Weight function and multi-brightness fusion-based analysis and detection method and device

Abstract

The invention discloses an analysis and detection method and device based on a weighting function and multi-brightness fusion, and belongs to the field of immunochromatography analysis and detection. The analysis and detection method comprises the steps that a multi-brightness image set is obtained under multiple illumination intensities, in the process of restoring a brightness response function of an image acquisition system, an HDR algorithm based on multi-brightness fusion is used, weights of different brightness images are adjusted by adopting a segmented weight function, parameters of the segmented weight function are finely regulated and controlled, and the brightness response function of the image acquisition system is recovered. The characteristics of each concentration section in immunochromatography are reflected more finely, and the optimal weighting function is established to enhance the accuracy of brightness response function fitting, so that the immunochromatography HDR image with the highest quality is recovered, and the linear range of quantitative detection is expanded.

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 smart phones, using the cameras of these portable devices for immunochromatographic image detection has become a new trend. These immunoassay devices based on the principle of image imaging can quickly, conveniently and cost-effectively capture all image information of the entire test strip card, providing a basis for subsequent data processing and global analysis.

[0003] The commonly used detection algorithm is based on the grayscale image of the test strip, and then fits the concentration quantitative curve. However, due to the limited dynamic range of the image captured by the camera, it is easy to have the problem of overexposure of bright parts or loss of details in dark parts when shooting the immunochromatographic test strip image, which seriously affects the accuracy and reproducibility of the test results. For fluorescent immunochromatographic technology with higher sensitivity requirements and wider signal range, the defect of too narrow dynamic range of image imaging detection equipment is more prominent.

[0004] In the field of consumer digital cameras, the development of HDR technology has expanded the dynamic range of photos, solved the problem of loss of dark or brightness details in photos, and made it possible to show both bright and dark parts in a photo. However, traditional HDR technology is committed to showing the details of both highlight and shadow areas in a photo, and mainly focuses on improving the contrast and visual effects of the image. It does not pay attention to the precise correspondence between the synthesis result and the actual brightness value, nor does it consider the signal distortion caused by nonlinear conversion.

[0005] In lateral flow imaging detection, the HDR algorithm not only needs to retain 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 paper, meet the wider concentration range of the detection sample (especially in the field of fluorescent immunochromatography, where the fluorescence brightness spans from 0 to 10,000+ average fluorescence intensity), and fit 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 view of the defects of the related art, the purpose of the present invention is to provide an analysis and detection method based on weight function and multi-brightness fusion, aiming to solve the problem that the dynamic range of photos in traditional HDR technology is limited, which leads to the problem that the immunochromatographic detector based on the image imaging principle has a narrow detection range, slow detection speed and low detection accuracy.

[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] 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 actual concentration value of the paper card to be tested is obtained;

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

[0012] S1, taking LDR images of X standard immunochromatographic test paper cards 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 paper card images and a second multi-brightness image set including X2 standard immunochromatographic test paper card images; wherein L≥3, X=X1+X2;

[0013] S2, acquiring intensity value changes of N pixels in the T-line region of each image in the first multi-brightness 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, 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 area and the true intensity value, and obtaining a target segmentation weight function; the calculation method of the average intensity value of the T-line area includes: obtaining a brightness response function according to the segmentation weight function combined with the intensity value matrix; obtaining an HDR image according to the brightness response function, the second multi-brightness image set and the corresponding light intensity; performing tone mapping on the HDR image to obtain the average intensity value of the T-line area;

[0018] S5, obtaining a target brightness response function according to the target segment weight function combined with the intensity value matrix;

[0019] S7. According to the target brightness response function, the multi-brightness image set of the standard immunochromatographic test paper card and the corresponding light intensity, the average intensity value of the T line area of ​​the standard immunochromatographic test paper card is obtained, and the average intensity value of the T line area of ​​the standard immunochromatographic test paper 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 the 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 , acquiring 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 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 paper card according to a target brightness response function, a multi-brightness image set of a standard immunochromatographic test paper card, and a 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 fluorescent 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, which is used to perform the analysis and detection method as described in any one of the first aspects, including:

[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 according to 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 comprises:

[0038] Taking LDR images of X standard immunochromatographic test paper cards 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 paper card images and a second multi-brightness image set including X2 standard immunochromatographic test paper card 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-brightness 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] 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 area and the true intensity value, and obtain the target segmentation weight function; the calculation method of the average intensity value of the T-line area includes: obtaining a brightness response function according to the segmentation weight function combined with the intensity value matrix; obtaining an HDR image according to the brightness response function, the second multi-brightness image set and the corresponding light intensity; and performing tone mapping on the HDR image to obtain the average intensity value of the T-line area;

[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 paper card and the corresponding light intensity, the average intensity value of the T line area of ​​the standard immunochromatographic test paper card is obtained, and the average intensity value of the T line area of ​​the standard immunochromatographic test paper card is fitted with the corresponding concentration to generate a standard concentration quantitative curve.

[0046] Compared with the prior art, the above technical solution 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 function 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 that the detection range of the current immunochromatographic detector based on the image imaging principle 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 overexposed and underexposed 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-curve, which can significantly improve the accuracy of HDR image synthesis and make the analysis and detection results based on the immunochromatographic test paper card more accurate.

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

[0049] Figure 1 It is a flowchart 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 There are 17 standard colloidal gold immunochromatographic test strip cards;

[0051] Figure 3 (A) shows images 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 mark in (B) indicates that Q pixels are selected 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 the 3D image of the blue channel of the standard fluorescent test paper card;

[0053] Figure 5 The RGB image and three channel images of the standard fluorescent color scale immunochromatographic test paper 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 the red component is extracted and 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 fitted 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] Fig. 9 (A) is the MAPE of different (a, b) parameter combinations. Fig. 9 (B) RMSE of different (a, b) parameter combinations;

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

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

[0060] Fig.12 Photo collection and restored HDR images of the fluorescent immunochromatographic test strip card under 7 currents;

[0061] Fig.13The fitting curves of the fluorescent immunochromatographic test strips before and after the multi-brightness fusion HDR algorithm are shown, including (A) the comparison of the standard concentration quantitative curves before and after the multi-brightness fusion HDR algorithm (B) the comparison of the detection linear range before and after the multi-brightness fusion HDR algorithm;

[0062] Fig.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 purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain 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 can 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] We can learn from the photo enhancement technology of digital cameras to solve the problem of dynamic range expansion of immunochromatographic detection systems. During immunochromatographic detection, it is necessary to flexibly adjust different parameters in the experiment according to the needs of the detection object, such as light source brightness, exposure time, etc., so as to produce a series of images under different light intensities, which will help to more effectively restore the most realistic immunochromatographic HDR images in immunochromatographic detection, surpassing the images obtained under traditional HDR technology.

[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] 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 actual concentration value of the paper card to be tested is obtained;

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

[0070] S1, taking LDR images of X standard immunochromatographic test paper cards 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 paper card images and a second multi-brightness image set including X2 standard immunochromatographic test paper card images; wherein L≥3, X=X1+X2;

[0071] S2, acquiring intensity value changes of N pixels in the T-line region of each image in the first multi-brightness 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, 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 area and the true intensity value, and obtaining a target segmentation weight function; the calculation method of the average intensity value of the T-line area includes: obtaining a brightness response function according to the segmentation weight function combined with the intensity value matrix; obtaining an HDR image according to the brightness response function, the second multi-brightness image set and the corresponding light intensity; performing tone mapping on the HDR image to obtain the average intensity value of the T-line area;

[0076] S5, obtaining a target brightness response function according to the target segment weight function combined with the intensity value matrix;

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

[0078] Optionally, S2 specifically includes:

[0079] S21, extracting the 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 , acquiring 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 paper card according to a target brightness response function, a multi-brightness image set of a standard immunochromatographic test paper card, and a 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 fluorescent 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 value 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 fluorescent 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 a quality control strip area frame of corresponding size is laid out and designed, including the quality control line (C line) and the detection line (T line). The size of the quality control strip and the positions 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 paper card is designed, it is printed on Pantech 230g enhanced rough paper by a high-resolution color photocopying device, and in this embodiment, a standard immunochromatographic test paper card with 36 different T-line grayscales with pixels ranging from 0 to 255 is designed with a pixel difference of 7, and the C-line pixel value of each test paper card is the same. After all the test paper cards are printed and encased, they represent 36 standard colloidal gold immunochromatographic test paper cards with the same C-line grayscale value and different T-line grayscale values.

