Image processing method and system in analyte testing, medium, and device

Abstract

The invention provides an analyte detection method, an analyte detection system, an analyte image processing method, an analyte detection system, a medium and analyte detection equipment. The analyte detection method comprises the following steps: an image acquisition step: acquiring an infrared grayscale image of a first region; a first target screening step: converting the infrared grayscale image into a two-dimensional matrix, recording a pre-selected first target area, and screening out maximum values and minimum values of horizontal and vertical coordinates: xmax, xmin, ymax and ymin; a retrieval area determination step: taking xmax + m, xmin-m, ymax + m and ymin-m as retrieval area boundaries, and taking m as a preset expansion distance to obtain a retrieval area; and a second target screening step: sorting the retrieval regions from large to small according to the gray levels, and selecting the first preset number of coordinates with the maximum gray level as a second target region. According to the invention, the blood-vessel-free skin area in the infrared grayscale image can be effectively distinguished, so that the spectral data of the blood-vessel-free skin area in the image can be accurately acquired subsequently.

Description

(19) State Intellectual Property Office (12) Invention Patent Application (10) Application Publication Number (43) Application Publication Date (21) Application Number 202410951416.5 (22) Application Date 2024.07.16 (71) Applicant Guangzhou Ruixin Microelectronics Co., Ltd. Address 510535, Room 1703, No. 188, Kaitai Avenue, Huangpu District, Guangzhou, Guangdong Province (72) Inventor Zheng Hongzhi (74) Patent Agency Shanghai Duan & Duan Law Firm 31334 Patent Attorney Shi Jiawei (51) Int.Cl. A61B 5 / 1455 (2006.01) A61B 5 / 145 (2006.01) A61B 5 / 00 (2006.01) G06V 40 / 145 (2022.01) G06V 10 / 143 (2022.01) G06V 10 / 25 (2022.01) G06T 7 / 00 (2017.01) (54) Invention Title: Detection of Analytes, Image Processing Method, System, Medium and Device (57) Abstract: This invention provides a detection of analytes, image processing method, system, medium and device, including: Image acquisition step: acquiring an infrared grayscale image of a first region; First target screening step: converting the infrared grayscale image into a two-dimensional matrix, recording the pre-selected first target region, and screening out the maximum and minimum values ​​of the horizontal and vertical coordinates: xmax, xmin, ymax, ymin; Search region determination step: using xmax+m, xmin-m, ymax+m, ymin-m as the search region boundary, where m is a preset extension distance, to obtain the search region; Second target screening step: sorting the search regions from largest to smallest according to grayscale, and selecting the first preset number of coordinates with the largest grayscale as the second target region. This invention can effectively distinguish avascular skin regions in infrared grayscale images, so as to accurately acquire the spectral data of avascular skin regions in the image. Claims 1 page, Description 13 pages, Drawings 7 pages, CN 121337326 A 2026.01.16 CN 1 21 33 73 26 A 1. An image processing method for detecting an analyte, characterized in that it includes: an image acquisition step: acquiring an infrared grayscale image of a first region; a first target screening step: converting the infrared grayscale image into a two-dimensional matrix, recording a pre-selected first target region, and screening out the maximum and minimum values ​​of the horizontal and vertical coordinates: xmax, xmin, ymax, ymin; a search region determination step: using xmax+m, xmin-m, ymax+m, ymin-m as the search region boundaries, where m is a preset extension distance, to obtain the search region; a second target screening step: sorting the search regions from largest to smallest according to grayscale, and selecting the first preset number of coordinates with the largest grayscale as the second target region.2. The image processing method for analyte detection according to claim 1, wherein the step of determining the retrieval region further includes: filtering out areas within a range L from the edge of the infrared grayscale image from the retrieval region, where L is the size of the color-shifted area at the edge of the imaging lens. 3. The image processing method for analyte detection according to claim 1, wherein the higher the image spatial resolution of the infrared grayscale image, the larger the upper limit of the value of m. 4. A method for analyte detection, comprising the image processing method for analyte detection according to any one of claims 1-3. 5. An image processing system for analyte detection, characterized in that it comprises: an image acquisition module for acquiring an infrared grayscale image of a first region; a first target screening module for converting the infrared grayscale image into a two-dimensional matrix, recording a pre-selected first target region, and screening out the maximum and minimum values ​​of the horizontal and vertical coordinates: xmax, xmin, ymax, ymin; a search region determination module for using xmax+m, xmin-m, ymax+m, ymin-m as the search region boundaries, where m is a preset extension distance, to obtain the search region; and a second target screening module for sorting the search regions from largest to smallest grayscale, and selecting the top preset number of coordinates with the largest grayscale as the second target region. 6. The image processing system for analyte detection according to claim 5, characterized in that the search region determination module further comprises: filtering out regions within a range L from the edge of the infrared grayscale image from the search region, where L is the size of the color cast region at the edge of the imaging lens. 7. The image processing system for analyte detection according to claim 5, characterized in that the higher the image spatial resolution of the infrared grayscale image, the larger the upper limit of the value of m. 8. A system for detecting an analyte, characterized in that it includes an image processing system for detecting an analyte as described in any one of claims 5-7. 9. A computer-readable storage medium storing a computer program, characterized in that, when the computer program is executed by a processor, it implements the steps of the image processing method for detecting an analyte as described in any one of claims 1 to 3. 10. An electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, when the computer program is executed by a processor, it implements the steps of the image processing method for detecting an analyte as described in any one of claims 1 to 3. Claims 1 / 1 page 2 CN 121337326 A Detection of analytes, Image Processing Method, System, Medium and Device Technical Field

[0001] This invention relates to the field of optical analysis, specifically to a detection of analytes, image processing method, system, medium and device. Background Art

[0002] Fluorescence analysis refers to the use of certain substances being irradiated by ultraviolet light to enter an excited state, and the excited-state molecules undergoing aThe fluorescence generated during the de-excitation process of collision and emission can reflect the properties of the substance and can be analyzed qualitatively or quantitatively.

[0003] When using fluorescence analysis to detect blood glucose in the human body, the acquired image includes areas with veins and areas without veins. Since the blood glucose content in blood vessels and blood glucose content in skin differ greatly, if spectral data acquisition is required, it is necessary to first distinguish between areas with veins and areas without veins; otherwise, the detection results will be inaccurate.

[0004] Patent document US20160287147A1 discloses a device for non-invasive in vivo measurement using Raman spectroscopy, which uses Raman spectroscopy to measure in vivo blood glucose concentration. The advantage of this approach is that it has higher accuracy compared to the electrochemical method of US20100065441A1, but its disadvantage is that it currently requires a laboratory-grade Raman spectroscopy system, which is bulky and expensive.

