Analysis method for treating primary skin based on lattice CO2 laser

Abstract

The invention discloses an analysis method for treating primary skin based on dot matrix CO2 laser, which relates to a computer processing technology and comprises the following steps: S01, shooting a target skin area of a patient by using a high-definition camera to obtain original image data of the skin; s02, analyzing the skin image data by using an image processing algorithm so as to judge and obtain skin attributes and a treatment area; s03, searching in an expert knowledge base based on the acquired skin attributes to obtain a treatment strategy; and S04, transmitting ultra-pulse CO2 dot matrix laser to the treatment area by using the ultra-pulse CO2 dot matrix laser and the treatment strategy transmitting index. According to the invention, through high-precision image acquisition and processing and in combination with an expert knowledge base, precise diagnosis and personalized treatment of skin diseases are realized.

Description

Technical Field

[0001] The present invention relates to computer processing technology, and particularly to a method for analyzing primary skin treatment based on fractional CO 2 laser. Background Art

[0002] The Ultrapulsed fractional CO 2 laser combines the advantages of ultrapulsed CO 2 laser and fractional laser, and is currently the main means for clinical scar treatment. However, the setting of the energy parameters of the Ultrapulsed fractional CO 2 laser highly depends on the experience of the operating physician. If the energy is too shallow, the single-treatment effect is poor; if the energy is too deep, the healing difficulty after treatment increases, and it is easy to form an infection, increasing the risk of forming new scars.

[0003] Therefore, the energy parameters of the Ultrapulsed fractional CO 2 laser are determined according to the conditions of the scar area, that is, it is necessary to perform pixel decomposition on the collected skin area to judge the conditions of the scar area, and then according to the preset database, the problem of setting the energy parameters of the Ultrapulsed fractional CO 2 laser can be simply solved, and the problems caused by too deep or too shallow energy can be well avoided. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for analyzing primary skin treatment based on fractional CO 2 laser to solve the above problems.

[0005] To achieve the above purpose, the present invention provides the following technical solutions:

[0006] A method for analyzing primary skin treatment based on fractional CO 2 laser includes the following steps:

[0007] S01. Use a high-definition camera to photograph the target skin area of the patient to obtain the original image data of the skin;

[0008] S02. Use an image processing algorithm to analyze the skin image data to judge the skin attributes and the treatment area;

[0009] S03. Search through the expert knowledge base based on the obtained skin attributes to obtain a treatment strategy;

[0010] S04. Use an Ultrapulsed fractional CO 2 laser, and emit an Ultrapulsed fractional CO 2 laser to the treatment area according to the emission index of the treatment strategy.

[0011] Preferably, the image data of the target skin area of the patient taken in step S01 is no less than 20 consecutive snapshots, and every five snapshots during the shooting process, it increases by 200 pixels to obtain at least four sets of image sets A n , where n takes 1, 2, 3,..., n.

[0012] Preferably, obtaining the original image data of the skin in step S01 includes:

[0013] S11. Create a data channel matching the n items, and sequentially import the five photos in the four sets of image sets A n ;

[0014] S12. Determine the input value of the spatial variation tone mapping intensity of the photo;

[0015] S13. Process the photo and the corresponding input value of the photo to obtain the output photo data of the output image applied with the number of spatial variation tone mappings;

[0016] S14. Create grids for the obtained output photo data of the four data channels respectively, and then extract the pixel points in each grid;

[0017] S15. Enlarge the output photo data of the image set A n to be pixel-consistent with the output photo data of the image set A n+1 , then perform matching according to the obtained pixel points, and perform superposition calculation after the matching is completed;

[0018] S16. Repeat step S15 to perform superposition calculation on the four sets of image sets A n in sequence to obtain a superimposed original image data of the skin.

[0019] Preferably, the spatial variation tone mapping in step S13 adopts a local adaptive method. Based on multiple extracted reference pixels and the color level change values of the adjacent surrounding pixels corresponding to one reference pixel, the color level change values of the same pixels are weighted and averaged to obtain the color level change values.

