An analysis and detection method and device based on weight function and multi-brightness fusion

Abstract

The present invention discloses an analytical detection method and device based on a weight function and multi-brightness fusion, belonging to the field of immunochromatographic analytical detection. The analytical detection method includes obtaining a multi-brightness image set under multiple light intensities, and in the process of restoring the brightness response function of the image acquisition system, using an HDR algorithm based on multi-brightness fusion. The weights of the different brightness images are adjusted by adopting a segmented weight function. By fine-tuning the parameters of the segmented weight function to more accurately reflect the characteristics of each concentration segment in the immunochromatography, an optimal weight function is established to enhance the accuracy of the brightness response function fitting, thereby restoring the highest quality immunochromatographic HDR image and improving the linear range of quantitative detection.

Description

Technical Field

[0001] The present invention belongs to the field of immunochromatographic analysis and detection, and more specifically, relates to an analysis and detection method and device based on weight function and multi-brightness fusion. Background Art

[0002] In recent years, with the rapid development of mobile devices such as handheld devices and smartphones, utilizing the cameras of these portable devices for immunochromatographic imaging has become a new trend. These imaging-based immunoassay devices can quickly, conveniently, and cost-effectively capture the entire image information of the test strip, providing a foundation for subsequent data processing and global analysis.

[0003] Currently, commonly used detection algorithms process the grayscale image of the test strip and then fit a concentration quantification curve. However, due to the limited dynamic range of images captured by cameras, images of immunochromatographic test strips can easily result in overexposure of bright areas or loss of detail in dark areas, severely impacting the accuracy and reproducibility of test results. This limitation of the narrow dynamic range of imaging-based detection equipment is even more pronounced for fluorescent immunochromatographic techniques, which require higher sensitivity and a wider signal range.

[0004] In the consumer digital camera market, the development of HDR technology has expanded the dynamic range of photos, solving the problem of loss of detail in dark or bright areas, allowing a single photo to display both highlights and dark areas. However, traditional HDR technology focuses on simultaneously displaying details in both highlights and shadows within a single photo, primarily focusing on enhancing image contrast and visual quality. It neglects the precise correspondence between the synthesized result and the actual brightness value, and fails to consider signal distortion caused by nonlinear transformations.

[0005] In lateral flow imaging, the HDR algorithm not only needs to preserve the light and dark details in the image, but more importantly, it must ensure that the image can accurately reflect the actual reaction intensity of each area on the test strip, meet the wide concentration range of the test sample (especially in the field of fluorescence immunochromatography, where the fluorescence brightness spans from 0 to 10,000+ average fluorescence intensity), and conform to the characteristics of the S-shaped concentration quantification curve in this field.

[0006] Due to the limited dynamic range of photos in traditional HDR technology, the immunochromatographic detector based on the image imaging principle has problems such as narrow detection range, slow detection speed and low detection accuracy. Summary of the Invention

[0007] In response to the defects of related technologies, the purpose of the present invention is to provide an analysis and detection method based on weight functions and multi-brightness fusion, aiming to solve the problems of limited dynamic range of photos in traditional HDR technology, which leads to the problems of narrow detection range, slow detection speed and low detection accuracy in immunochromatographic detectors based on image imaging principles.

[0008] To achieve the above objectives, in a first aspect, the present invention provides an analysis and detection method based on a weight function and multi-brightness fusion, comprising:

[0009] Photographing the paper card to be tested under different light intensities to obtain a multi-brightness image set of the paper card to be tested;

[0010] Obtaining the actual concentration value of the paper card to be tested according to the standard concentration quantitative curve, the multi-brightness image set of the paper card to be tested and the corresponding light intensity;

[0011] The method for generating the standard concentration quantitative curve includes:

[0012] S1. Take X LDR images of standard immunochromatographic test strips with different concentrations under L different light intensities to obtain a multi-brightness LDR immunochromatographic image set; divide the multi-brightness LDR immunochromatographic image set into a first multi-brightness image set including X1 images of standard immunochromatographic test strips and a second multi-brightness image set including X2 images of standard immunochromatographic test strips; wherein L≥3, X=X1+X2;

[0013] S2. Obtain intensity value changes of N pixels in the T-line region of each image in the first multi-intensity image set to obtain an intensity value matrix of N×L pixels;

[0014] S3. Construct the following piecewise weight function:

[0015]

[0016] Wherein, the function forms of f1(z) and f2(z) include linear function, quadratic polynomial function, exponential function or logarithmic function, and z1 and z2 are segmentation points;

[0017] S4. Determine the function forms of f1(z) and f2(z) and the segmentation points z1 and z2 that minimize the error between the average intensity value of the T-line region and the true intensity value, and obtain a target segmentation weight function; calculate the average intensity value of the T-line region by: obtaining a brightness response function based on the segmentation weight function and the intensity value matrix; obtain an HDR image based on the brightness response function, the second multi-brightness image set, and the corresponding illumination intensity; and perform tone mapping on the HDR image to obtain the average intensity value of the T-line region;

[0018] S5. Obtaining a target brightness response function based on the target segment weight function and the intensity value matrix;

[0019] S7. According to the target brightness response function, the multi-brightness image set of the standard immunochromatographic test strip card, and the corresponding light intensity, the average intensity value of the T-line area of ​​the standard immunochromatographic test strip card is obtained, and the average intensity value of the T-line area of ​​the standard immunochromatographic test strip card is fitted with the corresponding concentration to generate a standard concentration quantitative curve.

[0020] Optionally, the standard immunochromatographic test paper cards have different T line concentrations and the same C line concentrations.

[0021] Optionally, S2 specifically includes:

[0022] S21, extracting a T-line region of each image in the first multi-brightness image set using an image segmentation algorithm;

[0023] S22, vertically stitching the T-line regions of each image in order of concentration to obtain a guide image;

[0024] S23 . Obtain intensity value changes of N / X1 pixels in each T-line region in the guide image to obtain an intensity value matrix of N×L pixels.

[0025] Optionally, the error between the average intensity value and the true intensity value is the root mean square error (RMSE) and the mean absolute percentage error (MAPE) between the average intensity value of the calculated T-line area and the true intensity value of the standard immunochromatographic test paper card.

[0026] Optionally, S7 specifically includes:

[0027] S71, obtaining an average intensity value of a T-line region of the standard immunochromatographic test strip card according to a target brightness response function, a multi-brightness image set of the standard immunochromatographic test strip card, and corresponding illumination time;

[0028] S72. With the average intensity value in S71 as the y-axis and the actual concentration of the standard immunochromatographic test paper card as the x-axis, a four-parameter logistic fit is used to obtain a standard concentration quantitative curve.

