Chemiluminescence immunodetection method and system combined with electroluminescent compound label

Abstract

The embodiment of the invention provides a chemiluminescence immunodetection method and system combined with an electroluminescent compound label, and belongs to the technical field of data processing. The method comprises the following steps: performing optical stimulation on a target compound corresponding to a target antigen by using a photoelectric detector to obtain a target stimulation result; wherein the target compound is obtained by combining an electroluminescent compound with an initial compound, and the initial compound is obtained by combining a primary antibody with a target antigen; performing image acquisition on the target stimulation result by using an image sensor to obtain an initial optical image corresponding to the target antigen; performing image processing on the initial optical image to obtain a target optical image; performing image segmentation on the target optical image to obtain a target area, and determining brightness intensity corresponding to the target area according to the target optical image; and determining the antigen concentration corresponding to the target antigen according to the brightness intensity.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a chemiluminescent immunoassay method and system combined with electroluminescent compound labeling. Background Art

[0002] The electroluminescent compound labeling method is a biological detection technology based on the electroluminescence phenomenon, which is mainly used to detect specific target antigens. This method uses a specific primary antibody to directly bind to the target antigen (such as cytokines, proteins, etc.). Next, a secondary antibody corresponding to the primary antibody is selected, which is usually labeled with an electroluminescent compound (such as certain quantum dots or electroluminescent dyes). Then, the target antigen-primary antibody-secondary antibody complex bound to the electroluminescent compound is placed in an electroluminescent detector, and an electric field is applied to excite the electroluminescent compound to generate a light signal. Finally, a photodetector (such as a photomultiplier tube or a CCD camera) is used to detect the emitted light signal to obtain the antigen concentration of the target antigen. However, in the related art, when a photodetector is used to detect the emitted light signal, it may be affected by various external interference factors. These interference sources may include ambient light, instrument noise, etc. Due to the presence of these factors, the determination result of the antigen concentration corresponding to the target antigen may be inaccurate, thereby reducing the reliability of the detection result.

[0003] Therefore, it is urgent to design a technical solution to solve at least one of the above technical problems. Summary of the invention

[0004] The main purpose of the embodiments of the present invention is to provide a chemiluminescent immunoassay method and system combined with electroluminescent compound labeling, aiming to solve the problem in the related art that the determination results of the antigen concentration corresponding to the target antigen are not accurate enough, thereby reducing the reliability of the detection results.

[0005] In a first aspect, an embodiment of the present invention provides a chemiluminescent immunoassay method combined with an electroluminescent compound label, comprising:

[0006] A target complex corresponding to the target antigen is optically stimulated by a photodetector to obtain a target stimulation result; wherein the target complex is obtained by combining an electroluminescent compound with an initial complex, and the initial complex is obtained by combining a primary antibody with the target antigen;

[0007] Using an image sensor to collect images of the target stimulation results to obtain an initial optical image corresponding to the target antigen;

[0008] Performing image processing on the initial optical image to obtain a target optical image;

[0009] Performing image segmentation on the target optical image to obtain a target area, and determining a brightness intensity corresponding to the target area according to the target optical image;

[0010] The antigen concentration corresponding to the target antigen is determined according to the brightness intensity.

[0011] In a second aspect, an embodiment of the present invention provides a chemiluminescent immunoassay system combined with an electroluminescent compound marker, comprising:

[0012] An optical stimulation module, used to use a photodetector to optically stimulate a target complex corresponding to a target antigen to obtain a target stimulation result; wherein the target complex is obtained by combining an electroluminescent compound with an initial complex, and the initial complex is obtained by combining a primary antibody with the target antigen;

[0013] A data acquisition module, used to acquire images of the target stimulation results using an image sensor to obtain an initial optical image corresponding to the target antigen;

[0014] An image processing module, used for performing image processing on the initial optical image to obtain a target optical image;

[0015] An image segmentation module, used to perform image segmentation on the target optical image to obtain a target area, and determine the brightness intensity corresponding to the target area according to the target optical image;

[0016] A concentration calculation module is used to determine the antigen concentration corresponding to the target antigen according to the brightness intensity.

[0017] In a third aspect, an embodiment of the present invention further provides a terminal device, comprising a processor, a memory, a computer program stored in the memory and executable by the processor, and a data bus for realizing connection and communication between the processor and the memory, wherein when the computer program is executed by the processor, the steps of any one of the chemiluminescent immunoassay methods combined with electroluminescent compound labeling provided in the specification of the present invention are realized.

[0018] In a fourth aspect, an embodiment of the present invention further provides a storage medium for computer-readable storage, characterized in that the storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement any step of the chemiluminescent immunoassay method combined with electroluminescent compound labeling as provided in the specification of the present invention.

[0019] The embodiment of the present invention provides a chemiluminescent immunoassay method and system combined with an electroluminescent compound label, which uses a photodetector to optically stimulate a target complex associated with a target antigen to obtain a target stimulation result. The target complex is formed by combining an electroluminescent compound and an initial complex, and the initial complex is formed by combining a primary antibody and a target antigen. Subsequently, the target stimulation result is imaged by an image sensor to obtain an initial optical image corresponding to the target antigen. Next, the initial optical image is processed to generate a target optical image, which provides higher accuracy and reliability for subsequent target area analysis, thereby providing support for improving the determination accuracy of antigen concentration. The target optical image is then segmented to extract the target area. This process can further improve the recognition rate and analysis accuracy of the target antigen, making the detection result more real and reliable. At the same time, the brightness intensity of the target area is determined based on the target optical image. Finally, the antigen concentration corresponding to the target antigen is determined based on the extracted brightness intensity. Through the above method, the present invention effectively solves the problem of inaccurate determination of the target antigen concentration in the related art, thereby improving the reliability of the detection result. In addition, the method realizes quantitative detection of the target antigen, significantly improving the accuracy of antigen concentration detection. This innovation is of great significance for biomedical research and clinical testing, and also makes quantitative results more valuable for reference. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0021] Figure 1 A schematic flow chart of a chemiluminescent immunoassay method combined with electroluminescent compound labeling provided in an embodiment of the present invention;

[0022] Figure 2 A schematic diagram of the module structure of a chemiluminescent immunoassay system combined with an electroluminescent compound marker provided in an embodiment of the present invention;

[0023] Figure 3 A schematic block diagram of the structure of a terminal device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0024] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0025] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0026] It should be understood that the terms used in this specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0027] The embodiment of the present invention provides a chemiluminescent immunoassay method and system combined with electroluminescent compound labeling. The chemiluminescent immunoassay method combined with electroluminescent compound labeling can be applied to a terminal device, which can be an electronic device such as a tablet computer, a laptop computer, a desktop computer, a personal digital assistant, and a wearable device. The terminal device can be a server or a server cluster.