[0096] When the standard immunochromatographic test paper card adopts the fluorescent immunochromatographic test paper, 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 101-10 4 The fluorescence intensity is high and the dynamic range is wide.

[0097] In this embodiment, the measurement of the standard fluorescent immunochromatographic test paper card is carried out under dark conditions. A sample set of 17 test paper cards is photographed under ultraviolet light and a specific current to cover various conditions from underexposure to overexposure. The brightness light image set covers 0-255 pixel values ​​under different light intensities. Then a set of multi-brightness fluorescent immunochromatographic image sets is obtained. In this embodiment, L is taken as 17, such as 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 of the 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 in the figure. Figure 3 shown.

[0098] By observing the image set of the standard fluorescent color scale test paper card, it can be seen that when the current is small, the light is weak, so the digital image is dark, the gray value of the pixel is small, and large areas of underexposure are prone to appear. As the current increases, the light intensity also increases, the image becomes brighter, and the differentiation of the standard color scale card increases until overexposure occurs.

[0099] After acquiring the multi-brightness image set, the image needs to be preprocessed. The acquired fluorescent immunochromatography images are collected by CMOS image sensors, and these images often contain noise, and the shorter the exposure time, the more noise points there are in the image. The multi-brightness image set can be preprocessed by a median filter algorithm, a Gaussian filter algorithm, a bilateral filter algorithm or a guided filter algorithm. In this embodiment, the most effective guided filter algorithm is used to preprocess the exposure image set, which can effectively reduce the noise in the image and enhance the visibility of the target.

[0100] Optionally, S2 specifically includes:

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

[0102] The HDR image in .hdr format is converted into a high-quality LDR image through a tone mapping algorithm to adapt to the dynamic range of conventional display devices. The position and size of the region of interest (such as the T-line area) in the standard immunochromatographic test strip card are determined to extract the ROI area and convert it into a single-channel grayscale image. The image is divided into two parts, upper and lower, at the vertical midpoint of the grayscale image. The sliding window threshold segmentation is applied to the two parts. The window moves along the vertical direction of the image, and the average pixel value in each window is calculated. By comparing the pixel values ​​of different windows, the area with the smallest pixel value is found, corresponding to the T line and the 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] As an alternative, other threshold segmentation algorithms may 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 guide image.

[0106] S23 , acquiring 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 the guide image, 5 pixels are obtained in each T-line area, and a total of 50 pixels are selected. The changes in the pixel values ​​of the 50 pixels under 7 multi-brightness sequences are grayscale value matrices, which will participate in fitting the brightness response function curve. For the pixel value range (Zmax-Zmin) = 255, and each test card has 7 multi-brightness photos, select N = 50 > [255 / (7-1) = 43] pixels, then in the 12 T-line ROI areas, each select Q = 50 / 10 = 5 pixels.

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

[0109] The standard concentration quantitative curve in the field of immunochromatography often presents an S-shaped characteristic. In the middle of this quantitative curve, there is a linear relationship between the measured pixel value and the concentration of the target substance, while at both ends it enters a plateau period where the correlation becomes less obvious. Therefore, the strategy of piecewise weight function is adopted to more finely reflect the characteristics of each concentration segment, determine the boundary of the linear and nonlinear intervals according to the linear range of the standard concentration quantitative curve, and set the weight coefficient to 1 in the linear interval, thereby retaining 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 microscopic image acquisition device.

[0113] If 50 pixels with different gray 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 accordingly.