[0029] Optionally, the standard immunochromatographic test paper card includes a colloidal gold immunochromatographic card and a fluorescent immunochromatographic card;

[0030] When the standard immunochromatographic test paper card is a colloidal gold immunochromatographic card, the intensity value is a grayscale value;

[0031] When the standard immunochromatographic test paper card is a fluorescent immunochromatographic card, the intensity value is a fluorescence intensity value.

[0032] Optionally, the multi-brightness image set covers underexposure to overexposure at different light intensities.

[0033] Optionally, after obtaining the multi-brightness image set, the method further includes preprocessing the multi-brightness image set using a median filtering algorithm, a Gaussian filtering algorithm, a bilateral filtering algorithm or a guided filtering algorithm.

[0034] In a second aspect, the present invention further provides an analysis and detection device based on a weight function and multi-brightness fusion, for performing the analysis and detection method as described in any one of the first aspects, comprising:

[0035] An image acquisition module is used to photograph the paper card to be tested under different light intensities to obtain a multi-brightness image set of the paper card to be tested;

[0036] The concentration analysis and detection module is used to obtain the actual concentration value of the paper card to be tested based on the standard concentration quantitative curve, the multi-brightness image set of the paper card to be tested and the corresponding light intensity;

[0037] The method for generating the standard concentration quantitative curve includes:

[0038] Taking LDR images of X standard immunochromatographic test strips with different concentrations under L different light intensities to obtain a multi-brightness LDR immunochromatographic image set; dividing the multi-brightness LDR immunochromatographic image set into a first multi-brightness image set including X1 standard immunochromatographic test strip images and a second multi-brightness image set including X2 standard immunochromatographic test strip images; wherein L≥3, X=X1+X2;

[0039] Obtain intensity value changes of N pixels in the T-line region of each image in the first multi-intensity image set to obtain an intensity value matrix of N×L pixels;

[0040] Construct the following piecewise weight function:

[0041]

[0042] Wherein, the function forms of f1(z) and f2(z) include linear function, quadratic polynomial function, exponential function or logarithmic function, and z1 and z2 are segmentation points;

[0043] Determining the function forms of f1(z) and f2(z) and the segmentation points z1 and z2 that minimize the error between the average intensity value of the T-line region and the true intensity value, and obtaining a target segmentation weight function; calculating the average intensity value of the T-line region by: obtaining a brightness response function based on the segmentation weight function and an intensity value matrix; obtaining an HDR image based on the brightness response function, a second multi-brightness image set, and corresponding illumination intensities; and performing tone mapping on the HDR image to obtain an average intensity value of the T-line region;

[0044] The target brightness response function is obtained according to the target segment weight function combined with the intensity value matrix;

[0045] According to the target brightness response function, the multi-brightness image set of the standard immunochromatographic test strip card and the corresponding light intensity, the average intensity value of the T-line area of ​​the standard immunochromatographic test strip card is obtained. The average intensity value of the T-line area of ​​the standard immunochromatographic test strip card is fitted with the corresponding concentration to generate a standard concentration quantitative curve.

[0046] Compared with the prior art, the above technical solutions conceived by the present invention can achieve the following beneficial effects:

[0047] 1. The present invention provides an analysis and detection method based on weight functions and multi-brightness fusion. Aiming at the specific needs of quantitative processing of immunochromatographic images, based on the idea of ​​multi-brightness fusion high dynamic range imaging technology, the brightness response function of the imaging system is first restored, and then the brightness response function is combined with the biological response curve, thereby expanding the linear range of the immunochromatographic standard quantitative curve to solve the limitation of the current immunochromatographic detector based on the image imaging principle that the detection range is too narrow. In the process of restoring the brightness response function of the imaging system, the weights of images of different brightness are adjusted by using a segmented weight function to reduce the influence of over-exposed and under-exposed areas in the image on the fitting accuracy of the brightness response function, so that the standard quantitative curve obtained by fitting fits the characteristics of the immunochromatographic S-shaped curve, which can significantly improve the accuracy of HDR image synthesis and make the analysis and detection results based on the immunochromatographic test strip card more accurate.

[0048] 2. The present invention provides an analytical detection method based on a weighting function and multi-brightness fusion. After generating a standard concentration quantitative curve, a set of multi-brightness images of the test paper card is captured under multiple illumination intensities, thereby obtaining a highly accurate HDR image and an accurate average intensity value of the T-line region of the test paper card. The actual concentration value of the test paper card can be obtained by referring to the standard concentration quantitative curve. This method can be applied to both colloidal gold immunochromatographic test strips and fluorescent immunochromatographic test strips, and can produce accurate concentration detection results. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 This is a flow chart of an analysis and detection method based on a weight function and multi-brightness fusion provided by an embodiment of the present invention;

[0050] Figure 2 17 standard colloidal gold immunochromatographic test strips;

[0051] Figure 3 (A) is an image of 10 self-made standard fluorescent color-scale immunochromatographic test strips. Figure 3 Middle (B) is a guide image formed by combining the T-line area of ​​10 fluorescent immunochromatographic test strip images. Figure 3The red marks in (B) indicate the selection of Q pixels in each ROI area;

[0052] Figure 4 (A) is the 3D image of the red channel of the standard fluorescent test paper card. Figure 4 Middle (B) is the 3D image of the green channel of the standard fluorescent test paper card. Figure 4 Middle (C) is a 3D image of the blue channel of a standard fluorescent test strip card;

[0053] Figure 5 The RGB image and three channel images of the standard fluorescent color scale immunochromatographic test strip card; (A) RGB color fluorescence image, (B) green channel image, (C) blue channel image, (D) red channel image;

[0054] Figure 6 (A) is the RGB image of the standard fluorescent color scale immunochromatographic test paper card. Figure 6 Middle (B) is a single-channel image after extracting the red component and applying guided filtering;

[0055] Figure 7 (A) is the exposure offset under the identity function, Figure 7 (B) is the exposure offset under the hat function. Figure 7 Middle (C) is the exposure offset under the Gaussian function;

[0056] Figure 8 The results of the brightness response function fitting under different weight functions; among them, (A) is the identity function, (B) is the hat function, (C) is the Gaussian function, parameters a = 2, b = 4, (D) is the CRF curve fitted under the identity function, (E) is the CRF curve fitted under the hat function, and (F) is the CRF curve fitted under the Gaussian function;

[0057] Figure 9 (A) is the MAPE of different (a, b) parameter combinations, Figure 9 (B) RMSE of different (a, b) parameter combinations;

[0058] Figure 10 is the image of the Gaussian function when the parameters a=10, b=4;

[0059] Figure 11 The brightness response function curve fitted to the standard fluorescent color scale test paper card;

[0060] Figure 12 A collection of photos and restored HDR images of the fluorescent immunochromatographic test strip card under 7 currents;

[0061] Figure 13Fitting curves of the fluorescent immunochromatographic test strips before and after the multi-brightness fusion HDR algorithm. (A) Comparison of the standard concentration quantitative curves before and after the multi-brightness fusion HDR algorithm. (B) Comparison of the detection linear range before and after the multi-brightness fusion HDR algorithm.

