Methods, systems, media and devices for analyte detection and image processing

Abstract

To provide methods, systems, media, and devices for analyte detection and image processing.SOLUTION: An image acquisition step of acquiring an infrared grayscale image of the first region, a first target screening step of converting the infrared grayscale image into a two dimensional matrix, recording a preselected first target region, and screening a maximum value and a minimum value xmax, xmin, ymax, and ymin of an abscissa and an ordinate, xmax + m, xmin-m, ymax + m, and ymin-m being search region boundaries, and m being a preset extension distance, to obtain a search region, and a second target screening step of sorting the search region according to grayscales in descending order, and selecting coordinates with a preset number of highest grayscales as a second target region. The skin region without blood vessels in the infrared grayscale image can be effectively distinguished, and the spectral data of the skin region without blood vessels in the image can be accurately obtained subsequently.SELECTED DRAWING: Figure 14

Description

[Technical Field]

[0001] The present invention relates to the field of optical analysis, and in particular to analyte detection, image processing methods, systems, media and devices. [Background technology]

[0002] Fluorescence analysis refers to the process in which a specific substance is excited after being irradiated with ultraviolet light, and the excited molecules undergo a de-excitation process of collision and radiation, resulting in fluorescence that can reflect the properties of the substance, and can be used in qualitative or quantitative analysis.

[0003] When testing blood glucose in the human body using fluorescence analysis, the collected image will contain areas with veins and areas without veins. Because the blood glucose content in blood vessels is significantly different from that in the skin, when collecting spectral data from the image, it is necessary to first distinguish between areas with veins and areas without veins, otherwise the test results will be inaccurate.

[0004] Patent document US20160287147A1 discloses a non-invasive in vivo measurement device using Raman spectroscopy to measure blood glucose concentrations in a living body. The advantage of such a solution is that it is more accurate than the electrochemical method of US20100065441A1, but its disadvantage is that it currently requires implementation relying on large and expensive laboratory-level Raman spectroscopy systems.

[0005] Furthermore, Patent Document CN118078277A discloses a non-invasive blood glucose detection method based on hyperspectral data analysis, which enables non-invasive detection. In this document, absorption spectroscopy is used, and the collected and analyzed spectral signals include not only the spectral signal of blood glucose but also the spectral signals of components such as skin tissue, resulting in a mixture of spectral signals of different wavelengths, making it difficult to finely separate and extract the spectral signals related to blood glucose. At the same time, factors such as differences in the excitation light source, human skin color, and epidermal layer thickness also affect the intensity of the spectral signals, resulting in differences in the intensity of the spectral signals. The final collected spectral signals are easily affected and cannot be strongly correlated with blood glucose concentration, thereby affecting the accurate measurement of blood glucose concentration. Patent Document CN108542402A also has similar problems. Summary of the Invention [Problem to be solved by the invention]

[0006] SUMMARY OF THE INVENTION In view of the shortcomings of the prior art, it is an object of the present invention to provide analyte detection and image processing methods, systems, media and devices. [Means for solving the problem]

[0007] The image processing method for analyte detection provided by the present invention comprises: an image acquisition stage for acquiring an infrared grayscale image of the first region; Convert the infrared grayscale image into a two-dimensional matrix, record the preselected first target area, and calculate the maximum and minimum values ​​of the abscissa and ordinate: x max ,x min ,y max ,y min a first target screening stage for screening; x max +m,x min -m,y max +m,y min a search area determination step, where m is a search area boundary, m being a predetermined extension distance, to obtain a search area; and a second target screening step of sorting the search areas in descending order according to grayscale and selecting a predetermined number of coordinates with the largest grayscale from the top as the second target area. Preferably, the step of determining the search area further includes filtering out an area within a range of L from the edge of the infrared grayscale image relative to the search area, where L is the size of a color cast area at the edge of the imaging lens.

[0008] Preferably, the higher the image spatial resolution of the infrared grayscale image, the higher the upper limit of the value of m.

[0009] The present invention also provides a method for detecting an analyte, which comprises the image processing method for detecting an analyte described above.

[0010] The present invention also provides an image capture module for capturing an infrared grayscale image of the first region; Convert the infrared grayscale image into a two-dimensional matrix, record the preselected first target area, and calculate the maximum and minimum values ​​of the abscissa and ordinate: x max ,x min ,y max ,y min a first target screening module for screening; x max +m,x min -m,y max +m,y min a search area determination module for obtaining a search area with m as a search area boundary, m being a predetermined expansion distance; and a second target screening module that arranges the search areas in descending order according to grayscale and selects a predetermined number of coordinates with the largest grayscale as second target areas.

[0011] Preferably, the search area determination module further includes filtering out an area within a range of L from the edge of the infrared grayscale image relative to the search area, where L is the size of a color cast area at the edge of the imaging lens.

[0012] Preferably, the higher the image spatial resolution of the infrared grayscale image, the higher the upper limit of the value of m.

[0013] The present invention also provides an analyte detection system including the image processing system for detecting an analyte described above.

[0014] The present invention also provides a computer-readable storage medium having stored thereon a computer program that, when executed by a processor, implements the steps of the image processing method for analyte detection described above.

[0015] The present invention also provides an electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, the electronic device performs the steps of the image processing method for analyte detection described above. [Effects of the Invention]

[0016] Compared with the prior art, the present invention has the following beneficial effects: 1. The technical solution of the present invention can effectively distinguish the blood vessel-free skin areas in the infrared grayscale image, and later accurately obtain the spectral data of the blood vessel-free skin areas in the image, while minimizing the interference of the blood vessel-located areas. 2. The detection method of the present application does not require an electrochemical reaction with the analyte, making the detection method simpler and more convenient, and can achieve the goal of non-invasive detection, especially when detecting an analyte in a living body. 3. The detection method of the present application can obtain spectral data of various regions by utilizing the characteristics of the non-uniform distribution of the analyte in the imaging area, and since the distribution of components other than the analyte in the imaging area is relatively uniform, the difference in spectral data in different regions directly reflects information correlated with the spectral data of the analyte, such as the concentration of the analyte, which basically eliminates the influence of components other than the analyte. 4. The detection method of the present application uses fluorescence spectrum for detection, avoiding the traditional method of using Raman method to measure the analyte, realizing low-cost and miniaturized detection system, and achieving the goal of real-time detection.