[0005] In addition, patent document CN118078277A discloses a non-invasive blood glucose detection method based on hyperspectral data analysis, achieving non-invasive detection. This document uses absorption spectroscopy, and the spectral signals collected and analyzed not only include the spectral signals of blood glucose, but also the spectral signals of components such as skin tissue. The spectral signals of different wavelengths are superimposed, making it difficult to finely separate and extract the spectral signals related to blood glucose. At the same time, factors such as the excitation light source, human skin color, and differences in epidermal layer thickness will 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, affecting the accurate measurement of blood glucose concentration. Similarly, patent document CN108542402A also has the same problem. Summary of the Invention

[0006] In view of the deficiencies in the prior art, the purpose of this invention is to provide an analyte detection, image processing method, system, medium, and device.

[0007] An image processing method for detecting analytes according to the present invention includes:

[0008] Image acquisition step: acquiring an infrared grayscale image of a first region;

[0009] First target screening step: converting the infrared grayscale image into a two-dimensional matrix, recording a pre-selected first target region, and screening out the maximum and minimum values ​​of the horizontal and vertical coordinates: xmax, xmin, ymax, ymin;

[0010] Search region determination step: using xmax+m, xmin-m, ymax+m, ymin-m as the search region boundary, where m is a preset extension distance, to obtain the search region;

[0011] Second target screening step: sorting the search regions from largest to smallest according to grayscale, and selecting the first preset number of coordinates with the largest grayscale as the second target region.

[0012] Preferably, the search region determination step further includes: filtering out the edges of the search region that are far from the infrared grayscale image. (Page 1 / 13 of the specification)3 CN 121337326 A The area within which L is the size of the color-shifted area at the edge of the imaging lens.

[0013] Preferably, the higher the image spatial resolution of the infrared grayscale image, the larger the upper limit of the value of m.

[0014] The present invention also provides a method for detecting analytes, including the above-described image processing method for detecting analytes.

[0015] The present invention also provides an image processing system for detecting analytes, comprising:

[0016] an image acquisition module: acquiring an infrared grayscale image of a first region;

[0017] a first target screening module: converting the infrared grayscale image into a two-dimensional matrix, recording a pre-selected first target region, and screening out the maximum and minimum values ​​of the horizontal and vertical coordinates: xmax, xmin, ymax, ymin;

[0018] a search region determination module: using xmax+m, xmin-m, ymax+m, ymin-m as the search region boundary, where m is a preset extension distance, to obtain the search region;

[0019] a second target screening module: sorting the search regions from largest to smallest according to grayscale, and selecting the first preset number of coordinates with the largest grayscale as the second target region.

[0020] Preferably, the search region determination module further comprises: filtering out the region within a range L from the edge of the infrared grayscale image from the search region, where L is the size of the color cast region at the edge of the imaging lens.

[0021] Preferably, the higher the image spatial resolution of the infrared grayscale image, the larger the upper limit of the value of m.

[0022] The present invention also provides a system for detecting analytes, including the image processing system for detecting analytes described above.

[0023] The present invention also provides a computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the steps of the image processing method for detecting analytes described above.

[0024] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the image processing method for detecting analytes described above.

[0025] Compared with the prior art, the present invention has the following beneficial effects:

[0026] 1. The technical solution of this application can effectively distinguish avascular skin areas in infrared grayscale images, so as to facilitate subsequent accurate acquisition of spectral data of avascular skin areas in the image, and minimize interference from areas where blood vessels are located.

[0027] 2. The detection method of this application does not require electrochemical reaction with the analyte, making the detection method more convenient, especially when detecting analytes in living organisms, it can achieve the purpose of non-invasive detection.

[0028] 3. The detection method of this application utilizes the non-uniform distribution of the analyte in the imaging region to obtain spectral data of different regions. Since the distribution of other components besides the analyte in the imaging region is relatively uniform, different regions...The difference in spectral data can directly reflect the information related to the analyte and spectral data after the influence of non-analytes has been basically excluded, such as the concentration of the analyte.

[0029] 4. The detection method of this application uses fluorescence spectroscopy for detection, avoiding the traditional method of measuring analytes using Raman spectroscopy, thereby achieving low cost and miniaturization of the detection system and achieving the purpose of real-time detection.

[0030] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0031] FIG1 is a flowchart of Embodiment 1;

[0032] FIG2 is a schematic diagram of the first image acquired in Embodiment 2;

[0033] FIG3 is a schematic diagram of the second image acquired in Embodiment 2; Specification 2 / 13 pages 4 CN 121337326 A

[0034] FIG4 is a schematic diagram of the detection model of Embodiment 2;

[0035] FIG5 is a schematic diagram of the detection point-reference point spectral data obtained in Embodiment 2;

[0036] FIG6 is the experimental results of the accuracy of the analysis results of the analysis model;

[0037] FIG7 is a structural schematic diagram of an analyte detection device provided in Embodiment 7;

[0038] FIG8 is a structural schematic diagram of an electronic device provided in Embodiment 8;

[0039] FIG9 is a structural schematic diagram of an analyte detection watch provided in Embodiment 7;

[0040] FIG10 is a schematic diagram of the back of the analyte detection watch;

[0041] Figure 11 is an exploded view of the analyte detection watch;

[0042] Figure 12 is a schematic diagram of the analyte detection watch in use;

[0043] Figure 13 is a schematic diagram and cross-sectional view of blood vessel distribution under infrared light source;

[0044] Figure 14 is a flowchart of the image processing method in analyte detection in Example 3.

[0045] In the figures:

[0046] 100: Imaging area; 200: Detection device;

[0047] 201: Light source; 202: Imaging spectral detection device;

[0048] 203: Controller; 204: First bandpass filter;

[0049] 205: Lens; 206: Second bandpass filter;

[0050] 207: Circuit board; 501: Processor;

[0051] 502: Memory. Detailed Description

[0052] The present invention will be described in detail below with reference to specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but do not limit the present invention in any way. It should be noted that those skilled in the art can make several changes and improvements without departing from the concept of the present invention. These all fall within the protection scope of the present invention.

[0053] Example 1

[0054] Figure 1 is a flowchart of this embodiment. This embodiment provides a method for detecting an analyte, including:

[0055] Imaging Steps: The first region is illuminated by light within a preset wavelength range provided by a light source, and the first region is imaged by an imaging spectral detection device to obtain an image of the imaging region. Illumination by light within a preset wavelength range allows the image to reflect the distribution data and spectral data of the reflected or excitation signals generated by the analyte under light irradiation within the imaging region. The first region can be a region on the surface of human skin. To avoid the influence of external light such as ambient light on the detection, the acquisition window of the imaging spectral detection device needs to be tightly attached to the surface of the human skin in the first region. The imaging region refers to the area within the lens range of the imaging spectral detection device. In general, the imaging region can be a part of the first region or the same region as the first region.