[0062] Figure 14 This is the quantitative curve of colloidal gold immunochromatography standard concentration before and after multi-brightness fusion HDR algorithm. DETAILED DESCRIPTION

[0063] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely for the purpose of explaining the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.

[0064] The contents involved in the above embodiment are described below in conjunction with a preferred embodiment.

[0065] Photo enhancement techniques used in digital cameras can be leveraged to address the dynamic range expansion issue in immunochromatographic testing systems. During immunochromatographic testing, different parameters, such as light source brightness and exposure time, must be flexibly adjusted based on the needs of the test subject. This generates a series of images at varying light intensities. This helps to more effectively restore the most realistic immunochromatographic HDR images during immunochromatographic testing, surpassing those obtained using traditional HDR techniques.

[0066] An analysis and detection method based on weight function and multi-brightness fusion, comprising:

[0067] Photographing the paper card to be tested under different light intensities to obtain a multi-brightness image set of the paper card to be tested;

[0068] Obtaining the actual concentration value of the paper card to be tested according to the standard concentration quantitative curve, the multi-brightness image set of the paper card to be tested and the corresponding light intensity;

[0069] The method for generating the standard concentration quantitative curve includes:

[0070] S1. Take X LDR images of standard immunochromatographic test strips with different concentrations under L different light intensities to obtain a multi-brightness LDR immunochromatographic image set; divide the multi-brightness LDR immunochromatographic image set into a first multi-brightness image set including X1 images of standard immunochromatographic test strips and a second multi-brightness image set including X2 images of standard immunochromatographic test strips; wherein L≥3, X=X1+X2;

[0071] S2. Obtain intensity value changes of N pixels in the T-line region of each image in the first multi-intensity image set to obtain an intensity value matrix of N×L pixels;

[0072] S3. Construct the following piecewise weight function:

[0073]

[0074] Wherein, the function forms of f1(z) and f2(z) include linear function, quadratic polynomial function, exponential function or logarithmic function, and z1 and z2 are segmentation points;

[0075] S4. Determine the function forms of f1(z) and f2(z) and the segmentation points z1 and z2 that minimize the error between the average intensity value of the T-line region and the true intensity value, and obtain a target segmentation weight function; calculate the average intensity value of the T-line region by: obtaining a brightness response function based on the segmentation weight function and the intensity value matrix; obtain an HDR image based on the brightness response function, the second multi-brightness image set, and the corresponding illumination intensity; and perform tone mapping on the HDR image to obtain the average intensity value of the T-line region;

[0076] S5. Obtaining a target brightness response function based on the target segment weight function and the intensity value matrix;

[0077] S7. According to the target brightness response function, the multi-brightness image set of the standard immunochromatographic test strip card, and the corresponding light intensity, the average intensity value of the T-line area of ​​the standard immunochromatographic test strip card is obtained, and the average intensity value of the T-line area of ​​the standard immunochromatographic test strip card is fitted with the corresponding concentration to generate a standard concentration quantitative curve.

[0078] Optionally, S2 specifically includes:

[0079] S21, extracting a T-line region of each image in the first multi-brightness image set using an image segmentation algorithm;

[0080] S22, vertically stitching the T-line regions of each image in order of concentration to obtain a guide image;

[0081] S23 . Obtain intensity value changes of N / X1 pixels in each T-line region in the guide image to obtain an intensity value matrix of N×L pixels.

[0082] Optionally, S7 specifically includes:

[0083] S71, obtaining an average intensity value of a T-line region of the standard immunochromatographic test strip card according to a target brightness response function, a multi-brightness image set of the standard immunochromatographic test strip card, and corresponding illumination time;

[0084] S72. With the average intensity value in S71 as the y-axis and the actual concentration of the standard immunochromatographic test paper card as the x-axis, a four-parameter logistic fit is used to obtain a standard concentration quantitative curve.

[0085] Optionally, the standard immunochromatographic test paper card includes a colloidal gold immunochromatographic card and a fluorescent immunochromatographic card;

[0086] When the standard immunochromatographic test paper card is a colloidal gold immunochromatographic card, the intensity value is a grayscale value;

[0087] When the standard immunochromatographic test paper card is a fluorescent immunochromatographic card, the intensity value is a fluorescence intensity value.

[0088] Optionally, the multi-brightness image set covers underexposure to overexposure at different light intensities.

[0089] Optionally, after obtaining the multi-brightness image set, the method further includes preprocessing the multi-brightness image set using a median filtering algorithm, a Gaussian filtering algorithm, a bilateral filtering algorithm or a guided filtering algorithm.

[0090] The specific process can be divided into (1) initialization stage: establishing and optimizing the luminance response function (LRF) curve of the imaging system; (2) use stage: taking a multi-luminance image set of a standard immunochromatographic test strip card with different concentrations, using the established luminance response function curve to guide the multi-luminance image set to generate an HDR image, and then extracting the characteristic values ​​of the region of interest (ROI) in the HDR image to expand the linear range of the standard quantitative curve.

[0091] Before acquiring the multi-intensity image set, it also includes:

[0092] Design X standard immunochromatographic test strips with different T-line concentrations, and set the C-line concentration of each standard immunochromatographic test strip to be the same.

[0093] Among them, the standard immunochromatographic test paper card includes colloidal gold immunochromatographic card and fluorescent immunochromatographic card. When the standard immunochromatographic test paper card is a colloidal gold immunochromatographic card, the intensity value is a grayscale value; when the standard immunochromatographic test paper card is a fluorescent immunochromatographic card, the intensity value is a fluorescence intensity value.