[0017] Other features, objects and advantages of the present invention will become more apparent through the detailed description given below, given by way of non-limiting example with reference to the drawings in which: [Brief explanation of the drawings]

[0018] [Figure 1] 1 is a flowchart of the first embodiment. [Figure 2] FIG. 1 is a schematic diagram of a first image collected in Example 2. [Figure 3] FIG. 10 is a schematic diagram of a second image collected in Example 2. [Figure 4] FIG. 10 is a diagram illustrating the principle of a detection model according to a second embodiment. [Figure 5] FIG. 1 is a schematic diagram of detection point-reference point spectrum data obtained in Example 2. [Figure 6] These are experimental results of the accuracy of the analytical results of the analytical model. [Figure 7] FIG. 10 is a structural schematic diagram of an analyte detection device provided by Example 7. [Figure 8] FIG. 1 is a structural schematic diagram of an electronic device provided by Example 8. [Figure 9] FIG. 10 is a structural schematic diagram of a wristwatch for detecting an analyte provided by Example 7. [Figure 10] FIG. 1 is a schematic diagram of the back of an analyte detection wristwatch. [Figure 11]FIG. 1 is an exploded view of a wristwatch for detecting an analyte. [Figure 12] FIG. 1 is a schematic diagram of a wristwatch for detecting an analyte in use. [Figure 13] 1A and 1B are schematic and cross-sectional views of blood vessel distribution under an infrared light source. [Figure 14] 10 is a flow chart of steps of an image processing method for analyte detection in Example 3. DETAILED DESCRIPTION OF THE INVENTION

[0019] The present invention will be described in detail below with reference to specific examples. The following examples will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any way. It should be noted that those skilled in the art may make some modifications and improvements without departing from the concept of the present invention, all of which fall within the protection scope of the present invention.

[0020] Example 1 FIG. 1 is a flow chart of this embodiment, and the method for detecting an analyte of this embodiment includes the following steps:

[0021] Imaging step: Irradiating a first region with light within a predetermined wavelength range through a light source, and imaging the first region through an imaging spectrum detector to obtain an image of the imaging region. By irradiating the first region with light within the predetermined wavelength range, distribution data and spectral data of the reflected or excited signals generated by the analyte when the light is irradiated in the imaging region can be reflected in the image. The first region may be a specific region on the surface of human skin. To prevent external light such as ambient light from affecting the detection, the collection window of the imaging spectrum detector must be tightly attached to the surface of the human skin in the first region. The imaging region refers to the region within the lens range of the imaging spectrum detector. Generally, the imaging region may be a part of the first region or the same region as the first region.

[0022] Since light rays with different wavelength ranges are required to obtain analyte distribution data and spectral data, two methods can be used: light rays with a wider wavelength range provided by one light source, or light rays with narrower wavelength ranges provided by two light sources. When one light source is used, the wavelength range of the light rays provided by the light source must simultaneously cover the wavelength range for obtaining analyte distribution data and the wavelength range for obtaining analyte spectral data. When two light sources are used, the two light sources provide different light rays, with the wavelength of one light ray covering the wavelength range for obtaining analyte distribution data and the wavelength of the other light ray covering the wavelength range for obtaining analyte spectral data. At the same time, when one light source is used, only one image is captured, and when two light sources are used, two images are captured. For ease of processing, the capturing areas of the two images must be the same, i.e., the collection window of the imaging spectrum detection device does not move on the surface of human skin.

[0023] In the present application, the analyte may be vascular glucose, ketones, alcohol, lactate, oxygen, hemoglobin A1C, acetylcholine, amylase, bilirubin, cholesterol, chorionic gonadotropin, creatine kinase (e.g., CK-MB), creatine, creatinine, DNA, fructosamine, glutamine, growth hormone, hormones, peroxides, prostate-specific antigen, prothrombin, RNA, thyroid-stimulating hormone, or troponin, or may be a drug such as an antibiotic (e.g., gentamicin, vancomycin, etc.), digitoxin, digoxin, a drug of abuse, theophylline, or warfarin. In embodiments where more than one analyte is detected, the analytes may be monitored at the same or different times. In other embodiments, the analyte may be any other substance within a body surface that can be noninvasively detected using the present invention.

[0024] Spectral acquisition step: Acquire spectral data from the image, which reflects the non-uniform distribution of reflected or excited signals generated by the object to be analyzed when the light beam is irradiated in the imaging region via the imaging spectral detection device. Specifically, the imaging region can be divided based on different distribution status data, and positions for acquiring spectral data from different sections can be selected.

[0025] Analysis stage: Based on the acquired spectral data, information about the analyte in the imaging area is acquired, including information about the analyte correlated with the spectral data. Because the distribution of the analyte in different sections is different, the reflected or excited signals generated by the analyte when irradiated with light also differ. Taking human skin as an example, it is divided into three parts: the epidermis, the dermis, and the subcutaneous tissue, and blood vessels such as veins are located in the subcutaneous tissue. UV light can be used to irradiate skin areas with blood vessels and skin areas without blood vessels to acquire corresponding spectral data, or to irradiate skin areas with thick blood vessels and skin areas with thin blood vessels to acquire corresponding spectral data. The difference between the two spectral data can reflect information about the analyte correlated with the spectral data in the blood vessels. Intermediate information, such as data about the degree of impact of the analyte on the spectral data, can be acquired for further analysis, or information such as the concentration of the analyte can be directly acquired through an analytical model.

[0026] Example 2 This embodiment is based on the first embodiment and takes glucose detection in human blood vessels as an example to provide a non-invasive glucose detection method, which includes the following steps:

[0027] Imaging step: Irradiate the skin where veins are located on the wrist, back of the hand, etc. with infrared light in a first wavelength range of 800 to 1000 nanometers, preferably in the near-infrared band, to capture a first image of the imaging area, and then irradiate the same location with ultraviolet light in a second wavelength range of 300 to 390 nanometers to capture a second image of the imaging area.

