Analysis and detection method and device based on multi-exposure and multi-brightness double parameters

Abstract

The invention discloses an analysis and detection method and device based on multi-exposure and multi-brightness double parameters, and belongs to the field of immunochromatography analysis and detection. The analysis and detection method comprises the steps of obtaining a multi-exposure multi-brightness image set under a plurality of multi-exposure time sequences and different illumination intensities, and in the process of restoring a camera response function of an image acquisition system, using an HDR algorithm based on multi-exposure and multi-brightness fusion. The weights of images with different exposures and different current values are adjusted by adopting a segmented weighting function, the characteristics of each concentration section in immunochromatography are reflected more finely by finely regulating and controlling parameters of the segmented weighting function, and the optimal weighting function is established to enhance the fitting accuracy of a response function of a two-parameter camera, so that the accuracy of the response function fitting is improved. Therefore, the immunochromatography HDR image with the highest quality is recovered, and the linear range of quantitative detection is widened.

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 multi-exposure and multi-brightness dual parameters. Background Art

[0002] As an easy-to-operate on-site rapid detection technology, the immunochromatographic method has been widely used in many fields. The immunochromatographic detection device based on the principle of image imaging can quickly, conveniently and inexpensively capture all the image information of the entire test card, providing a basis for subsequent data processing and global analysis. However, traditional HDR (high dynamic range) technology usually takes a set of photos with different exposure times under fixed light conditions, while immunochromatographic detection requires flexible adjustment of different parameters (such as light source brightness and exposure time) in the experiment according to the needs of the test object, thereby generating a series of images under different brightness conditions. This helps to more effectively restore the most realistic immunochromatographic HDR images in immunochromatographic detection, surpassing the images obtained under traditional HDR technology. This is also what traditional HDR technology has not covered.

[0003] The immunochromatographic image sensor is essentially a special camera, and its basic working principle is the same as that of the digital cameras used in daily life. Therefore, the photo enhancement technology of digital cameras can be used to solve the problem of dynamic range expansion of the immunochromatographic detection system. However, since the fluorescence signal is extremely sensitive to changes in ambient light, for the HDR algorithm that fuses images of different brightness, it is necessary to accurately adjust the current to change the brightness and wait for the current to stabilize before accurate recording and shooting. Relatively speaking, the traditional multi-exposure fusion HDR algorithm can quickly and conveniently collect the required image set by simply adjusting the exposure time parameters in the shooting software, which is suitable for dynamically changing experimental conditions.

[0004] In real applications, images usually obtained are image information of different brightness and exposure levels. The dynamic range of photos in traditional HDR technology is limited. The camera response function that only focuses on exposure time or light brightness cannot accurately detect the results of immunochromatography images, resulting in the immunochromatography detector having problems such as narrow detection range, slow detection speed and low detection accuracy. Summary of the invention

[0005] 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 multi-exposure and multi-brightness dual parameters, aiming to solve the problems of narrow detection range, slow detection speed and low detection accuracy of immunochromatographic detectors for image information with different brightness and exposure levels.

[0006] To achieve the above objectives, in a first aspect, the present invention provides an analysis and detection method based on multi-exposure and multi-brightness dual parameters, comprising:

[0007] The paper card to be tested is photographed in a multi-exposure time sequence and at different light intensities to obtain a multi-exposure and multi-brightness image set of the paper card to be tested;

[0008] According to the standard concentration quantitative curve, the multi-exposure and multi-brightness image set of the paper card to be tested, the corresponding exposure time and the corresponding current value, the actual concentration value of the paper card to be tested is obtained;

[0009] The method for generating the standard concentration quantitative curve comprises:

[0010] S1, photographing X images of standard immunochromatographic test strips with different concentrations in P multi-exposure time sequences and L light intensities to obtain a multi-exposure multi-brightness image set; dividing the multi-exposure multi-brightness image set into a first multi-exposure multi-brightness image set including X1 images of standard immunochromatographic test strips and a second multi-exposure multi-brightness image set including X2 images of standard immunochromatographic test strips; wherein P≥3, L≥3, X=X1+X2;

[0011] S2, obtaining gray value changes of N pixels in the T-line area of ​​each image in the first multi-exposure multi-brightness image set to obtain a gray value matrix of N×P×L pixels;

[0012] S3, constructing an initial dual-parameter camera response function based on the gray value matrix, exposure time and current value;

[0013] S4. Construct the following piecewise weight function:

[0014]

[0015] 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;

[0016] S5, determining the function forms of f1(z) and f2(z) and the segmentation points z1 and z2 that minimize the error between the average pixel value of the T-line area and the true pixel value, and obtaining a target segmentation weight function; the calculation method of the average pixel value of the T-line area includes: optimizing the initial two-parameter camera response function by using the segmentation weight function; acquiring an HDR image according to the optimized camera response function, the second multi-exposure multi-brightness image set, the corresponding exposure time and the corresponding current value; performing tone mapping on the HDR image to obtain the average pixel value of the T-line area;

[0017] S6, optimizing the initial dual-parameter camera response function using the target segmented weight function to obtain the target dual-parameter camera response function;

[0018] S7. According to the target dual-parameter camera response function, the multi-exposure and multi-brightness image set of the standard immunochromatographic test paper card, the corresponding exposure time and the corresponding current value, the average pixel value of the T-line area of ​​the standard immunochromatographic test paper card is obtained, and the average pixel 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.