[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 proportionality 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, we can get the irradiance E and the light source intensity I e The experiment in this paper is strictly controlled to be carried out in a dark environment, so the influence of changes in ambient light on the experimental data can be excluded. Only the LED light source is considered to be irradiated on the test paper card, and the algorithm is applied to the actual test paper card detector, which is to insert the test paper card into the black box to test the test paper. Therefore, simplified experimental conditions and complex influencing factors are considered. Combining formula (2) and formula (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 unchanged, 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 ith point at the mth current. Since Δt is kept constant, 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 Minimize K 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 the identity function, the hat function and the 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 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 leads to 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, which 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 that of 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 in 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 value 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, so that the pixel value of the T line in the corrected HDR image is closer to the actual printed grayscale value. The comparison results are shown in Figure 2. Fig. 9 shown.

[0143] The experimental results show that when the parameter combination is (a=10, b=4), the obtained MAPE and RMSE values ​​reach the minimum, 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 so that its T-line pixel value can be closest to the true intensity value.

[0144] According to the optimal function form of the Gaussian function, we can draw the following Fig.10 From the weight function diagram shown, it can be found that the weight value begins to turn when the pixel value is 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 areas 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 segment weight function, it is necessary to solve the segment weight function. First, set the values ​​of the initial segment points z1 and z2 to obtain the first segment weight function; in this embodiment, set the value of segment point z1 to 50 and the value of segment point z2 to 200, and then select the appropriate functions of f1(z) and f2(z) on this basis.

[0149] Select linear, quadratic polynomial, exponential, logarithmic and other functions as candidates for f1(z) and f2(z), and obtain the current brightness response function according to the current segment weight function, i.e., the first segment weight function, combined with the intensity value matrix. Correct the brightness response function curve to obtain the mean absolute percentage error (MAPE) and root mean square error (RMSE) between the intensity value of the T line in the HDR image and the actual printed grayscale value of the T line. Replace the function type of f1(z) and f2(z), update the first segment weight function, and thus update the brightness response function, update the calculated MAPE and RMSE values, and use MAPE and RMSE as the objective function to solve f1(z) and f2(z) that minimize MAPE and RMSE as the final expression of the segment function. Find the optimal segment function form and compare the results 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 according to the values ​​of MAPE and RMSE. The MAPE and RMSE values ​​under different piecewise functions are calculated and 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, the value range of the segment point needs to be optimized. The search range of z1 and z2 is related to the performance of the CMOS image sensor used. The search range of segment points z1 and z2 is set to obtain the second segment weight function.

[0152] Perform a cycle on z1 and z2 of the segmented weight function, set the step size to 1, gradually update the second segmented weight function, calculate the MAPE and RMSE values ​​between the pixel value of the T line in the HDR image corrected with different z1 and z2 and the actual printed T line grayscale value, and determine the segmentation point of the target weight function based on the minimum MAPE and RMSE results. Through the optimization algorithm, it is found that when z1 = 50 and z2 = 215, the MAPE of the 7 test strips is 40.23% and the RMSE is 12.48, achieving the minimum error.

[0153] Therefore, when the standard immunochromatographic test paper card is a fluorescent immunochromatographic card, the form of 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 the format of the fluorescent image is RGB (red, green, blue) channels, and the RGB channels are stored as three two-dimensional channels in the computer, the standard RGB is 24 bits, and each component occupies 8 bits. For example, when the emission wavelength of the fluorescent microsphere is at 610nm, which is within the red light wavelength range, according to the characteristics of the fluorescent image, the red component image of each image needs to be extracted first to obtain an image containing more fluorescent information and less noise to continue image segmentation and feature extraction. Figure 3 The three-dimensional images of each channel of the homemade standard fluorescent chromatographic analysis test card image in (B) are as follows Figure 4 As shown, through the three-dimensional graph, it is intuitively shown that the red component of the fluorescence image contains more information than the other two channels, and is closest to the distribution of 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, it proves that 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 And F value, after calculation by Python software, the brightness response function curves of the two imaging systems are obtained, as shown in Fig.11 shown.

[0161] The experimental results show that the LRF curve trends of the two data sets 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 different values ​​of real irradiance.