[0028] 2, where the horizontal axis is the horizontal coordinate of the first image, the vertical axis is the vertical coordinate of the first image, the white box represents the pixel point block of the detection point selected on the venous blood vessel, and the black box represents the pixel point block of the reference point selected on the surrounding skin. In the first image, part of the infrared light passes through the human skin, and part is absorbed by the human skin. At the same time, the area where the venous blood vessel is located is absorbed in large amounts by the venous blood vessel, so the grayscale value of the pixel in the area where the venous blood vessel is located is smaller, and the grayscale value of the pixel outside the area where the venous blood vessel is located is larger, which makes it easy to divide the imaging area into the area where the venous blood vessel is located and the area outside the area where the venous blood vessel is located.

[0029] As shown in Figure 3, the horizontal axis represents the horizontal coordinate of the second image, the vertical axis represents the vertical coordinate of the second image, the white box represents the pixel point block of the detection point selected on the venous blood vessel, and the black box represents the pixel point block of the reference point selected on the surrounding skin. Because it is difficult to distinguish between the area where the venous blood vessel is located and the area other than the area where the venous blood vessel is located in the second image, it is necessary to distinguish the first image. The purpose of using excitation light in the second wavelength range of 300-390 nanometers is to obtain a high-quality effective fluorescence spectrum signal. This is because the main response band of the imaging spectrum detection device is 400-800 nm. If the wavelength of the excitation light used is less than 300 nm, the main peak of the fluorescence spectrum of the excited fluorescence emission signal is located in the band <400 nm, making it difficult for the imaging spectrum detection device to obtain a high-quality effective fluorescence spectrum signal. If the wavelength of the excitation light used exceeds 390 nm, the excitation light itself will be visible light, and the spectral signal of the excitation light will be superimposed on the fluorescence spectral signal, making it difficult to extract an effective fluorescence spectral signal without interference from the excitation light. Glucose in venous blood vessels absorbs ultraviolet light in the wavelength range of 300-390 nanometers and then emits a fluorescent emission signal in the visible light band of 400-800 nm, which is within the effective response range of the imaging spectral detector. The characteristic spectral intensity of this fluorescent emission signal is positively correlated with the glucose concentration, resulting in higher fluorescence excitation efficiency.

[0030] Spectrum acquisition step: based on the grayscale distribution of the pixel points in the first image, divide the imaging area into an area where venous blood vessels are located and an area other than the area where venous blood vessels are located, select a detection point from the position of the area where venous blood vessels are located corresponding to the second image, select a reference point from the position of the area where non-venous blood vessels are located corresponding to the second image, and respectively obtain spectral data of the detection point and the reference point in the second image. Specifically, based on the grayscale values ​​of the pixel points in the second image, select one pixel point having a grayscale value that meets a predetermined requirement as the detection point from the area where venous blood vessels are located, or select a combination of this pixel point and adjacent pixel points as the detection point, and select one pixel point whose grayscale value of the selected detection point is within a predetermined deviation range from the area other than the area where venous blood vessels are located, or select a combination of this pixel point and multiple adjacent pixel points as reference points, and calculate and obtain fluorescence spectral data of the detection point and the reference point in the second image. The spectral data can be selected from a single pixel point of the detection point, the reference point, or an average of a combination of multiple pixel points, and can be appropriately selected based on the width of the blood vessel. Averaging a combination of multiple pixel points improves the signal-to-noise ratio but is limited by the width of the blood vessel and avoids acquiring data from areas outside the blood vessel. Selecting a single pixel point provides high spatial resolution and is suitable for situations with thin blood vessels, but has a lower signal-to-noise ratio. As a preset requirement for grayscale values, it is possible to use the point with the smallest grayscale value as the detection point, but this application is not limited to this. The calculation results are shown in Figure 5, where the horizontal axis is wavelength (unit: nm) and the vertical axis is relative radiance (unit: W / nm). The solid line represents the spectral data of the detection point, and the dotted line represents the spectral data of the reference point.Here, the reason why the grayscale value of the reference point and the grayscale value of the selected detection point are within the predetermined deviation range is that the skin in the imaging area has influencing factors such as skin color, blemishes, and cosmetics, which may directly affect the spectral data of the reference point. However, the first image does not distinguish the areas of these influencing factors, so by setting a predetermined deviation range of the grayscale value, these influencing factors can be effectively excluded. Furthermore, because the grayscale value and the grayscale value of the selected detection point are within the predetermined deviation range, it is guaranteed that a reference point close to the detection point, such as the edge of a venous blood vessel, will be selected. This ensures that, excluding the blood vessels, the parameters of the remaining epidermis, dermis, and subcutaneous tissue, such as color and thickness, are closest. Therefore, the deviation between the spectral data of the detection point and the spectral data of the reference point can eliminate the influence of non-analyte objects as much as possible.

[0031] In addition to the spectral reconstruction algorithm, the method of obtaining the spectral data is to form the radiation calibration coefficient through pre-radiation calibration, and then calculate the grayscale value * radiation calibration coefficient to obtain the spectral line.

[0032] When a combination of multiple pixel points is selected from the detection point, the fluorescence spectrum data of the detection point can be the average value of the fluorescence spectrum data of these pixel points. At the same time, the number of detection points and reference points can be one or more. When the number of detection points and reference points is multiple, the average value of the fluorescence spectrum data of all the detection points and the average value of the fluorescence spectrum data of all the reference points can be calculated respectively.

[0033] Analysis stage: The acquired spectral data of the detection points and reference points is preprocessed and then input into a trained detection model, which outputs the glucose concentration or an intermediate result showing the correlation between glucose and the spectral data. When training the detection model, it is necessary to simultaneously acquire the subject's spectral data and accurate test results such as blood test results, and use the spectral data as the input of the detection model and the blood test results as the output of the detection model to train the detection model.

[0034] The detection model may employ a convolutional neural network model, which sequentially includes an input layer, at least two convolutional layers, at least two activation function layers, a flattened layer, a fully connected layer, and an output layer, where the convolutional layers and the activation function layers are spaced apart, and the activation function used in the activation function layer is a Relu function.