[0028] The electroluminescent compound labeling method is a biological detection technology based on the electroluminescence phenomenon, which is mainly used to detect specific target antigens. This method uses a specific primary antibody to directly bind to the target antigen (such as cytokines, proteins, etc.). Next, a secondary antibody corresponding to the primary antibody is selected, which is usually labeled with an electroluminescent compound (such as certain quantum dots or electroluminescent dyes). Then, the target antigen-primary antibody-secondary antibody complex bound to the electroluminescent compound is placed in an electroluminescent detector, and an electric field is applied to excite the electroluminescent compound to generate a light signal. Finally, a photodetector (such as a photomultiplier tube or a CCD camera) is used to detect the emitted light signal to obtain the antigen concentration of the target antigen. However, in the related art, when a photodetector is used to detect the emitted light signal, it may be affected by various external interference factors. These interference sources may include ambient light, instrument noise, etc. Due to the presence of these factors, the determination result of the antigen concentration corresponding to the target antigen may be inaccurate, thereby reducing the reliability of the detection result.

[0029] To this end, the present invention uses a photodetector to optically stimulate a target complex associated with a target antigen to obtain a target stimulation result. The target complex is formed by combining an electroluminescent compound and an initial complex, and the initial complex is formed by combining a primary antibody and a target antigen. Subsequently, the target stimulation result is imaged by an image sensor to obtain an initial optical image corresponding to the target antigen. Next, the initial optical image is processed to generate a target optical image, which provides higher accuracy and reliability for subsequent target area analysis, thereby providing support for improving the determination accuracy of antigen concentration. The target optical image is then segmented to extract the target area. This process can further improve the recognition rate and analysis accuracy of the target antigen, making the test results more realistic and reliable. At the same time, the brightness intensity of the target area is determined based on the target optical image. Finally, the antigen concentration corresponding to the target antigen is determined based on the extracted brightness intensity. Through the above method, the present invention effectively solves the problem of inaccurate determination of target antigen concentration in the related art, thereby improving the reliability of the test results.

[0030] Some embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0031] Please refer to Figure 1 , Figure 1 A schematic flow chart of a chemiluminescent immunoassay method combined with electroluminescent compound labeling provided in an embodiment of the present invention.

[0032] like Figure 1 As shown, the chemiluminescent immunoassay method combined with electroluminescent compound labeling includes steps S101 to S105.

[0033] Step S101, using a photodetector to optically stimulate a target complex corresponding to a target antigen to obtain a target stimulation result; wherein the target complex is obtained by combining an electroluminescent compound with an initial complex, and the initial complex is obtained by combining a primary antibody with the target antigen.

[0034] Exemplarily, a specific primary antibody is used to directly bind to a target antigen (such as a cytokine, protein, etc.) to obtain an initial complex. A secondary antibody corresponding to the primary antibody is then selected to obtain a target complex, and the secondary antibody is usually labeled with an electroluminescent compound (such as certain quantum dots or electroluminescent dyes).

[0035] Exemplarily, a target complex combined with an electroluminescent compound is placed in a photodetector, and an electric field is applied to excite the electroluminescent compound to generate a light signal to obtain a target stimulation result.

[0036] Step S102: using an image sensor to collect images of the target stimulation results to obtain an initial optical image corresponding to the target antigen.

[0037] Exemplarily, a high-resolution image sensor is selected according to the experimental requirements to ensure that the details of the target stimulation results can be accurately captured. Try to use a low-light environment or a light shielding device to avoid interference of ambient light on image acquisition. Ensure that the image sensor and light source are fixed in place to avoid vibration or displacement to obtain a stable image. Use a photodetector to emit a light signal to optically stimulate the target complex, ensuring that the stimulation intensity and duration are sufficient to produce a clear response of the target complex. At the same time as the optical stimulation, turn on the image sensor to start acquisition. The image sensor receives reflected or emitted light from the target complex to generate an initial optical image.

[0038] Step S103: performing image processing on the initial optical image to obtain a target optical image.

[0039] Exemplarily, a denoising algorithm (eg, Gaussian filtering or median filtering) is used to reduce noise in the initial optical image to improve image clarity and obtain a target optical image.

[0040] In some embodiments, the image processing of the initial optical image to obtain the target optical image includes: determining a target window, and performing window image extraction on the initial optical image according to the target window to obtain a window optical image corresponding to the initial optical image; determining a first window image from the window optical image, and determining the remaining image after excluding the first window image from the window optical image as a second window image; performing image denoising on the first window image according to the second window image to obtain an initial denoised image corresponding to the first window image; performing image merging on the initial denoised image to obtain a target denoised image corresponding to the initial optical image; and performing brightness adjustment on the target denoised image to obtain the target optical image.

[0041] Exemplarily, a target window is determined, and the target window may be a window of size L*L, where L is a positive integer smaller than the size of the initial optical image, and then a window image is extracted from the target window in the initial optical image, thereby obtaining a plurality of window optical images corresponding to the initial optical image. That is, the initial optical image can be obtained by splicing the plurality of window optical images.

[0042] Exemplarily, one window optical image is arbitrarily determined from a plurality of window optical images as the first window image, and then the remaining window optical images after excluding the first window image from the plurality of window optical images are determined as the second window image, that is, the second window image is all the remaining window optical images after excluding the first window image.

[0043] Exemplarily, first, the second window image is denoised to obtain a clear denoising result. Afterwards, the denoising result is compared with the original second window image to calculate the quality difference between them. This quality difference can be evaluated by a variety of indicators, such as peak signal-to-noise ratio, structural similarity, etc. If the calculated quality difference exceeds a preset threshold, this generally indicates that the denoising result effectively improves the image quality, and the denoising algorithm corresponding to the second window image is determined. Then, the similarity between the second window image and the first window image is calculated, and the denoising algorithm corresponding to the second window image is fused according to the similarity and applied to the first window image for denoising, thereby obtaining an initial denoised image corresponding to the first window image.

