Hyperspectral imaging-based stratum corneum moisture characteristic spectrum construction method and system

Abstract

The invention discloses a stratum corneum moisture characteristic spectrum construction method and system based on hyperspectral imaging, and relates to the technical field of spectral data processing, and the method comprises the steps: collecting hyperspectral image data of to-be-detected skin; measuring the cuticle thickness of the skin to be measured by using an optical coherence tomography technology; obtaining a correlation coefficient between the percutaneous moisture loss rate of the to-be-detected skin and a stratum corneum scattering coefficient; and calculating and extracting a moisture absorption peak and generating a characteristic spectrum based on a K-M equation in combination with the cuticle thickness and the correlation coefficient. The cuticle thickness is obtained through an optical coherence tomography technology, the influence of the cuticle thickness on a spectrum model is corrected according to a K-M method based on the cuticle thickness, in addition, the incidence relation between TEWL and a cuticle scattering coefficient is established, therefore, correction parameters are provided for each detection in the later period, and an accurate cuticle moisture characteristic spectrum is generated. And the accuracy of the detection result of the water content of the cuticle is greatly improved.

Description

Technical Field

[0001] The present application relates to the technical field of spectral data processing, and in particular to a method and system for constructing a stratum corneum moisture characteristic map based on hyperspectral imaging. Background Art

[0002] The stratum corneum of human skin is located in the outermost layer of the epidermis and is mainly composed of multiple layers of non-nucleated keratin cells and lipids. The main components of lipids are ceramide (about 50%), cholesterol (about 25%) and free fatty acids (about 15%). Lipids and keratin cells together form the protective barrier of the skin, which can lock in moisture, protect against external microorganisms and ultraviolet radiation, and prevent the loss of nutrients.

[0003] The water content of stratum corneum lipids is typically 10%-20%. Once the water content decreases, gaps appear between the lipids and keratin, disrupting the orderly arrangement of the lipids. This, in turn, accelerates water loss from the skin and allows for the invasion of external microorganisms. This directly manifests as a compromised skin barrier function. Therefore, maintaining the water content of the stratum corneum is key to keeping the skin soft, smooth, and elastic.

[0004] Currently, most methods for detecting the moisture content of the stratum corneum rely on skin bioelectrical impedance measurement or skin crease testing. Practice has shown that the moisture content of the stratum corneum measured using these indirect methods is significantly affected by the test environment, resulting in inaccurate test results and hindering the development of subsequent skin repair plans. Spectral analysis can be used to directly analyze the moisture content of the stratum corneum. The detection principle is to obtain a hyperspectral image of the skin using a hyperspectral imaging device and then compare it with a characteristic map of stratum corneum moisture to determine the moisture content of the stratum corneum. From this detection principle, it can be seen that the accuracy of the stratum corneum moisture test results is actually closely related to the accuracy of the characteristic map used as a reference. In actual applications, the stratum corneum thickness and baseline moisture content of different users vary. This means that before performing a stratum corneum moisture test, it is necessary to obtain a specific characteristic map of the user's stratum corneum moisture to improve the accuracy of subsequent test results.

[0005] From the above, it can be seen that how to eliminate interference and obtain an accurate and effective stratum corneum moisture characteristic map that is adapted to the user is crucial and is also a technical problem that needs to be solved urgently. Summary of the Invention

[0006] In view of the problem that the detection of stratum corneum moisture content by spectral analysis in actual applications is easily affected by individual differences of users and environmental factors, and it is impossible to accurately construct a stratum corneum moisture characteristic map for reference, the purpose of this application is to provide a stratum corneum moisture characteristic map construction method based on hyperspectral imaging, which obtains the thickness of the stratum corneum through optical coherence tomography (OCT) technology, and based on the above-mentioned stratum corneum thickness, corrects the influence of the stratum corneum thickness on the spectral model according to the KM method. In addition, a correlation between TEWL and the stratum corneum scattering coefficient is established, thereby providing correction parameters for each subsequent detection, generating an accurate stratum corneum moisture characteristic map, and greatly improving the accuracy of the stratum corneum moisture content detection results. In order to achieve the above-mentioned stratum corneum moisture characteristic map construction method based on hyperspectral imaging, this application also proposes a stratum corneum moisture characteristic map construction system based on hyperspectral imaging, and the specific scheme is as follows:

[0007] A method for constructing a stratum corneum moisture characteristic map based on hyperspectral imaging, comprising:

[0008] Collecting hyperspectral image data of the skin to be tested;

[0009] The thickness of the stratum corneum of the skin to be tested is measured using optical coherence tomography technology;

[0010] Obtaining the correlation coefficient between the transepidermal water loss rate of the skin to be tested and the stratum corneum scattering coefficient;

[0011] Based on the KM equation combined with the above stratum corneum thickness and correlation coefficient, the water absorption peak is calculated and extracted and a characteristic spectrum is generated. The calculation method is:

[0012]

[0013] Among them, K 1940 is the stratum corneum water absorption peak, U TEWL is the transepidermal water loss value, TEWL is the transepidermal water loss rate, S is the reference scattering coefficient of the stratum corneum, d0 is the reference thickness of the stratum corneum, d is the actual thickness of the stratum corneum measured by optical coherence tomography, μ is the correlation coefficient, and R is the diffuse reflectance spectrum.

[0014] Through the above technical solution, when obtaining the stratum corneum water absorption peak, not only the influence of the stratum corneum thickness on the characteristic spectrum is considered, but also the influence of the internal structure of the stratum corneum on the stratum corneum scattering coefficient is considered. The transepidermal water loss rate is used to characterize the internal structural state of the stratum corneum, and the TEWL value can be used to reflect the stratum corneum scattering coefficient. Therefore, correction parameters are further introduced to make the final extracted stratum corneum water absorption peak more accurate, and the characteristic spectrum constructed thereby is also more precise.

[0015] Furthermore, the correlation coefficient between the transepidermal water loss rate of the skin to be measured and the stratum corneum scattering coefficient is obtained, including:

[0016] Acquire a first TEWL value and first hyperspectral image data of the skin to be tested in a first state, and acquire a first spectral curve representing a moisture absorption peak;

[0017] Applying a set dose of a chemical penetration enhancer to the skin area to disrupt the lipid arrangement in the stratum corneum to produce a second state of the skin to be tested;

[0018] Obtaining a second TEWL value and second hyperspectral image data of the skin to be tested in a second state, and obtaining a second spectral curve representing a moisture absorption peak;

[0019] Select a skin area adjacent to the current skin area to be tested and repeat the above process to obtain multiple groups of TEWL value change curves and corresponding spectral curves of the skin to be tested under the action of different chemical penetration enhancers;

[0020] Based on multiple sets of TEWL value change curves and corresponding spectral curves, the correlation between TEWL value and absorbance change amplitude was analyzed and obtained, and the first correlation coefficient between TEWL value and stratum corneum reflectance was generated using linear regression algorithm.

[0021] The use of chemical penetration enhancers can disrupt the lipid arrangement structure inside the stratum corneum of the skin area to be tested. While changing the TEWL value, it will also change the propagation path of light inside the stratum corneum, thereby affecting the absorbance of the stratum corneum (the above-mentioned absorbance change caused by the change in optical path length is usually misjudged as an increase in water content), which is manifested in the spectrum as a change in the reflectance coefficient of the stratum corneum. Therefore, the above-mentioned technical solution can more accurately obtain the correlation between the TEWL value and the reflectance coefficient of the stratum corneum, and then the reflectance coefficient of the stratum corneum can be obtained by measuring the TEWL. Finally, the reflectance coefficient of the stratum corneum is further corrected by the TEWL value, making the extracted stratum corneum water absorption peak more accurate, which is conducive to improving the accuracy of constructing the stratum corneum water characteristic map.

