Method for measuring blood glucose in sweat gland area based on OCT (optical coherence tomography)

Abstract

The invention relates to a method for measuring blood glucose based on an OCT sweat gland area, which comprises the following steps of: S1, acquiring a three-dimensional OCT image of a measured object before each blood glucose experiment is started, and determining a template; regulating and controlling the blood glucose concentration of the tested object for at least five times, and acquiring a three-dimensional OCT image and a blood glucose value of the tested object after each regulation and control; s2, aligning the surface of the three-dimensional OCT image, and calculating a 2D surface sweat gland image; s3, calculating a fixed area of the 2D surface sweat gland image by using a sweat gland area positioning method according to the template; s4, performing 3D image reconstruction according to the fixed area; calculating a scattering coefficient; s5, calculating the relationship between the scattering coefficient and the blood glucose value; s6, after the blood glucose experiment is started, a tested three-dimensional OCT image of the tested object is collected; executing the steps S2-S4 on the measured three-dimensional OCT image to obtain a scattering coefficient; and S7, calculating the blood sugar value according to the results of the steps S5 and S6. The three-dimensional OCT image of the rough area of the measured object is obtained every time, the same fixed area is calculated through a sweat gland area positioning method, and label paper does not need to be positioned.

Description

Technical Field

[0001] The invention relates to a method for measuring blood sugar, in particular to a method for measuring blood sugar in an OCT sweat gland area. Background Art

[0002] Diabetes is a chronic metabolic disease that can cause a series of complications such as renal failure, retinopathy, nerve paralysis, cerebrovascular disease, etc. However, according to data released by the International Diabetes Federation, there are about 537 million adults with diabetes in the world in 2021. Compared with 2019, the number of diabetes patients has increased by 74 million, an increase of 16%. It is estimated that by 2025, the number of patients will increase to 783 million. Blood sugar level is one of the important detection indicators of diabetes. Traditional invasive and minimally invasive blood sugar testing can cause pain to the patient's body and easily cause wound infection. Therefore, non-invasive blood sugar testing is of great significance.

[0003] The non-invasive blood glucose monitoring technology based on optical coherence tomography (OCT) mainly uses near-infrared light to perform tomographic imaging of human skin, and calculates the glucose concentration of the human body based on the change in skin scattering coefficient caused by different glucose values ​​inside the tissue. Studies have shown that when the blood glucose concentration increases from 4 mmol / L to 30 mmol / L, the slope of the OCT signal decreases significantly by 40%, and presents a linear relationship, and the accuracy of the scattering coefficient measurement can reach 1%. Therefore, OCT has huge advantages in non-invasive blood glucose testing.

[0004] However, there are still some shortcomings in OCT blood sugar testing. In some blood sugar experiments, for example, to observe the blood sugar change trend of the subject, it is necessary to collect a large number of OCT images of the subject within a few hours. When collecting images, it is required to collect a fixed area of ​​the skin, and the fixed area is generally 650×650μm. 2 In order to achieve accurate measurement, the OCT images collected in one experiment must be exactly the same, and positioning labels are required, which brings a certain degree of inconvenience to blood glucose testing. In addition, the OCT probe will exert pressure on the skin tissue, change the scattering coefficient of the skin tissue, and affect the accuracy of blood glucose testing. Summary of the invention

[0005] The purpose of the present invention is to provide a method for measuring blood sugar based on OCT sweat gland area to solve the problem that the prior art is inconvenient for blood sugar detection and has low detection accuracy.

[0006] The purpose of the present invention is achieved like this:

[0007] A method for measuring blood sugar based on OCT sweat gland area, comprising the following steps:

[0008] S1. Before each blood glucose experiment, collect the three-dimensional OCT images of the subject to be measured; determine the template based on the first three-dimensional OCT image; regulate the change of the blood glucose concentration of the subject to be measured at least 5 times, and after each regulation, collect the three-dimensional OCT images of the subject to be measured and measure the blood glucose value;

[0009] S2. Perform surface alignment operations on the three-dimensional OCT images of the subject to be measured, and calculate the 2D surface sweat gland images at different depths;

[0010] S3. Select the image to be located in the 2D surface sweat gland image, and calculate the position of the fixed area of the image to be located using the sweat gland area positioning method based on the template;

[0011] S4. Perform 3D image reconstruction based on the fixed areas of the 2D surface sweat gland images at different depths; calculate the scattering coefficients of the 3D images reconstructed from each three-dimensional OCT image;