[0044] Exemplarily, the above steps are performed on each image in the window optical image to obtain an initial denoised image corresponding to each image, and then all the initial denoised images are merged according to the position of the window optical image in the initial optical image to obtain a target denoised image corresponding to the initial optical image.

[0045] Exemplarily, the brightness of the target denoised image is corrected using a brightness adjustment model, thereby obtaining a target optical image corresponding to the initial optical image. The brightness adjustment model can be a global brightness adjustment model or a local brightness enhancement model, so that the selected brightness adjustment model is applied to calculate and adjust the brightness value of the target denoised image. The brightness adjustment model can be based on some mathematical models, such as linear transformation, gamma correction, etc., to ensure that the brightness of the target optical image meets expectations.

[0046] In some embodiments, performing image denoising on the first window image according to the second window image to obtain an initial denoised image corresponding to the first window image includes: performing similarity calculation on a first pixel value corresponding to the first window image and a second pixel value corresponding to the second window image to obtain a target similarity between the first window image and the second window image; determining an association distance between the first window image and the second window image according to the target similarity; normalizing the association distance to obtain an association weight between the first window image and the second window image; performing image denoising on the first window image using the second window image according to the association weight to obtain the initial denoised image corresponding to the first window image; wherein the association distance is obtained according to the following formula:

[0047]

[0048]

[0049] Among them, disij represents the correlation distance between the i-th image in the first window and the j-th image in the second window, ave represents the average pixel value corresponding to the initial optical image, sim ij represents the target similarity between the i-th image in the first window and the j-th image in the second window, N represents the number of pixels corresponding to the first window image, img it represents the first pixel value corresponding to the i-th first window image at the t-th position, img jt Represents the second pixel value corresponding to the j-th second window image at the t-th position.

[0050] Exemplarily, the first pixel value corresponding to the first window image and the second pixel value corresponding to the second window image are similarly calculated according to the following formula to obtain the target similarity between the first window image and the second window image:

[0051]

[0052] Among them, sim ij represents the target similarity between the i-th first window image and the j-th second window image, N represents the number of pixels corresponding to the first window image, img it represents the first pixel value corresponding to the tth position of the i-th first window image, img jt Represents the second pixel value corresponding to the j-th second window image at the t-th position.

[0053] Exemplarily, the similarity calculation of the first pixel value corresponding to the first window image and the second pixel value corresponding to the second window image is performed according to the above formula, which can numerically reflect the similarity between the first window image and the second window image in terms of brightness, contrast and texture, and provide support for subsequent image denoising processing of the first window image based on the second window image.

[0054] Exemplarily, the association distance between the first window image and the second window image is determined using the target similarity according to the following formula:

[0055]

[0056] Among them, dis ij represents the correlation distance between the i-th first window image and the j-th second window image, ave represents the average pixel value corresponding to the initial optical image, sim ij represents the target similarity between the i-th first window image and the j-th second window image, N represents the number of pixels corresponding to the first window image, img itrepresents the first pixel value corresponding to the tth position of the i-th first window image, img jt Represents the second pixel value corresponding to the j-th second window image at the t-th position.

[0057] Exemplarily, according to the above formula, the relationship between the first window image and the second window image can be quantified by using the target similarity to determine the association distance between the first window image and the second window image. In this formula, the pixel average, target similarity, and the specific difference between the pixel values ​​are comprehensively considered, thereby achieving a more comprehensive and accurate image relationship evaluation. By combining the pixel average and target similarity, the difference in overall image brightness and contrast can be accurately reflected, making image analysis more accurate. Using target similarity to calculate the association distance between the first window image and the second window image not only realizes the quantitative analysis of the image relationship, but also provides strong support for subsequent noise removal and image quality optimization.

[0058] Exemplarily, the first window image is obtained and the associated distance is calculated with each second window image, so that all the obtained associated distances are normalized, and then the associated weight between the first window image and the second window image is obtained.

[0059] Exemplarily, a denoising algorithm corresponding to the second window image is obtained. After the denoising algorithm corresponding to the second window image is applied to the first window image, an initial processing result of the denoising algorithm corresponding to the second window image on the first window image is obtained, and then all the initial processing results are weighted summed using the associated weights, thereby obtaining an initial denoised image corresponding to the first window image.

[0060] Specifically, by adopting the denoising algorithm of the second window image, more information and features can be introduced, making the denoising effect of the first window image more significant. The denoising algorithm involved in the second window image may be optimized and have the ability to effectively suppress noise and retain details, so that the noise problem of the first window image can be carefully processed. By weighted summing of multiple initial processing results, the final denoised image will be visually smoother and more harmonious. The weighted summation process can effectively integrate information from multiple processing results, and even suppress artifacts and noise introduced due to the insufficiency of a single algorithm, thereby improving the overall visual quality.

[0061] In some embodiments, the brightness adjustment of the target denoised image to obtain the target optical image includes: extracting brightness information from the target denoised image using a guided filtering algorithm to obtain an initial brightness component; performing gamma correction on the initial brightness component to obtain a target brightness component; extracting a reflection component from the target denoised image using a retinal enhancement algorithm to obtain a target reflection component corresponding to the target denoised image; and performing data synthesis based on the target reflection component and the target brightness component to obtain the target optical image.

[0062] Exemplarily, a guided filtering algorithm, such as guided filtering or local weighted regression, is selected as a tool for extracting brightness information. These algorithms can reduce noise while maintaining image edge features, thereby ensuring that the extracted brightness information is smoother and more accurate. The target denoised image itself is used as a guided image so that smoothing can be performed during the guided filtering process, and then the target denoised image is used as an input of the guided filtering algorithm, and the brightness information in the target denoised image is extracted by the guided filtering algorithm to obtain a relatively smooth initial brightness component.

[0063] Exemplarily, the initial brightness component is subjected to gamma correction. The gamma value is usually selected between 2.2 and 2.8, because this range can effectively adjust the brightness and contrast of the image, which is more in line with the perception characteristics of the human eye. When the previously extracted initial brightness component is subjected to gamma correction, the brightness value of each pixel is increased to the power of the selected gamma value. After gamma correction, the target brightness component is obtained.