[0020] Preferably, analyzing the skin image data using the image processing algorithm in step S02 includes:

[0021] S21. Perform preprocessing operations on the original image data of the skin, including denoising, color correction, and image enhancement;

[0022] S22. Based on applying an image processing algorithm to the original image data of the preprocessed skin, extracting local differential color patches, and magnifying the pixels to identify the attributes corresponding to the differential color patches on the foot;

[0023] S23. Evaluating the multiple classifications obtained to determine that the corresponding classification belongs to skin characteristics;

[0024] S24. Based on the given skin characteristics, frame the target area and extract the grid coordinates of the original image data of the skin.

[0025] Preferably, the attributes in step S22 include skin color depth, skin texture, pores, wrinkles, pigmentation, and local hyperplasia;

[0026] In step S23, the evaluation is to determine whether the differential color patch conforms to the attribute type. If not, it is determined that step S24 does not need to be executed.

[0027] Preferably, the image processing algorithm includes edge detection, texture analysis, color space conversion, histogram equalization, and feature extraction algorithms, which are executed in sequence.

[0028] Preferably, the expert knowledge base in step S03 is the pixel sample of the collected diseased skin image, and includes the super-pulse CO 2 dot matrix laser emission strategy.

[0029] In the above technical solution, a method for analyzing primary skin based on dot matrix CO 2 laser therapy provided by the present invention has the following beneficial effects: First, a high-definition camera is used to photograph the target skin area of the patient to obtain high-quality original image data. Subsequently, through advanced image processing algorithms, the skin image is deeply analyzed to accurately judge the skin attributes and determine the treatment area. Then, based on the skin attributes, a matching treatment strategy is searched in the expert knowledge base to ensure the accuracy and effectiveness of the treatment plan. Finally, a super-pulse CO 2 dot matrix laser is used to accurately emit laser to the treatment area according to the treatment strategy to achieve precise treatment. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0031] Figure 1 It is a flowchart provided by an embodiment of the present invention;

[0032] Figure 2 It is a flowchart of S01 provided by an embodiment of the present invention;

[0033] Figure 3 It is a flowchart of S02 provided by an embodiment of the present invention. Detailed implementation manners

[0034] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0035] As Figure 1 shown, a method for analyzing primary skin by dot matrix CO 2 laser treatment includes the following steps:

[0036] S01. Use a high-definition camera to capture the target skin area of the patient and obtain the original image data of the skin;

[0037] S02. Use an image processing algorithm to analyze the skin image data to judge the skin attributes and the treatment area;

[0038] S03. Search through the expert knowledge base based on the obtained skin attributes to obtain a treatment strategy;

[0039] S04. Use an ultra-pulse CO 2 dot matrix laser to emit ultra-pulse CO 2 dot matrix laser to the treatment area according to the treatment strategy.

[0040] As described above, first, a high-definition camera is used to capture the target skin area of the patient to obtain high-quality original image data. Subsequently, through an advanced image processing algorithm, the skin image is deeply analyzed to accurately judge the skin attributes and determine the treatment area. Then, based on the skin attributes, a matching treatment strategy is searched in the expert knowledge base to ensure the accuracy and effectiveness of the treatment plan. Finally, an ultra-pulse CO 2 dot matrix laser is used to accurately emit laser to the treatment area according to the treatment strategy to achieve precise treatment.

[0041] Specifically, the image data of the target skin area of the patient captured in step S01 is a continuous snapshot of no less than 20 frames, and every five frames are incremented by 200 pixels during the shooting process to obtain at least four sets of image sets A n , where n takes 1, 2, 3,..., n.

[0042] As described above, by taking snapshots continuously for no less than 20 frames and increasing by 200 pixels every five frames, image sets of multiple sets are obtained. It can capture skin details more comprehensively, improve the richness and accuracy of image data, and provide more powerful support for subsequent skin property judgment and treatment area determination.