[0022] Furthermore, obtaining the correlation coefficient between the transepidermal water loss rate of the skin to be measured and the stratum corneum scattering coefficient also includes:

[0023] Select at least two skin areas to be tested, maintain the natural transepidermal water loss state of the first area, and apply a glycerin agent for preventing transepidermal water loss to the second area;

[0024] After a set time, the second area is cleaned, TEWL values ​​and hyperspectral image data of the first and second areas are collected, and spectral curves representing stratum corneum water absorption peaks of the first and second areas are obtained based on the hyperspectral image data;

[0025] storing the natural TEWL value and the natural spectrum curve of the first area, and the comparative TEWL value and the comparative spectrum curve of the second area to form a comparison data set;

[0026] The correlation between the TEWL value and the absorbance variation was obtained based on the analysis of multiple control data sets, and the second correlation coefficient between the TEWL value and the stratum corneum reflectance was generated using a linear regression algorithm.

[0027] Through the above technical solution, multiple sets of skin TEWL values ​​and spectral curves in natural conditions can be obtained, and then the correlation coefficient between the TEWL value and the reflectance coefficient can be obtained.

[0028] Furthermore, before collecting the NIR diffuse reflectance spectrum data of the skin to be tested, the method further includes:

[0029] Obtain the correlation between the scattering effect and the thickness of the stratum corneum;

[0030] Based on the above correlation, a stratum corneum thickness interval is set to reduce light scattering;

[0031] Determine the skin area to be tested and adjust the stratum corneum thickness to the set range based on physical or chemical methods.

[0032] Through the above technical solution, the light scattering effect caused by excessive or insufficient stratum corneum thickness can be reduced, the drift of subsequent spectral baselines can be reduced, and it is helpful to improve the peak position identification of the stratum corneum water absorption peak.

[0033] Furthermore, the method further includes a characteristic spectrum verification step, including:

[0034] preparing in vitro stratum corneum samples of different thicknesses, obtaining and correlating and storing the water content and spectrum of the in vitro stratum corneum samples to form an analysis array;

[0035] Adjust the water content of the isolated stratum corneum and obtain corresponding spectral data to generate multiple analysis arrays;

[0036] The correspondence between the moisture absorption peak and the moisture content in the stratum corneum moisture spectrum image in each analysis array is compared, and the accuracy of the current stratum corneum moisture characteristic map is determined based on the comparison results.

[0037] Through the above technical solution, the accuracy of the currently generated stratum corneum moisture characteristic map can be evaluated, and in subsequent practical applications, the corresponding reliability weight can be configured for the stratum corneum moisture content measurement results based on the above evaluation results.

[0038] Furthermore, the method further comprises:

[0039] The reliability of obtaining the first correlation coefficient and the second correlation coefficient is verified by big data;

[0040] The comprehensive correlation coefficient μ is obtained by weighting the reliability values ​​of the first correlation coefficient and the second correlation coefficient respectively;

[0041] Among them, μ=ω1*μ1+ω2*+μ2, ω1 and ω2 are the weight values ​​corresponding to the first correlation coefficient and the second correlation coefficient respectively; μ1 and μ2 are the first correlation coefficient and the second correlation coefficient.

[0042] A system for constructing a stratum corneum moisture characteristic map based on hyperspectral imaging, comprising:

[0043] A spectral data acquisition unit configured to acquire hyperspectral image data of the skin to be tested;

[0044] a stratum corneum thickness obtaining unit, configured to measure the stratum corneum thickness of the skin to be measured by optical coherence tomography;

[0045] a correlation coefficient generating unit configured to obtain a correlation coefficient between the transepidermal water loss rate of the skin to be measured and the stratum corneum scattering coefficient;

[0046] Data storage unit, used to store various types of data and algorithm models;

[0047] A characteristic spectrum generating unit is configured to calculate and extract the water absorption peak and generate a characteristic spectrum based on the KM equation combined with the stratum corneum thickness and the correlation coefficient;

[0048] The calculation method of the water absorption peak is implemented by using the calculation formula included in the method for constructing the stratum corneum water characteristic map based on hyperspectral imaging as described above.