[0064] Exemplarily, a retina enhancement algorithm is used to extract the reflection component from the target denoised image. The retina enhancement algorithm simulates the perception mechanism of the human eye and can effectively separate the reflection component and the illumination component by analyzing the color and brightness changes of the image, thereby obtaining the target reflection component corresponding to the target denoised image.

[0065] Exemplarily, a simple linear combination, weighted summation or matrix operation method is used to fuse the target brightness component and the target reflection component to obtain a target optical image.

[0066] Step S104: performing image segmentation on the target optical image to obtain a target area, and determining a brightness intensity corresponding to the target area according to the target optical image.

[0067] Exemplarily, an edge detection algorithm (such as the Canny operator) is used to perform edge detection and identification on the target optical image and extract the corresponding target area.

[0068] Exemplarily, the calculation is performed based on the brightness value of each pixel in the target area. If the image is a grayscale image, the pixel value is used directly; if it is a color image, a weighted method (e.g., YUV or RGB channel weighting) can be used to obtain the corresponding brightness value. The brightness value of the target area is statistically analyzed to calculate the average brightness of the target area, so that the average brightness is determined as the brightness intensity corresponding to the target area.

[0069] In some embodiments, performing image segmentation on the target optical image to obtain a target area includes: determining initial segmentation pixels, performing image segmentation on the target optical image according to the initial segmentation pixels to obtain a first segmented image and a second segmented image; performing pixel distribution statistics on the target optical image to obtain a pixel distribution result; determining first distribution information corresponding to the first segmented image and second distribution information corresponding to the second segmented image according to the pixel distribution result; determining a distribution gap between the first segmented image and the second segmented image according to the first distribution information and the second distribution information; determining a segmentation error corresponding to the initial segmentation pixels according to the first segmented image and the second segmented image; determining a segmentation quality corresponding to the initial segmentation pixels according to the distribution gap and the segmentation error; adjusting the initial segmentation pixels according to the segmentation quality to obtain target segmentation pixels corresponding to the target optical image; performing image segmentation on the target optical image according to the target segmentation pixels to obtain the target area.

[0070] Exemplarily, the pixels of the target optical image are clustered to obtain a clustering result, and then the central pixel corresponding to each cluster in the clustering result is used as the initial segmentation pixel, so as to perform image segmentation on the target optical image according to the initial segmentation pixel to obtain a first segmented image and a second segmented image.

[0071] Exemplarily, the frequency of occurrence of each brightness value or color value in the target optical image is counted, and the frequency distribution is normalized to a probability distribution, so as to obtain a pixel distribution result corresponding to the target optical image.

[0072] Exemplarily, the probability distribution value corresponding to each pixel in the first segmented image is obtained according to the pixel distribution result, and then all the probability distribution values ​​contained in the first segmented image are summed to obtain the first distribution information corresponding to the first segmented image. Similarly, the probability distribution value corresponding to each pixel in the second segmented image is obtained according to the pixel distribution result, and then all the probability distribution values ​​contained in the second segmented image are summed to obtain the second distribution information corresponding to the second segmented image.

[0073] Exemplarily, the pixel values ​​of the first segmented image are averaged to obtain a first average pixel, and the pixel values ​​of the second segmented image are averaged to obtain a second average pixel, and then the first distribution information, the first average pixel value, the second distribution information, and the second average pixel value are weightedly summed to obtain a target average pixel value corresponding to the target optical image. Then, the first average pixel and the target average pixel value are differenced to obtain a first difference value, and the second average pixel and the target average pixel value are differenced to obtain a second difference value, and then the first difference value is squared to obtain a first value, and the second difference value is squared to obtain a second value, so that the first distribution information, the first value, the second distribution information, and the second value are weightedly summed to obtain a distribution gap corresponding to the target optical image.

[0074] Exemplarily, pixel clustering is performed on the first segmented image to obtain a first clustering result and pixel clustering is performed on the second segmented image to obtain a second clustering result, and then pixel clustering is performed on the target optical image to obtain a third clustering result, a first number of pixel values ​​in each cluster in the first clustering result that are not in the same cluster in the third clustering result is obtained, and a second number of pixel values ​​in each cluster in the second clustering result that are not in the same cluster in the third clustering result is obtained, thereby determining the segmentation error corresponding to the initial segmentation pixel based on the sum of the first number and the second number.

[0075] For example, the distribution gap is converted to the range of 0 to 1 to obtain a first conversion value, and the larger the first conversion value is, the better the segmentation effect is. Similarly, the segmentation error is also converted to the range of 0 to 1, and the smaller the segmentation error is, the better the segmentation effect is. Then, the second conversion value is obtained by subtracting the converted segmentation error from 1. Thus, the first conversion value and the second conversion value are summed to obtain the segmentation quality corresponding to the initial segmentation pixel.

[0076] Exemplarily, when the segmentation quality is greater than or equal to a preset value, the initial segmentation pixel is determined as the target segmentation pixel. When the segmentation quality is less than the preset value, the initial segmentation pixel is re-determined, and the above steps are re-executed to obtain a new segmentation quality until the new segmentation quality is greater than or equal to the preset value, and then the new initial segmentation pixel is determined as the target segmentation pixel.

[0077] Exemplarily, the target optical image is segmented by using a threshold segmentation method according to the target segmentation pixels to obtain the target area.

[0078] Specifically, the initial segmentation pixels are adjusted according to the segmentation quality feedback, so that the segmentation process has dynamic adaptability, and the segmentation results can be continuously optimized, thereby improving the segmentation effect of the target area, thereby providing good support for the subsequent improvement of the accuracy of the antigen concentration of the target antigen.

[0079] In some embodiments, determining the segmentation error corresponding to the initial segmentation pixel based on the first segmented image and the second segmented image includes: calculating a first mean and a first variance corresponding to the first segmented image and calculating a second mean and a second variance corresponding to the second segmented image; determining a first image distribution corresponding to the first segmented image based on the first mean and the first variance and determining a second image distribution corresponding to the second segmented image based on the second mean and the second variance; calculating a first distribution difference of first pixel information corresponding to the first segmented image in the second image distribution; calculating a second distribution difference of second pixel information corresponding to the second segmented image in the first image distribution; and determining the segmentation error corresponding to the initial segmentation pixel based on the first distribution difference and the second distribution difference.