[0094] When colloidal gold immunochromatographic test strips are used in standard immunochromatographic test strips, image processing software is used to produce standard colloidal gold immunochromatographic test strips, and the quality control strip area frame of the corresponding size is designed, including the design of the quality control line (C line) and the detection line (T line). The size of the quality control strip and the position of the C line and T line are cut to be exactly the same as the size and position of the actual test strip card product, and three test strips of the same concentration are produced at a time, reserved for subsequent repeatability tests.

[0095] After the standard colloidal gold immunochromatographic test strips were designed, they were printed on Pantac 230g reinforced matte paper using a high-resolution color photocopying device. In this example, a standard immunochromatographic test strip with 36 different T-line grayscales ranging from 0 to 255 pixels was designed with a pixel difference of 7. The C-line pixel values ​​on each test strip were identical. After all the printed and encased test strips were encased, they represented 36 standard colloidal gold immunochromatographic test strips with identical C-line grayscale values ​​and different T-line grayscale values.

[0096] When the standard immunochromatographic test paper card adopts the fluorescent immunochromatographic test paper card, the standard fluorescent immunochromatographic test paper card is composed of 19 self-made fluorescent immunochromatographic test paper cards with different T line concentrations. The T line concentrations of the 19 fluorescent immunochromatographic test paper cards range from low to high, covering from 101 to 10 4 The fluorescence intensity is high and the dynamic range is wide.

[0097] In this example, the measurement of standard fluorescent immunochromatographic test strips was performed under dark conditions. A sample set of 17 test strips was captured under UV light and a specific current to cover various conditions from underexposure to overexposure. The brightness image set covered pixel values ​​from 0 to 255 at different light intensities. A set of multi-brightness fluorescent immunochromatographic images was then obtained. In this example, L was set to 17. Figure 2 As shown, the standard fluorescent color scale test paper card was photographed at 7 currents (26.6mA, 58.6mA, 91.5mA, 129.5mA, 166.6mA, 206.1mA, 228.6mA), and a set of 17×7 multi-exposure photos from underexposure to overexposure was obtained. 10 test paper cards were evenly selected to cover the fluorescence intensity of 101-104 as much as possible, namely 1# test paper card, 3# test paper card, 5# test paper card, 7# test paper card, 9# test paper card, 10# test paper card, 11# test paper card, 13# test paper card, 15# test paper card, and 17# test paper card. These 10×7 images are the first multi-brightness image set, which will be used to establish the brightness response function curve. The obtained multi-brightness photo set of the standard fluorescent color scale test paper card is shown as follows: Figure 3 shown.

[0098] By observing the image set of standard fluorescent color scale test strips, we can see that when the current is low, the illumination is weak, resulting in a dark digital image and low pixel grayscale values, which easily leads to large underexposed areas. As the current increases, the illumination intensity also increases, the image becomes brighter, and the standard color scale card's discrimination increases, until overexposure occurs.

[0099] After acquiring the multi-intensity image set, the images need to be preprocessed. Fluorescent immunochromatographic images are acquired using CMOS image sensors, which often contain noise. The shorter the exposure time, the more noise there is in the image. The multi-intensity image set can be preprocessed using a median filter, Gaussian filter, bilateral filter, or guided filter. In this embodiment, the most effective guided filter algorithm is used to preprocess the exposed image set, effectively reducing image noise and enhancing target visibility.

[0100] Optionally, S2 specifically includes:

[0101] S21 . Extract the T-line region of each image in the first multi-brightness image set using an image segmentation algorithm.

[0102] A tone mapping algorithm is used to convert HDR images in .hdr format into high-quality LDR images to accommodate the dynamic range of conventional displays. The location and size of the region of interest (e.g., the T-line region) on a standard immunochromatographic test strip are determined to extract the ROI and convert it into a single-channel grayscale image. The grayscale image is divided into two parts at its vertical midpoint. A sliding window threshold is applied to the two parts, with the window moving vertically along the image. The average pixel value within each window is calculated. By comparing the pixel values ​​of different windows, the region with the minimum pixel value is found, corresponding to the T-line and C-line.

[0103] The segmented image clearly identifies the T-line and C-line regions, and the average pixel value of these two regions is calculated.

[0104] Alternatively, other threshold segmentation algorithms can be used, such as the maximum between-class variance (OTSU) algorithm.

[0105] S22 , vertically stitching the T-line regions of each image in the order of fluorescence concentration to obtain a guidance image.

[0106] S23 . Obtain intensity value changes of N / X1 pixels in each T-line region in the guide image to obtain an intensity value matrix of N×L pixels.

[0107] In this guide image, five pixels are acquired from each T-line region, for a total of 50 pixels. The changes in pixel values ​​for these 50 pixels under seven multi-brightness sequences form a grayscale matrix, which will be used to fit the brightness response function curve. For a pixel value range of (Zmax - Zmin) = 255 and seven multi-brightness images per test card, N = 50 > [255 / (7-1) = 43] pixels is selected. Therefore, in each of the 12 T-line ROI regions, Q = 50 / 10 = 5 pixels is selected.

[0108] A weight function is constructed to optimize the brightness response function curve. The form of the weight function needs to be determined first.

[0109] Standard concentration quantification curves in immunochromatography often exhibit S-shaped characteristics. In the middle of this curve, a linear relationship exists between the measured pixel value and the target substance concentration, while at the ends, the relationship plateaus and becomes less apparent. Therefore, a piecewise weighting function strategy is employed to more precisely reflect the characteristics of each concentration segment. The linear range of the standard concentration quantification curve is used to determine the boundaries between the linear and nonlinear intervals. The weight coefficient is set to 1 in the linear interval, preserving the linear characteristics of the original signal.

[0110] The multi-intensity image set was taken at 7 different currents and calculated according to the formula:

[0111]

[0112] Therefore, at least 43 pixels need to be selected to meet the minimum requirement of the number of pixels required for fitting the brightness response function curve. In this embodiment, 50 pixels are selected to fit the brightness response function curve of the colloidal gold immunochromatography microscopy image acquisition device.

[0113] If 50 pixels with different grayscale values ​​are selected from the image, the digital intensity value matrix of the 50 pixels in the image under 7 different currents is obtained, and the corresponding brightness response function F curve and irradiance Ei are derived based on this.