[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, according to the image situation, the appropriate current value is selected to shoot a multi-brightness image set, for example Fig.12 The multi-brightness image set of the B4 fluorescent immunochromatographic test strip card taken at 7 current values ​​and the Fig.11 The HDR image synthesized under the LRF curve shows that the contrast of the HDR image and the clarity of the C and T lines are better than those of the 7 multi-brightness LDR photos.

[0166] The multi-brightness fluorescent immunochromatographic test paper card is photographed Fig.12 As shown in the figure, 7 multi-brightness photos taken by B4 fluorescent immunochromatographic test strips and HDR images synthesized under the CRF curve. The contrast of HDR images and the clarity of C and T lines are better than those of multi-brightness LDR image sets, and the HDR image of the fluorescence image not only improves the contrast but also handles the halo of strong fluorescence well.

[0167] After obtaining the 7 fluorescent immunochromatographic HDR image sets, the T line of the image is extracted using the sliding window algorithm, and the T line pixel values ​​are recorded in Table 4-2. For the fluorescent immunochromatographic test strips, the fluorescence values ​​of B1-B7 fluorescent test strips are measured using a commercial fluorescent immunochromatographic detection instrument before testing, and the measured values ​​are recorded as "commercial instrument quantitative T line", and the LDR images of the 7 test strips taken at 91.5mA are also segmented to calculate the pixel values ​​of the target T line. The measured values ​​are recorded as "T line before HDR".

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

[0169]

[0170] Finally, the standard concentration quantitative curve of the average pixel value of the T line of the fluorescent immunochromatography HDR image set and the measured value of the commercial fluorescent immunochromatography analyzer was fitted. The experimental data were fitted with a four-parameter logistic model to obtain the standard concentration quantitative S-shaped curve of the fluorescent test paper card. After obtaining the standard concentration quantitative curve, the fluorescent test paper card of any concentration can be quantitatively analyzed.

[0171] Optionally, S7 specifically includes:

[0172] S71, obtaining an average intensity value of a T-line region of the standard immunochromatographic test paper card according to a target brightness response function, a multi-brightness image set of a standard immunochromatographic test paper 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 standard concentration quantitative curve of immunochromatography is fitted using a four-parameter logistic fitting model, which 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 measured image; A2 represents the minimum eigenvalue of the curve, that is, the minimum average pixel value of the T line in the measured image; x represents the independent variable, that is, the printed gray value of the T line; x0 is EC50 (half-maximum effect concentration), that is, the printed gray value corresponding to the middle pixel value of the T line in the measured image; p controls the slope of the curve. At the same time, the four-parameter logistic fitting model has an important evaluation parameter, the fitting degree R 2 It reflects the fitting degree of the standard concentration quantitative curve. The closer its value is to 1, the more accurate the fitted curve is. By fitting the experimental data to the four-parameter logistic model, these parameters can be estimated and an S-shaped curve suitable for the data can be obtained.

[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, Fig.13In (A), the horizontal axis is the fluorescence intensity value of the T line measured by the fluorescence immunoassay analyzer on 7 fluorescent immunochromatographic test strips. The vertical axis of the black fitting curve is the average pixel value of the T line before the multi-brightness fusion HDR algorithm is used, and the vertical axis of the red fitting curve is the average pixel value of the T line after the multi-brightness fusion HDR algorithm is used. The results show that after using the multi-brightness fusion HDR algorithm, the fitting degree of the standard concentration quantitative curve increased from 0.97329 to 0.98632. Fig.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), and the fitting degree is 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, which proves the effectiveness of the multi-brightness fusion HDR algorithm for quantitative fluorescence immunochromatography images. Fig.14 shown.

[0181] On the basis of the above embodiments, the influence of the number of multi-brightness images required for the multi-brightness fusion HDR algorithm is further considered. The 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. The HDR image synthesis of fluorescent immunochromatographic test strips requires a more sophisticated set of multi-brightness images to ensure the high quality of the final image and a more accurate expansion of the detection range.

[0182] Although the above scheme takes 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 piecewise 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-curve, and obtains a wider brightness range under different illumination intensities, effectively overcoming the limitation of the loss of highlights 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 paper 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 paper cards.