[0080] Exemplarily, the pixel values ​​of the first segmented image are averaged to obtain a corresponding first mean, and the pixel values ​​of the first segmented image are variance-calculated based on the first mean to obtain a first variance. Similarly, the pixel values ​​of the second segmented image are averaged to obtain a corresponding second mean, and the pixel values ​​of the second segmented image are variance-calculated based on the second mean to obtain a second variance.

[0081] Exemplarily, the first mean and the first variance are combined with the normal distribution to obtain a first image distribution corresponding to the first segmented image, and the second mean and the second variance are combined with the normal distribution to obtain a second image distribution corresponding to the second segmented image.

[0082] Exemplarily, the first pixel information corresponding to the first segmented image is substituted into the second image distribution to obtain the first result, the first pixel information corresponding to the first segmented image is substituted into the first image distribution to obtain the second result, and then the difference between the first result and the second result is summed to determine the first distribution difference. Similarly, the second pixel information corresponding to the second segmented image is substituted into the first image distribution to obtain the third result, the second pixel information corresponding to the second segmented image is substituted into the second image distribution to obtain the fourth result, and then the difference between the third result and the fourth result is summed to determine the second distribution difference.

[0083] Exemplarily, the first pixel information corresponding to the first segmented image is substituted into the second image distribution to obtain a first result, and the first pixel information corresponding to the first segmented image is substituted into the first image distribution to obtain a second result. When the difference between the first result and the second result is large, it indicates that the segmentation effect is better; when the difference between the first result and the second result is small, it indicates that the segmentation effect is worse.

[0084] Exemplarily, a weighted sum is performed based on the first distribution information, the first distribution difference, and the second distribution information and the second distribution difference to obtain the segmentation error corresponding to the initial segmentation pixel. At this time, the larger the segmentation error, the better, so when calculating the segmentation quality, there is no need to subtract the conversion value of the segmentation error from 1, and the conversion value corresponding to the segmentation difference can be directly summed to obtain the corresponding segmentation quality.

[0085] In some embodiments, determining the brightness intensity corresponding to the target area according to the target optical image includes: obtaining a target pixel value corresponding to the target area according to the target optical image; performing pixel clustering on the target pixel value to obtain a target clustering result; determining the number of target pixels corresponding to each target cluster in the target clustering result, and determining a target weight corresponding to the target cluster according to the number of target pixels; performing brightness calculation according to the target weight and the target clustering result to obtain the brightness intensity corresponding to the target area.

[0086] Exemplarily, the target pixel value corresponding to the target area is obtained from the target optical image, so as to select a suitable clustering algorithm according to the characteristics of the target pixel value, and commonly used algorithms include K-means, mean shift, DBSCAN, etc. The target pixel value is clustered using the selected clustering algorithm to obtain the target clustering result.

[0087] Exemplarily, according to the target clustering result, the number of target pixels in each target cluster is counted, and the corresponding target weight is obtained by calculating the ratio of the number of target pixels in each target cluster to the number of all target pixel values.

[0088] Exemplarily, a weighted sum is performed on the brightness value corresponding to each pixel in the target cluster according to the target weight and the target clustering result to obtain the brightness intensity corresponding to the target area.

[0089] Step S105: determining the antigen concentration corresponding to the target antigen according to the brightness intensity.

[0090] For example, the brightness intensity data corresponding to the antigen standard solution of known concentration is plotted to establish a calibration curve between concentration and brightness intensity. This curve can help establish a quantitative relationship between concentration and brightness. Thus, the brightness intensity is compared in the calibration curve to find the antigen concentration corresponding to the brightness intensity.

[0091] The embodiment of the present invention provides a chemiluminescent immunoassay method and system combined with an electroluminescent compound label, which uses a photodetector to optically stimulate a target complex associated with a target antigen to obtain a target stimulation result. The target complex is formed by combining an electroluminescent compound and an initial complex, and the initial complex is formed by combining a primary antibody and a target antigen. Subsequently, the target stimulation result is imaged by an image sensor to obtain an initial optical image corresponding to the target antigen. Next, the initial optical image is processed to generate a target optical image, which provides higher accuracy and reliability for subsequent target area analysis, thereby providing support for improving the determination accuracy of antigen concentration. The target optical image is then segmented to extract the target area. This process can further improve the recognition rate and analysis accuracy of the target antigen, making the detection result more real and reliable. At the same time, the brightness intensity of the target area is determined based on the target optical image. Finally, the antigen concentration corresponding to the target antigen is determined based on the extracted brightness intensity. Through the above method, the present invention effectively solves the problem of inaccurate determination of the target antigen concentration in the related art, thereby improving the reliability of the detection result. In addition, the method realizes quantitative detection of the target antigen, significantly improving the accuracy of antigen concentration detection. This innovation is of great significance for biomedical research and clinical testing, and also makes quantitative results more valuable for reference.

[0092] See also Figure 2 , Figure 2 A chemiluminescent immunoassay system 200 combined with an electroluminescent compound marker is provided in an embodiment of the present application. The chemiluminescent immunoassay system 200 combined with an electroluminescent compound marker includes an optical stimulation module 201, a data acquisition module 202, an image processing module 203, an image segmentation module 204, and a concentration calculation module 205, wherein the optical stimulation module 201 is used to use a photodetector to perform optical stimulation on a target complex corresponding to a target antigen to obtain a target stimulation result; wherein the target complex is obtained by combining an electroluminescent compound with an initial complex, and the initial complex is obtained by combining a primary antibody with the target antigen; the data acquisition module 202 is used to use an image sensor to perform image acquisition on the target stimulation result to obtain an initial optical image corresponding to the target antigen; the image processing module 203 is used to perform image processing on the initial optical image to obtain a target optical image; the image segmentation module 204 is used to perform image segmentation on the target optical image to obtain a target area, and determine the brightness intensity corresponding to the target area according to the target optical image; the concentration calculation module 205 is used to determine the antigen concentration corresponding to the target antigen according to the brightness intensity.

[0093] In some implementations, the image processing module 203 performs, during the process of performing image processing on the initial optical image to obtain a target optical image:

[0094] Determine a target window, and perform window image extraction on the initial optical image according to the target window to obtain a window optical image corresponding to the initial optical image;

[0095] Determine a first window image from the window optical image, and determine a remaining image of the window optical image after excluding the first window image as a second window image;

[0096] Performing image denoising processing on the first window image according to the second window image to obtain an initial denoised image corresponding to the first window image;

[0097] Merging the initial denoised images to obtain a target denoised image corresponding to the initial optical image;

[0098] The target denoised image is brightness adjusted to obtain the target optical image.