[0114] When the current is less than a certain value, the current passing through the LED is proportional to the light intensity:

[0115] I e =k1I(2)

[0116] I e is the light intensity, k1 is the proportional coefficient. e The irradiance generated by an isotropic point light source at a distance r is:

[0117]

[0118] Among them, r is the distance from the camera lens to the test paper card, and θ is the angle between the incident light and the vertical plane of the image sensor. So when all positions remain unchanged, the irradiance E and the light source intensity I can be obtained. e The experiments in this paper were conducted strictly in a dark environment, so the influence of changes in ambient light on the experimental data can be eliminated. Only the LED light source is considered when irradiating the test paper card. When the algorithm is applied to the actual test paper card detector, the test paper card is inserted into the black box for testing. Therefore, simplified experimental conditions and complex influencing factors are considered. Combining formulas (2) and (3), the following conclusion is obtained: If various conditions are simplified and changes in other factors are not considered, the irradiance E is proportional to the input current I.

[0119] f is the function that maps exposure to digital image pixel value and can be expressed as:

[0120] Z=f(E,I e ,Δt)(4)

[0121] Z=f(E,I,Δt)(5)

[0122] According to the formula of Z and I, a function transformation is designed to record the current set as I m , m=1, 2, ..., L, let the mth current be I m , the irradiance of the i-th pixel is:

[0123] E im =E i I m (6)

[0124] I j The different values ​​of are set manually, so I j is also known. And E i is a constant, because when the light intensity remains constant, the irradiance E of each pixel i is a constant value, formula (6) can be written as:

[0125] f -1 (Z im )=E im Δt=E i I m Δt (7)

[0126] Z ij is the pixel value of the i-th point under the m-th current. Since Δt remains unchanged, formula (7) can be written as:

[0127] f -1 (Z im )=E im Δt=E i I m Δt=Ki I m , K i =E i Δt (8)

[0128] F(Z im )=lnK i +lnI m (9)

[0129] F is the brightness response function, and the problem is transformed into using the least squares method to solve the following objective function O I K under minimization i And the estimated value of F:

[0130]

[0131] This is a K i and F. Through the optimization algorithm, we can find K that minimizes the objective function. i and the values ​​of F, thereby obtaining the optimal brightness response function curve F.

[0132] This embodiment analyzes the application of identity function, hat function and Gaussian function in the brightness response function curve fitting, and obtains the following Figure 7 The five pixels shown have different exposure offsets calculated under different weight functions and Figure 8 The three response function curves g are shown.

[0133] Among them, the identity function form is:

[0134] w1(z)=1,z∈[0,255]

[0135] The functional form of the hat function is:

[0136]

[0137] The functional form of the Gaussian function is:

[0138]

[0139] Among them, parameter a controls the shape of the Gaussian function. A larger a value results in a wider range of pixels with a weight value of 1 in the weight function. Parameter b controls the width of the Gaussian function. A larger b value makes the distribution range of pixels with a weight value greater than 0 narrower.

[0140] pass Figure 8The experimental results are obtained, and the residuals of the brightness response function curve obtained under three different weight functions, namely the identity function, the hat function and the Gaussian function, are calculated. They are 6.24503, 0.27919 and 0.19764 respectively. The experimental results show that the fitting degree of the CRF curve under the Gaussian function is better than the curve fitting results under the identity function and the hat function.

[0141] Furthermore, different (a, b) parameter combinations of the Gaussian function will fit different CRF results, and different fitting results will directly affect the pixel value of each pixel in the HDR image. Therefore, this embodiment establishes a parameter selection algorithm to optimize the selection of a and b of the Gaussian function. The a and b of the Gaussian function are respectively taken within a certain interval, where a is between [2, 50] and a is an even number, and b is between [1, 4]. Because when a = 50 and b = 4, the Gaussian function has set the weights of most pixel values ​​to 1, which is similar to the identity function.

[0142] The MAPE and RMSE values ​​between the pixel values ​​of the T line in the HDR image corrected by the CRF curve fitted with different parameters a and b and the actual T line printed grayscale value are calculated. By comparing the MAPE and RMSE results of 36 test strips under different a and b parameter combinations, the optimal CRF curve is determined, which makes the pixel values ​​of the T line in the corrected HDR image closer to the actual printed grayscale value. The comparison results are shown in Figure 2. Figure 9 shown.

[0143] Experimental results show that when the parameter combination is (a=10, b=4), the obtained MAPE and RMSE values ​​reach their minimum values, which are 45.16% and 18.21 respectively. This shows that under this parameter combination, the CRF curve fitted by the Gaussian function can effectively correct the HDR image, making its T-line pixel values ​​closest to the true intensity value.

[0144] According to the optimal function form of the Gaussian function, the following is drawn: Figure 10 As shown in the weight function diagram, it can be found that the weight values ​​begin to turn when the pixel values ​​are 50 and 200, which means that these two pixel values ​​approximately mark the end of the low-brightness area and the beginning of the high-brightness area in the response function.

[0145] So we construct a piecewise function that behaves differently in low-brightness and high-brightness areas. The functional form of the piecewise function is:

[0146]

[0147] Wherein, the function forms of f1(z) and f2(z) include linear function, quadratic polynomial function, exponential function or logarithmic function, and z1 and z2 are segmentation points;

[0148] After determining the form of the piecewise weight function, it is necessary to solve the piecewise weight function. First, set the values ​​of the initial segmentation points z1 and z2 to obtain the first piecewise weight function. In this embodiment, the value of segmentation point z1 is set to 50 and the value of segmentation point z2 is set to 200. Based on this, the appropriate functions f1(z) and f2(z) are selected.

[0149] Functions such as linear, quadratic polynomial, exponential, and logarithmic are selected as candidates for f1(z) and f2(z). The current segmented weight function (i.e., the first segmented weight function) is combined with the intensity matrix to obtain the current brightness response function. The mean absolute percentage error (MAPE) and root mean square error (RMSE) between the intensity values ​​of the T-line in the HDR image and the actual printed grayscale values ​​of the T-line are calculated by correcting the brightness response function curve. The function types of f1(z) and f2(z) are changed, and the first segmented weight function is updated to update the brightness response function. The calculated MAPE and RMSE values ​​are updated. Using MAPE and RMSE as the objective function, the f1(z) and f2(z) values ​​that minimize the MAPE and RMSE are solved as the final expression of the segmented function. The optimal segmented function form is found and compared with the optimal function form of the Gaussian function.