[0019] Optionally, the standard immunochromatographic test paper cards have different T line concentrations and the same C line concentrations.

[0020] Optionally, S2 specifically includes:

[0021] S21, extracting a T-line region of each image in the first multi-exposure multi-brightness image set using an image segmentation algorithm;

[0022] S22, vertically stitching the T-line regions of each image in order of concentration to obtain a guide image;

[0023] S23, acquiring grayscale value changes of N / X1 pixels in each T-line region in the guide image to obtain a grayscale value matrix of N×P×L pixels.

[0024] Optionally, the error between the average pixel value and the true pixel value is the root mean square error RMSE and the mean absolute percentage error MAPE of the average pixel value of the T-line area and the true pixel value of the standard immunochromatographic test paper card.

[0025] Optionally, S7 specifically includes:

[0026] S71, obtaining an average pixel value of the T-line area of ​​the standard immunochromatographic test paper card according to the target dual-parameter camera response function, the multi-exposure and multi-brightness image set of the standard immunochromatographic test paper card, the corresponding exposure time, and the corresponding current value;

[0027] 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.

[0028] Optionally, the standard immunochromatographic test paper card includes a colloidal gold immunochromatographic card and a fluorescent immunochromatographic card.

[0029] Optionally, the multi-exposure multi-brightness image set covers 0-255 pixel values ​​under different exposure time sequences and light intensities.

[0030] Optionally, after obtaining the multi-exposure multi-brightness image set, the method further includes: preprocessing the multi-exposure multi-brightness image set using a median filtering algorithm, a Gaussian filtering algorithm, a bilateral filtering algorithm or a guided filtering algorithm.

[0031] In a second aspect, the present invention further provides an analysis and detection device based on multi-exposure and multi-brightness dual parameters, which is used to perform the analysis and detection method as described in any one of the first aspects, including:

[0032] An image acquisition module is used to photograph the paper card to be tested under multiple exposure time sequences and different light intensities to obtain a multi-exposure and multi-brightness image set of the paper card to be tested;

[0033] A concentration analysis and detection module, used to obtain the actual concentration value of the paper card to be tested according to the standard concentration quantitative curve, the multi-exposure and multi-brightness image set of the paper card to be tested, the corresponding exposure time and the corresponding current value;

[0034] The method for generating the standard concentration quantitative curve comprises:

[0035] Taking images of X standard immunochromatographic test strips with different concentrations in P multi-exposure time sequences and L light intensities to obtain a multi-exposure multi-brightness image set; dividing the multi-exposure multi-brightness image set into a first multi-exposure multi-brightness image set including X1 images of standard immunochromatographic test strips and a second multi-exposure multi-brightness image set including X2 images of standard immunochromatographic test strips; wherein P≥3, L≥3, X=X1+X2;

[0036] Obtain grayscale value changes of N pixels in the T-line area of ​​each image in the first multi-exposure multi-brightness image set to obtain a grayscale value matrix of N×P×L pixels;

[0037] Constructing an initial two-parameter camera response function based on the gray value matrix, exposure time and current value;

[0038] Construct the following piecewise weight function:

[0039]

[0040] 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;

[0041] Determine the function form of f1(z) and f2(z) and the segmentation points z1 and z2 that minimize the error between the average pixel value of the T-line area and the true pixel value, and obtain the target segmentation weight function; the calculation method of the average pixel value of the T-line area includes: optimizing the initial two-parameter camera response function by using the segmentation weight function; acquiring an HDR image according to the optimized camera response function, the second multi-exposure multi-brightness image set, the corresponding exposure time and the corresponding current value; and performing tone mapping on the HDR image to obtain the average pixel value of the T-line area;

[0042] The target segmented weight function is used to optimize the initial two-parameter camera response function to obtain the target two-parameter camera response function;

[0043] According to the target dual-parameter camera response function, the multi-exposure and multi-brightness image set of the standard immunochromatographic test paper card, the corresponding exposure time and the corresponding current value, the average pixel value of the T-line area of ​​the standard immunochromatographic test paper card is obtained, and the average pixel 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.