[0035] Here, the size of the convolution kernel of each layer in the convolutional neural network model is 1, the number of convolution kernels in the first convolutional layer is 32, and the number of convolution kernels in the second layer is 64. Both are used to extract blood glucose features and nonlinearly transform the output of the convolutional layer through an activation function. The Flatten layer flattens the output of the convolutional layer into a one-dimensional vector to facilitate connection to the subsequent fully connected layer, and the final output dimension is 1. In the model training process, the Adam optimizer is used to train the model, and the mean squared error is used as the loss function, and the mean absolute error is simultaneously calculated as the performance indicator for model evaluation.

[0036] If the output result of the detection model is a glucose concentration, the training for obtaining the detection model is stopped when the error between the output result and the measured standard glucose concentration value satisfies a preset condition. If the output result of the detection model is an intermediate result correlating glucose with spectral data, such as an intermediate neuron result, the training for obtaining the detection model is stopped when the error between the output result and the intermediate neuron result satisfies a preset condition. The intermediate neuron result is further processed by model correction to obtain the glucose concentration.

[0037] As shown in Figure 4, the input layer is a spectral data input layer obtained by preprocessing the original spectral data. The hidden layer is an intermediate hidden layer that uses deep learning convolutional operations to combine features and output the final predicted blood glucose concentration value as an output layer. Deep learning convolutional operations can also output one neuron, Output1, as an intermediate result after combining features. The intermediate result, Output1, and the two infrared IR feature intensity values ​​are then used for further model training to further correct the blood glucose prediction error and output the final predicted blood glucose concentration value, Output2. The degree of training of the detection model can be determined by setting different parameters as needed. The extracted glucose feature values ​​are continuously trained according to different parameter settings. When the error between the output result and the standard glucose value of the above label value meets the requirements, the training for obtaining the detection model is stopped.

[0038] Through multiple repeated training, the neurons can learn the corresponding change rules between different glucose concentrations and glucose spectrum features of different samplers, thereby improving the generality of the detection model and achieving the goal of predicting the glucose concentrations of different users.

[0039] The entire glucose detection process does not require blood sampling or skin puncture or skin implantation, but rather obtains the subject's spectral information based on the fluorescence spectrum, and then obtains the subject's glucose detection result based on the spectral information, thereby avoiding pain and discomfort and improving the discomfort and convenience of detection. This method allows for detailed distinction between the spectral signals at the blood vessel site and the skin site, allowing for accurate extraction of the subsequent glucose signal, and at the same time, the intensity of the spectral signal and the glucose concentration are closely correlated, achieving accurate measurement of glucose concentration, more accurate detection results, and more convenient processing.

[0040] Figure 6 shows a schematic diagram of the experimental results of the trained detection model. The horizontal axis represents the reference blood glucose concentration (unit: mmol / L) collected by the blood glucose meter, and the vertical axis represents the blood glucose concentration (unit: mmol / L) predicted by the patented method. The total number of samples collected by the subjects was 2,037, of which 1,537 samples were in the training set and 500 samples were in the prediction set. The figure shows the distribution of the detection results of the detection model. The MARD value of the predicted samples was 11.32%, and they were classified into region A and region B. The samples in region A accounted for 87.03%, and the samples in region B accounted for 12.77%, indicating that the detection accuracy of the detection model is relatively high.

[0041] Example 3 This embodiment further describes an image processing method for analyte detection based on Example 1 and Example 2. As shown in FIG. 14, this embodiment provides an image processing method for analyte detection, which includes the following steps:

[0042] Image acquisition step: An infrared grayscale image of the first region is acquired. First target screening step: Convert the infrared grayscale image into a two-dimensional matrix to record the preselected first target area, and calculate the maximum and minimum values ​​of the abscissa and ordinate: x max ,x min ,y max ,y min Screening.

[0043] Search area determination stage: x max +m,x min -m,y max +m,y min-m is a search area boundary, where m is a preset expansion distance, to obtain a search area, and the search area determination step further includes filtering out an area within a range of L from the edge of the infrared grayscale image relative to the search area, where L is the size of a color cast area at the edge of the imaging lens, and the higher the image spatial resolution of the infrared grayscale image, the higher the upper limit of the value of m.

[0044] Second target screening stage: The search areas are sorted in descending order according to the gray scale, and the coordinates with the highest gray scale among the predetermined number of coordinates are selected as the second target area.

[0045] This embodiment also provides a method for detecting an analyte, the method including the image processing method for detecting an analyte. This embodiment also provides a computer-readable storage medium having a computer program stored thereon, the computer program being executed by a processor to realize the steps of the image processing method for detecting an analyte. This embodiment also provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being executed by the processor to realize the steps of the image processing method for detecting an analyte.

[0046] In infrared grayscale images, dark spots and bright spots are distinguished based on pixel gradients, with dark spots representing vascular areas and bright spots representing skin areas. First, the grayscale gradient of a 2x2 cycle around each pixel point in the subregion is calculated, then the peaks (including local maxima (dark spots) and minima (bright spots)) of the subregion are found, and 20 consecutive maximum values ​​are selected as candidate locations for dark spots. The specific vascular screening method is as follows:

[0047] 1. Vessel Positioning An infrared light source with a central wavelength of 940 nm is used as the light source to determine the location of blood vessels. However, hemoglobin in human blood strongly absorbs light waves in the infrared band, so the skin reflectance of blood vessels under the infrared light source collected by the detector is lower than that of the skin area. On the other hand, skin pigmentation such as age spots does not absorb infrared light waves, so the method of selecting blood vessel areas based on grayscale is not affected by skin quality and is highly versatile.

[0048] As shown in Figure 13, the infrared reflection intensity of the skin surrounding the blood vessel varies uniformly along the cross-sectional direction of the blood vessel, making it difficult to accurately identify the specific blood vessel area. At the same time, due to the uneven distribution of image light irradiation, grayscale intensity alone cannot accurately reflect the absorption of light energy by hemoglobin. The trend in grayscale change in this direction must be taken into account, and therefore, the minimum point can be used as a criterion for identifying blood vessels. Ideally, assuming a blood vessel is a standard cylinder, the center of the blood vessel absorbs infrared light most strongly, resulting in a lower grayscale in the image than the surrounding areas. The image is treated as a two-dimensional matrix, and the matrix for each row and column in the x and y directions is sequentially derived to find the minimum point. The common minimum point in the x and y directions is the center of the blood vessel, and the line connecting the minimum points is the line connecting the blood vessel centers. The area surrounding the blood vessel is the skin area. The left image in Figure 13 is a schematic diagram of skin under an infrared light source, and the right image is a schematic diagram of the grayscale distribution in the cross-section.