[0150] The optimal f1(z) and f2(z) function forms are selected by adjusting the piecewise function form based on the MAPE and RMSE values. The MAPE and RMSE values ​​calculated for different piecewise functions are shown in Table 1. The experimental results show that when f1(x) is an exponential function and f2(x) is a linear function, the obtained MAPE and RMSE values ​​are minimized, indicating that the piecewise function under this combination can achieve the optimal correction of HDR images.

[0151] After updating the expression of the first segment weight function, it is necessary to optimize the value range of the segment points. The search range of z1 and z2 is related to the performance of the CMOS image sensor used. Setting the search range of segment points z1 and z2 yields the second segment weight function.

[0152] The second segmented weight function was updated incrementally using a loop with a step size of 1 for z1 and z2. The MAPE and RMSE values ​​between the pixel values ​​of the T-line in the HDR image corrected for different z1 and z2 values ​​and the grayscale values ​​of the actual printed T-line were calculated. The segmentation point for the target weight function was determined based on the minimum MAPE and RMSE. The optimization algorithm determined that when z1 = 50 and z2 = 215, the MAPE for the seven test strips was 40.23% and the RMSE was 12.48, achieving the minimum error.

[0153] Therefore, when the standard immunochromatographic test paper card is a fluorescent immunochromatographic card, the target weight function for the fluorescent immunochromatographic image is as follows:

[0154]

[0155] After obtaining the target segment weight function, the target brightness response function can be obtained according to the target segment weight function combined with the intensity value matrix.

[0156] Because captured fluorescence images are formatted as RGB (red, green, and blue) channels, and these channels are stored as three two-dimensional channels in a computer, the standard RGB format is 24 bits, with each component occupying 8 bits. For example, when the emission wavelength of fluorescent microspheres is 610nm, which is within the red wavelength range, the characteristics of fluorescence images require the first step of extracting the red component of each image to obtain an image with more fluorescence information and less noise, allowing for further image segmentation and feature extraction. Figure 3 The three-dimensional images of each channel of the homemade standard fluorescent color chromatographic analysis test card image in (B) are as follows Figure 4 As shown in the figure, the three-dimensional graph intuitively shows that the red component of the fluorescence image contains more information than the other two channels, and is closest to the distribution of the RGB image fluorescence with the least burrs.

[0157] For example, for fluorescence images with fluorescence emission wavelengths in the red light band, the R component images in all images are extracted as color-scale images to obtain images containing more fluorescence information and less noise; for colloidal gold images, the original grayscale images can be directly used as color-scale images.

[0158] Figure 5 The single-channel image after separating the three channels is also shown. Compared with the RGB color image, the red channel image contains the most fluorescence information.

[0159] As shown in the flowchart, after obtaining the red component photo, the standard fluorescent color scale card photo set is guided filtered and then input into the algorithm. The PSNR of the filtered image is 49.55 and the SSIM is 0.9998. Figure 6 As shown, the filtering effect is very good.

[0160] The image set of 13 standard fluorescent color scale test paper cards is selected, and N = 50 pixels are selected to solve the K that minimizes the objective function of formula (10) i After calculation by Python software, the brightness response function curves of the two imaging systems are obtained, as shown in Figure 11 shown.

[0161] The experimental results show that the LRF curve trends of the two datasets obtained under the two shooting systems are consistent, but it is noted that the logarithmic exposure values ​​corresponding to the vertical axis are different because the two cameras convert digital pixel values ​​into real irradiance values ​​differently.

[0162] The irradiance recovery of the multi-brightness fusion high dynamic range algorithm is as follows:

[0163]

[0164] Once the brightness response curves of the two image acquisition devices are successfully restored, the weighted high dynamic range irradiance value of each pixel in the multi-brightness image set will be calculated using formula (11). Figure 2 A multi-brightness image set of 7 fluorescent immunochromatographic test strips with different concentrations is used to correct the irradiance value and synthesize the HDR image.

[0165] Since the fluorescence intensity of each fluorescent test paper card is different, in the experiment, the appropriate current value is selected according to the image situation to shoot a multi-brightness image set, for example Figure 12 The multi-brightness image set of B4 fluorescent immunochromatographic test paper card taken at 7 current values ​​and the Figure 11 The HDR image synthesized under the medium LRF curve shows that the contrast of the HDR image and the clarity of the C line and T line are better than those of the 7 multi-brightness LDR photos.

[0166] The multi-brightness fluorescent immunochromatographic test paper card is photographed Figure 12 Figure 2 shows seven multi-brightness images taken of a B4 fluorescent immunochromatographic test strip and an HDR image synthesized under the CRF curve. The HDR image's contrast and C- and T-line clarity are superior to those of the multi-brightness LDR image set. Furthermore, the HDR image of the fluorescence image improves contrast while also effectively handling haloes from strong fluorescence.

[0167] After obtaining a set of seven fluorescent immunochromatographic HDR images, a sliding window algorithm was used to extract the T lines from the images, and the T-line pixel values ​​were recorded in Table 4-2. For the fluorescent immunochromatographic test strips, fluorescence values ​​were measured on the B1-B7 fluorescent test strips using a commercial fluorescent immunochromatographic instrument before testing. The measured values ​​are recorded as "commercial instrument quantitative T lines." The LDR images of the seven test strips, taken at 91.5 mA, were also segmented to determine the target T lines and calculate their pixel values. These measured values ​​are recorded as "pre-HDR T lines."

[0168] Table 4-2 Average pixel values ​​of T lines before and after the multi-brightness fusion HDR algorithm for fluorescent immunochromatographic test strips

[0169]

[0170] Finally, a standard concentration quantification curve was fitted, comparing the average T-line pixel values ​​of the fluorescence immunochromatography HDR image set with the values ​​measured by a commercial fluorescence immunochromatography analyzer. A four-parameter logistic model was used to fit the experimental data, resulting in an S-shaped curve for the standard concentration quantification of the fluorescence test strips. Once this standard concentration quantification curve is obtained, quantitative analysis of fluorescence test strips of any concentration can be performed.

[0171] Optionally, S7 specifically includes:

[0172] S71, obtaining an average intensity value of a T-line region of the standard immunochromatographic test strip card according to a target brightness response function, a multi-brightness image set of the standard immunochromatographic test strip card, and a corresponding exposure time;

[0173] S72. With the average pixel value in S71 as the y-axis and the actual concentration of the standard immunochromatographic test paper card as the x-axis, a four-parameter logistic fit is used to obtain a standard concentration quantitative curve.