[0043] Specifically, in combination with Figure 2 , the original image data of the skin is obtained in step S01, including:

[0044] S11. Create a data channel matching the n items, and sequentially import five photos from the image set A n therein;

[0045] S12. Determine the input value of the spatial variation tone mapping intensity of the photo;

[0046] S13. Process the photo and the corresponding input value of the photo to obtain the output photo data of the output image to which the spatial variation tone mapping of a quantity is applied;

[0047] S14. Create grids for the output photo data obtained from the four data channels respectively, and then extract the pixel points of each grid within the grids;

[0048] S15. Enlarge the output photo data of the image set A n to be pixel-consistent with the output photo data of the image set A n+1 , then perform matching according to the obtained pixel points, and perform superposition calculation after the matching is completed;

[0049] S16. Repeat step S15 to perform superposition calculation on the image sets An of the four sets in sequence to obtain the original image data of a superimposed skin.

[0050] As described above, when obtaining the original image data of the skin, through steps such as creating a data channel, determining the input value of the spatial variation tone mapping intensity, processing the photo to obtain the output photo data, creating a grid and extracting pixel points, matching and superposition calculation, etc., fine processing of multiple image sets is realized. It can eliminate the differences between images, improve the consistency and accuracy of image data, and provide a reliable basis for subsequent skin property analysis.

[0051] Specifically, in step S13, the spatial variation tone mapping adopts a local adaptive method. Based on multiple extracted reference pixels and the color level change values of the adjacent surrounding pixels corresponding to one reference pixel, the color level change values of the same pixels are subjected to weighted average processing to obtain the color level change value.

[0052] As described above, the local adaptive method is adopted to achieve spatially-varying tone mapping. By extracting the tone change values of multiple reference pixels and their adjacent surrounding pixels and performing weighted average processing, it can more accurately reflect the true tone change of the skin, improving the accuracy and effect of image processing.

[0053] Specifically, in combination with Figure 3 the data analysis of the skin image using the image processing algorithm in step S02 includes:

[0054] S21. Perform preprocessing operations on the original image data of the skin, including denoising, color correction, and image enhancement;

[0055] S22. Based on applying the image processing algorithm to the original image data of the preprocessed skin, extract local difference color patches, and magnify the pixels to identify the attributes corresponding to the difference color patches;

[0056] S23. Evaluate the multiple classifications obtained to determine whether the corresponding classification belongs to skin characteristics;

[0057] S24. Based on the given skin characteristics, frame the target area and extract the grid coordinates of the original image data of the skin.

[0058] Preferably, the attributes in step S22 include skin tone depth, skin texture, pores, wrinkles, pigmentation, local hyperplasia;

[0059] In step S23, the evaluation is to determine whether the difference color patch conforms to the attribute type. If it does not conform, it is determined that step S24 does not need to be executed.

[0060] It should be noted that the image processing algorithm includes edge detection, texture analysis, color space conversion, histogram equalization, and feature extraction algorithms, which are executed in sequence.

[0061] As described above, when analyzing the skin image data using the image processing algorithm, through steps such as preprocessing operations, extraction of local difference color patches, magnification of pixels and identification of attributes, and evaluation of multiple classifications, the skin attributes can be analyzed comprehensively and accurately. At the same time, framing the target area and extracting the grid coordinates based on the skin characteristics provides accurate positioning information for the subsequent formulation of treatment strategies.

[0062] Moreover, the extracted attributes include skin tone depth, skin texture, pores, wrinkles, pigmentation, local hyperplasia, etc., which can comprehensively reflect the health status of the skin. At the same time, in the evaluation in step S23, if the difference color patch does not conform to the attribute type, it is determined that the subsequent steps do not need to be executed, which can avoid unnecessary processing and analysis and improve the efficiency and accuracy of the method.

[0063] Secondly, in the image processing algorithm, by successively executing various algorithms such as edge detection, texture analysis, color space conversion, histogram equalization, and feature extraction algorithms, the skin image can be comprehensively and deeply analyzed, extracting more useful information and providing more powerful support for subsequent skin attribute judgment and treatment strategy formulation.

[0064] Specifically, in step S03, the expert knowledge base is the pixel samples of the collected diseased skin images, as well as the super-pulse CO 2 dot matrix laser emission strategy.