[0099] In some implementations, the image processing module 203, in the process of performing image denoising on the first window image according to the second window image to obtain an initial denoised image corresponding to the first window image, executes:

[0100] Calculate the similarity between a first pixel value corresponding to the first window image and a second pixel value corresponding to the second window image to obtain a target similarity between the first window image and the second window image;

[0101] Determine an association distance between the first window image and the second window image according to the target similarity;

[0102] Normalizing the association distance to obtain an association weight between the first window image and the second window image;

[0103] The first window image is subjected to image denoising processing using the second window image according to the association weight to obtain the initial denoised image corresponding to the first window image; wherein the association distance is obtained according to the following formula:

[0104]

[0105] Among them, dis ij represents the correlation distance between the i-th image in the first window and the j-th image in the second window, ave represents the average pixel value corresponding to the initial optical image, sim ijrepresents the target similarity between the i-th image in the first window and the j-th image in the second window, N represents the number of pixels corresponding to the first window image, img it represents the first pixel value corresponding to the i-th first window image at the t-th position, img jt Represents the second pixel value corresponding to the j-th second window image at the t-th position.

[0106] In some implementations, the image processing module 203 performs, during the process of adjusting the brightness of the target denoised image to obtain the target optical image:

[0107] Extracting brightness information from the target denoised image using a guided filtering algorithm to obtain an initial brightness component;

[0108] Performing gamma correction on the initial brightness component to obtain a target brightness component;

[0109] Extracting the reflection component of the target denoised image by using a retina enhancement algorithm to obtain the target reflection component corresponding to the target denoised image;

[0110] Data synthesis is performed according to the target reflection component and the target brightness component to obtain the target optical image.

[0111] In some implementations, the image segmentation module 204, during the process of segmenting the target optical image to obtain the target area, performs:

[0112] Determining initial segmentation pixels, and performing image segmentation on the target optical image according to the initial segmentation pixels to obtain a first segmented image and a second segmented image;

[0113] Performing pixel distribution statistics on the target optical image to obtain a pixel distribution result;

[0114] Determine first distribution information corresponding to the first segmented image and second distribution information corresponding to the second segmented image according to the pixel distribution result;

[0115] determining a distribution gap between the first segmented image and the second segmented image according to the first distribution information and the second distribution information;

[0116] Determine a segmentation error corresponding to the initial segmentation pixel according to the first segmentation image and the second segmentation image;

[0117] Determining the segmentation quality corresponding to the initial segmentation pixel according to the distribution gap and the segmentation error;

[0118] Adjusting the initial segmentation pixels according to the segmentation quality to obtain target segmentation pixels corresponding to the target optical image;

[0119] The target area is obtained by performing image segmentation on the target optical image according to the target segmentation pixels.

[0120] In some implementations, the image segmentation module 204, during the process of determining the segmentation error corresponding to the initial segmentation pixel according to the first segmented image and the second segmented image, performs:

[0121] Calculating a first mean and a first variance corresponding to the first segmented image and calculating a second mean and a second variance corresponding to the second segmented image;

[0122] determining a first image distribution corresponding to the first segmented image according to the first mean and the first variance, and determining a second image distribution corresponding to the second segmented image according to the second mean and the second variance;

[0123] Calculating a first distribution difference of first pixel information corresponding to the first segmented image in the second image distribution;

[0124] Calculating a second distribution difference of second pixel information corresponding to the second segmented image in the first image distribution;

[0125] A segmentation error corresponding to the initial segmentation pixel is determined according to the first distribution difference and the second distribution difference.

[0126] In some implementations, the image segmentation module 204, during the process of determining the brightness intensity corresponding to the target area according to the target optical image, performs:

[0127] Obtaining a target pixel value corresponding to the target area according to the target optical image;

[0128] Performing pixel clustering on the target pixel value to obtain a target clustering result;

[0129] Determine the number of target pixels corresponding to each target cluster in the target clustering result, and determine the target weight corresponding to the target cluster according to the number of target pixels;

[0130] Brightness calculation is performed according to the target weight and the target clustering result to obtain the brightness intensity corresponding to the target area.

[0131] In some embodiments, the chemiluminescent immunoassay system 200 combined with an electroluminescent compound label can be applied to a terminal device.

[0132] It should be noted that technicians in the relevant field can clearly understand that for the convenience and simplicity of description, the specific working process of the chemiluminescent immunoassay system 200 combined with electroluminescent compound labeling described above can refer to the corresponding process in the aforementioned embodiment of the chemiluminescent immunoassay method combined with electroluminescent compound labeling, and will not be repeated here.

[0133] See also Figure 3 , Figure 3 A schematic block diagram of the structure of a terminal device provided in an embodiment of the present invention.

[0134] like Figure 3 As shown, the terminal device 300 includes a processor 301 and a memory 302, and the processor 301 and the memory 302 are connected via a bus 303, such as an I2C (Inter-integrated Circuit) bus.

[0135] Specifically, the processor 301 is used to provide computing and control capabilities to support the operation of the entire terminal device. The processor 301 can be a central processing unit (CPU), and the processor 301 can also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc.

[0136] Specifically, the memory 302 may be a Flash chip, a read-only memory (ROM) disk, an optical disk, a USB flash drive, or a mobile hard disk.

[0137] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a partial structure related to the embodiment of the present invention, and does not constitute a limitation on the terminal device to which the embodiment of the present invention is applied. The specific server may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0138] The processor is used to run a computer program stored in the memory, and implement any one of the chemiluminescent immunoassay methods combined with electroluminescent compound labeling provided by the embodiments of the present invention when executing the computer program.

[0139] In one embodiment, the processor is used to run a computer program stored in the memory, and implements the following steps when executing the computer program:

[0140] A target complex corresponding to the target antigen is optically stimulated by a photodetector to obtain a target stimulation result; wherein the target complex is obtained by combining an electroluminescent compound with an initial complex, and the initial complex is obtained by combining a primary antibody with the target antigen;

[0141] Using an image sensor to collect images of the target stimulation results to obtain an initial optical image corresponding to the target antigen;

[0142] Performing image processing on the initial optical image to obtain a target optical image;

[0143] Performing image segmentation on the target optical image to obtain a target area, and determining a brightness intensity corresponding to the target area according to the target optical image;

[0144] The antigen concentration corresponding to the target antigen is determined according to the brightness intensity.