[0044] Compared with the prior art, the above technical solution conceived by the present invention can achieve the following beneficial effects:

[0045] 1. The present invention provides an analysis and detection method based on multi-exposure multi-brightness dual parameters. For immunochromatographic images with different brightness and exposure levels, immunochromatographic detection and analysis require specific quantitative processing. Based on a dual parameter adjustment method combining limited multiple current values ​​and multiple exposure time values, the acquisition technology of high dynamic range images is expanded. First, the camera response function of the image acquisition system is restored, and then the camera response function curve is combined with the biological response curve, thereby expanding the linear range of the immunochromatographic standard quantitative curve to solve the current limitation of the narrow detection range of the immunochromatographic detector based on the image imaging principle. By limiting the current value and exposure time respectively, multiple camera response function curves under multiple light intensities are obtained in turn, and image information from different brightness and exposure levels is merged to form a comprehensive dynamic range. The weights of different parameter images are adjusted by using a segmented weight function to reduce the influence of over-exposure and under-exposure areas in the image on the fitting accuracy of the camera 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 paper card more accurate.

[0046] 2. The present invention provides an analysis and detection method based on multi-exposure multi-brightness dual parameters. After generating a standard concentration quantitative curve, a multi-exposure multi-brightness image set of the paper card to be tested is taken under multi-exposure multi-light intensity parameters, thereby obtaining a high-precision HDR image and an accurate average pixel 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

[0047] Figure 1 It is a flow chart of an analysis and detection method based on multi-exposure and multi-brightness dual parameters provided by an embodiment of the present invention;

[0048] Figure 2 This is an example of a standard immunochromatographic test strip card;

[0049] Figure 3 It is 36 standard colloidal gold immunochromatographic test strip cards;

[0050] Figure 4 This is a set of images of 30# colloidal gold immunochromatographic test strips at 5 currents and 3 exposures;

[0051] Figure 5 (A) shows the images of 8 immunochromatographic test strips. Figure 5 Middle (B) is a guide image formed by combining the T-line area of ​​8 immunochromatographic test strip images. Figure 5 Middle (C) is to select Q pixels in each ROI area;

[0052] Figure 6 The results of the 5# test paper card under four filtering algorithms are shown in Figure 2, where (A) is the original image of the 5# test paper card, (B) is the bilateral filtering result, (C) is the median filtering result, (D) is the Gaussian filtering result, and (E) is the guided filtering result.

[0053] Figure 7 It is a flow chart of the sliding window threshold segmentation algorithm;

[0054] Figure 8 (A) is the sliding window threshold segmentation algorithm to extract the C line and T line of the immunochromatographic test paper card. Figure 8 (B) is the area with the minimum gray value selected by the sliding window of the ROI image of the immunochromatography image. Figure 8 Middle (C) shows the C-line and T-line regions after the sliding window segmentation;

[0055] Fig. 9 (A) is the three-dimensional dual-parameter response function curve fitted under 5 currents and 3 exposure times. Fig. 9 (B) is a set of multi-brightness camera response function curves under different exposure times. Fig. 9 Middle (C) is a set of multi-exposure camera response function curves under different currents;

[0056] Fig.10 (A) is the exposure offset under the identity function. Fig.10 (B) is the exposure offset under the hat function. Fig.10 Middle (C) is the exposure offset under the Gaussian function;

[0057] Fig.11 The camera response function results fitted under different weight functions; where (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;

[0058] Fig.12 (A) is the MAPE of different (a, b) parameter combinations. Fig.12 (B) RMSE of different (a, b) parameter combinations;

[0059] Fig.13 is the image of the Gaussian function when the parameters a=10, b=4;

[0060] Fig.14 (A) is the quantitative curve of 36 test strips divided into 9 groups and spliced ​​at 9 exposure times. Fig.14 Middle (B) is the slope of the mapping between the T-line pixel value and the actual printed grayscale value of the 9 groups of test strips;

[0061] Fig.15 is the response function curve of a two-dimensional dual-parameter camera based on the optimal weight function fitting;

[0062] Fig.16 The image set and synthesized HDR image of 30# colloidal gold immunochromatographic test paper card at 5 currents and 3 exposure times;

[0063] Fig.17 This is the standard concentration quantitative curve of colloidal gold immunochromatography before and after the dual-parameter HDR algorithm;

[0064] Fig.18 This is an example of a fluorescent immunochromatographic test strip card;

[0065] Fig.19 This is a set of images of the B4 fluorescent immunochromatographic test strip card at 5 currents and 3 exposure times;

[0066] Fig. 20 It is a single channel image after extracting the red component and guided filtering;

[0067] Fig.21 (A) The three-dimensional dual-parameter response function curve fitted by the standard fluorescence color scale test paper card. Fig.21 (B) is a set of multi-exposure camera response function curves under different currents. Fig.21 Middle (C) is a set of multi-brightness camera response function curves under different exposure times;

[0068] Fig. 22 A two-dimensional dual-parameter response function curve fitted to a standard fluorescent color scale test paper card;

[0069] Fig.23 This is the image set and synthesized HDR image of the B4 fluorescent immunochromatographic test strip card at 5 currents and 3 exposure times;

[0070] Fig.24The fitting curve results of the fluorescent immunochromatographic test paper card before and after using the dual-parameter HDR algorithm; among them, (A) is the comparison of the standard concentration quantitative curves before and after the dual-parameter HDR algorithm, and (B) is the comparison of the detection linear range in the standard concentration quantitative curve. DETAILED DESCRIPTION

[0071] 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.