[0174] The four-parameter logistic fitting model is usually expressed as follows:

[0175]

[0176] Among them, A1 represents the maximum eigenvalue of the curve, that is, the maximum average pixel value of the T line in the measurement image; A2 represents the minimum eigenvalue of the curve, that is, the minimum average pixel value of the T line in the measurement image; x represents the independent variable, that is, the printed grayscale value of the T line; x0 is the EC50 (half-maximal effect concentration), that is, the printed grayscale value corresponding to the middle pixel value of the T line in the measurement image; p controls the slope of the curve. At the same time, the four-parameter logistic fitting model has an important evaluation parameter, the goodness of fit R 2 This value reflects the goodness of fit of the quantitative curve of the standard concentration. The closer its value is to 1, the more accurate the fitted curve is. By fitting the experimental data to a four-parameter logistic model, these parameters can be estimated and an S-shaped curve can be obtained that fits the data well.

[0177] After obtaining the standard concentration quantitative curve, it also includes:

[0178] Photographing the paper card to be tested under different light intensities to obtain a multi-brightness image set of the paper card to be tested;

[0179] The actual concentration value of the paper card to be tested is obtained according to the standard concentration quantitative curve, the multi-brightness image set of the paper card to be tested and the corresponding light intensity.

[0180] Analysis of the standard concentration quantitative curve fitting, Figure 13In the figure (A), the horizontal axis represents the T-line fluorescence intensity values ​​measured on seven fluorescent immunochromatographic test strips using a fluorescent immunoassay analyzer. The vertical axis of the black fitting curve represents the average T-line pixel value before and after the multi-intensity fusion HDR algorithm is used. The vertical axis of the red fitting curve represents the average T-line pixel value after the multi-intensity fusion HDR algorithm is used. The results show that the goodness of fit of the standard concentration quantitative curve increased from 0.97329 to 0.98632 after the multi-intensity fusion HDR algorithm was used. Figure 13 (B) compares the linear range before and after using the multi-brightness fusion HDR algorithm. The results show that before using the HDR algorithm, there is a good linear relationship between 1200-10000 (fluorescence characteristic value), with a fitting degree of 0.99207. After using the HDR algorithm, the linear range is extended to 1200-18000 (fluorescence characteristic value), and the fitting degree reaches 0.99287, proving the effectiveness of the multi-brightness fusion HDR algorithm for quantitative fluorescence immunochromatography images. Figure 14 shown.

[0181] Based on the above examples, the impact of the number of multi-brightness images required for the multi-brightness fusion HDR algorithm was further considered. Experimental results show that for fluorescent immunochromatographic test strips, due to the characteristics of the fluorescent signal, the method of using three multi-brightness photos instead of nine photos is not applicable. HDR image synthesis of fluorescent immunochromatographic test strips requires a more refined set of multi-brightness images to ensure high quality of the final image and a more accurate expansion of the detection range.

[0182] Although the above scheme uses fluorescent immunochromatography as an example, this method is also applicable to colloidal gold immunochromatography and can effectively improve its detection performance.

[0183] In the process of restoring the brightness response function of the image acquisition system, the embodiment of the present invention uses a segmented weight function to adjust the weights of images with different illumination intensities, accurately adjusts the fusion of multiple brightness images, and reduces the influence of over-exposure and under-exposure areas in the image on the fitting accuracy of the brightness response function, so that the standard quantitative curve obtained by fitting fits the characteristics of the immunochromatographic S-shaped curve, obtains a wider brightness range under different illumination intensities, effectively overcomes the limitation of loss of highlight or dark details in a single brightness image, and can significantly improve the accuracy of HDR image synthesis. It effectively reduces the influence of image noise and quantization errors, makes the synthesized immunochromatographic image closer to the real scene, and makes the analysis and detection results based on the immunochromatographic test strip card more accurate. It solves the technical problem that the dynamic range of photos in traditional HDR technology is limited, resulting in the immunochromatographic detector based on the image imaging principle having a narrow detection range, slow detection speed and low detection accuracy, and achieves improved detection speed and detection accuracy, and expands the scope of analysis and detection of immunochromatographic test strip cards.

[0184] Example 2

[0185] Based on the above embodiments, the present invention further provides an analysis and detection device based on a weight function and multi-brightness fusion, characterized in that it is used to perform the analysis and detection method as described in any one of the first embodiments, including:

[0186] An image acquisition module is used to photograph the paper card to be tested under different light intensities to obtain a multi-brightness image set of the paper card to be tested;

[0187] The concentration analysis and detection module is used to obtain the actual concentration value of the paper card to be tested based on the standard concentration quantitative curve, the multi-brightness image set of the paper card to be tested and the corresponding light intensity;

[0188] The method for generating the standard concentration quantitative curve includes:

[0189] Taking LDR images of X standard immunochromatographic test strips with different concentrations under L different light intensities to obtain a multi-brightness LDR immunochromatographic image set; dividing the multi-brightness LDR immunochromatographic image set into a first multi-brightness image set including X1 standard immunochromatographic test strip images and a second multi-brightness image set including X2 standard immunochromatographic test strip images; wherein L≥3, X=X1+X2;

[0190] Obtain intensity value changes of N pixels in the T-line region of each image in the first multi-intensity image set to obtain an intensity value matrix of N×L pixels;

[0191] Construct the following piecewise weight function:

[0192]

[0193] Wherein, the function forms of f1(z) and f2(z) include linear function, quadratic polynomial function, exponential function or logarithmic function, and z1 and z2 are segmentation points;

[0194] Determining the function forms of f1(z) and f2(z) and the segmentation points z1 and z2 that minimize the error between the average intensity value of the T-line region and the true intensity value, and obtaining a target segmentation weight function; calculating the average intensity value of the T-line region by: obtaining a brightness response function based on the segmentation weight function and an intensity value matrix; obtaining an HDR image based on the brightness response function, a second multi-brightness image set, and corresponding illumination intensities; and performing tone mapping on the HDR image to obtain an average intensity value of the T-line region;

[0195] The target brightness response function is obtained according to the target segment weight function combined with the intensity value matrix;

[0196] According to the target brightness response function, the multi-brightness image set of the standard immunochromatographic test strip card and the corresponding light intensity, the average intensity value of the T-line area of ​​the standard immunochromatographic test strip card is obtained. The average intensity value of the T-line area of ​​the standard immunochromatographic test strip card is fitted with the corresponding concentration to generate a standard concentration quantitative curve.