[0049] In actual collection, there are still many interference factors to consider, so this model needs to be optimized and filters added to eliminate interference. a. Actual blood vessels are not standard cylindrical. Also, due to the influence of noise, the local minimum points in the x and y directions in the blood vessel region are not necessarily located at the same position. Therefore, in actual screening, the distance between the extreme points in both directions may be close. b. In actual images, there are many interferences such as noise points and fine wrinkles in the skin region, and these interferences also appear as extreme points in the image. The characteristics of noise points and fine wrinkles are that they are small in area and discretely distributed. The design scheme is based on the following two considerations: (1) The blood vessel region has a certain width and is mathematically represented as a monotonic region within a certain distance on both sides of a minimum point, with no other extreme points. Therefore, single-pixel noise points and fine wrinkles can be removed accordingly. (2) Because the blood vessel region is a continuous region with densely distributed extreme points, the remaining discrete noise points can be filtered accordingly.

[0050] The above solution can be expressed as the following steps when converted into an algorithm: Convert the infrared grayscale image into a two-dimensional matrix, and find the local minimum by sequentially deriving the matrix for each row and column in the x and y directions. If the grayscale values ​​within n pixels on either side of the local minimum remain monotonic, mark that location. Here, n depends on the width of the blood vessel; the larger the value of n, the wider the selection range, and vice versa. Create a two-dimensional matrix of equal size, plot the distribution of the local minimum points in this matrix, and replace them with 1. If there are overlapping local minimum points, replace them with 2. Convolve the created matrix with a matrix of size (n / 2) * (n / 2) (truncated) as the convolution kernel. The larger the value of the convolution matrix element, the higher the probability that the location corresponding to that point is a blood vessel area. The elements of the convolution matrix and their corresponding locations can be sorted in descending order to screen for blood vessel areas.

[0051] 2. Skin Positioning The skin positioning can be done by finding the maximum intensity points in the vicinity of the blood vessel regions. The coordinates of all selected blood vessel regions are recorded and the x max , x min , y max , y min Screen the maximum and minimum values ​​to find x max +m, x min -m,y max +m,y min-m is used as the boundary of the search area, where m is the dilation distance and depends on the spatial resolution of the image. If the spatial resolution of the image is high, the m value can be increased appropriately, and vice versa. Finally, the area within L at the edge of the image is filtered, where L depends on the size of the color cast area that may exist at the edge of the lens. The final search area is sorted based on grayscale; the higher the grayscale, the more likely it is to be a skin area.

[0052] 3. Screening of skin pigmentation areas Because a 365nm UV light source can better reflect pigmented areas of the skin, after sampling under infrared light conditions, the same skin area is sampled using a 365nm UV light source. The coordinates of all pre-screened blood vessel and skin areas are recorded, their locations are marked on the UV image, and the image grayscale values ​​D of these locations are recorded. The mean value Mu and variance Sigma of these grayscale values ​​are calculated, respectively. If |D-Mu|>Sigma×threshold, this means that the selected point is covered by a pigmented skin area or a fluorescently marked area and can be filtered. The coordinates remaining after filtering are the available coordinates of the filtered blood and skin areas.

[0053] Outlier detection is performed on candidate points for blood vessels and skin, a threshold is set based on the standard deviation, and filtering and screening are performed to remove abnormal values ​​such as dull skin and fluorescence. The threshold determination condition is |X-μ|≦T*σ, where μ represents the mean value of dark or bright points, σ ​​represents the standard deviation of dark or bright points, and T is a threshold value, usually a multiple of the standard deviation σ. Here, a threshold value of 1 is set as the basis for determining whether a selected data point is an outlier (abnormal value), and more accurate coordinate positions of dark points, i.e., the coordinate positions of blood vessels, are obtained.

[0054] The present invention also provides an image processing system for analyte detection, which may be realized by performing the flow steps of the image processing method for analyte detection, i.e., a person skilled in the art can understand that the image processing method for analyte detection is a preferred embodiment of the image processing system for analyte detection.

[0055] Example 4 This embodiment provides an image processing system for analyte detection, which includes the following steps:

[0056] Image acquisition module: acquires an infrared grayscale image of a first region. First target screening module: convert the infrared grayscale image into a two-dimensional matrix, record the preselected first target area, and calculate the maximum and minimum values ​​of the horizontal and vertical coordinates: x max ,x min ,y max ,y min Screening.

[0057] Search area determination module:x max +m,x min -m,y max +m,y min -m is a search area boundary, where m is a preset expansion distance to obtain a search area. The search area determination module further includes filtering out an area within a range of L from an edge of the infrared grayscale image with respect to the search area, where L is the size of a color cast area at the edge of the imaging lens, and the higher the image spatial resolution of the infrared grayscale image, the higher the upper limit of the value of m.

[0058] Second target screening module: sort the search areas in descending order according to gray scale, and select the coordinates with the highest gray scale among a predetermined number of coordinates as the second target area. The present embodiment also provides an analyte detection system including the image processing system for analyte detection described above.

[0059] Example 5 This example is based on Example 2, and replaces infrared light with visible light to provide a non-invasive glucose detection method, which includes the following steps:

[0060] Imaging step: Visible light is irradiated onto the skin where veins are located, such as on the wrist or back of the hand, to capture a first image of the imaging area, and ultraviolet light within a second wavelength range of 300 to 390 nanometers is irradiated onto the same location to capture a second image of the imaging area.

[0061] In the first image, the color of the area where the venous blood vessels are located is different from the color of the area other than the area where the venous blood vessels are located, so the imaging area can be easily divided into the area where the venous blood vessels are located and the area other than the area where the venous blood vessels are located.