[0072] The contents involved in the above embodiment are described below in conjunction with a preferred embodiment.

[0073] The immunochromatographic image sensor in the image acquisition system is essentially a special camera, and its basic working principle is the same as that of the digital cameras used in daily life. Therefore, the photo enhancement technology of digital cameras can be used to solve the dynamic range expansion problem of the immunochromatographic detection system. 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 brightness conditions, which helps to more effectively restore the most realistic immunochromatographic HDR images in immunochromatographic detection, surpassing the images obtained under traditional HDR technology.

[0074] An analysis and detection method based on multi-exposure and multi-brightness dual parameters, comprising:

[0075] The paper card to be tested is photographed in a multi-exposure time sequence and at different light intensities to obtain a multi-exposure and multi-brightness image set of the paper card to be tested;

[0076] According to the standard concentration quantitative curve, the multi-exposure and multi-brightness image set of the paper card to be tested, the corresponding exposure time and the corresponding current value, the actual concentration value of the paper card to be tested is obtained;

[0077] The method for generating the standard concentration quantitative curve comprises:

[0078] S1, photographing X images of standard immunochromatographic test strips with different concentrations in P multi-exposure time sequences and L light intensities to obtain a multi-exposure multi-brightness image set; dividing the multi-exposure multi-brightness image set into a first multi-exposure multi-brightness image set including X1 images of standard immunochromatographic test strips and a second multi-exposure multi-brightness image set including X2 images of standard immunochromatographic test strips; wherein P≥3, L≥3, X=X1+X2;

[0079] S2, obtaining gray value changes of N pixels in the T-line area of ​​each image in the first multi-exposure multi-brightness image set to obtain a gray value matrix of N×P×L pixels;

[0080] S3, constructing an initial dual-parameter camera response function based on the gray value matrix, exposure time and current value;

[0081] S4. Construct the following piecewise weight function:

[0082]

[0083] 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;

[0084] S5, determining the function forms of f1(z) and f2(z) and the segmentation points z1 and z2 that minimize the error between the average pixel value of the T-line area and the true pixel value, and obtaining a target segmentation weight function; the calculation method of the average pixel value of the T-line area includes: optimizing the initial two-parameter camera response function by using the segmentation weight function; acquiring an HDR image according to the optimized camera response function, the second multi-exposure multi-brightness image set, the corresponding exposure time and the corresponding current value; performing tone mapping on the HDR image to obtain the average pixel value of the T-line area;

[0085] S6, optimizing the initial dual-parameter camera response function using the target segmented weight function to obtain the target dual-parameter camera response function;

[0086] S7. According to the target dual-parameter camera response function, the multi-exposure and multi-brightness image set of the standard immunochromatographic test paper card, the corresponding exposure time and the corresponding current value, the average pixel value of the T-line area of ​​the standard immunochromatographic test paper card is obtained, and the average pixel 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.

[0087] like Figure 1 As shown in FIG. 1 , the specific process can be divided into (1) an initialization phase: establishing and optimizing a multi-exposure and multi-brightness dual-parameter response function curve of an image acquisition device; and (2) an application phase: using an image acquisition device to capture a multi-exposure and multi-brightness image set of a standard immunochromatographic test strip card of different concentrations, using the established camera response function curve to guide the multi-exposure and multi-brightness 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.

[0088] In this solution, the standard immunochromatographic test paper cards include colloidal gold immunochromatographic cards and fluorescent immunochromatographic cards. The specific implementation methods of the two different test paper cards are different, and here, the colloidal gold immunochromatographic card is used as an example for description.

[0089] Before acquiring a multi-exposure and multi-brightness image set, it also includes:

[0090] 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.

[0091] Image processing software is used to make Figure 2 The standard immunochromatographic test strip card shown has a layout designed with a quality control strip area frame of corresponding size, including the quality control line (C line) and the test 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 made at a time, reserved for subsequent repetitive tests.

[0092] 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. 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. Figure 3 As shown, it represents 36 standard colloidal gold immunochromatographic test paper cards with the same C line gray value and different T line gray values.

[0093] In this embodiment, the measurement of the standard immunochromatographic test strip card is carried out under dark conditions, and a sample set of 36 test strip cards is taken at specific exposures and different current values ​​to cover various conditions from underexposure to overexposure, and the multi-exposure multi-brightness image set covers 0-255 pixel values ​​at different exposure time sequences. The measurement is carried out under dark conditions, and the light intensity is adjusted by changing the current, and a sample set of 36 test strip cards is taken at three exposures to cover various conditions from underexposure to overexposure.