[0056] Since different wavelength ranges of light are needed to obtain the distribution data and spectral data of the analyte, there are two possible implementation methods: the light is a light with a large wavelength range provided by one light source or light with a smaller wavelength range provided by two different light sources. When there is only one light source, the wavelength range of the light provided by that light source needs to simultaneously cover the wavelength range capable of acquiring the distribution data of the analyte and the wavelength range capable of acquiring the spectral data of the analyte. When there are two light sources, they provide different wavelengths of light. One light source covers the wavelength range that can acquire data on the distribution of the analyte, while the other light source covers the wavelength range that can acquire spectral data of the analyte. Furthermore, according to the instruction manual (page 3 / 13, CN 121337326 A), when there is only one light source, one image is formed; when there are two light sources, two images are formed. For ease of processing, the imaging areas of the two images must be identical, meaning the acquisition window of the imaging spectral detection device must not move on the surface of the human skin.

[0057] In this application, the analyte may be glucose, ketones, alcohols, lactate, oxygen, hemoglobin A1C, acetylcholine, amylase, bilirubin, cholesterol, human 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 it may be a drug such as antibiotics (e.g., gentamicin, vancomycin, etc.), digitalis, digoxin, abused drugs, theophylline, or warfarin. In embodiments that detect two or more analytes, the analytes may be monitored at the same or different times. In other embodiments, the analytes may also be other substances within the body surface, and non-invasive detection can be achieved through this invention.

[0058] Spectral acquisition step: Spectral data reflecting the uneven distribution of reflected or excitation signals generated by the analyte under light irradiation in the imaging area is acquired from the image using an imaging spectral detection device. Specifically, depending on differentDistribution data can be used to partition the imaging area to facilitate the selection of locations from different partitions for obtaining spectral data.

[0059] Analysis steps: Based on the acquired spectral data, information about the analytes in the imaging area is obtained. This information includes the correlation between the analytes and the spectral data. Since the distribution of analytes differs in different partitions, the reflected or excitation signals generated by the analytes when exposed to light will also differ. Taking human skin as an example, it is divided into three parts: epidermis, dermis, and subcutaneous tissue. Veins and other blood vessels are located in the subcutaneous tissue. Ultraviolet light can be used to irradiate both vascular and non-vascular skin areas to obtain corresponding spectral data, or between skin areas with thicker and thinner blood vessels. The difference between the two spectral data can reflect the correlation between the analytes in the blood vessels and the spectral data, such as the degree of influence of the analytes on the spectral data, for further analysis, or the concentration of the analytes can be obtained directly through the analysis model.

[0060] Example 2

[0061] This example is based on Example 1, taking glucose detection in human blood vessels as an example, and provides a non-invasive glucose detection method, including:

[0062] Imaging step: Irradiating the skin at the location of the vein, such as the wrist or back of the hand, with infrared light in the first wavelength range of 800-1000 nm, and acquiring a first image of the imaging area. The first wavelength range is preferably near-infrared. And irradiating the same location with ultraviolet light in the second wavelength range of 300-390 nm, and acquiring a second image of the imaging area.

[0063] As shown in Figure 2, 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 selected detection point pixel block on the vein, and the black box represents the selected reference point pixel block on the surrounding skin. In the first image, part of the infrared light penetrates the human skin and part is absorbed by the skin. Simultaneously, it is also heavily absorbed by the veins, resulting in smaller pixel grayscale values ​​in the vein area and larger pixel grayscale values ​​in the non-vein area. This allows for easy division of the imaging area into vein and non-vein regions.

[0064] As shown in Figure 3, the horizontal axis represents the horizontal coordinate of the second image, and the vertical axis represents the vertical coordinate of the second image. White boxes represent selected detection point pixel blocks on the veins, and black boxes represent selected reference point pixel blocks on the surrounding skin. In the second image, it is difficult to distinguish between vein and non-vein areas, therefore the first image is needed for differentiation. The excitation light in the second wavelength range of 300-390 nm is used to obtain high-quality effective fluorescence spectral signals. This is because the main response band of the imaging spectral detection device is located in the 400-800 nm range. (The instruction manual is used...)When the excitation wavelength of CN 121337326 A is less than 300 nm, the main peak of the excited fluorescence radiation signal is located in the <400 nm band, making it difficult for imaging spectral detection devices to obtain high-quality effective fluorescence spectral signals. When the wavelength of the excitation light used is >390 nm, the excitation light itself is also visible light, and the spectral signal of the excitation light is superimposed on the fluorescence spectral signal, making it difficult to eliminate the interference of the spectral signal of the excitation light and extract the effective fluorescence spectral signal. After absorbing ultraviolet light in the 300-390 nm wavelength range, glucose in venous blood vessels can emit fluorescence radiation signals in the 400-800 nm visible light band. This band is within the effective response range of imaging spectral detection devices, and the characteristic spectral intensity of this fluorescence radiation signal is positively correlated with the glucose concentration, exhibiting high fluorescence excitation efficiency.

[0065] Spectral acquisition steps: Based on the grayscale distribution of pixels in the first image, the imaging area is divided into areas where veins are located and areas where non-vein vessels are located. Detection points are selected from the locations in the second image corresponding to the locations in the vein areas, and reference points are selected from the locations in the second image corresponding to the locations in the non-vein areas. The spectral data of the detection points and the spectral data of the reference points in the second image are acquired respectively. Specifically, based on the grayscale values ​​of pixels in the second image, a pixel with a grayscale value that meets a preset requirement is selected from the vein areas as a detection point, or a combination of that pixel and its adjacent pixels is selected as a detection point. A pixel with a grayscale value within a preset deviation range from the selected detection point is selected from the non-vein areas as a reference point, or a combination of that pixel and multiple adjacent pixels is selected as a reference point. The fluorescence spectral data of the detection points and the fluorescence spectral data of the reference points are calculated in the second image. Spectral data is taken from a single pixel or an average of multiple pixels at the detection point and reference point, which can be appropriately selected based on the blood vessel width. Averaging multiple pixels can improve the signal-to-noise ratio, but is limited by the blood vessel width, avoiding the capture of areas outside the blood vessel. Selecting a single pixel offers high spatial resolution, suitable for thinner blood vessels, but has a lower signal-to-noise ratio. The preset grayscale value can be set to use the point with the smallest grayscale value as the detection point, but this application does not impose this limitation. The calculation results are shown in Figure 5, where the horizontal axis represents wavelength (in nm), the vertical axis represents relative radiance (in W / nm), the solid line represents the spectral data of the detection point, and the dashed line represents the spectral data of the reference point. The reason why the grayscale value of the reference point and the grayscale value of the selected detection point are within the preset deviation range is that the skin in the imaging area may have influencing factors such as skin color, blemishes, and cosmetics, which will directly affect the spectral data of the reference point. The first image cannot distinguish areas with these influencing factors. By setting the preset deviation range for the grayscale value, these influencing factors can be effectively eliminated. Furthermore, the grayscale value being within the preset deviation range of the selected detection point...This ensures that the reference point is close to the detection point, such as at the edge of a vein. This ensures that the color, thickness, and other parameters of the epidermis, dermis, and subcutaneous tissue, except for the blood vessel, are as close as possible, thus minimizing the influence of non-analytes on the deviation between the spectral data of the detection point and the spectral data of the reference point.