[0012] S5. Calculate the relationship between the scattering coefficient and the blood glucose value of the subject to be measured based on the scattering coefficient of the three-dimensional OCT image and the corresponding blood glucose value;

[0013] S6. After the blood glucose experiment starts, collect the three-dimensional OCT images of the subject to be measured; perform surface alignment operations on the three-dimensional OCT images of the subject to be measured in steps S2 - S4, calculate the 2D surface sweat gland images at different depths; select the image to be located in the 2D surface sweat gland image, calculate the fixed area of the 2D surface sweat gland image of the three-dimensional image of the subject to be measured using the sweat gland area positioning method based on the template, perform 3D image reconstruction based on the fixed areas of the 2D surface sweat gland images at different depths, and calculate the scattering coefficients of the 3D images reconstructed from the three-dimensional OCT images of the subject to be measured; there is an overlapping area in the parts where the three-dimensional OCT images of the subject to be measured are collected each time in the same experiment;

[0014] S7. Determine the blood glucose value corresponding to the three-dimensional OCT image of the subject to be measured based on the scattering coefficient of the 3D image of the three-dimensional OCT image of the subject to be measured and the relationship between the scattering coefficient and the blood glucose value.

[0015] Furthermore, the specific method for determining the template is as follows:

[0016] Calculate the 2D surface sweat gland images corresponding to different depths of the first three-dimensional OCT image, and use the central area of any one of the 2D surface sweat gland images as the template; the side length of the template is less than or equal to 1 / 2 of the original 2D surface sweat gland image.

[0017] Furthermore, the specific method for calculating the 2D surface sweat gland image in step S2 is as follows:

[0018] S2a-1. Calculate the light intensity maps at different depths based on the light intensity values in the three-dimensional OCT image; calculate the phase delay maps at different depths based on the phase delay data in the three-dimensional OCT image.

[0019] S2a-2. Subtract the light intensity map and the phase delay map at the same depth to obtain the 2D surface sweat gland image at that depth, and continue this operation for all depths of the light intensity and phase delay maps to obtain 2D surface sweat gland images at different depths.

[0020] Further, the specific method for determining the position of the fixed region on the image to be located in step S3-2 is as follows:

[0021] S3-1. Select the image to be located from the 2D surface sweat gland images generated from the three-dimensional OCT image according to the image contrast.

[0022] S3-2. Slide the template from the upper left vertex to the lower right vertex of the image to be located, and calculate the cross-correlation value each time it slides.

[0023] S3-3. Rotate the image to be located. Each time it rotates, execute step S3-2-1, and the rotation angle each time does not exceed 3°; a cross-correlation matrix is obtained each time it rotates.

[0024] S3-4. Compare the obtained cross-correlation values, rotate the image to be located by the rotation angle corresponding to the maximum cross-correlation value, and determine the position of the fixed region on the image to be located according to the position of the cross-correlation value in the cross-correlation matrix.

[0025] Further, the rotation range of the image to be located is between -30° and 30°.

[0026] Further, the specific method for performing three-dimensional OCT image reconstruction is as follows:

[0027] Rotate the 2D surface sweat gland images at different depths by the rotation angle corresponding to the maximum cross-correlation value, select the sub-images of the 2D surface sweat gland images at different depths according to the position of the fixed region; recombine the sub-images at different depths according to the depth position to obtain the reconstructed three-dimensional OCT image.

[0028] The present invention does not require the regions for collecting OCT images to be exactly the same each time, as long as there are overlapping parts, and no positioning tags are needed. The fixed regions are calculated using the sweat gland region positioning method. After obtaining multiple three-dimensional OCT images in an experiment, multiple 2D surface sweat gland images are generated from the three-dimensional OCT images. One 2D surface sweat gland image is selected from each three-dimensional OCT image, and the cross-correlation calculation is performed between each selected 2D surface sweat gland image and the template in this experiment. The sub-images with high cross-correlation values calculated in this way all have overlapping parts, and the sub-images are stacked according to the depth to reconstruct a 3D image. The images obtained in this way are all 3D images at the same position; the scattering coefficient of the reconstructed 3D image is calculated, and according to the relationship between the scattering coefficient and the blood glucose value, as well as the scattering coefficient of the reconstructed 3D image, the blood glucose value is calculated.

