* channels of the color fundus image to be corrected. The calculation formula is as follows: in, and These are the original chromaticity values ​​of the a* and b* channels of the color fundus image to be corrected, respectively. and These are the mean values ​​of the a* and b* channels of the reference color fundus image, respectively. and These are the mean values ​​of the a* and b* channels of the color fundus image to be corrected, respectively.

[0016] A second objective of the present invention is to provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described method.

[0017] A third object of the present invention is to provide a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-described method.

[0018] Compared with the prior art, the beneficial effects of the present invention are: This invention provides a color correction method, device, and storage medium for fundus images. The method uses a nonlinear specular suppression algorithm to smooth and compress the bright areas after basic brightness correction, effectively avoiding the overexposure and detail loss problems of bright areas such as the optic disc caused by traditional linear transformation methods. It preserves the texture details inside the bright areas and improves the visibility of details in the optic disc area in fundus images.

[0019] This invention, by setting two adjustable parameters—a high-brightness threshold and a suppression intensity control coefficient—can adaptively adjust the degree of high-brightness suppression according to the characteristics of different fundus images, and has good robustness and generalization performance for fundus images with different degrees of color deviation.

[0020] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it according to the contents of the specification, the preferred embodiments of the present invention are described in detail below with reference to the accompanying drawings. Specific embodiments of the present invention are given in detail below with reference to the accompanying drawings. Attached Figure Description

[0021] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings: Figure 1 This is a flowchart of a color correction method for fundus images; Figure 2 For reference color fundus images and color fundus images to be corrected; Figure 3 Flowchart for determining the effective fundus region using the circular mask method; Figure 4 It is a binary mask image; Figure 5 The nonlinear mapping curve for specular highlight suppression processing; Figure 6 Images before and after luminance channel correction; Figure 7 Images before and after chroma channel correction; Figure 8 Images before and after color correction; Figure 9 This is a schematic diagram of a computer device. Figure 10 This is a schematic diagram of a computer-readable storage medium. Detailed Implementation

[0022] The present invention will now be further described with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. It should be noted that, without conflict, the various embodiments or technical features described below can be arbitrarily combined to form new embodiments.

[0023] Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of this invention.

[0024] The drawing numbers in this application are only used to distinguish the steps in the scheme and are not used to limit the execution order of the steps. The specific execution order is as described in the specification.

[0025] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.

[0026] Example 1 A color correction method for fundus images, such as Figure 1 As shown, it includes the following steps: S100: Obtain the reference color fundus image and the color fundus image to be corrected; In some alternative embodiments, a color fundus image with normal color is selected as a reference color fundus image, such as... Figure 2 As shown in (a). The color fundus image to be corrected is as follows. Figure 2 As shown in (b).

[0027] S200: Convert the color space of the reference color fundus image and the color fundus image to be corrected, and statistically analyze the parameters; Specifically, the color spaces of the reference color fundus image and the color fundus image to be corrected are converted to the CIELAB color space, and the mean values ​​of the L*, a*, and b* channels and the standard deviation of the L* channel are calculated respectively.

[0028] In some embodiments, after the step of converting the color space of the reference color fundus image and the color fundus image to be corrected and statistically analyzing the parameters, the method further includes: The effective fundus region in the color fundus image to be corrected is selected to exclude the interference of the black background region at the image edge on the calculation of statistical features.

[0029] Specifically, such as Figure 3 As shown, the effective fundus area is determined using a circular mask method, which specifically includes the following steps: S210. Extract the height h and width w of the color fundus image to be corrected; S220. Calculate the center coordinates, with the horizontal coordinate being w / 2 and the vertical coordinate being h / 2. S230. Calculate the radius of the circular mask, which is equal to half the smaller of the height h and the width w; S240. Create two-dimensional grid coordinate matrices X and Y, where the elements of matrix X represent the horizontal coordinates of pixels and the elements of matrix Y represent the vertical coordinates of pixels. S250. Calculate the distance from each pixel to the center according to the equation of the circle, and determine whether the distance is less than or equal to the radius. S260. If so, the pixel belongs to the effective fundus region, and the value at the corresponding position in the mask matrix is ​​set to the true value. S270. If not, then the pixel does not belong to the effective fundus region, and the value at the corresponding position in the mask matrix is ​​set to false.

[0030] Through the above steps, a binary mask matrix with the same size as the color fundus image to be corrected is obtained, as shown in the following figure. Figure 4 As shown.

[0031] S300, Perform linear intensity transformation on the brightness channel of the color fundus image to be corrected to correct the base brightness; Specifically, a linear intensity transformation is performed on the L* channel of the color fundus image to be corrected to obtain the basic luminance value of the L* channel. The formula for calculating the linear intensity transformation is as follows: in, p represents the original luminance value of the L* channel of the color fundus image to be corrected, where p denotes any pixel. and These are the mean and standard deviation of the L* channel of the color fundus image to be corrected, respectively. and These are the mean and standard deviation of the L* channel of the reference color fundus image, respectively.