[0066] In addition to the spectral reconstruction algorithm, spectral data can also be obtained by generating radiometric calibration coefficients through prior radiometric calibration, and then calculating the spectral line by multiplying the gray value by the radiometric calibration coefficients.

[0067] When selecting a combination of multiple pixels as the detection point, the fluorescence spectral data of that detection point can be the average of the fluorescence spectral data of these pixels. Simultaneously, the number of detection points and reference points can be one or more. When there are multiple detection points and reference points, the average of the fluorescence spectral data of all detection points and the average of the fluorescence spectral data of all reference points can be calculated separately.

[0068] Analysis steps: After preprocessing, the obtained spectral data of the detection points and reference points are input into the trained detection model, and the glucose concentration or intermediate results of the correlation between glucose and spectral data are output. During training, the detection model needs to simultaneously acquire the spectral data of the tested object and accurate test results, such as blood test results. The spectral data is used as the input of the detection model, and the blood test results are used as the output of the detection model to train the detection model.

[0069] The detection model can adopt a convolutional neural network model, which sequentially includes an input layer, at least two convolutional layers, at least two activation function layers, a Flatten layer, a fully connected layer, and an output layer. The convolutional layers and the activation function layers are distributed alternately. The activation function used in the activation function layer is the ReLU function.

[0070] In the convolutional neural network model, the kernel size of each convolutional layer is 1. The number of convolutional kernels in the first convolutional layer is 32, and the number of convolutional kernels in the second convolutional layer is 64. Both are used to extract blood glucose features, and the output of the convolutional layer is nonlinearly transformed by the activation function. The Flatten layer flattens the output of the convolutional layer into a one-dimensional vector, which is convenient for connecting the subsequent fully connected layers. The final output dimension is 1. During model training, the Adam optimizer is used for model training, and the mean squared error is used as the loss function. Simultaneously, the mean absolute error is calculated as the performance metric for model evaluation.

[0071] When the output of the detection model is glucose concentration, if the error between the output and the measured standard glucose concentration value meets a preset condition, training is stopped to obtain the detection model. When the output of the detection model is an intermediate result related to glucose and spectral data, such as an interneuron result, if the error between the output and the interneuron result meets a preset condition, training is stopped to obtain the detection model. Further model correction processing is performed on the interneuron result to obtain the glucose concentration.

[0072] As shown in Figure 4, the Input layer is the spectral data input layer, which is obtained after preprocessing the original spectral data. The Hidden layer is the intermediate hidden layer, which outputs the final predicted blood glucose concentration value after feature combination through deep learning via convolution operation. Alternatively, deep learning via convolution operation can be used to output a neuron Output1 as an intermediate result value after feature combination. The intermediate result value Output1 and two infrared IR feature brightness values ​​are then used to train the model again to further correct the blood glucose prediction error and output the final predicted blood glucose concentration value Output2. The training degree of the detection model needs to be set with different parameters as needed. The extracted glucose feature values ​​will continuously learn according to the different parameter settings until the error between the output result and the standard glucose value of the above label value meets the requirements, then the training stops and the detection model is obtained.

[0073] Through multiple iterative training, the neurons learn the corresponding change rules between different glucose concentrations and glucose spectral features of different samplers, thereby improving the universality of the detection model and enabling the prediction of glucose concentrations of different users.

[0074] The entire glucose detection process does not require puncturing the skin to collect blood or implanting a needle. It obtains the spectral information of the test subject based on fluorescence spectroscopy and obtains the glucose detection result based on this spectral information, avoiding pain and discomfort and improving the comfort and convenience of the test. This method can finely distinguish the spectral signals of blood vessels and skin, providing the possibility for accurate extraction of glucose signals in the future. At the same time, it also makes the spectral signal strongly correlated with glucose concentration, realizing accurate measurement of glucose concentration, making the detection results more accurate and the processing more convenient.

[0075] Figure 6 shows a schematic diagram of the experimental effect of the trained detection model. The horizontal axis is the reference blood glucose concentration collected by the blood glucose meter (unit: mmol / L), and the vertical axis is the blood glucose concentration predicted by the method of this patent (unit: mmol / L). The total sample size of the test subjects was 2037, of which 1537 were training set samples and 500 were prediction set samples. As can be seen from the figure, the distribution of detection results of the detection model shows that the MARD value of the predicted samples is 11.32%, and the vast majority of samples fall into areas A and B. Among them, the proportion of samples falling into area A is 87.03%, and the proportion of samples falling into area B is 12.77%, indicating that the detection accuracy of the detection model is high.

[0076] Example 3

[0077] This example further illustrates the image processing method in analyte detection based on Examples 1 and 2. As shown in Figure 14, this example provides an image processing method in analyte detection, including:

[0078] Image acquisition step: acquiring the infrared grayscale image of the first region;

[0079] First target screening step: Convert the infrared grayscale image into a two-dimensional matrix, record the pre-selected first target area, and filter out the maximum and minimum values ​​of the horizontal and vertical coordinates: xmax, xmin, ymax, ymin; Manual 6 / 13 page 8 CN 121337326 A

[0080] Search area determination step: Take xmax+m, xmin-m, ymax+m, ymin-m as the search area boundary, m is the preset extension distance, and obtain the search area; The search area determination step also includes: filtering out the area within L of the edge of the infrared grayscale image from the search area, where L is the size of the color cast area at the edge of the imaging lens; The higher the image spatial resolution of the infrared grayscale image, the larger the upper limit of m;

[0081] Second target screening step: Sort the search areas from largest to smallest according to grayscale, and select the first preset number of coordinates with the largest grayscale as the second target area.