[0145] In some implementations, the processor 301, during the process of performing image processing on the initial optical image to obtain a target optical image, executes:

[0146] Determine a target window, and perform window image extraction on the initial optical image according to the target window to obtain a window optical image corresponding to the initial optical image;

[0147] Determine a first window image from the window optical image, and determine a remaining image of the window optical image after excluding the first window image as a second window image;

[0148] Performing image denoising processing on the first window image according to the second window image to obtain an initial denoised image corresponding to the first window image;

[0149] Merging the initial denoised images to obtain a target denoised image corresponding to the initial optical image;

[0150] The target denoised image is brightness adjusted to obtain the target optical image.

[0151] In some implementations, the processor 301, in the process of performing image denoising on the first window image according to the second window image to obtain an initial denoised image corresponding to the first window image, executes:

[0152] Calculate the similarity between a first pixel value corresponding to the first window image and a second pixel value corresponding to the second window image to obtain a target similarity between the first window image and the second window image;

[0153] Determine an association distance between the first window image and the second window image according to the target similarity;

[0154] Normalizing the association distance to obtain an association weight between the first window image and the second window image;

[0155] The first window image is subjected to image denoising processing using the second window image according to the association weight to obtain the initial denoised image corresponding to the first window image; wherein the association distance is obtained according to the following formula:

[0156]

[0157] Among them, dis ij represents the correlation distance between the i-th image in the first window and the j-th image in the second window, ave represents the average pixel value corresponding to the initial optical image, sim ij represents the target similarity between the i-th image in the first window and the j-th image in the second window, N represents the number of pixels corresponding to the first window image, img it represents the first pixel value corresponding to the i-th first window image at the t-th position, img jt Represents the second pixel value corresponding to the j-th second window image at the t-th position.

[0158] In some implementations, the processor 301, during the process of adjusting the brightness of the target denoised image to obtain the target optical image, executes:

[0159] Extracting brightness information from the target denoised image using a guided filtering algorithm to obtain an initial brightness component;

[0160] Performing gamma correction on the initial brightness component to obtain a target brightness component;

[0161] Extracting the reflection component of the target denoised image by using a retina enhancement algorithm to obtain the target reflection component corresponding to the target denoised image;

[0162] Data synthesis is performed according to the target reflection component and the target brightness component to obtain the target optical image.

[0163] In some implementations, the processor 301, during the process of performing image segmentation on the target optical image to obtain the target area, executes:

[0164] Determining initial segmentation pixels, and performing image segmentation on the target optical image according to the initial segmentation pixels to obtain a first segmented image and a second segmented image;

[0165] Performing pixel distribution statistics on the target optical image to obtain a pixel distribution result;

[0166] Determine first distribution information corresponding to the first segmented image and second distribution information corresponding to the second segmented image according to the pixel distribution result;

[0167] determining a distribution gap between the first segmented image and the second segmented image according to the first distribution information and the second distribution information;

[0168] Determine a segmentation error corresponding to the initial segmentation pixel according to the first segmentation image and the second segmentation image;

[0169] Determining the segmentation quality corresponding to the initial segmentation pixel according to the distribution gap and the segmentation error;

[0170] Adjusting the initial segmentation pixels according to the segmentation quality to obtain target segmentation pixels corresponding to the target optical image;

[0171] The target area is obtained by performing image segmentation on the target optical image according to the target segmentation pixels.

[0172] In some implementations, during the process of determining the segmentation error corresponding to the initial segmentation pixel according to the first segmented image and the second segmented image, the processor 301 performs:

[0173] Calculating a first mean and a first variance corresponding to the first segmented image and calculating a second mean and a second variance corresponding to the second segmented image;

[0174] determining a first image distribution corresponding to the first segmented image according to the first mean and the first variance, and determining a second image distribution corresponding to the second segmented image according to the second mean and the second variance;

[0175] Calculating a first distribution difference of first pixel information corresponding to the first segmented image in the second image distribution;

[0176] Calculating a second distribution difference of second pixel information corresponding to the second segmented image in the first image distribution;

[0177] A segmentation error corresponding to the initial segmentation pixel is determined according to the first distribution difference and the second distribution difference.

[0178] In some implementations, during the process of determining the brightness intensity corresponding to the target area according to the target optical image, the processor 301 executes:

[0179] Obtaining a target pixel value corresponding to the target area according to the target optical image;

[0180] Performing pixel clustering on the target pixel value to obtain a target clustering result;

[0181] Determine the number of target pixels corresponding to each target cluster in the target clustering result, and determine the target weight corresponding to the target cluster according to the number of target pixels;

[0182] Brightness calculation is performed according to the target weight and the target clustering result to obtain the brightness intensity corresponding to the target area.

[0183] It should be noted that technicians in the relevant field can clearly understand that for the convenience and simplicity of description, the specific working process of the terminal device described above can refer to the corresponding process in the aforementioned embodiment of the chemiluminescent immunoassay method combined with electroluminescent compound labeling, and will not be repeated here.

[0184] An embodiment of the present invention also provides a storage medium for computer-readable storage, wherein the storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement any step of the chemiluminescent immunoassay method combined with electroluminescent compound labeling provided in the description of the embodiment of the present invention.

[0185] The storage medium may be an internal storage unit of the terminal device described in the foregoing embodiment, such as a hard disk or memory of the terminal device. The storage medium may also be an external storage device of the terminal device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc., equipped on the terminal device.

[0186] It will be appreciated by those skilled in the art that all or some of the steps, systems, and functional modules / units in the methods disclosed above may be implemented as software, firmware, hardware, and appropriate combinations thereof. In a hardware embodiment, the division between the functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed by several physical components in cooperation. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or implemented as hardware, or implemented as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or non-transitory medium) and a communication medium (or temporary medium). As known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer. In addition, it is well known to those of ordinary skill in the art that communication media typically contain computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0187] It should be understood that the term "and / or" used in the present specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, including these combinations. It should be noted that, in this article, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "including a..." does not exclude the presence of other identical elements in the process, method, article or system including the element.