[0049] Furthermore, the correlation coefficient generating unit includes:

[0050] A TEWL data acquisition module is configured to acquire and output a TEWL value of the skin to be tested;

[0051] A stratum corneum structure adjustment module is configured to apply a set dose of a chemical penetration enhancer to the skin area to be tested to disrupt the orderliness of the lipid arrangement inside the stratum corneum;

[0052] a spectral data acquisition module, configured to be data-connected to the spectral data acquisition unit, for acquiring hyperspectral image data of the skin to be tested and generating a spectral curve;

[0053] The data processing module is configured to analyze and obtain the correlation between the TEWL value and the absorbance change amplitude based on multiple sets of TEWL value change curves and corresponding spectral curves, and use a linear regression algorithm to generate a correlation coefficient between the TEWL value and the stratum corneum reflectance coefficient.

[0054] This application has at least one of the following beneficial effects:

[0055] (1) By adopting the KM optical model equation, the influence of stratum corneum thickness on the accuracy of obtaining water spectral characteristic maps is effectively reduced, making the corrected water spectrum more accurate;

[0056] (2) By establishing a correlation between the TEWL value and the stratum corneum scattering coefficient, the scattering coefficient is corrected using the detected TEWL value, thereby making the extracted water absorption peak more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 Schematic diagram of the method for constructing the stratum corneum moisture characteristic map of the present application;

[0058] Figure 2 Schematic diagram of the method for obtaining the correlation coefficient between transepidermal water loss rate and stratum corneum scattering coefficient;

[0059] Figure 3 Schematic diagram of the functional modules of the stratum corneum moisture characteristic map construction system of this application.

[0060] Figure numerals: 100, spectral data acquisition unit; 200, stratum corneum thickness acquisition unit; 300, correlation coefficient generation unit; 310, TEWL data acquisition module; 320, stratum corneum structure adjustment module; 330, spectral data acquisition module; 340, data processing module; 400, data storage unit; 500, feature map generation unit. DETAILED DESCRIPTION

[0061] The following describes the embodiments of the present application in detail, and examples of the embodiments are shown in the attached Figure 1-3 Shown in.

[0062] Throughout this specification, reference to the terms "certain embodiments," "one embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0063] A method for constructing a stratum corneum moisture feature map based on hyperspectral imaging, such as Figure 1 As shown, it mainly includes the following steps:

[0064] S100: Acquire hyperspectral image data of the skin to be tested. In the embodiment of the present application, near-infrared (NIR) diffuse reflectance spectral data is mainly collected, mainly for collecting stratum corneum moisture spectral data with a thickness of 10-50 μm in the forearm or cheek area.

[0065] S200: Measure the stratum corneum thickness of the skin to be tested using optical coherence tomography (OCT). The OCT system utilizes a Michelson interferometer structure, primarily utilizing light beam interference to generate an interference signal carrying depth information to measure stratum corneum thickness. The specific thickness acquisition principles and processes are well known in the art and will not be further described here.

[0066] S300: Obtaining a correlation coefficient between the transepidermal water loss rate of the skin to be measured and the stratum corneum scattering coefficient.

[0067] S400, based on the KM equation combined with the above stratum corneum thickness and correlation coefficient, calculates and extracts the water absorption peak and generates a characteristic spectrum. The calculation method is:

[0068]

[0069] Among them, K 1940 is the stratum corneum water absorption peak, U TEWL is the transepidermal water loss value, TEWL is the transepidermal water loss rate, S is the reference scattering coefficient of the stratum corneum, d0 is the reference thickness of the stratum corneum, d is the actual thickness of the stratum corneum measured by optical coherence tomography, μ is the correlation coefficient, and R is the diffuse reflectance spectrum.

[0070] The implementation principle of the above step S300 is as follows:

[0071] The stratum corneum of the skin is mainly composed of anucleated keratin cells and intercellular lipids. Under normal circumstances, these lipids are layered and arranged in an orderly manner, which can resist the invasion of external microorganisms and also lock the moisture inside the skin and reduce its loss rate. When the internal structure of the stratum corneum changes, that is, the orderliness of the lipid arrangement is disrupted, the moisture in the skin changes and loses faster, which is manifested externally as an increase in the transepidermal water loss rate (TEWL) of the skin. The unit of measurement of TEWL is g / hm 2 The higher the value, the more water is lost through the skin and the poorer the barrier function of the stratum corneum. From the above analysis, it can be seen that the TEWL value of the skin is closely related to the structure of the stratum corneum.