[0029] The present invention also takes into account that during multiple blood glucose measurements, the finger direction of the subject being measured may deviate. Therefore, when calculating the cross-correlation value, the image to be located is rotated, and the cross-correlation calculation is performed between the template and the rotated image to be located, reducing the error in collecting three-dimensional OCT images. Description of the Drawings

[0030] Figure 1 is the flow chart of the present invention.

[0031] Figure 2 are different optical images of sweat gland-related characteristics. Among them, (a) is the light intensity map of sweat gland distribution, (b) is the phase delay map of sweat gland distribution, and (c) is the result map obtained by subtracting (a) from (b).

[0032] Figure 3 is the image after being matched by the sweat gland region positioning method. Among them, the images in column (a) are the parts collected from the finger, the images in column (b) are the three-dimensional OCT images collected, and the images in column (c) are the images after being matched by the sweat gland region positioning method.

[0033] Figure 4 is the 3D calibration map and the curve graph of OCT blood glucose detection and fingertip blood glucose without the sweat gland region positioning and recognition algorithm. Among them, (a) is the 3D calibration map without the sweat gland region positioning and recognition algorithm, and (b) is the curve graph of OCT blood glucose detection and fingertip blood glucose without the sweat gland region positioning and recognition algorithm.

[0034] Figure 5 is the 3D calibration map and the curve graph of OCT blood glucose detection and fingertip blood glucose after the sweat gland region positioning and recognition algorithm. Among them, (a) is the 3D calibration map after the sweat gland region positioning and recognition algorithm, and (b) is the curve graph of OCT blood glucose detection and fingertip blood glucose after the sweat gland region positioning and recognition algorithm. Detailed Embodiment

[0035] The present invention will be further described in detail below with reference to the accompanying drawings.

[0036] As Figure 1 shown, the present invention provides a method for measuring blood glucose based on OCT sweat gland area, including the following steps:

[0037] S1. Before each blood glucose experiment, collect the three-dimensional OCT image of the object to be measured; determine the template according to the first three-dimensional OCT image; regulate the change of the blood glucose concentration of the object to be measured at least 5 times, and after each regulation, collect the three-dimensional OCT image of the object to be measured and measure the blood glucose value.

[0038] As Figure 2 shown, before regulating the blood glucose concentration of the object to be measured, perform a surface alignment operation on the obtained first three-dimensional OCT image, calculate the light intensity map of the fingertip at different depths according to the light intensity values in the three-dimensional OCT image ( Figure 2 a), calculate the phase delay map of the fingertip at different depths according to the phase values in the three-dimensional OCT image ( Figure 2 b), subtract the light intensity map and the phase delay map at the same depth position to obtain a new image of the enhanced sweat gland position characteristics at different depths, that is, the 2D surface sweat gland image.

[0039] Take the central area of the first 2D sweat gland image as the template T, and the side length of the central area is 1 / X of the original 2D sweat gland image, where X≥2.

[0040] Use the OCT instrument to collect the three-dimensional OCT image of the fingertip of the object to be measured.

[0041] Conduct an oral glucose tolerance test (OGTT) on the object to be measured, regulate the change of the blood glucose concentration of the object to be measured, collect fingertip blood once every preset time interval, and measure the blood glucose value of the object to be measured. Use a blood glucose meter to collect fingertip blood as the standard blood glucose value.

[0042] Wipe the finger of the object to be measured clean with alcohol, place the finger under the OCT probe, collect the finger skin image, and collect two three-dimensional OCT images of the object to be measured, with an interval of 1 minute between each collection.

[0043] S2. Perform a surface alignment operation on the three-dimensional OCT image of the object to be measured, and calculate the 2D surface sweat gland image of the object to be measured.

[0044] After collecting the three-dimensional OCT images of the object to be measured, perform surface alignment operations on all the collected three-dimensional OCT images, calculate the light intensity maps of the fingertips at different depths according to the light intensity values in the three-dimensional OCT images, calculate the phase delay maps at different depths between the fingers according to the phase values in the three-dimensional OCT images, and subtract the light intensity map and the phase delay map at the same depth position to obtain a new image of the enhanced sweat gland position characteristics at different depths, that is, a 2D surface sweat gland image.

[0045] The present invention generates a series of 2D surface sweat gland images from three-dimensional OCT images according to single-pixel depth.