[0094] In this embodiment, the value of P is 3, and the value of L is 5. Figure 4 As shown, the standard grayscale test paper card was photographed at 3 exposure times and 5 currents (0.122ms, 0.977ms, 31.25ms, 0.9mA, 1.7mA, 4.4mA, 8.5mA, 16.5mA), and a set of 36×3×5 multi-exposure photos from underexposure to overexposure were obtained. Figure 4 It shows 15 multi-exposure brightness photos taken with the 30# colloidal gold immunochromatographic test paper card.

[0095] like Figure 5As shown in (A), 8 test strips are evenly selected to cover the grayscale value range of 0-255 as much as possible, namely, 1# test strip, 5# test strip, 10# test strip, 15# test strip, 20# test strip, 25# test strip, 30# test strip, and 35# test strip. These 8×3×5 images will be used to establish the dual-parameter response function curve.

[0096] After obtaining a set of multi-exposure and multi-brightness images, the images need to be preprocessed. The acquired immunochromatographic images are acquired 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. Figure 6 As shown, the multi-exposure multi-brightness image set can be preprocessed by using 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.

[0097] Optionally, S2 specifically includes:

[0098] S21 . Extracting a T-line region of each image in the first multi-exposure multi-brightness image set using an image segmentation algorithm.

[0099] 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. Determine the position and size of the region of interest (such as the T-line area) in the standard immunochromatographic test strip card, extract the ROI area, and convert it into a single-channel grayscale image. Divide the image into two parts, upper and lower, at the vertical midpoint of the grayscale image. Apply sliding window threshold segmentation to the two parts. The window moves along the vertical direction of the image, and calculates the average pixel value in each window. By comparing the pixel values ​​of different windows, find the area with the smallest pixel value, which corresponds to the T line and C line. The process of the sliding window threshold segmentation algorithm is as follows Figure 7 shown.

[0100] The segmented image is Figure 8 As shown, the T-line and C-line areas are clearly identified, and the average pixel value of the two areas is calculated.

[0101] As an alternative, other threshold segmentation algorithms may be used, such as the maximum between-class variance (OTSU) algorithm.

[0102] S22, vertically stitching the T-line regions of each image in order of concentration to obtain a guide image;

[0103] S23, acquiring grayscale value changes of N / X1 pixels in each T-line region in the guide image to obtain a grayscale value matrix of N×P×L pixels.

[0104] The T-line ROI areas of the eight immunochromatographic test strip card images taken at three exposure times and five currents are stitched together to form an image with eight color levels, which is called the guide image. Figure 8 As shown. 40 pixels are selected in the guide image. The changes in the pixel values ​​of the 40 pixels under 3 exposure times and 5 currents will be used to fit the dual-parameter response function curve. For the pixel value range (Zmax-Zmin) = 255, and each test card has 15 dual-parameter photos, select N = 40 > [255 / (15-1) = 19] pixels. Then, in the 8 T-line ROI areas, select Q = 40 / 8 = 5 pixels each, as shown in Figure 5 As shown in (C).

[0105] Construct a weight function to optimize the camera response function curve, and the form of the weight function needs to be determined first.

[0106] 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.

[0107] Changing the exposure time and light intensity at the same time requires combining the two HDR algorithms to adjust the input matrix of current value and exposure time. In this case, a three-dimensional data set needs to be constructed, which contains pixel values, exposure time and current value. The exposure time set is recorded as Δt j , j = 1, 2, ..., P; the current set is denoted as I m , m = 1, 2, ..., Q; use Z ijm represents the pixel value, where i = 1, 2, 3, ..., N, and represents the three-dimensional index of the position in the image. These three parameters work together to form the camera response function. Here are the general steps to adjust the algorithm to adapt to this change:

[0108] f is the brightness response function that needs to be solved, which can be expressed as:

[0109] Z=f(E,I,Δt) (1-1)

[0110] According to the formula of Z and I, let the mth current be I m , the irradiance of the i-th pixel is:

[0111] E im =E i I m (1-2)

[0112] I m is known, and k i is a constant, because when the light intensity does not change, the irradiance of each pixel is constant. Let the pixel value of the i-th pixel at the m-th current and the j-th exposure time be Z ijm , formula (5-2) can be written as:

[0113] f -1 (Z ijm )=E im Δt j =E i I m Δt j (1-3)

[0114] G(Z ijm )=lnE i +lnI m +lnΔt j (1-4)

[0115] Need to restore irradiance E i and the estimated value of G, use the least squares method to solve the following objective function O dual , to minimize:

[0116]

[0117] Among them, I m and Δt j It is known that w(Z ijm ) is the weight function corresponding to the pixel point, so through the optimization algorithm, we can find the E that minimizes the objective function i and G values ​​to obtain the optimal brightness response function curve.