[0072] The KM equation (Kubelka-Munk) is a classic model for describing light propagation in turbid media, such as skin. It is widely used in spectral analysis, particularly for diffuse reflectance spectroscopy. In skin stratum corneum moisture detection, the KM equation can be used to correct for the effects of stratum corneum thickness and scattering effects, improving the accuracy of stratum corneum moisture spectral signatures. The scattering coefficient (S) involved in the KM equation refers to the medium's ability to scatter light, which is generally related to the structure of the stratum corneum. The absorption coefficient (K) involved in the KM equation refers to the medium's ability to absorb light, which, in the present embodiment, is related to the water content of the stratum corneum.

[0073] The above analysis shows that the TEWL value can be used to understand the structural state of the stratum corneum, and thus indirectly the scattering coefficient. However, due to individual differences, the correlation coefficient between the TEWL value and the scattering coefficient is not the same.

[0074] To this end, in one embodiment of the present application, step S300 is to obtain the correlation coefficient between the transepidermal water loss rate of the skin to be measured and the stratum corneum scattering coefficient, such as Figure 2 As shown, specifically including:

[0075] S311, obtaining a first TEWL value and first hyperspectral image data of the skin to be tested in a first state,

[0076] generating a first spectrum curve representing a water absorption peak;

[0077] S312, applying a set dose of a chemical penetration enhancer to the skin area to be tested, which is used to disrupt the orderly arrangement of lipids in the stratum corneum, to generate a second state of the skin to be tested;

[0078] S313, obtaining a second TEWL value and second hyperspectral image data of the skin to be tested in a second state,

[0079] generating a second spectrum curve representing a water absorption peak;

[0080] S314, selecting a skin area adjacent to the current skin area to be tested and repeating the above process to obtain multiple sets of TEWL value change curves and corresponding spectral curves of the skin to be tested under the action of different chemical penetration enhancers;

[0081] S315 , based on the multiple sets of TEWL value change curves and the corresponding spectral curves, analyzing and obtaining the correlation between the TEWL value and the absorbance change amplitude, and generating a first correlation coefficient between the TEWL value and the stratum corneum reflectance using a linear regression algorithm.

[0082] The chemical penetration enhancer may be a reagent containing isopropyl alcohol. Isopropyl alcohol can cause rheological changes and disturbances in the free double-layer structure of lipids between keratin cells, thereby changing the scattering coefficient and water loss rate of the free double-layer structure.

[0083] A higher TEWL value means a more disordered stratum corneum structure, resulting in an overall decrease in absorbance relative to an ordered lipid arrangement.

[0084] In another embodiment, obtaining the correlation coefficient between the transepidermal water loss rate of the skin to be measured and the stratum corneum scattering coefficient further includes:

[0085] S321, selecting at least two skin areas to be tested, maintaining the natural TEWL state of the first area, and applying a glycerin agent for preventing TEWL to the second area;

[0086] S322, cleaning the second area after a set time, collecting TEWL values ​​and hyperspectral image data of the first and second areas, and obtaining spectral curves representing stratum corneum water absorption peaks of the first and second areas based on the hyperspectral image data;

[0087] S323, storing the natural TEWL value and the natural spectrum curve of the first area, and the comparative TEWL value and the comparative spectrum curve of the second area to form a comparison data set;

[0088] S324 , obtaining a correlation between the TEWL value and the absorbance variation amplitude based on analysis of multiple control data sets, and generating a correlation coefficient between the TEWL value and the stratum corneum reflectance using a linear regression algorithm.

[0089] The above technical solution can obtain multiple sets of skin TEWL values ​​and spectral curves in natural conditions, and then obtain the second correlation coefficient between the TEWL value and the reflectance coefficient.