[0062] In the second image, it is difficult to distinguish between areas where venous blood vessels are located and areas where they are not, so it is necessary to distinguish between the first image. The purpose of using excitation light in the second wavelength range of 300-390 nanometers is to obtain a high-quality effective fluorescence spectral signal. This is because the main response band of the imaging spectral detection device is 400-800 nm. If the wavelength of the excitation light used is less than 300 nm, the main peak of the fluorescence spectrum of the excited fluorescence emission signal is located in the band <400 nm, making it difficult for the imaging spectral detection device to obtain a high-quality effective fluorescence spectral signal. If the wavelength of the excitation light used is greater than 390 nm, the excitation light itself is visible light, and the spectral signal of the excitation light is superimposed on the fluorescence spectral signal, making it difficult to extract the effective fluorescence spectral signal without interference from the spectral signal of the excitation light. Glucose in the venous blood vessels absorbs ultraviolet light in the wavelength range of 300 to 390 nanometers, and then emits fluorescent radiation signals in the visible light band of 400 to 800 nm, which is located within the effective response range of the imaging spectrum detection device. The characteristic spectral intensity of the fluorescent radiation signals is positively correlated with the glucose concentration and has higher fluorescence excitation efficiency.

[0063] Spectrum acquisition step: based on the grayscale distribution of the pixel points in the first image, divide the imaging area into an area where venous blood vessels are located and an area other than the area where venous blood vessels are located, select a detection point from the area where venous blood vessels are located, select a reference point from the area other than the area where venous blood vessels are located, and obtain spectral data of the detection point and spectral data of the reference point, respectively. Specifically, based on the grayscale values ​​of the pixel points in the second image, select one pixel point having a grayscale value that meets a predetermined requirement from the area where venous blood vessels are located, or select a combination of this pixel point and adjacent pixel points as the detection point, select one pixel point from the area other than the area where venous blood vessels has a grayscale value within a predetermined deviation range, or select a combination of this pixel point and multiple adjacent pixel points as reference points, and calculate and obtain fluorescence spectral data of the detection point and fluorescence spectral data of the reference point. Here, the reason why the grayscale value of the reference point and the grayscale value of the selected detection point are within a preset deviation range is that the skin in the imaging area has influencing factors such as skin color, blemishes, cosmetics, etc., which may directly affect the spectral data of the reference point. However, in the first image, the areas of all influencing factors are not distinguished at the same time, and by setting a preset deviation range of the grayscale values, these influencing factors can be effectively excluded.

[0064] When a combination of multiple pixel points is selected from the detection point, the fluorescence spectrum data of the detection point can be the average value of the fluorescence spectrum data of these pixel points. At the same time, the number of detection points and reference points can be one or more. When the number of detection points and reference points is multiple, the average value of the fluorescence spectrum data of all the detection points and the average value of the fluorescence spectrum data of all the reference points can be calculated respectively.

[0065] Analysis stage: The acquired spectral data of the detection point and reference point are preprocessed and then input into a trained detection model to output the glucose concentration. When training the detection model, it is necessary to simultaneously acquire the subject's spectral data and accurate test results such as blood test results, use the spectral data as the input of the detection model, and use the blood test results as the output of the detection model to train the detection model.

[0066] The detection model may employ a convolutional neural network model, which sequentially includes an input layer, at least two convolutional layers, at least two activation function layers, a flattened layer, a fully connected layer, and an output layer, where the convolutional layers and the activation function layers are spaced apart, and the activation function used in the activation function layer is a Relu function.

[0067] Here, the size of the convolution kernel of each layer in the convolutional neural network model is 1, the number of convolution kernels in the first convolutional layer is 32, and the number of convolution kernels in the second layer is 64. Both are used to extract blood glucose features and nonlinearly transform the output of the convolutional layer through an activation function. The Flatten layer flattens the output of the convolutional layer into a one-dimensional vector to facilitate connection to the subsequent fully connected layer, and the final output dimension is 1. In the model training process, the Adam optimizer is used to train the model, and the mean squared error is used as the loss function, and the mean absolute error is simultaneously calculated as the performance indicator for model evaluation.

[0068] When the error between the output result of the detection model and the standard glucose value satisfies a preset condition, the training for obtaining the detection model is stopped.

[0069] The degree of training of the detection model needs to set different parameters as needed, and the extracted multiple glucose feature values ​​are continuously learned according to different parameter settings. When the error between the output result and the standard glucose value of the above label value meets the requirement, the training to obtain the detection model is stopped.

[0070] Through multiple repeated training, the neurons can learn the corresponding change rules between different glucose concentrations and glucose spectrum features of different samplers, thereby improving the generality of the detection model and achieving the goal of predicting the glucose concentrations of different users.

[0071] The entire glucose detection process does not require blood sampling or skin puncture, but rather obtains the subject's spectral information based on the fluorescence spectrum, and then obtains the subject's glucose detection result based on the spectral information, thereby avoiding pain and discomfort and improving the discomfort and convenience of detection. This method can finely distinguish the spectral signals from the blood vessel site and the skin site, allowing the subsequent glucose signal to be accurately extracted, and at the same time, the intensity of the spectral signal and the glucose concentration are closely correlated, achieving accurate measurement of the glucose concentration, more accurate detection results, and more convenient processing.

[0072] Example 6 This embodiment provides an analyte detection system, which can be realized by performing the process steps of the analyte detection method, i.e., those skilled in the art can understand the analyte detection method as a preferred embodiment of the analyte detection system. The analyte detection system includes:

[0073] The imaging module includes a light source that provides light within a predetermined wavelength range to illuminate a first region, and an imaging spectrum detector that captures the first region to obtain an image of the imaging region. By irradiating the imaging region with light within the predetermined wavelength range, the image can reflect distribution data and spectral data of the reflected or excited signals generated by the analyte when the light is irradiated in the imaging region. Because different wavelength ranges are required to obtain distribution data and spectral data of the analyte, the light may be light of two corresponding wavelength ranges, or one wavelength range may be wider and cover the two required wavelength ranges. When two types of light are used, two images are obtained, and for ease of processing, the imaging regions of the two images typically need to be the same.