[0118] When the product of the "exposure time-current" pair is used as a whole to calculate the least square method to solve the objective function, that is, ln(I m ×Δt j ) as a variable to replace the exposure time variable lnΔt of the multi-exposure fusion HDR algorithm j Or the current variable ln(I m ×Δt j ), a "single parameter" response curve fitting was performed. Because the exposure time and the irradiance of the pixel are independent parameters and will not affect each other. At this time, formula (1-5) can be transformed into:

[0119]

[0120] The multi-exposure multi-brightness image set was taken at 3 different exposure times and 5 different currents, and was calculated according to the formula:

[0121]

[0122] Therefore, at least 19 pixels need to be selected to meet the minimum requirement of the number of pixels required for fitting the dual-parameter response function curve. In this embodiment, 50 pixels are selected to fit the dual-parameter camera response function curve of the colloidal gold immunochromatography image acquisition device.

[0123] A 3D dual-parameter response function graph is established, with the logarithmic value of the exposure time set as the X-axis, the logarithmic value of the current set as the Y-axis, and the dual-parameter response function value G set as the Z-axis. In this way, the position of any point in this three-dimensional space can reflect the brightness value under a specific exposure time and current value. After calculation by Python software, the 3D dual-parameter response function graph of the colloidal gold immunochromatography image acquisition device is obtained as shown in the figure. Fig. 9 As shown in (A), Fig. 9 (B) and (C) show the response curves of the device at different currents and exposure times:

[0124] This embodiment analyzes the application of the identity function, the hat function and the Gaussian function in the camera response function curve fitting, and obtains the following Fig.10 The five pixels shown have different exposure offsets calculated under different weight functions and Fig.11 Three response function curves g are shown.

[0125] Among them, the identity function form is:

[0126] w1(z)=1,z∈[0,255]

[0127] The functional form of the hat function is:

[0128]

[0129] The functional form of the Gaussian function is:

[0130]

[0131] 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.

[0132] pass Fig.11The experimental results are obtained, and the residuals of the camera 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 fitted under the identity function and the hat function.

[0133] 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.

[0134] 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.12 shown.

[0135] 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 41.16% and 11.42 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 actual printed grayscale value.

[0136] According to the optimal function form of the Gaussian function, we can draw the following Fig.13 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.

[0137] 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:

[0138]

[0139] 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;

[0140] 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.

[0141] Select linear, quadratic polynomial, exponential, logarithmic and other functions as candidates for f1(z) and f2(z), and obtain the current camera response function according to the current segmented weight function, i.e., the first segmented weight function, combined with the gray value matrix. The average absolute percentage error MAPE and root mean square error RMSE values ​​between the pixel value of the T line in the HDR image and the actual printed gray value of the T line are corrected by the camera response function curve. Replace the function type of f1(z) and f2(z), update the first segmented weight function, and thus update the camera 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 segmented function. Find the optimal segmented function form and compare the results with the optimal function form of the Gaussian function.

[0142] Specifically, for the calculation of the weight function, this embodiment uses multi-exposure images of 36 test strips at 9 exposure times. The pixel values ​​of the 36 test strips are divided into 9 groups at 9 exposure times. Fig.14 Middle (A). The T-line pixel values ​​of test strips 1-4 (group 1, and so on) were taken at 31.25ms, the T-line pixel values ​​of test strips 5-8 were taken at 15.625ms, the T-line pixel values ​​of test strips 9-12 were taken at 7.812ms, the T-line pixel values ​​of test strips 13-16 were taken at 3.906ms, the T-line pixel values ​​of test strips 17-20 were taken at 1.953ms, the T-line pixel values ​​of test strips 21-24 were taken at 0.977ms, the T-line pixel values ​​of test strips 25-28 were taken at 0.488ms, the T-line pixel values ​​of test strips 29-32 were taken at 0.244ms, and the T-line pixel values ​​of test strips 33-36 were taken at 0.122ms. Fig.14 The slope of the mapping between the pixel value of the T line of each test paper card and the actual printed grayscale value shown in (B) reflects the sensitivity of the camera to the change of grayscale value under a specific exposure time. The group with a higher slope shows the high sensitivity of the camera to the change of grayscale value, while the group with a lower slope shows that the camera is less sensitive to the change of grayscale value in this range.

[0143] The optimal f1(z) and f2(z) function forms are selected by adjusting the piecewise function form through the values ​​of MAPE and RMSE. The results of calculating the MAPE and RMSE values ​​under 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 ​​reach the minimum, indicating that the piecewise function under this combination can achieve the optimal correction of HDR images. The MAPE of the T-line pixel values ​​extracted from the original image without HDR algorithm processing and the T-line printed grayscale values ​​is calculated, and the result reaches 117%.

[0144] Table 1 Error results of different f1(x) and f2(x) in piecewise function

[0145]

[0146]

[0147] After updating the expression of the first segment weight function, the value range of the segment points 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. In this embodiment, by observing Fig. 20 In the T-line pixel value of the digital image of the test paper card, when the printed grayscale of the test paper card is around 50, the slope of the multi-exposure pixel value becomes 0.9, which is close to linear, indicating that the camera response is good at this time, so the search range of z1 is set to 40-70 (grayscale value). For the test paper card with a high printed grayscale value, the slope decreases to 0.74 when the pixel value is around 200, so the search range of z2 is set to 190-230 (grayscale value).