[0082] This embodiment also provides a method for detecting analytes, including the above-mentioned image processing method for detecting analytes. This embodiment also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the image processing method for detecting analytes described above. 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. When executed by the processor, the computer program implements the steps of the image processing method for detecting analytes described above.

[0083] In an infrared grayscale image, dark spots and bright spots are distinguished based on pixel gradients. Dark spots represent blood vessel regions, and bright spots represent skin regions. First, the grayscale gradient of a 2*2 period around each pixel in a sub-region is calculated. Then, the peak values ​​of the sub-region are found, including local maximum values ​​(dark spots) and local minimum values ​​(bright spots). Twenty consecutive maximum values ​​are selected as candidate positions for dark spots. Specific blood vessel screening method:

[0084] 1. Blood vessel localization

[0085] An infrared light source with a center wavelength of 940nm is selected as the blood vessel localization light source. On the one hand, because hemoglobin in human blood has a strong absorption of infrared light waves, the skin reflectivity of the blood vessels collected by the detector under the infrared light source is lower than that of the skin area. On the other hand, thanks to the fact that pigment deposits such as age spots on the skin do not absorb infrared light waves, this means that the method of selecting blood vessel areas based on grayscale is not affected by skin type and has good versatility.

[0086] As shown in Figure 13, along the direction of the blood vessel cross section, the infrared reflection intensity of the skin around the blood vessel changes uniformly, making it difficult to accurately delineate the specific blood vessel area. At the same time, due to the uneven distribution of image illumination, the intensity of grayscale alone cannot correctly reflect the absorption of light energy by hemoglobin. The trend of grayscale change in this direction needs to be considered. Therefore, the minimum value point can be used as the basis for judging blood vessels. In an ideal case, assuming that the blood vessel is a standard cylinder, the center of the blood vessel absorbs infrared light the strongest, and the grayscale value in the image is the highest.The intensity is lower than that of the surrounding area. The image is regarded as a two-dimensional matrix. The derivative of each row and column matrix in the x and y directions is calculated in turn to find the minimum point. The minimum point in both x and y directions is the center of the blood vessel. The line connecting the minimum points is the line connecting the centers of the blood vessels. The area around the blood vessels is the skin area. In Figure 13, the left image is a schematic diagram of the blood vessel skin with infrared light source, and the right image is a schematic diagram of the gray-scale distribution at the cross section.

[0087] There are still many interference factors to consider in actual acquisition. This means that the model needs to be optimized and filters need to be added to eliminate interference:

[0088] a. The real blood vessels are not standard cylinders. In addition, due to the influence of noise, the minimum points in the x and y directions in the blood vessel area are not necessarily in the same position. Therefore, in actual screening, the extreme points in the two directions should be close to each other.

[0089] b. There are a lot of noise, fine lines and other interference objects in the skin area of ​​the actual image. These interference objects are also extreme points in the image. The characteristics of noise and fine lines are that they are small in area and discretely distributed. Based on these two considerations, the design scheme is as follows: (1) The blood vessel region has a certain width, which mathematically means that there are monotonic regions within a certain distance on both sides of the minimum point, and there are no other extreme points. Single-pixel noise and fine lines can be screened out accordingly; (2) The blood vessel region is a continuous region, and the distribution of its extreme points is relatively dense. Discrete noise can be filtered out accordingly.

[0090] The above scheme is converted into an algorithm as follows: Convert the infrared grayscale image into a two-dimensional matrix, and take the derivative of each row and column matrix in the x and y directions in turn to find the minimum point. If the grayscale value within n pixels on both sides of the minimum point remains monotonic, then mark the position. Here, n depends on the width of the blood vessel. The larger the value of n, the larger the selection range, and vice versa. Create a two-dimensional matrix of the same size 0 matrix, and draw the distribution of the minimum point in the matrix and replace it with 1. If there is an overlap of minimum points, replace it with 2. A matrix of size (n / 2)*(n / 2) (rounded down) is used as the kernel to convolve the created matrix. The larger the value of each element in the convolution matrix, the greater the probability that the corresponding position is a blood vessel region. The blood vessel regions can be filtered out by sorting the elements and their corresponding positions in the convolution matrix from largest to smallest.

[0091] 2. Skin localization

[0092] Skin localization is performed by finding the point with the maximum brightness near the above-mentioned blood vessel regions. Record the coordinates of all selected blood vessel regions, filter out the maximum and minimum values ​​of xmax, xmin, ymax, and ymin, and use xmax+m, xmin-m, ymax+m, and ymin-m as the boundaries of the search region, where m is the extension distance, which depends on the image spatial resolution. If the image spatial resolution is high, the value of m can be increased appropriately, and vice versa. Finally, filter out the region within the range of the image edge L, where L depends on the possible deviation of the lens edge.Size of the color region. The final search area is sorted according to gray level. The higher the gray level, the greater the probability that it is a skin region.

[0093] 3. Skin pigment region screening

[0094] The 365nm ultraviolet light source can better reflect the skin pigment deposition area. Therefore, after the infrared light source is collected, the same skin area is collected by the 365nm ultraviolet light source. Record the coordinates of all previously screened blood vessel areas and skin areas, and mark the positions in the ultraviolet image. Record the image gray value D of the above positions. Calculate the mean Mu and variance Sigma of the above gray values ​​respectively. When |D-Mu|>Sigma×threshold, it means that the selected point is covered by the skin pigment area or the fluorescent marked area and can be filtered out. The remaining coordinates after filtering are the usable coordinates of the screened blood light area and skin area.

[0095] Outlier detection is performed on the blood vessel and skin candidate points. The threshold is set by the standard deviation for filtering and screening to remove abnormal values ​​such as skin spots and fluorescence. The threshold determination condition is: |X–μ|≤T*σ, where μ represents the average value of dark spots or bright spots, σ ​​represents the standard deviation of dark spots or bright spots, and T is used to set the threshold size, usually a multiple of the standard deviation σ. Here, the threshold is set to 1 as the basis for judging whether the selected data points are outliers (abnormal values), so as to obtain more accurate coordinate positions of dark spots, i.e., coordinate positions of blood vessels.

[0096] The present invention also provides an image processing system for analyte detection. The image processing system for analyte detection can be implemented by executing the process steps of the image processing method for analyte detection. That is, those skilled in the art can understand the image processing method for analyte detection as a preferred embodiment of the image processing system for analyte detection.