[0074] In the present application, the analyte may be glucose, ketones, alcohol, lactate, oxygen, hemoglobin A1C, acetylcholine, amylase, bilirubin, cholesterol, chorionic gonadotropin, creatine kinase (e.g., CK-MB), creatine, creatinine, DNA, fructosamine, glutamine, growth hormone, hormones, peroxide, prostate-specific antigen, prothrombin, RNA, thyroid-stimulating hormone, troponin, or drugs such as antibiotics (e.g., gentamicin, vancomycin, etc.), digitoxin, digoxin, drugs of abuse, theophylline, and warfarin in an animal's blood vessels. In embodiments where more than one analyte is detected, the analytes can be monitored at the same or different times. In other embodiments, the analyte may be other substances in the fluid.

[0075] Spectral acquisition module: Acquires spectral data from the image, which reflects the non-uniform distribution of reflected or excited signals generated by the object to be analyzed when the imaging area is irradiated with light via the imaging spectrum detection device. Specifically, the imaging area can be divided based on different distribution status data, and positions for acquiring spectral data from different sections can be selected.

[0076] Analysis module: Based on the acquired spectral data, the analysis module acquires information about the analyte in the imaging area, including information about the analyte correlated with the spectral data. Because the distribution of the analyte in different sections is different, the reflected or excited signals generated by the analyte when irradiated with light are also different. By utilizing this characteristic, the difference between the spectral data can be obtained, and the information about the analyte correlated with the spectral data, such as the concentration of the analyte, can be accurately reflected.

[0077] Those skilled in the art will recognize that in addition to realizing the system provided by the present invention and its respective devices, modules, and units purely in the form of computer-readable program codes, the system provided by the present invention and its respective devices, modules, and units can also realize similar functions in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc. by performing logic programming in method steps. Therefore, the system provided by the present invention and its various devices, modules, and units can be considered as hardware components, and the devices, modules, and units included therein that realize various functions can also be considered as structures within the hardware components, and the devices, modules, and units for realizing various functions can also be considered as both software modules for realizing methods or structures within the hardware components.

[0078] Example 7 Shown in Figure 7 is an electronic device of this embodiment, specifically an analyte detection device 200. The detection device 200 is a portable, non-invasive detection device for the human body, which can be used as a standalone detection device or integrated into a wristwatch or mobile phone, realizing convenient and rapid detection of analytes on the body surface.

[0079] The detection device 200 includes a light source 201, an imaging spectrum detector 202, a controller 203, a first bandpass filter 204, a second bandpass filter 206, and a lens 205. The controller 203 establishes an electrical or communication connection with the light source 201 and the imaging spectrum detector 202, respectively.

[0080] The light source 201 can provide light within a predetermined wavelength range. Since light of different wavelength ranges is required to obtain distribution data and spectral data of an analyte, two methods can be used: one light source that can provide light of a wider wavelength range, or two light sources that provide light of narrower wavelength ranges. When a single light source is used, the wavelength range of the light provided by the light source must simultaneously cover the wavelength range for obtaining distribution data of the analyte, such as a halogen lamp, and the wavelength range for obtaining spectral data of the analyte. When two light sources are used, the two light sources provide different light beams, with one light beam having a wavelength covering the wavelength range for obtaining distribution data of the analyte, and the other light beam having a wavelength covering the wavelength range for obtaining spectral data of the analyte, such as a combination of infrared and ultraviolet light, or a combination of visible light and ultraviolet light.

[0081] To uniformly illuminate the imaging area 100, a ring-shaped light source can be used, which has multiple light-emitting modules uniformly distributed on the same circumference. When there are two types of light sources, the light-emitting modules of the two types of light sources are arranged relative to each other.

[0082] The imaging spectral detector 202 can image the imaging region 100 to obtain a corresponding image upon command, and can also obtain corresponding spectral data upon command. The imaging spectral detector 202 includes a sensor and a periodic pixel-level optical filter structure disposed on a surface of the sensor. The periodic pixel-level optical filter structure is used to perform spectral modulation on an incident optical signal, thereby enabling the sensor to generate an image containing the spectral information of interest.

[0083] The periodic pixel-level optical filter structure includes a plurality of optical filter pixel channels with different shapes of pixel-level structures, each having the same specifications and size, uniformly arranged, and each having a length and width that is an integer multiple of the pixel point size of the pixel sensor. The optical filter pixel channels of the different shapes of the pixel-level optical filter structures correspond to different spectral wave filter coefficients, and the pixel-level optical filter structures with different spectral wave filter coefficients are periodically arranged after being combined in a fixed order. The sensor modulates the received first detected light through the periodic pixel-level optical filter structures arranged on its surface to form a mosaic image containing spectral information, and then reconstructs the spectral data using an algorithm.

[0084] The controller 203 is configured to control the light source to provide light within a predetermined wavelength range to illuminate a first region, control the imaging spectrum detector to image the first region, and acquire an image of the imaging region. The controller 203 is configured to control the imaging spectrum detector to acquire spectral data from the image, the spectral data reflecting the non-uniform distribution of reflected or excited signals generated by the analyte when the light is irradiated in the imaging region. Based on the acquired spectral data, information about the analyte in the imaging region is acquired, where the information about the analyte correlates with the spectral data. When the light source 201 is one type, one image is acquired; when the light source 201 is two types, two images are acquired. When the first light source is turned on, the second light source is turned off; similarly, when the second light source is turned on, the first light source is turned off, so that the two light sources do not interfere with each other.

[0085] The first bandpass filter 204 is located between the light source 201 and the imaging area 100, and its function is to pass light within a predetermined wavelength range and block light outside the predetermined wavelength range, thereby reducing the influence of other external light on the detection results.

[0086] The second bandpass filter 206 is located between the imaging spectrum detector 202 and the lens 205, and its function is to pass light in the wavelength range in which the reflection signal or excitation signal generated by the analyte when irradiated with light is located, and to block light in other wavelength ranges, thereby reducing the influence of reflection signals or excitation signals other than those of the analyte on the detection results.

[0087] The lens 205 can be used for focusing to obtain a clear image. In another embodiment, the second bandpass filter 206 can be located on one side of the lens 205, away from the imaging spectrum detector 202, but the present invention is not limited thereto.