[0032] This embodiment centers the brightness distribution by subtracting the mean value of the L* channel of the color fundus image to be corrected from the original brightness value of the L* channel of the color fundus image to be corrected, then multiplying it by the standard deviation ratio to adjust the brightness variation range, and finally adding the mean value of the L* channel of the reference color fundus image to shift the overall brightness to the target brightness level.

[0033] S400: Perform nonlinear highlight suppression processing on the base brightness value of the brightness channel of the color fundus image to be corrected in order to correct the final brightness. Specifically, the improved Reinhard tone mapping algorithm is applied to the base luminance values ​​of the L* channel of the color fundus image to be corrected. Nonlinear specular suppression processing is performed to obtain the final corrected luminance value of the color fundus image to be corrected. The method works by smoothly and progressively compressing pixels whose brightness values ​​exceed a preset highlight threshold (e.g., ...). Figure 5 As shown), it effectively darkens the bright areas without affecting the medium and low brightness areas, thus suppressing overexposure in the bright areas. Figure 6 As shown, the calculation formula for the nonlinear specular highlight suppression processing is as follows: in, The set high gloss threshold value ranges from 0.6 to 1.0. In this embodiment, The value is set to 0.95, and T is the set suppression intensity control coefficient, which ranges from 0.3 to 3.0. In this embodiment, T is set to 0.5.

[0034] S500 performs chromaticity correction on the chromaticity channels of the color fundus image to be corrected through mean shift transformation.

[0035] Specifically, chromaticity correction is performed on the a* and b* channels of the color fundus image to be corrected through mean shift transformation. The original chromaticity values ​​of the pixels in the a* and b* channels are added to the differences between the mean values ​​of the a* and b* channels of the reference color fundus image and the mean values ​​of the a* and b* channels of the color fundus image to be corrected, thereby achieving precise correction of color deviation and obtaining the corrected chromaticity values ​​of the a* and b* channels of the color fundus image to be corrected. , ,like Figure 7 As shown, the calculation formula is as follows: in, and These are the original chromaticity values ​​of the a* and b* channels of the color fundus image to be corrected, respectively. and These are the mean values ​​of the a* and b* channels of the reference color fundus image, respectively. and These are the mean values ​​of the a* and b* channels of the color fundus image to be corrected, respectively.

[0036] S600: Perform channel merging on the final corrected luminance and chromaticity values ​​of the color fundus image to be corrected and convert it back to the original color space to obtain the fundus image after color correction, such as... Figure 8 As shown.

[0037] Example 2 A computer device 600, such as Figure 9 As shown, the system includes a memory 610, a processor 620, and a computer program 630 stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of a color correction method for fundus images. For a detailed description of the method, please refer to the corresponding description in the above method embodiments; it will not be repeated here.

[0038] Example 3 A computer-readable storage medium, such as Figure 10 As shown, a computer program is stored thereon, which, when executed by a processor, implements the steps of a color correction method for fundus images. For a detailed description of the method, please refer to the corresponding description in the above method embodiments, and will not be repeated here.

[0039] The number of devices and processing scale described herein are for the purpose of simplifying the description of the invention. Applications, modifications, and variations of the invention will be readily apparent to those skilled in the art.

[0040] Although embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the specification and embodiments. They can be applied to various fields suitable for the present invention. For those skilled in the art, other modifications can be easily made. Therefore, without departing from the general concept defined by the claims and their equivalents, the present invention is not limited to the specific details and illustrations shown and described herein.

[0041] The apparatus, computer device, and non-volatile computer storage medium and method provided in the embodiments of this specification are corresponding. Therefore, the apparatus, computer device, and non-volatile computer storage medium also have similar beneficial technical effects as the corresponding method. Since the beneficial technical effects of the method have been described in detail above, the beneficial technical effects of the corresponding apparatus, computer device, and non-volatile computer storage medium will not be repeated here.

[0042] Those skilled in the art will also know that, besides implementing the controller in the form of purely computer-readable program code, the same functions can be achieved by logically programming the method steps, making the controller take the form of logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers (PLCs), and embedded microcontrollers. Therefore, such a controller can be considered a hardware component, and the devices included within it for implementing various functions can also be considered structures within that hardware component. Alternatively, the devices for implementing various functions can be considered as both software units implementing the method and structures within a hardware component.

[0043] The systems, apparatuses, or units described in the above embodiments can be implemented by computer chips or physical entities, or by products with certain functions. For ease of description, the above apparatuses are described separately by function as various units. Of course, when implementing one or more embodiments of this specification, the functions of each unit can be implemented in one or more software and / or hardware.

[0044] Those skilled in the art will understand that the embodiments of this specification can be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the embodiments of this specification can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0045] This specification is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this specification. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, produce a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0046] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0047] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0048] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0049] This specification may be described in the general context of computer-executable instructions, such as program units, that are executed by a computer. Generally, program units include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. This specification may also be practiced in distributed computing environments, where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program units may reside in local and remote computer storage media, including storage devices.