[0097] Example 4:

[0098] This example provides an image processing system for analyte detection, including:

[0099] Image acquisition module: acquiring an infrared grayscale image of a first region;

[0100] First target screening module: converting the infrared grayscale image into a two-dimensional matrix, recording the pre-selected first target region, and screening out the maximum and minimum values ​​of the horizontal and vertical coordinates: xmax, xmin, ymax, ymin;

[0101] Search region determination module: using xmax+m, xmin-m, ymax+m, ymin-m as the search region boundary, where m is a preset extension distance, to obtain the search region; the search region determination module further includes: filtering out the region within a range L from the edge of the infrared grayscale image from the search region, where L is the size of the color cast region at the edge of the imaging lens; the higher the image spatial resolution of the infrared grayscale image, the larger the upper limit of m;

[0102] Second target screening module: sorting the search regions from largest to smallest according to grayscale, and selecting the first preset number of coordinates with the largest grayscale as the second target region.

[0103] This embodiment also provides a system for analyte detection, including the image processing system for analyte detection described above.

[0104] Embodiment 5

[0105] This embodiment is based on Embodiment 2, replacing infrared light with visible light, and provides another non-invasive glucose detection method, including:

[0106] Imaging steps: Irradiating the skin at the location of the vein, such as the wrist or back of the hand, with visible light to acquire a first image of the imaging area. And irradiating the same location with ultraviolet light in the second wavelength range of 300-390 nm to acquire a second image of the imaging area.

[0107] In the first image, since the color of the area where the vein is located differs from the color of the area where the non-vein is located, the imaging area can be easily divided into the area where the vein is located and the area where the non-vein is located.

[0108] In the second image, since it is difficult to distinguish the area where the vein is located and the area where the non-vein is located, the first image is needed for differentiation. The excitation light in the second wavelength range of 300-390 nm is used to obtain a high-quality effective fluorescence spectrum signal. Because the main response band of imaging spectral detection devices is located in the 400-800 nm range. When the excitation light wavelength used is less than 300 nm, the main peak of the excited fluorescence radiation signal is located in the <400 nm band, making it difficult for the imaging spectral detection device to obtain a high-quality effective fluorescence spectral signal. When the excitation light wavelength used is greater than 390 nm, the excitation light itself is also visible light, and the spectral signal of the excitation light is superimposed on the fluorescence spectral signal, making it difficult to eliminate the interference of the excitation light spectral signal and extract the effective fluorescence spectral signal. Glucose in venous blood vessels, after absorbing ultraviolet light in the 300-390 nm wavelength range, can emit fluorescence radiation signals in the 400-800 nm visible light band. This band is within the effective response range of the imaging spectral detection device, and the characteristic spectral intensity of this fluorescence radiation signal is positively correlated with the glucose concentration, exhibiting high fluorescence excitation efficiency.

[0109] Spectral acquisition steps: Based on the grayscale distribution of pixels in the first image, the imaging area is divided into areas where veins are located and areas where non-vein vessels are located. Detection points are selected from the areas where veins are located, and reference points are selected from the areas where non-vein vessels are located. The spectral data of the detection points and the spectral data of the reference points are acquired respectively. Specifically, based on the grayscale values ​​of pixels in the second image, a pixel whose grayscale value meets a preset requirement or a combination of that pixel and its adjacent pixels is selected from the areas where veins are located as a detection point. A pixel whose grayscale value is within a preset deviation range from the selected detection point or a combination of that pixel and multiple adjacent pixels is selected from the areas where non-vein vessels are located as a reference point.The fluorescence spectral data of the detection point and the fluorescence spectral data of the reference point are calculated. The reason why the gray value of the reference point is within a preset deviation range from the gray value of the selected detection point is that the skin in the imaging area may have influencing factors such as skin color, blemishes, and cosmetics, which will directly affect the spectral data of the reference point. The first image cannot simultaneously distinguish all influencing factors. By setting a preset deviation range for the gray value, these influencing factors can be effectively eliminated.

[0110] When selecting a combination of multiple pixels as the detection point, the fluorescence spectral data of that detection point can be the average of the fluorescence spectral data of these pixels. Simultaneously, the number of detection points and reference points can be one or more. When there are multiple detection points and reference points, the average value of the fluorescence spectral data of all detection points and the average value of the fluorescence spectral data of all reference points can be calculated separately.

[0111] Analysis steps: After preprocessing, the obtained spectral data of the detection points and reference points are input into the trained detection model, and the glucose concentration is output. During training, the detection model needs to simultaneously acquire the spectral data of the tested object and accurate test results, such as blood test results. The spectral data is used as the input of the detection model, and the blood test results are used as the output of the detection model to train the detection model.

[0112] The detection model can adopt a convolutional neural network model, which sequentially includes an input layer, at least two convolutional layers, at least two activation function layers, a Flatten layer, a fully connected layer, and an output layer. The convolutional layers and the activation function layers are distributed alternately. The activation function used in the activation function layer is the ReLU function.

[0113] In the convolutional neural network model, the kernel size of each convolutional layer is 1. The number of kernels in the first convolutional layer is 32, and the number of kernels in the second convolutional layer is 64. Both are used to extract blood glucose features, and the output of the convolutional layer is nonlinearly transformed by the activation function. The Flatten layer flattens the output of the convolutional layer into a one-dimensional vector, which is convenient for connecting the subsequent fully connected layers. The final output dimension is 1. The Adam optimizer is used for model training during the model training process, and the mean squared error is used as the loss function. The mean absolute error is calculated as the performance metric for model evaluation.

[0114] If the error between the output of the detection model and the standard glucose value meets the preset conditions, training is stopped to obtain the detection model.

[0115] The training level of the detection model needs to be set with different parameters as required. The extracted glucose feature values ​​will continuously learn according to the different parameter settings until the error between the output and the standard glucose value of the above label value meets the requirements, at which point training is stopped to obtain the detection model.

[0116] Through multiple iterative training, the neurons learn different glucose concentrations and glucose spectra of different samplers.The corresponding change patterns between features improve the universality of the detection model and enable the prediction of glucose concentration for different users.

[0117] The entire glucose detection process does not require blood collection or skin puncture. It obtains the spectral information of the test subject based on fluorescence spectroscopy and obtains the glucose detection result of the test subject based on the spectral information, avoiding pain and discomfort and improving the comfort and convenience of the detection. This method can finely distinguish the spectral signals of blood vessels and skin, providing the possibility for the accurate extraction of glucose signals in the future. At the same time, it also makes the spectral signal strongly correlated with the glucose concentration, realizing the accurate measurement of glucose concentration, making the detection results more accurate and the processing more convenient.