[0188] The serial numbers of the embodiments of the present invention are only for description and do not represent the advantages and disadvantages of the embodiments. The above description is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A chemiluminescent immunoassay method combined with an electroluminescent compound label, characterized in that: The method comprises: A target complex corresponding to the target antigen is optically stimulated by a photodetector to obtain a target stimulation result; wherein the target complex is obtained by combining an electroluminescent compound with an initial complex, and the initial complex is obtained by combining a primary antibody with the target antigen; Using an image sensor to collect images of the target stimulation results to obtain an initial optical image corresponding to the target antigen; Performing image processing on the initial optical image to obtain a target optical image; Performing image segmentation on the target optical image to obtain a target area, and determining a brightness intensity corresponding to the target area according to the target optical image; The antigen concentration corresponding to the target antigen is determined according to the brightness intensity.

2. The method according to claim 1, characterized in that The performing image processing on the initial optical image to obtain a target optical image comprises: Determine a target window, and perform window image extraction on the initial optical image according to the target window to obtain a window optical image corresponding to the initial optical image; Determine a first window image from the window optical image, and determine a remaining image of the window optical image after excluding the first window image as a second window image; Performing image denoising processing on the first window image according to the second window image to obtain an initial denoised image corresponding to the first window image; Merging the initial denoised images to obtain a target denoised image corresponding to the initial optical image; The target optical image is obtained by performing brightness adjustment on the target denoised image.

3. The method according to claim 2, characterized in that The performing image denoising on the first window image according to the second window image to obtain an initial denoised image corresponding to the first window image includes: Calculate the similarity between a first pixel value corresponding to the first window image and a second pixel value corresponding to the second window image to obtain a target similarity between the first window image and the second window image; Determine an association distance between the first window image and the second window image according to the target similarity; Normalizing the association distance to obtain an association weight between the first window image and the second window image; The first window image is subjected to image denoising processing using the second window image according to the association weight to obtain the initial denoised image corresponding to the first window image; wherein the association distance is obtained according to the following formula: Among them, dis ij represents the correlation distance between the i-th image in the first window and the j-th image in the second window, ave represents the average pixel value corresponding to the initial optical image, sim ij represents the target similarity between the i-th image in the first window and the j-th image in the second window, N represents the number of pixels corresponding to the first window image, img it represents the first pixel value corresponding to the i-th first window image at the t-th position, img jt Represents the second pixel value corresponding to the j-th second window image at the t-th position.

4. The method according to claim 2, characterized in that: The step of adjusting the brightness of the target denoised image to obtain the target optical image comprises: Extracting brightness information from the target denoised image using a guided filtering algorithm to obtain an initial brightness component; Performing gamma correction on the initial brightness component to obtain a target brightness component; Extracting the reflection component of the target denoised image by using a retina enhancement algorithm to obtain the target reflection component corresponding to the target denoised image; Data synthesis is performed according to the target reflection component and the target brightness component to obtain the target optical image.

5. The method according to claim 1, characterized in that The step of performing image segmentation on the target optical image to obtain the target area comprises: Determining initial segmentation pixels, and performing image segmentation on the target optical image according to the initial segmentation pixels to obtain a first segmented image and a second segmented image; Performing pixel distribution statistics on the target optical image to obtain a pixel distribution result; Determine first distribution information corresponding to the first segmented image and second distribution information corresponding to the second segmented image according to the pixel distribution result; determining a distribution gap between the first segmented image and the second segmented image according to the first distribution information and the second distribution information; Determine a segmentation error corresponding to the initial segmentation pixel according to the first segmentation image and the second segmentation image; Determining the segmentation quality corresponding to the initial segmentation pixel according to the distribution gap and the segmentation error; Adjusting the initial segmentation pixels according to the segmentation quality to obtain target segmentation pixels corresponding to the target optical image; The target area is obtained by performing image segmentation on the target optical image according to the target segmentation pixels.

6. The method according to claim 5, characterized in that The determining the segmentation error corresponding to the initial segmentation pixel according to the first segmentation image and the second segmentation image includes: Calculating a first mean and a first variance corresponding to the first segmented image and calculating a second mean and a second variance corresponding to the second segmented image; determining a first image distribution corresponding to the first segmented image according to the first mean and the first variance, and determining a second image distribution corresponding to the second segmented image according to the second mean and the second variance; Calculating a first distribution difference of first pixel information corresponding to the first segmented image in the second image distribution; Calculating a second distribution difference of second pixel information corresponding to the second segmented image in the first image distribution; A segmentation error corresponding to the initial segmentation pixel is determined according to the first distribution difference and the second distribution difference.

7. The method according to claim 1, characterized in that The step of determining the brightness intensity corresponding to the target area according to the target optical image includes: Obtaining a target pixel value corresponding to the target area according to the target optical image; Performing pixel clustering on the target pixel value to obtain a target clustering result; Determine the number of target pixels corresponding to each target cluster in the target clustering result, and determine the target weight corresponding to the target cluster according to the number of target pixels; Brightness calculation is performed according to the target weight and the target clustering result to obtain the brightness intensity corresponding to the target area.

8. A chemiluminescent immunoassay system combined with an electroluminescent compound label, characterized in that: include: An optical stimulation module, used to use a photodetector to optically stimulate a target complex corresponding to a target antigen to obtain a target stimulation result; wherein the target complex is obtained by combining an electroluminescent compound with an initial complex, and the initial complex is obtained by combining a primary antibody with the target antigen; A data acquisition module, used to acquire images of the target stimulation results using an image sensor to obtain an initial optical image corresponding to the target antigen; An image processing module, used for performing image processing on the initial optical image to obtain a target optical image; An image segmentation module, used to perform image segmentation on the target optical image to obtain a target area, and determine the brightness intensity corresponding to the target area according to the target optical image; A concentration calculation module is used to determine the antigen concentration corresponding to the target antigen according to the brightness intensity.

9. A terminal device, characterized in that: The terminal device includes a processor and a memory; The memory is used to store computer programs; The processor is used to execute the computer program and implement the chemiluminescent immunoassay method combined with electroluminescent compound labeling as described in any one of claims 1 to 7 when executing the computer program.

10. A computer storage medium for computer storage, characterized in that: The computer storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of the chemiluminescent immunoassay method combined with electroluminescent compound labeling as described in any one of claims 1 to 7.

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