[0148] 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 results of MAPE and RMSE. Through the optimization algorithm, it is found that when z1=50 and z2=200, the MAPE of 36 test strips is 36.21% and the RMSE is 11.23, achieving the minimum error. And this result is better than the MAPE=41.16% and RMSE=11.42 calculated by the Gaussian function objective function form. Therefore, the form of the target weight function in this embodiment is as follows:

[0149]

[0150] After obtaining the target segment weight function, the target dual-parameter camera response function can be obtained according to the target segment weight function combined with the gray value matrix.

[0151] Take the "exposure time-current" product pair as a whole, and input the image set taken under each product pair to solve the objective function in formula (1-6). The pixel value of the digital image is the horizontal coordinate, and lnE i +ln(I m ×Δt j ) as the ordinate, and draw a 2D dual-parameter response function curve. The 2D dual-parameter response function diagram of the colloidal gold immunochromatography microscopic image acquisition device is obtained as shown in Fig.15 shown.

[0152] Optionally, S7 specifically includes:

[0153] S71, obtaining an average pixel value of the T-line area of ​​the standard immunochromatographic test paper card according to the target dual-parameter camera response function, the multi-exposure and multi-brightness image set of the standard immunochromatographic test paper card, the corresponding exposure time, and the corresponding current value;

[0154] 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.

[0155] High dynamic range radiation pattern k i Restore as follows:

[0156]

[0157] Once the dual-parameter response function curve G of the colloidal gold immunochromatography device is successfully restored, it will guide Figure 3 36 colloidal gold immunochromatographic test strips with different printing concentrations and T values ​​are used to realize the operation of fusion generation of HDR images from dual-parameter LDR image sets.

[0158] Fig.16 The 15 multi-exposure brightness photos taken by 30# colloidal gold immunochromatography test paper and the HDR images synthesized under the DPRF curve are shown. It is found that the contrast of the colloidal gold immunochromatography HDR image and the clarity of the C line and T line are better than the 15 dual-parameter LDR photos.

[0159] The standard concentration quantitative curve of immunochromatography is fitted using a four-parameter logistic fitting model, which is usually expressed as follows:

[0160]

[0161] 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.

[0162] After obtaining the standard concentration quantitative curve, it also includes:

[0163] The paper card to be tested is photographed in a multi-exposure time sequence and at different light intensities to obtain a multi-exposure and multi-brightness image set of the paper card to be tested;

[0164] The actual concentration value of the paper card to be tested is obtained according to the standard concentration quantitative curve, the multi-exposure and multi-brightness image set of the paper card to be tested, the corresponding exposure time and the corresponding current value.

[0165] The results of 30 test strips were selected to fit the standard concentration quantitative curve, such as Fig. 22 As shown, the horizontal axis is the grayscale value of the T line print of 30 test strips, and the vertical axis is the change of the T line pixel value in the HDR image. By observing the curve, the data points are closely distributed around the fitting curve, and the fitting degree is 0.99953, and the fitting effect is very good.

[0166] In order to study the effectiveness of the algorithm designed in this study, the standard concentration quantitative curves of the colloidal gold immunochromatography microscopy image acquisition device before and after the HDR algorithm was used were compared, that is, the T-line pixel values ​​of 30 test strip card LDR images taken under normal exposure (1.953ms) were compared with the T-line pixel values ​​of 36 HDR images synthesized using the HDR algorithm, and the upper and lower limits of the measurable signal value area S of the two standard concentration quantitative curves were plotted. The results are shown in Figure 2. Fig.17 As shown in the figure, before the HDR algorithm, the range of test strips from 5# to 34# can be measured, and after the HDR algorithm, the range is extended to test strips from 3# to 36#, showing the effectiveness of the dual-parameter fusion HDR algorithm for the extended range of immunochromatography images. In addition, the linear measurement range of the standard concentration quantitative curve has also been significantly extended.

[0167] The specific implementation method of the standard immunochromatographic test paper card being a fluorescent immunochromatographic card includes the following steps:

[0168] like Fig.18As shown, 7 fluorescent immunochromatographic test strip cards with different fluorescence concentrations were made.