[0118] Example 6

[0119] This embodiment provides an analyte detection system. The analyte detection system can be implemented by executing the process steps of the analyte detection method. That is, those skilled in the art can understand the analyte detection method as the preferred implementation of the analyte detection system. The analyte detection system includes:

[0120] Imaging module: The light source provides light within a preset wavelength range to irradiate the first area, and the imaging spectral detection device images the first area to obtain an image of the imaging area. By illuminating the image with light within a preset wavelength range, the image can reflect the distribution and spectral data of the reflected or excitation signals generated by the analyte under light illumination within the imaging area. Since different wavelength ranges are required to obtain the analyte distribution and spectral data, the light source can be two corresponding wavelength ranges, or a single light source with a larger wavelength range covering both required wavelengths. When two types of light are used, two images are obtained. For ease of processing, it is usually required that the imaging areas of the two images are identical.

[0121] In this application, the analyte may be glucose, ketones, alcohols, lactate, oxygen, hemoglobin A1C, acetylcholine, amylase, bilirubin, cholesterol, human 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, troponin, or drugs such as antibiotics (e.g., gentamicin, vancomycin, etc.), digitalis, digoxin, abused drugs, theophylline, and warfarin. In embodiments that detect more than one analyte, the analytes may be monitored at the same or different times. In other embodiments, the analyte may also be other substances in a liquid.

[0122] Spectral acquisition module: Acquires spectral data from an image using an imaging spectral detection device, reflecting the uneven distribution of the reflected or excitation signals generated by the analyte under light irradiation in the imaging area. Specifically, according to different instructions, page 10 / 13, 12 CN 121337326A. Distribution data can be used to partition the imaging area to facilitate the selection of locations from different partitions for obtaining spectral data.

[0123] Analysis module: Based on the acquired spectral data, information about the analytes in the imaging area is obtained. The information about the analytes includes information related to the analytes and the spectral data. Since the distribution of analytes in different partitions is different, the reflected or excitation signals generated by the analytes when irradiated by light will also be different. Utilizing this characteristic, the difference in spectral data between the two can be obtained, thereby accurately reflecting the information related to the analytes and the spectral data, such as the concentration of the analytes.

[0124] Those skilled in the art know that, in addition to implementing the system and its various devices, modules, and units provided by the present invention in a purely computer-readable program code manner, the same functions can be achieved by logically programming the method steps so that the system and its various devices, modules, and units provided by the present invention can be implemented in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, the system and its various devices, modules, and units provided by the present invention can be considered as a hardware component, and the devices, modules, and units included therein for realizing various functions can also be considered as structures within the hardware component; the devices, modules, and units for realizing various functions can also be considered as both software modules for implementing methods and structures within the hardware component.

[0125] Embodiment 7

[0126] Figure 7 shows 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. It can be a standalone detection device or integrated into a watch or mobile phone, thereby enabling convenient and quick detection of analytes within the body surface.

[0127] The detection device 200 includes: a light source 201, an imaging spectral detection device 202, a controller 203, a first bandpass filter 204, a second bandpass filter 206, and a lens 205. The controller 203 establishes an electrical connection or a communication connection with the light source 201 and the imaging spectral detection device 202, respectively.

[0128] The light source 201 is capable of providing light within a preset wavelength range. Since acquiring analyte distribution data and spectral data requires illumination from light sources with different wavelength ranges, there are two possible implementation methods: one light source capable of providing light with a large wavelength range; or two light sources each providing light with a smaller wavelength range. When there is only one light source, the wavelength range of the light provided by this light source needs to simultaneously cover the wavelength range capable of acquiring analyte distribution data and the wavelength range capable of acquiring analyte spectral data, such as a halogen lamp. When there are two light sources, the two light sources provide different light; one light source's wavelength covers the wavelength range capable of acquiring analyte distribution data, and the other light source's wavelength...Long coverage can acquire the wavelength range of analyte spectral data, such as infrared lamp combined with ultraviolet lamp, or visible light lamp combined with ultraviolet lamp.

[0129] In order to make the illumination of the imaging area 100 uniform, a ring light source can be used. The light source has multiple light-emitting modules, which are uniformly distributed on the same circumference. When there are two types of light sources, the light-emitting modules of the two light sources are arranged alternately.

[0130] The imaging spectral detection device 202 can image the imaging area 100 according to the instruction to obtain the corresponding image, and can obtain the corresponding spectral data according to the instruction. The imaging spectral detection device 202 includes a sensor and a periodic pixel-level filter structure set on the sensor surface. The periodic pixel-level filter structure is used to perform spectral modulation on the incoming light signal so that the sensor can generate an image containing the spectral information to be measured.

[0131] The periodic pixel-level filter structure includes multiple filter pixel channels with different shapes of pixel-level structures. The multiple filter pixel channels have the same size and are uniformly arranged. Their length and width are integer multiples of the pixel size in the image sensor. Different pixel-level filter structures of different shapes correspond to different spectral filtering coefficients. Pixel-level filter structures with different spectral filtering coefficients are arranged periodically after being combined in a fixed order. The sensor modulates the received first detection light through the periodic pixel-level filter structure set on its surface to form a mosaic image containing spectral information. Then, the spectral data is reconstructed using the algorithm (page 11 / 13, CN 121337326 A).

[0132] The controller 203 is configured to control the light source to provide light within a preset wavelength range to illuminate the first area, and to control the imaging spectral detection device to image the first area to obtain an image of the imaging area. The controller controls the imaging spectral detection device to obtain spectral data from the image that reflects the uneven distribution of the reflection signal or excitation signal generated by the analyte under light illumination in the imaging area. The information of the analyte in the imaging area is obtained based on the obtained spectral data. The information of the analyte includes information related to the analyte and the spectral data. When the light source 201 is of one type, the image is one; when the light source 201 is of two types, the image is two. 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, and the two do not interfere with each other.

[0133] The first bandpass filter 204 is located between the light source 201 and the imaging area 100. Its function is to allow light within a preset wavelength range to pass through, while blocking light outside the preset wavelength range, thereby reducing the influence of other external light on the detection results.

[0134] The second bandpass filter 206 is located between the imaging spectral detection device 202 and the lens 205. Its function is to allow light within the wavelength range of the reflected signal or excitation signal generated by the analyte being irradiated to pass through, while blocking light outside the preset wavelength range.The surrounding light is blocked, thereby reducing the influence of non-analyte reflection signals or excitation signals on the detection results.

[0135] Lens 205 can be used for fixed focus in order to obtain a clear image. In other embodiments, the second bandpass filter 206 may also be located on the side of lens 205 away from imaging spectral detection device 202, which is not limited by the present invention.