[0088] As described above, FIG. 9 shows a wristwatch for detecting an analyte provided by this embodiment. The front of the wristwatch is a display, and as shown in FIG. 10, the back of the wristwatch is a light-transmitting window with a built-in detection device 200. As shown in FIG. 11, the light source 201 and the first band-pass filter 204 are all in a ring-like structure. The light-emitting modules of the light source 201 are arranged in a ring shape, and the emitted light is optically filtered by the first band-pass filter 204 to output light of a required wavelength, which is then irradiated onto the human body through the light-transmitting window on the back of the wristwatch. The reflected signal or excitation signal from the human body enters the light-transmitting window, passes through the central hollow part of the light source 201 and the first band-pass filter 204, and enters the second band-pass filter 206 through the lens 205. After being optically filtered by the second band-pass filter 206, it enters the imaging spectrum detection device 202. The imaging spectrum detector 202 is mounted on a circuit board 207, and at the same time, a controller 203 (not shown) of the detector device 200 is also mounted on the circuit board 207. As shown in Figure 12, the watch can also be worn on the inside of the wrist to more accurately identify the location of the venous blood vessels.

[0089] Example 8 FIG. 8 is a structural schematic diagram of an electronic device provided by an embodiment of the present application, which includes at least one processor 501, as shown in FIG. 8, and a memory 502 communicatively connected to the at least one processor 501, wherein the memory 502 stores instructions executable by the at least one processor 501, the instructions are executed by the at least one processor 501, and the at least one processor 501 can perform the above-mentioned analyte detection method.

[0090] Here, the memory 502 and the processor 501 are connected in a bus manner, which may include any number of interconnected buses and bridges, connecting various circuits of one or more processors 501 and memories 502. The bus may also connect various other circuits, such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and will not be further described in the present invention. A bus interface provides an interface between the bus and a transceiver. The transceiver may be a single component or multiple components, for example, multiple receivers and transmitters, providing a unit for communicating with various other devices via a transmission medium. Data processed by the processor 501 is transmitted over a wireless medium via an antenna, which receives data and transmits data to the processor 501.

[0091] The processor 501 is responsible for bus management and general processing, and may also provide a variety of functions including timing, peripheral interfaces, voltage regulation, power management, and other control functions. The memory 502 may be used to store data used by the processor 501 when performing operations.

[0092] The present invention further provides a computer-readable storage medium having stored thereon a computer program, which, when executed by a processor, implements the above-described method for detecting an analyte.

[0093] That is, those skilled in the art will understand that all or some of the steps of implementing the methods of the above embodiments can be completed by instructing relevant hardware through a program, and the program can be stored in a storage medium containing some instructions that enable a device (which may be a single-chip microcomputer, chip, etc.) or a processor to execute all or some of the steps of the methods described in each embodiment of the present application. The storage medium includes various media that can store program code, such as a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0094] Those skilled in the art will understand that the above embodiments are specific examples for realizing the present invention, and that in actual applications, various changes in form and details are possible without departing from the spirit and scope of the present invention.

[0095] The above describes specific examples of the present invention. The present invention is not limited to the above specific embodiments, and it should be understood that those skilled in the art can make various changes or modifications within the scope of the claims without affecting the essential content of the present invention. Unless inconsistent, the examples and features of the examples in this application can be combined with each other in any way. [Explanation of symbols]

[0096] 100: Imaging area 200:Detection device 201: Light source 202: Imaging spectrum detector 203: Controller 204: 1st bandpass filter 205: Lens 206: Second bandpass filter 207: Circuit board 501: Processor 502: Memory

Claims

1. 1. An image processing method for analyte detection, comprising: an image acquisition step of acquiring an infrared grayscale image of the first region; The infrared grayscale image is converted into a two-dimensional matrix to record the preselected first target area, and the maximum and minimum values ​​of the abscissa and ordinate: x ｍａｘ , x ｍｉｎ , y ｍａｘ , y ｍｉｎ a first target screening step of screening; x ｍａｘ +m, x ｍｉｎ -m, y ｍａｘ + m, y ｍｉｎ a search region determination step of obtaining a search region boundary, where m is a predetermined extension distance; a second target screening step of sorting the search area in descending order according to the gray scale and selecting a predetermined number of coordinates having the largest gray scale as a second target area. Image processing methods for analyte detection.

2. The step of determining the search area further includes filtering out an area within a range of L from the edge of the infrared grayscale image, where L is the size of a color cast area at the edge of the imaging lens.

10. The image processing method for analyte detection according to claim 1.

3. The infrared grayscale image is characterized in that the higher the image spatial resolution, the higher the upper limit of the value of m.

10. The image processing method for analyte detection according to claim 1.

4. 1. A method for detecting an analyte, comprising: A method for detecting an analyte, comprising the image processing method for detecting an analyte according to any one of claims 1 to 3.

5. 1. An image processing system for analyte detection, comprising: an image capture module for capturing an infrared grayscale image of the first region; The infrared grayscale image is converted into a two-dimensional matrix to record the preselected first target area, and the maximum and minimum values ​​of the abscissa and ordinate: x ｍａｘ , x ｍｉｎ , y ｍａｘ , y ｍｉｎ a first target screening module for screening; x ｍａｘ +m, x ｍｉｎ -m, y ｍａｘ + m, y ｍｉｎ a search area determination module for obtaining a search area boundary, where m is a predetermined expansion distance; a second target screening module that arranges the search areas in descending order according to the gray scale and selects a predetermined number of coordinates having the largest gray scale as the second target area. Image processing system for analyte detection.

6. The search area determination module further includes filtering out an area within a range of L from an edge of the infrared grayscale image from the search area, where L is a size of a color cast area at the edge of the imaging lens.

6. An image processing system for detecting an analyte according to claim 5.

7. The infrared grayscale image is characterized in that the higher the image spatial resolution, the larger the upper limit of the value of m.

6. An image processing system for detecting an analyte according to claim 5.

8. 1. An analyte detection system comprising: An analyte detection system, characterized in that it comprises an image processing system for analyte detection according to any one of claims 5 to 7.

9. A computer-readable storage medium having a computer program stored thereon, A computer-readable storage medium having stored thereon a computer program, characterized in that when the computer program is executed by a processor, the steps of the image processing method for analyte detection according to any one of claims 1 to 3 are realized.

10. 1. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, The electronic device, characterized in that when the computer program is executed by a processor, the steps of the image processing method for analyte detection according to any one of claims 1 to 3 are realized.

Images