[0065] As described above, the expert knowledge base is used to match and search for treatment strategies, where the expert knowledge base includes pixel samples of diseased skin images and the corresponding super-pulse CO 2 dot matrix laser emission strategy. It can quickly and accurately find the most suitable treatment plan for the patient based on rich experience and data, improving the pertinence and effectiveness of the treatment.

[0066] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) containing computer-usable program code.

[0067] The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be realized by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for realizing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0068] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device that realizes the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0069] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are performed on the computer or other programmable apparatus to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions specified in one process or multiple processes and / or blocks Figure 1 one process or multiple processes and / or blocks Figure 1 or steps for implementing the functions specified in one block or multiple blocks.

[0070] In the present invention, specific embodiments are used to elaborate on the principles and implementation manners of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present invention.

[0071] The embodiments of the present application also provide a specific implementation manner of an electronic device that can implement all the steps in the method in the above embodiments. The electronic device specifically includes the following content:

[0072] A processor, a memory, a communication interface, and a bus;

[0073] Wherein, the processor, the memory, and the communication interface complete communication with each other through the bus;

[0074] The processor is used to call the computer program in the memory. When the processor executes the computer program, all the steps in the method in the above embodiments are implemented. For example, when the processor executes the computer program, the following steps are implemented:

[0075] Use a high-definition camera to capture the target skin area of the patient and obtain the original image data of the skin;

[0076] Analyze the skin image data using an image processing algorithm to judge the skin attributes and the treatment area;

[0077] Search through the expert knowledge base based on the obtained skin attributes to obtain a treatment strategy;

[0078] Use an ultra-pulse CO2 fractional laser to emit ultra-pulse CO2 fractional laser towards the treatment area according to the treatment strategy.

[0079] Embodiments of the present application further provide a computer-readable storage medium capable of implementing all steps in the methods in the above embodiments. A computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, all steps in the methods in the above embodiments are implemented. For example, when the processor executes the computer program, the following steps are implemented:

[0080] Use a high-definition camera to capture the target skin area of the patient and obtain the original image data of the skin;

[0081] Analyze the skin image data using an image processing algorithm to determine the skin attributes and the treatment area;

[0082] Search through the expert knowledge base based on the obtained skin attributes to obtain a treatment strategy;

[0083] Use an ultra-pulse CO2 fractional laser to emit ultra-pulse CO2 fractional laser towards the treatment area according to the emission index according to the treatment strategy.

[0084] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other, and the differences between each embodiment and other embodiments are emphasized. In particular, for the hardware + program type of embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and reference can be made to the corresponding parts of the method embodiments for the relevant content. Although the method operation steps as described in the embodiments of this specification are provided, more or fewer operation steps may be included based on conventional or non-creative means. The order of steps listed in the embodiments is only one of the ways of the execution order of numerous steps and does not represent the only execution order. When the actual device or terminal product is executed, it can be executed in the order shown in the embodiments or the drawings or in parallel (for example, in an environment of parallel processors or multi-threaded processing, or even in a distributed data processing environment). The term "comprising", "including" or any other variation thereof is intended to cover non-exclusive inclusion, so that a process, method, product or device including a series of elements not only includes those elements but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, product or device. Without further limitations, it does not exclude the existence of additional identical or equivalent elements in the process, method, product or device including the said elements. For the convenience of description, the above device is described by dividing it into various modules according to functions. Of course, when implementing the embodiments of this specification, the functions of each module can be realized in the same or multiple software and / or hardware, or the modules implementing the same function can be realized by a combination of multiple sub-modules or sub-units, etc. The device embodiments described above are only illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling, direct coupling or communication connection to each other can be through some interfaces, and the indirect coupling or communication connection of the device or unit can be in electrical, mechanical or other forms. The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for realizing the functions specified in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks

[0085] Those skilled in the art should understand that the embodiments of this specification can be provided as a method, a system, or a computer program product. Therefore, the embodiments of this specification can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, 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.) that contain computer-usable program code. Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. The key point of each embodiment is to illustrate the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can refer to the partial description of the method embodiment. In the description of this specification, the description of reference terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the embodiments of this specification.