[0136] According to the above description, FIG9 shows an analyte detection watch provided in this embodiment. The front of the watch is a display, as shown in FIG10, and the back of the watch has a light-transmitting window and a built-in detection device 200. As shown in FIG11, the light source 201 and the first bandpass filter 204 are both ring structures. The light-emitting modules of the light source 201 are distributed in a ring, and the emitted light is filtered by the first bandpass filter 204 to output light with the required wavelength, which is then irradiated onto the human body through the light-transmitting window on the back of the watch. The reflected signal or excitation signal of the human body enters the light-transmitting window, passes through the light source 201 and the hollow part in the middle of the first bandpass filter 204, passes through the lens 205 and enters the second bandpass filter 206, and enters the imaging spectral detection device 202 after being filtered by the second bandpass filter 206. The imaging spectral detection device 202 is mounted on the circuit board 207, and the controller 203 (not shown in the figure) of the detection device 200 is also mounted on the circuit board 207. As shown in Figure 12, in order to more accurately identify the location of veins, the watch can be worn on the inside of the wrist.

[0137] Embodiment 8

[0138] Figure 8 is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. As shown in Figure 8, it includes at least one processor 501; and a memory 502 communicatively connected to at least one processor 501; wherein, the memory 502 stores instructions that can be executed by at least one processor 501, and the instructions are executed by at least one processor 501 to enable at least one processor 501 to perform the above-mentioned method for detecting analytes.

[0139] The memory 502 and the processor 501 are connected via a bus. The bus may include any number of interconnected buses and bridges, connecting various circuits of one or more processors 501 and memory 502 together. 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 therefore will not be further described here. The bus interface provides an interface between the bus and the transceiver. The transceiver may be a single element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by the processor 501 is transmitted over a wireless medium via an antenna, which further receives data and transmits it to the processor 501.

[0140] The processor 501 is responsible for managing the bus and general processing, and may also provide various functions, including timing, peripheral interfaces, and specifications.Page 12 / 13 14 CN 121337326 A

[0141] Voltage regulation, power management, and other control functions. The memory 502 can be used to store data used by the processor 501 during operation.

[0142] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-described method for detecting the analyte.

[0143] That is, those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. The program is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0144] Those skilled in the art will understand that the above embodiments are specific examples of implementing the present invention, and in practical applications, various changes can be made in form and detail without departing from the spirit and scope of the present invention.

[0145] Specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art can make various changes or modifications within the scope of the claims, which do not affect the substantive content of the present invention. In the absence of conflict, the embodiments and features in the embodiments of this application can be arbitrarily combined with each other. Instruction manual, page 13 / 13, 15 CN 121337326 A, Figure 1, Figure 2; Instruction manual, Figure 1 / 7, page 16 CN 121337326 A, Figure 3, Figure 4; Instruction manual, Figure 2 / 7, page 17 CN 121337326 A, Figure 5, Figure 6; Instruction manual, Figure 3 / 7, page 18 CN 121337326 A, Figure 7, Figure 8; Instruction manual, Figure 4 / 7, page 19 CN 121337326 A, Figure 9, Figure 10; Instruction manual, Figure 5 / 7, page 20 CN 121337326 A, Figure 11, Figure 12; Instruction manual, Figure 6 / 7, page 21 CN 121337326 A, Figure 13, Figure 14; Instruction manual, Figure 7 / 7, page 22 CN 121337326 A. Abstract: The present invention provides an analytical detection and image...processing method and system, a medium, and a device. The method comprises: an image acquisition step of acquiring an infrared grayscale image of a first area; a first target screening step of converting the infrared grayscale image into a two-dimensional matrix, recording a preselected first target region, and screening out maximum and minimum values of horizontal and vertical coordinates, namely , , and ; a search region determination step of using +, −, + and − as boundaries of a search region, where m is a preset expansion distance, to obtain the search region; and a second target screening step of sorting the search region in descending order according to grayscale values and selecting a preset number of coordinates with the largest grayscale values as a second target region. The present invention can effectively distinguish a skin region without blood vessels in the infrared grayscale image, so as to subsequently accurately acquire spectral data of the skin region without bloodvessels in the image. Abstract Abdominal ultrasound examination method, system and device

Claims

1. An image processing method for detecting analytes, characterized in that, include: Image acquisition steps: Acquire the infrared grayscale image of the first region; First target selection step: Convert the infrared grayscale image into a two-dimensional matrix, record the pre-selected first target region, and select the maximum and minimum values ​​of the horizontal and vertical coordinates: x max x min y max y min ; Steps for determining the search area: x max +m、x min -m、y max +m、y min -m serves as the boundary of the search area, where m is the preset extension distance, thus obtaining the search area; The second target selection step is to sort the search areas from largest to smallest based on their grayscale value, and select the top preset number of coordinates with the largest grayscale value as the second target areas.

2. The image processing method for analyte detection according to claim 1, characterized in that, The search area determination step further includes: filtering out areas within a range L from the edge of the infrared grayscale image from the search area, where L is the size of the color-shifted area at the edge of the imaging lens.

3. The image processing method for analyte detection according to claim 1, characterized in that, The higher the spatial resolution of the infrared grayscale image, the larger the upper limit of the value of m.

4. A method for detecting an analyte, characterized in that, The image processing method for detecting analytes as described in any one of claims 1-3.

5. An image processing system for detecting analytes, characterized in that, include: Image acquisition module: Acquires the infrared grayscale image of the first region; First target selection module: Converts the infrared grayscale image into a two-dimensional matrix, records the pre-selected first target region, and selects the maximum and minimum values ​​of the horizontal and vertical coordinates: x max x min y max y min ; Search area determination module: x max +m、x min -m、y max +m、y min -m serves as the boundary of the search area, where m is the preset extension distance, thus obtaining the search area; The second target filtering module sorts the search areas from largest to smallest based on grayscale, and selects the top preset number of coordinates with the largest grayscale as the second target areas.

6. The image processing system for analyte detection according to claim 5, characterized in that, The retrieval area determination module further includes: filtering out areas within a range L from the edge of the infrared grayscale image from the retrieval area, where L is the size of the color-shifted area at the edge of the imaging lens.

7. The image processing system for analyte detection according to claim 5, characterized in that, The higher the spatial resolution of the infrared grayscale image, the larger the upper limit of the value of m.

8. A system for detecting an analyte, characterized in that, The image processing system for detecting analytes as described in any one of claims 5-7.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the image processing method for detecting analytes as described in any one of claims 1 to 3.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the computer program is executed by the processor, it implements the steps of the image processing method for detecting analytes as described in any one of claims 1 to 3.

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