[0184] Embodiment 2

[0185] On the basis of the above embodiments, the present invention further provides an analysis and detection device based on 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 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 according to 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 comprises:

[0189] Taking LDR images of X standard immunochromatographic test paper cards 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 paper card images and a second multi-brightness image set including X2 standard immunochromatographic test paper card 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-brightness 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] 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 area and the true intensity value, and obtain the target segmentation weight function; the calculation method of the average intensity value of the T-line area includes: obtaining a brightness response function according to the segmentation weight function combined with the intensity value matrix; obtaining an HDR image according to the brightness response function, the second multi-brightness image set and the corresponding light intensity; and performing tone mapping on the HDR image to obtain the average intensity value of the T-line area;

[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 paper card and the corresponding light intensity, the average intensity value of the T line area of ​​the standard immunochromatographic test paper card is obtained, and the average intensity value of the T line area of ​​the standard immunochromatographic test paper 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 in an embodiment of the present invention is used to execute an analysis and detection method based on weight function and multi-brightness fusion provided in 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 only 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 protection scope 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; 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 actual concentration value of the paper card to be tested is obtained; The method for generating the standard concentration quantitative curve comprises: S1, taking LDR images of X standard immunochromatographic test paper cards 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 paper card images and a second multi-brightness image set including X2 standard immunochromatographic test paper card images; wherein L≥3, X=X1+X2; S2, acquiring intensity value changes of N pixels in the T-line region of each image in the first multi-brightness 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 functions, quadratic polynomial functions, exponential functions or logarithmic functions, and z1 and z2 are segmentation points; S4, 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 area and the true intensity value, and obtaining a target segmentation weight function; the calculation method of the average intensity value of the T-line area includes: obtaining a brightness response function according to the segmentation weight function combined with the intensity value matrix; obtaining an HDR image according to the brightness response function, the second multi-brightness image set and the corresponding light intensity; performing tone mapping on the HDR image to obtain the average intensity value of the T-line area; S5, obtaining a target brightness response function according to the target segment weight function combined with the intensity value matrix; S7. According to the target brightness response function, the multi-brightness image set of the standard immunochromatographic test paper card and the corresponding light intensity, the average intensity value of the T line area of ​​the standard immunochromatographic test paper card is obtained, and the average intensity value of the T line area of ​​the standard immunochromatographic test paper card is fitted with the corresponding concentration to generate a standard concentration quantitative curve.

2. The analysis and detection method according to claim 1, characterized in that: 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, characterized in that: S2 specifically includes: S21, extracting the 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 , acquiring 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, characterized in that: 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 of 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, characterized in that: S7 specifically includes: S71, obtaining an average intensity value of a T-line region of the standard immunochromatographic test paper card according to a target brightness response function, a multi-brightness image set of a standard immunochromatographic test paper card, and a 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, characterized in that: 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 fluorescent intensity value.

7. The analysis and detection method according to claim 1, characterized in that: The multi-brightness image set covers underexposure to overexposure under different light intensities.

8. The analysis and detection method according to claim 1, characterized in that: 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 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 comprises: Taking LDR images of X standard immunochromatographic test paper cards 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 paper card images and a second multi-brightness image set including X2 standard immunochromatographic test paper card 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-brightness 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 functions, quadratic polynomial functions, exponential functions or logarithmic functions, and z1 and z2 are segmentation points; 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 area and the true intensity value, and obtain the target segmentation weight function; the calculation method of the average intensity value of the T-line area includes: obtaining a brightness response function according to the segmentation weight function combined with the intensity value matrix; obtaining an HDR image according to the brightness response function, the second multi-brightness image set and the corresponding light intensity; and performing tone mapping on the HDR image to obtain the average intensity value of the T-line area; 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 paper card and the corresponding light intensity, the average intensity value of the T line area of ​​the standard immunochromatographic test paper card is obtained, and the average intensity value of the T line area of ​​the standard immunochromatographic test paper card is fitted with the corresponding concentration to generate a standard concentration quantitative curve.

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