[0050] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0051] The above description is merely an embodiment of this specification and is not intended to limit the scope of one or more embodiments of this specification. Various modifications and variations can be made to one or more embodiments of this specification by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of one or more embodiments of this specification should be included within the scope of the claims of one or more embodiments of this specification.

Claims

1. A color correction method for fundus images, characterized by, The method comprises the following steps: Obtaining a reference color fundus image and a color fundus image to be corrected; Converting the color space of the reference color fundus image and the color fundus image to be corrected and calculating parameters; Performing linear intensity transformation on the luminance channel of the color fundus image to be corrected to correct the base luminance; Performing nonlinear highlight suppression processing on the base luminance value of the luminance channel of the color fundus image to be corrected to correct the final luminance; Performing chroma correction on the chroma channel of the color fundus image to be corrected through mean shift transformation.

2. The color correction method for fundus images according to claim 1, wherein Further comprising the following steps: Performing channel merging processing on the final corrected luminance value and the chroma value of the color fundus image to be corrected and converting back to the original color space to obtain the color-corrected fundus image.

3. The method of claim 1, wherein the color correction of the fundus image is performed by using a color correction model. The step of converting the color space of the reference color fundus image and the color fundus image to be corrected and calculating parameters comprises: Converting the color space of the reference color fundus image and the color fundus image to be corrected to CIELAB color space and calculating the mean value of L*, a* and b* channels and the standard deviation of the L* channel, respectively.

4. The method of claim 1, wherein the color correction of the fundus image is performed by using a color correction model. After the step of converting the color space of the reference color fundus image and the color fundus image to be corrected and calculating parameters, further comprising: Selecting the effective fundus region in the color fundus image to be corrected to exclude the interference of the black background region at the edge of the image on the calculation of the statistical characteristics.

5. The method for color correction of fundus images according to claim 4, wherein: The effective fundus region is determined by a circular mask method, which comprises the following steps: Extracting the height h and width w of the color fundus image to be corrected; Calculating the center coordinates, with the horizontal coordinate being w / 2 and the vertical coordinate being h / 2; Calculating the radius of the circular mask, which is equal to half of the smaller value of the height h and the width w; Creating a two-dimensional grid coordinate matrix X and Y, with the elements of the matrix X representing the horizontal coordinates of the pixels and the elements of the matrix Y representing the vertical coordinates of the pixels; Calculating the distance from each pixel point to the center according to the equation of a circle and determining whether the distance is less than or equal to the radius; If yes, the pixel point belongs to the effective fundus region and the value at the corresponding position in the mask matrix is set to true; If no, the pixel point does not belong to the effective fundus region and the value at the corresponding position in the mask matrix is set to false.

6. The method of claim 3, wherein the color correction of the fundus image is performed by using a color correction table. The step of performing linear intensity transformation on the luminance channel of the color fundus image to be corrected to correct the base luminance comprises: performing a linear intensity transformation on the L* channel of the color fundus image to be corrected to obtain an L* channel base luminance value , the linear intensity transformation calculation formula is as follows: wherein, Lrawis the raw luminance value of the L* channel of the color fundus image to be corrected, p denotes any pixel, and are the mean and the standard deviation of the L* channel of the color fundus image to be corrected, respectively, and are the mean and the standard deviation of the L* channel of the reference color fundus image, respectively.

7. The method of color correction for fundus images according to claim 6, wherein, The step of performing nonlinear highlight suppression processing on the base luminance value of the luminance channel of the color fundus image to be corrected to correct the final luminance comprises: a basic brightness value of the to-be-corrected color fundus image L* channel performing a non-linear highlight suppression processing to obtain a final corrected brightness value of the to-be-corrected color fundus image , and a calculation formula of the non-linear highlight suppression processing is as follows: wherein, T is a set high brightness threshold, and T is a set suppression intensity control coefficient.

8. The method of color correction for fundus images according to claim 7, wherein, The step of performing chroma correction on the chroma channel of the color fundus image to be corrected through mean shift transformation comprises: Performing chroma correction on the a* and b* channels of the color fundus image to be corrected through mean shift transformation by adding the difference between the mean value of the a* and b* channels of the reference color fundus image and the mean value of the a* and b* channels of the color fundus image to be corrected to the original chroma value of the a* and b* channel pixels of the color fundus image to be corrected, to obtain the corrected chroma value of the a* and b* channels of the color fundus image to be corrected, with the calculation formula being as follows: wherein, and are the original chrominance values of the a* and b* channels of the color fundus image to be corrected, and are the mean values of the a* and b* channels of the reference color fundus image, and are the mean values of the a* and b* channels of the color fundus image to be corrected.

9. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the computer program to realize the steps of the method according to any one of claims 1-8.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program, which is executed by a processor, implements the steps of the method according to any one of claims 1 to 8.

Images