[0046] S3. Select the image to be located in the 2D surface sweat gland image, and use the template to calculate the fixed area for the image to be located using sweat gland area positioning.

[0047] Each three-dimensional OCT image corresponds to several 2D surface sweat gland images. Select a relatively clear image as the image to be located from the several 2D surface sweat gland images corresponding to each three-dimensional OCT image; a relatively clear image can be selected through the contrast or noise level of the 2D surface sweat gland image, and one three-dimensional OCT image corresponds to one image to be located.

[0048] Perform cross-correlation calculation on the template and the image to be located.

[0049] Slide the template from the upper left corner to the lower right corner of the image to be located in sequence. Perform cross-correlation calculation on the template and the sub-image I(x,y) of the image to be located that has been slid over. The size of the sub-image I(x,y) is the same as that of the template.

[0050] The calculation formula for the cross-correlation value is:

[0051]

[0052] where (i,j) is the coordinate of the pixel in the template, is the pixel average value of the sub-image I(x,y), is the pixel average value of the template T,

[0053] All the normalized cross-correlation values form a normalized cross-correlation matrix R. The larger the value in R, the higher the correlation between the template and the sub-image. The position corresponding to the maximum value in R is the position of the fixed area.

[0054] Such as Figure 3As shown, rotate the image to be located, each time rotating by Y°, where Y is less than or equal to 2, and the rotation range of the image to be located is between -30° and 30°. Each time after rotation, perform a cross-correlation operation between the template and the image to be located. After each rotation, the template slides on the image to be located to calculate the cross-correlation value matrix for cross-correlation calculations with different sub-images. Obtain the angle α corresponding to the maximum cross-correlation value. According to the position of the maximum cross-correlation value in the matrix, the corresponding sub-image can be deduced, and the position of the sub-image corresponding to the maximum cross-correlation value on the image to be located is the position of the fixed area. Calculate the coordinates of the four top corners of the fixed area on the image to be located.

[0055] Even if the maximum cross-correlation value has been selected, there may be certain errors in the three-dimensional OCT image itself. For example, when collecting the three-dimensional OCT image, the finger shakes, and the blood glucose value calculated using the three-dimensional OCT image collected at this time is not accurate. Therefore, after determining the maximum cross-correlation value, the three-dimensional OCT image is discriminated. The specific discrimination method is: set the threshold of the matrix where the maximum cross-correlation value is located. The threshold is the average value of the cross-correlation values in the area where the chessboard distance from the maximum cross-correlation value in the maximum cross-correlation value matrix is 2. When the maximum cross-correlation value in the matrix is less than the threshold, discard this three-dimensional OCT image and collect the OCT image again. When the maximum cross-correlation value is greater than the threshold, continue the calculation.

[0056] The significance of the threshold is that it can deviate a certain number of pixel points up, down, left, and right at the best position, corresponding to an actual distance at the μm level, which does not affect obtaining the image at the correct position.

[0057] Furthermore, it is also necessary to verify the selected sub-image. The verification is carried out according to the structural similarity. The verification formula is:

[0058]

[0059] where x is the template, y is the sub-image with the maximum cross-correlation value, u x is the average value of x, u y is the average value of y, is the variance of x, is the variance of y, σ xy is the covariance of x and y, c1 and c2 are constants used to maintain stability, c1 = (k1L) 2 and c2 = (k2L) 2 where L is the dynamic range of pixel values, k1 = 0.01, k2 = 0.03, and -1 ≤ SSIM(x, y) ≤ 1.

[0060] However, in image quality assessment, the effect of locally calculating the SSIM index is better than globally. The image can be divided into blocks using a sliding window, and the total number of blocks is Z. Considering the influence of the window shape on the block division, Gaussian weighting is used to calculate the mean, variance, and covariance of each window, and then the structural similarity SSIM of the corresponding block is calculated. Finally, the average value is used as the structural similarity measure of the two images.

[0061] S4. Reconstruct the 3D OCT image according to the fixed region of the 2D surface sweat gland image at different depths; calculate the scattering coefficient of the 3D image after reconstructing each 3D OCT image.

[0062] Rotate the 2D sweat gland images at other depths in the original 3D OCT image by the angle α. According to the coordinates of the four top corners of the fixed region, select the sub-images of all 2D sweat gland images corresponding to the 3D OCT image in the other 2D sweat gland images.

[0063] Stack the sub-images into a 3D image according to the depth where the sub-images are located. Calculate the scattering coefficient of the 3D image corresponding to each 3D OCT image.