[0090] In practical applications, the correlation coefficients obtained in steps S315 and S324 are defined as the first correlation coefficient and the second correlation coefficient. After their reliability is verified by big data, a weighted calculation is performed to obtain the comprehensive correlation coefficient. The comprehensive correlation coefficient μ = ω1*μ1+ω2*+μ2, where ω1 and ω2 are the weights corresponding to the first correlation coefficient and the second correlation coefficient, respectively; μ1 and μ2 are the first correlation coefficient and the second correlation coefficient, respectively.

[0091] In order to reduce the light scattering effect caused by excessive or insufficient stratum corneum thickness and reduce the drift of subsequent spectral baselines, in the embodiment of the present application, before collecting the NIR diffuse reflectance spectrum data of the skin to be measured, the method further includes:

[0092] A100, obtains the correlation between the scattering effect and the thickness of the stratum corneum. For example, the characteristic peak of moisture is masked by the scattering background, especially the significant difference between thin stratum corneum (<10um) and thick stratum corneum (>30um).

[0093] A110: Set a stratum corneum thickness range based on the above correlation to reduce light scattering.

[0094] A120 determines the skin area to be tested and adjusts the stratum corneum thickness to a set range using physical or chemical methods. In this application, physical methods are preferred for adjusting stratum corneum thickness, such as treating the skin with an abrasive disc, followed by measuring stratum corneum thickness using OCT technology. This technical solution helps improve the identification of the stratum corneum water absorption peak.

[0095] In the embodiment of the present application, the method further includes a characteristic spectrum verification step, including:

[0096] S500: Prepare ex vivo stratum corneum samples of varying thicknesses, obtain and associate the moisture content and spectra of the ex vivo stratum corneum samples to form an analysis array. In practical applications, multiple groups of stratum corneum samples are prepared, and the actual moisture content of the stratum corneum samples is obtained by weighing.

[0097] S510, adjusting the water content of the isolated stratum corneum and acquiring corresponding spectral data to generate multiple analysis arrays.

[0098] S520 , comparing the correspondence between the moisture absorption peak and the moisture content in the stratum corneum moisture spectrum image in each analysis array, and determining the accuracy of the current stratum corneum moisture characteristic map based on the comparison result.

[0099] Furthermore, if the accuracy judgment value of the current stratum corneum moisture characteristic map is too low, the correlation coefficient μ and / or the actual stratum corneum thickness d in the KM equation are revised, thereby evaluating the accuracy of the currently generated stratum corneum moisture characteristic map.

[0100] In subsequent practical applications, corresponding reliability weights can be configured for the stratum corneum moisture content measurement results based on the above evaluation results.

[0101] In order to realize the above-mentioned method for constructing a characteristic map of stratum corneum moisture, the present application also discloses a system for constructing a characteristic map of stratum corneum moisture based on hyperspectral imaging, such as Figure 3 As shown, it mainly includes a spectral data acquisition unit 100, a stratum corneum thickness acquisition unit 200, a correlation coefficient generation unit 300, a data storage unit 400 and a characteristic spectrum generation unit 500.

[0102] The spectral data acquisition unit 100 is configured to acquire and store hyperspectral image data of the skin to be tested. In specific applications, a hyperspectral camera, such as a SPECIM camera, is preferably used to acquire such hyperspectral image data. The stratum corneum thickness acquisition unit 200 is configured to measure the stratum corneum thickness of the skin to be tested using optical coherence tomography (OCT). In practical applications, a VivoSight Dx handheld probe can be used as the OCT instrument.

[0103] The correlation coefficient generating unit 300 is configured to obtain a correlation coefficient between the transepidermal water loss rate of the skin to be measured and the stratum corneum scattering coefficient.

[0104] In the embodiment of the present application, as described in detail, the correlation coefficient generation unit 300 includes a TEWL data acquisition module 310 , a stratum corneum structure adjustment module 320 , a spectrum data acquisition module 330 and a data processing module 340 .