[0197] An analysis and detection device based on weight function and multi-brightness fusion provided by an embodiment of the present invention is used to execute an analysis and detection method based on weight function and multi-brightness fusion provided by any embodiment 1 of the present invention, and has corresponding functional modules and beneficial effects.

[0198] It will be easily understood by those skilled in the art that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. An analysis and detection method based on weight function and multi-brightness fusion, characterized in that: include: Photographing the paper card to be tested under different light intensities to obtain a multi-brightness image set of the paper card to be tested; Obtaining the actual concentration value of the paper card to be tested according to the standard concentration quantitative curve, the multi-brightness image set of the paper card to be tested and the corresponding light intensity; The method for generating the standard concentration quantitative curve includes: S1. Take X LDR images of standard immunochromatographic test strips with different concentrations under L different light intensities to obtain a multi-brightness LDR immunochromatographic image set; divide the multi-brightness LDR immunochromatographic image set into a first multi-brightness image set including X1 images of standard immunochromatographic test strips and a second multi-brightness image set including X2 images of standard immunochromatographic test strips; wherein L≥3, X=X1+X2; S2. Obtain intensity value changes of N pixels in the T-line region of each image in the first multi-intensity image set to obtain an intensity value matrix of N×L pixels; S3. Construct the following piecewise weight function: Wherein, the function forms of f1(z) and f2(z) include linear function, quadratic polynomial function, exponential function or logarithmic function, and z1 and z2 are segmentation points; S4. Determine the function forms of f1(z) and f2(z) and the segmentation points z1 and z2 that minimize the error between the average intensity value of the T-line region and the true intensity value, and obtain a target segmentation weight function; calculate the average intensity value of the T-line region by: obtaining a brightness response function based on the segmentation weight function and the intensity value matrix; obtain an HDR image based on the brightness response function, the second multi-brightness image set, and the corresponding illumination intensity; and perform tone mapping on the HDR image to obtain the average intensity value of the T-line region; S5. Obtaining a target brightness response function based on the target segment weight function and the intensity value matrix; S7. According to the target brightness response function, the multi-brightness image set of the standard immunochromatographic test strip card, and the corresponding light intensity, the average intensity value of the T-line area of ​​the standard immunochromatographic test strip card is obtained, and the average intensity value of the T-line area of ​​the standard immunochromatographic test strip card is fitted with the corresponding concentration to generate a standard concentration quantitative curve.

2. The analysis and detection method according to claim 1, wherein The standard immunochromatographic test paper cards have different T line concentrations and the same C line concentration.

3. The analysis and detection method according to claim 1, wherein S2 specifically includes: S21, extracting a T-line region of each image in the first multi-brightness image set using an image segmentation algorithm; S22, vertically stitching the T-line regions of each image in order of concentration to obtain a guide image; S23 . Obtain intensity value changes of N / X1 pixels in each T-line region in the guide image to obtain an intensity value matrix of N×L pixels.

4. The analysis and detection method according to claim 1, wherein The error between the average intensity value and the true intensity value is the root mean square error (RMSE) and the mean absolute percentage error (MAPE) between the average intensity value of the T-line area and the true intensity value of the standard immunochromatographic test paper card.

5. The analysis and detection method according to claim 1, wherein S7 specifically includes: S71, obtaining an average intensity value of a T-line region of the standard immunochromatographic test strip card according to a target brightness response function, a multi-brightness image set of the standard immunochromatographic test strip card, and corresponding illumination time; S72. With the average intensity value in S71 as the y-axis and the actual concentration of the standard immunochromatographic test paper card as the x-axis, a four-parameter logistic fit is used to obtain a standard concentration quantitative curve.

6. The analysis and detection method according to claim 1, wherein The standard immunochromatographic test paper cards include colloidal gold immunochromatographic cards and fluorescent immunochromatographic cards; When the standard immunochromatographic test paper card is a colloidal gold immunochromatographic card, the intensity value is a grayscale value; When the standard immunochromatographic test paper card is a fluorescent immunochromatographic card, the intensity value is a fluorescence intensity value.

7. The analysis and detection method according to claim 1, wherein The multi-brightness image set covers underexposure to overexposure at different light intensities.

8. The analysis and detection method according to claim 1, wherein After obtaining the multi-brightness image set, the method further includes preprocessing the multi-brightness image set by using a median filtering algorithm, a Gaussian filtering algorithm, a bilateral filtering algorithm or a guided filtering algorithm.

9. An analysis and detection device based on weight function and multi-brightness fusion, characterized in that: Used to perform the analysis and detection method according to any one of claims 1 to 8, comprising: An image acquisition module is used to photograph the paper card to be tested under different light intensities to obtain a multi-brightness image set of the paper card to be tested; The concentration analysis and detection module is used to obtain the actual concentration value of the paper card to be tested based on the standard concentration quantitative curve, the multi-brightness image set of the paper card to be tested and the corresponding light intensity; The method for generating the standard concentration quantitative curve includes: Taking LDR images of X standard immunochromatographic test strips with different concentrations under L different light intensities to obtain a multi-brightness LDR immunochromatographic image set; dividing the multi-brightness LDR immunochromatographic image set into a first multi-brightness image set including X1 standard immunochromatographic test strip images and a second multi-brightness image set including X2 standard immunochromatographic test strip images; wherein L≥3, X=X1+X2; Obtain intensity value changes of N pixels in the T-line region of each image in the first multi-intensity image set to obtain an intensity value matrix of N×L pixels; Construct the following piecewise weight function: Wherein, the function forms of f1(z) and f2(z) include linear function, quadratic polynomial function, exponential function or logarithmic function, and z1 and z2 are segmentation points; Determining the function forms of f1(z) and f2(z) and the segmentation points z1 and z2 that minimize the error between the average intensity value of the T-line region and the true intensity value, and obtaining a target segmentation weight function; calculating the average intensity value of the T-line region by: obtaining a brightness response function based on the segmentation weight function and an intensity value matrix; obtaining an HDR image based on the brightness response function, a second multi-brightness image set, and corresponding illumination intensities; and performing tone mapping on the HDR image to obtain an average intensity value of the T-line region; The target brightness response function is obtained according to the target segment weight function combined with the intensity value matrix; According to the target brightness response function, the multi-brightness image set of the standard immunochromatographic test strip card and the corresponding light intensity, the average intensity value of the T-line area of ​​the standard immunochromatographic test strip card is obtained. The average intensity value of the T-line area of ​​the standard immunochromatographic test strip card is fitted with the corresponding concentration to generate a standard concentration quantitative curve.

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