[0064] S5. Calculate the relationship between the scattering coefficient of the measured object and the blood glucose value according to the scattering coefficient of the 3D OCT image and the corresponding blood glucose value.

[0065] Each 3D OCT image corresponds to a scattering coefficient and a blood glucose value. Fit the scattering coefficient and the blood glucose value to obtain the relationship between the scattering coefficient and the blood glucose value.

[0066] The relationship between the scattering coefficient and the blood glucose value of the measured object is y = kx + b, where y is the blood glucose value, x is the scattering coefficient, and k and b are the relationship coefficients of the measured object.

[0067] The coefficients of different parts of different measured objects are different. The relationship coefficients of the measured object need to be calculated before measurement, and the relationship coefficients of the measured object remain unchanged after measurement. When measuring the blood glucose of the same measured object, the relationship coefficients of the measured object can be directly used. For each measurement, only the scattering coefficient of the sub-block region of the 3D OCT image of the measured object needs to be calculated to obtain the blood glucose value of the measured object.

[0068] After the blood glucose experiment starts, collect the measured 3D OCT image of the measured object; perform the surface alignment operation on the measured 3D OCT image in steps S2 - S4 to calculate the 2D surface sweat gland images at different depths; select the image to be located in the 2D surface sweat gland image, calculate the fixed region according to the sweat gland region positioning method for the 2D surface sweat gland image of the measured 3D image according to the template, reconstruct the 3D image according to the fixed region of the 2D surface sweat gland images at different depths, and calculate the scattering coefficient of the 3D image after reconstructing the measured 3D OCT image.

[0069] Among them, there is an overlapping area in the part where the three-dimensional OCT image of the measured object is collected each time in the same experiment. That is, it is only necessary to ensure that the three-dimensional OCT images of the measured object collected in steps S1 and S6 have the same part during collection.

[0070] In the blood glucose experiment, the three-dimensional OCT image of the measured object is collected at preset time intervals according to the experimental requirements, and at this time, it is not necessary to measure the blood glucose value of the measured object again.

[0071] The processing of the measured three-dimensional OCT image is the same as the processing steps of the three-dimensional OCT image of the measured object when regulating the blood glucose of the measured object, and will not be elaborated here. Finally, the scattering coefficient of the reconstructed 3D image corresponding to the measured three-dimensional OCT image is obtained.

[0072] S7. Determine the blood glucose value corresponding to the measured three-dimensional OCT image according to the scattering coefficient of the 3D image of the measured three-dimensional OCT image and the relationship between the scattering coefficient and the blood glucose value.

[0073] After calculating the scattering coefficient of the 3D image calculated in step S6, substitute it into the relationship between the scattering coefficient and the blood glucose value of the measured object calculated in step S5 to obtain the blood glucose value corresponding to the measured three-dimensional OCT image.

[0074] In the blood glucose experiment, it is necessary to measure the blood glucose value of the measured object a large number of times in a short time. The traditional blood glucose measurement method brings different degrees of pain and discomfort to the experimenter; the non-invasive blood glucose value measurement method needs to use a positioning label sticker, which is easy to fall off. The present invention only needs to measure the blood glucose value with a blood glucose meter before the experiment starts, and then only needs to use the relationship between the scattering coefficient and the blood glucose value, and use the scattering coefficient of the reconstructed 3D image to calculate the blood glucose value of the measured object.

[0075] S8. Method evaluation.

[0076] As Figure 4 and Figure 5 shown, the present invention collects the three-dimensional OCT images of six testers, and measures the blood glucose values of the testers using the sweat gland area positioning and recognition method (i.e., the present invention) and without using the sweat gland area positioning and recognition method respectively. The Clarke grid analysis is a tool for evaluating the accuracy of a blood glucose monitoring system, which divides the error distribution of blood glucose values into five regions, and region A represents the most ideal accuracy.

[0077] Table 1. Sweat gland area positioning and recognition method and the method without using sweat gland area positioning and recognition

[0078]

[0079]

[0080] As shown in Table 1, when the present invention is compared with the method without using the sweat gland area positioning and recognition method, the mean absolute relative error and root mean square error of the present invention are lower; the average probability that the present invention falls in area A of the Clark grid is 91.5%, and even reaches 100% for individual testers. It can be seen from this that the accuracy rate of the present invention is relatively high.