[0105] The TEWL data acquisition module 310 is configured to acquire and output the TEWL value of the skin to be tested. In practical applications, this data can be directly acquired using an existing skin water loss tester. The stratum corneum structure adjustment module 320 is configured to apply a set dose of a chemical permeation enhancer to the skin area to be tested to disrupt the order of lipid arrangement within the stratum corneum. In practical applications, this can be achieved through chemical or physical means, such as using a chemical permeation enhancer to alter the stratum corneum structure. The spectral data acquisition module 330 is configured to be data-connected to the spectral data acquisition unit 100 and is used to acquire hyperspectral image data of the skin to be tested and generate a spectral curve. In this application, this spectral data acquisition model is directly implemented using a tablet computer wirelessly connected to a hyperspectral camera.

[0106] The data processing module 340 is configured to load relevant data processing algorithms in the above-mentioned tablet computer, such as linear regression models, etc., and analyze and obtain the correlation between the TEWL value and the absorbance change amplitude based on multiple sets of TEWL value change curves and corresponding spectral curves, and use the linear regression algorithm to generate and output the correlation coefficient between the TEWL value and the stratum corneum reflectance coefficient.

[0107] The characteristic spectrum generating unit 500 is configured to calculate and extract the water absorption peak and generate a characteristic spectrum based on the KM equation in combination with the stratum corneum thickness and the correlation coefficient.

[0108] The calculation method of the moisture absorption peak is as described above and will not be repeated here.

[0109] In order to realize the storage of relevant data and algorithm models, the system described in this application is also configured with a data storage unit 400 for storing various types of data and algorithm models. Preferably, the above-mentioned data storage unit 400 is configured in the cloud to facilitate data retrieval and upload.

[0110] The moisture absorption peak calculation method is stored in the data storage unit 400 in the form of a computer-readable module.

[0111] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for constructing a stratum corneum moisture characteristic map based on hyperspectral imaging, characterized in that: include: Collecting hyperspectral image data of the skin to be tested; The thickness of the stratum corneum of the skin to be tested is measured using optical coherence tomography technology; Obtaining the correlation coefficient between the transepidermal water loss rate of the skin to be tested and the stratum corneum scattering coefficient; Based on the KM equation combined with the above stratum corneum thickness and correlation coefficient, the water absorption peak is calculated and extracted to generate a characteristic spectrum: Among them, K 1940 is the stratum corneum water absorption peak, U TEWL is the transepidermal water loss value, TEWL is the transepidermal water loss rate, S is the reference scattering coefficient of the stratum corneum, d0 is the reference thickness of the stratum corneum, d is the actual thickness of the stratum corneum measured by optical coherence tomography, μ is the correlation coefficient, and R is the diffuse reflectance spectrum.

2. The method for constructing a stratum corneum moisture characteristic map based on hyperspectral imaging according to claim 1, characterized in that: Obtain the correlation coefficient between the transepidermal water loss rate of the skin to be measured and the stratum corneum scattering coefficient, including: Acquire a first TEWL value and first hyperspectral image data of the skin to be tested in a first state, and acquire a first spectral curve representing a moisture absorption peak; Applying a set dose of a chemical penetration enhancer to the skin area to disrupt the lipid arrangement in the stratum corneum to produce a second state of the skin to be tested; Obtaining a second TEWL value and second hyperspectral image data of the skin to be tested in a second state, and obtaining a second spectral curve representing a moisture absorption peak; Select a skin area adjacent to the current skin area to be tested and repeat the above process to obtain multiple groups of TEWL value change curves and corresponding spectral curves of the skin to be tested under the action of different chemical penetration enhancers; Based on multiple sets of TEWL value change curves and corresponding spectral curves, the correlation between TEWL value and absorbance change amplitude was analyzed and obtained, and the first correlation coefficient between TEWL value and stratum corneum reflectance was generated using linear regression algorithm.