[0086] In this specification, the schematic expression of the above terms does not necessarily refer to the same embodiment or example. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples. The above is only the embodiments of the embodiments of this specification and is not used to limit the embodiments of this specification. For those skilled in the art, the embodiments of this specification can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the embodiments of this specification shall be included in the scope of the claims of the embodiments of this specification.

Claims

1. A method for analyzing primary skin based on fractional CO2 laser treatment, characterized in that: The following steps are involved: S01. Use a high-definition camera to photograph the target skin area of ​​the patient to obtain original image data of the skin; S02, analyzing the skin image data using an image processing algorithm to determine skin properties and treatment areas; S03, searching the expert knowledge base based on the acquired skin attributes to obtain a treatment strategy; S04, using an ultra-pulse CO2 fractional laser, the treatment strategy emission indicator emits an ultra-pulse CO2 fractional laser to the treatment area.

2. A method for analyzing primary skin based on fractional CO2 laser treatment according to claim 1, characterized in that: The image data of the target skin area of ​​the patient captured in step S01 is a continuous snapshot of not less than 20 frames, and the number of frames is increased by 200 pixels every five frames during the capturing process to obtain at least four sets of image sets A. n , where n is 1, 2, 3, ..., n.

3. A method for analyzing primary skin based on fractional CO2 laser treatment according to claim 1, characterized in that: The step S01 of obtaining the original image data of the skin includes: S11, create a data channel matching the n items, and convert the image set A of the four sets into n Import the five photos in sequence; S12, determining an input value of a spatially varying tone mapping intensity of the photo; S13, processing the photo and the input value corresponding to the photo to obtain output photo data of an output image to which the number of spatially varying tone mappings are applied; S14, creating grids for the output photo data obtained from the four data channels respectively, and then extracting pixel points of each grid in the grid respectively; S15, image set A n The output photo data is enlarged to the same size as the image set A n+1 The output photo data pixels are consistent, and then matching is performed according to the obtained pixel points, and after the matching is completed, superposition calculation is performed; S16, repeat step S15 to convert the image set A of the four sets into n After sequential superposition and calculation, a superimposed original image data of the skin is obtained.

4. A method for analyzing primary skin based on fractional CO2 laser treatment according to claim 3, characterized in that: The spatial variation tone mapping in step S13 adopts a local adaptive method, based on the extracted multiple reference pixels and the color scale change values ​​of the adjacent surrounding pixels corresponding to one of the reference pixels, and performs weighted averaging processing on the color scale change values ​​of the same pixel to obtain the color scale change value.

5. The method for analyzing primary skin based on fractional CO2 laser treatment according to claim 1, characterized in that: The step S02 of analyzing the skin image data using an image processing algorithm includes: S21, performing preprocessing operations on the original image data of the skin, including denoising, color correction and image enhancement; S22, applying an image processing algorithm to the pre-processed original image data of the skin, extracting local difference color blocks, and zooming in on the pixels of the foot to identify the attributes corresponding to the difference color blocks; S23, evaluating the obtained multiple classifications to determine whether the corresponding classification belongs to skin characteristics; S24, selecting a target area based on the given skin characteristics, and extracting grid coordinates of the original image data of the skin.

6. The method for analyzing primary skin based on fractional CO2 laser treatment according to claim 4, characterized in that: The attributes in step S22 include skin color depth, skin texture, pores, wrinkles, pigmentation, and local hyperplasia; The evaluation in step S23 is to determine whether the difference color block meets the attribute type. If not, it is determined that step S24 does not need to be executed.

7. The method for analyzing primary skin based on fractional CO2 laser treatment according to claim 1, characterized in that: The image processing algorithms include edge detection, texture analysis, color space conversion, histogram equalization and feature extraction algorithms which are executed in sequence.

8. The method for analyzing primary skin based on fractional CO2 laser treatment according to claim 1, characterized in that: The expert knowledge base in step S03 is the pixel samples of the collected diseased skin image, and contains the ultra-pulse CO2 fractional laser emission strategy for the pixel samples.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of the primary skin analysis method based on fractional CO2 laser treatment according to any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the primary skin analysis method based on fractional CO2 laser treatment as described in any one of claims 1 to 8 are implemented.

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