Claims

1. A method for measuring blood sugar based on OCT sweat gland area, characterized in that: The steps include: S1. Before each blood glucose test, a three-dimensional OCT image of the subject is collected; based on the first three-dimensional OCT image, a template is determined; the change of the blood glucose concentration of the subject is regulated at least 5 times, and after each regulation, a three-dimensional OCT image is collected and a blood glucose value is measured for the subject; S2. performing a surface alignment operation on the three-dimensional OCT image of the object to be tested, and calculating the 2D surface sweat gland images at different depths; S3. Selecting an image to be located in the 2D surface sweat gland image, and calculating the position of the fixed area using a sweat gland area positioning method based on the template for the image to be located; S4. reconstructing a 3D image according to a fixed area of ​​the 2D surface sweat gland image at different depths; calculating a scattering coefficient of each 3D image reconstructed from the 3D OCT image; S5. Calculate the relationship between the scattering coefficient and the blood glucose value of the subject according to the scattering coefficient of the three-dimensional OCT image and the corresponding blood glucose value; S6. After the blood glucose experiment starts, a three-dimensional OCT image of the subject is acquired; Performing surface alignment operation on the three-dimensional OCT image under test in steps S2-S4, calculating 2D surface sweat gland images at different depths; selecting an image to be positioned in the 2D surface sweat gland image, calculating a fixed area of ​​the 2D surface sweat gland image of the three-dimensional image under test using a sweat gland area positioning method according to a template, reconstructing a 3D image according to the fixed area of ​​the 2D surface sweat gland image at different depths, and calculating the scattering coefficient of the reconstructed 3D image of the three-dimensional OCT image under test; in the same experiment, there are overlapping areas in the parts where the three-dimensional OCT images of the object under test are collected each time; S7. Determine the blood sugar level corresponding to the measured three-dimensional OCT image according to the scattering coefficient of the 3D image of the measured three-dimensional OCT image and the relationship between the scattering coefficient and the blood sugar level.

2. The method for measuring blood sugar based on OCT sweat gland area according to claim 1, characterized in that: The specific method to determine the template is: A 2D surface sweat gland image corresponding to the depth of the first three-dimensional OCT image is calculated, and the central area of ​​any 2D surface sweat gland image is used as a template; the side length of the template is less than or equal to 1 / 2 of the original 2D surface sweat gland image.

3. The method for measuring blood sugar based on OCT sweat gland area according to claim 1, characterized in that: The specific method of calculating the 2D surface sweat gland image in step S2 is: S2a-1. Calculate the intensity map at different depths according to the light intensity value in the three-dimensional OCT image; calculate the phase delay map at different depths according to the phase delay data in the three-dimensional OCT image; S2a-2. Subtract the light intensity map and phase delay map at the same depth to obtain a 2D surface sweat gland image at that depth, until the light intensity and phase delay maps at all depths are subtracted to obtain 2D surface sweat gland images at different depths.

4. The method for measuring blood sugar based on OCT sweat gland area according to claim 1, characterized in that: The specific method of determining the position of the fixed area on the image to be located in step S3 is: S3-1. Selecting an image to be located in the 2D surface sweat gland image generated by the three-dimensional OCT image according to the image contrast; S3-2. Slide the template from the upper left corner of the image to be located to the lower right corner, and calculate the cross-correlation value once each slide; S3-3. The positioning image is rotated, and step S3-2-1 is performed each time the rotation is performed, and a correlation matrix is ​​obtained each time the rotation angle does not exceed 3°; S3-4. Compare the obtained cross-correlation values, rotate the image to be located by the rotation angle corresponding to the maximum cross-correlation value, and determine the position of the fixed area on the image to be located according to the position of the cross-correlation value in the cross-correlation matrix.

5. The method for measuring blood sugar based on OCT sweat gland area according to claim 4, characterized in that: The rotation range of the image to be positioned is between -30° and 30°.

6. The method for measuring blood sugar based on OCT sweat gland area according to claim 4, characterized in that: The specific method of performing three-dimensional OCT image reconstruction is as follows: The 2D surface sweat gland images at different depths are rotated by the rotation angle corresponding to the maximum cross-correlation value, and sub-images of the 2D surface sweat gland images at different depths are selected according to the position of the fixed area; the sub-images at different depths are reorganized according to the depth position to obtain a reconstructed three-dimensional OCT image.

Images