3. The method for constructing a stratum corneum moisture characteristic map based on hyperspectral imaging according to claim 2, characterized in that: Obtaining the correlation coefficient between the transepidermal water loss rate of the skin to be measured and the stratum corneum scattering coefficient, also includes: Select at least two skin areas to be tested, maintain the natural transepidermal water loss state of the first area, and apply a glycerin agent for preventing transepidermal water loss to the second area; After a set time, the second area is cleaned, TEWL values ​​and hyperspectral image data of the first and second areas are collected, and spectral curves representing stratum corneum water absorption peaks of the first and second areas are obtained based on the hyperspectral image data; storing the natural TEWL value and the natural spectrum curve of the first area, and the comparative TEWL value and the comparative spectrum curve of the second area to form a comparison data set; The correlation between the TEWL value and the absorbance variation was obtained based on the analysis of multiple control data sets, and the second correlation coefficient between the TEWL value and the stratum corneum reflectance was generated using a linear regression algorithm.

4. The method for constructing a stratum corneum moisture characteristic map based on hyperspectral imaging according to claim 1, characterized in that: Before collecting the NIR diffuse reflectance spectrum data of the skin to be tested, the method further includes: Obtain the correlation between the scattering effect and the thickness of the stratum corneum; Based on the above correlation, a stratum corneum thickness interval is set to reduce light scattering; Determine the skin area to be tested and adjust the stratum corneum thickness to the set range based on physical or chemical methods.

5. The method for constructing a stratum corneum moisture characteristic map based on hyperspectral imaging according to claim 1, characterized in that: The method further comprises a characteristic spectrum verification step, comprising: preparing in vitro stratum corneum samples of different thicknesses, obtaining and correlating and storing the water content and spectrum of the in vitro stratum corneum samples to form an analysis array; Adjust the water content of the isolated stratum corneum and obtain corresponding spectral data to generate multiple analysis arrays; The correspondence between the moisture absorption peak and the moisture content in the stratum corneum moisture spectrum image in each analysis array is compared, and the accuracy of the current stratum corneum moisture characteristic map is determined based on the comparison results.

6. The method for constructing a stratum corneum moisture characteristic map based on hyperspectral imaging according to claim 3, characterized in that: The method further comprises: The reliability of obtaining the first correlation coefficient and the second correlation coefficient is verified by big data; The comprehensive correlation coefficient μ is obtained by weighting the reliability values ​​of the first correlation coefficient and the second correlation coefficient respectively; Among them, μ=ω1*μ1+ω2*+μ2, ω1 and ω2 are the weight values ​​corresponding to the first correlation coefficient and the second correlation coefficient respectively; μ1 and μ2 are the first correlation coefficient and the second correlation coefficient.

7. A system for constructing a stratum corneum moisture characteristic map based on hyperspectral imaging, characterized in that: A spectral data acquisition unit (100) configured to acquire hyperspectral image data of the skin to be tested; A stratum corneum thickness obtaining unit (200) is configured to measure the stratum corneum thickness of the skin to be tested by optical coherence tomography technology; A correlation coefficient generating unit (300) is configured to obtain a correlation coefficient between the transepidermal water loss rate of the skin to be measured and the stratum corneum scattering coefficient; A characteristic spectrum generating unit (500) is configured to calculate and extract the water absorption peak and generate a characteristic spectrum based on the KM equation in combination with the stratum corneum thickness and the correlation coefficient; The water absorption peak calculation method is implemented by using the calculation formula included in the method for constructing a stratum corneum water characteristic map based on hyperspectral imaging as described in any one of claims 1 to 6.

8. The system for constructing a stratum corneum moisture characteristic map based on hyperspectral imaging according to claim 7, characterized in that: The correlation coefficient generating unit (300) comprises: A TEWL data acquisition module (310) configured to acquire and output a TEWL value of the skin to be tested; A stratum corneum structure adjustment module (320) is configured to apply a set dose of a chemical penetration enhancer to the skin area to be tested so as to disrupt the orderliness of lipid arrangement inside the stratum corneum; A spectral data acquisition module (330) configured to be data-connected to the spectral data acquisition unit (100) and used to acquire hyperspectral image data of the skin to be tested and generate a spectral curve; The data processing module (340) is configured to analyze and obtain the correlation between the TEWL value and the absorbance variation amplitude based on multiple groups of TEWL value change curves and corresponding spectral curves, and generate a correlation coefficient between the TEWL value and the stratum corneum reflectance using a linear regression algorithm.

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