[0169] The measurement of this embodiment is carried out under dark conditions. By changing the current to adjust the light intensity, a sample set containing 7 test paper cards is photographed under three exposures to cover various conditions from underexposure to overexposure. This embodiment uses fluorescent color scale test paper cards to shoot at 3 exposure times and 5 currents (0.008ms, 0.033ms, 0.125ms; 58.6mA, 91.5mA, 129.5mA, 166.6mA, 206.1mA), and obtains a set of 7×3×5 multi-exposure photos from underexposure to overexposure. Because the format of the fluorescent image captured is RGB (red, green, blue) three channels, and the RGB three channels are stored as 3 two-dimensional channels in the computer, the standard RGB is 24 bits, and each component occupies 8 bits. Because the emission wavelength of fluorescent microspheres is at 610nm, which is within the red light wavelength range, according to the characteristics of fluorescence images, it is necessary to first extract the red component image of each image to obtain an image containing more fluorescence information and less noise in order to continue image segmentation and feature extraction. Fig.19 It shows 15 multi-exposure brightness photos taken of the B4 fluorescent immunochromatographic test paper card.

[0170] The T-line ROI areas of the non-characteristic values ​​of the 7 fluorescent immunochromatographic test strip images taken at 3 exposure times and 5 currents are spliced ​​to form an image with 12 different color levels, which is called the guide image, such as Fig. 20 shown.

[0171] After obtaining the multi-exposure multi-brightness image set, the image needs to be preprocessed. After the image preprocessing, the feature value is extracted by segmentation and positioning. The preprocessing process and the segmentation and positioning extraction process are the same as those in the embodiment of the colloidal gold immunochromatography card, and will not be repeated here.

[0172] Changing the exposure time and light intensity at the same time requires combining two HDR algorithms for adjustment, inputting a matrix of current value and exposure time. The calculation content of the dual-parameter camera response function is the same as that of the embodiment of the colloidal gold immunochromatography card above, and will not be repeated here.

[0173] A 3D dual-parameter response function graph is established, with the logarithmic value of the exposure time set as the X-axis, the logarithmic value of the current set as the Y-axis, and the dual-parameter response function value G set as the Z-axis. In this way, the position of any point in this three-dimensional space can reflect the brightness value under a specific exposure time and current value. After calculation by Python software, the 3D dual-parameter response function graph of the fluorescent immunochromatography image acquisition device is finally obtained as shown in the figure. Fig.21 As shown in (A), Fig.21 (B) and (C) show a set of response curves of the device at different currents and different exposure times.

[0174] The process of solving the piecewise weight function is similar to that of the embodiment of the colloidal gold immunochromatography card described above, and will not be described in detail here.

[0175] Therefore, the form of the target weight function in this embodiment is as follows:

[0176]

[0177] Take the "exposure time-current" product pair as a whole, and input the image set taken under each product pair to solve the objective function in formula (1-6). The pixel value of the digital image is the horizontal coordinate, and lnE i +ln(I m ×Δt j ) as the ordinate, and draw a 2D dual-parameter response function curve. The 2D dual-parameter response function diagram of the colloidal gold immunochromatography microscopic image acquisition device is obtained as shown in Fig. 22 shown.

[0178] High dynamic range radiation pattern k i Restore as follows:

[0179]

[0180] Once the dual-parameter response function curve G of the fluorescence immunochromatography device is successfully restored, Fig.10 There are 7 fluorescent immunochromatographic test strips with different concentrations in the paper, which realize the operation from dual-parameter LDR image set to fusion generation of HDR image.

[0181] Fig.23 The figure shows 15 multi-brightness photos taken of the B4 fluorescent immunochromatographic test paper card and the HDR image synthesized under the DPRF curve.

[0182] The standard concentration quantitative curve of the immunochromatography was fitted using four-parameter logistic fitting, which is similar to the embodiment of the colloidal gold immunochromatography card described above and will not be described in detail here.

[0183] After obtaining 7 sets of fluorescent immunochromatography dual-parameter HDR photos, the sliding window algorithm was used to segment and extract the T line of the image, and the average pixel value of the T line was recorded in Table 2, and the measured value was recorded as "T line after HDR". For the fluorescent immunochromatography test strips, the fluorescence value of B1-B7 fluorescent test strips was measured by a commercial fluorescent immunochromatography detection instrument before the test, and the measured value was recorded as "commercial instrument quantitative T line". And the average pixel value of the T line of the LDR images of the 7 fluorescent immunochromatography test strips taken at 129.5mA and 0.033ms was calculated and recorded as "T line before HDR".

[0184] Table 2 Average pixel values ​​of T lines before and after HDR algorithm of fluorescent immunochromatographic test paper card

[0185]

[0186] Fig.24 In (A), the horizontal axis is the fluorescence intensity 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 dual-parameter HDR algorithm is used, and the vertical axis of the red fitting curve is the average pixel value of the T line after the dual-parameter HDR algorithm is used. The results show that the dual-parameter HDR algorithm can improve the fitting degree of the standard concentration quantitative curve from 0.97399 to 0.99081. Fig.24 (B) compares the linear range before and after using the dual-parameter HDR algorithm. The results show that after using the dual-parameter HDR algorithm, the linear range of detection can be expanded from 1200 to 10000 (fluorescence characteristic value) to 600 to 18000 (fluorescence characteristic value), and the fitting degree reaches 0.99421.

[0187] In the process of restoring the dual-parameter camera 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 exposures and different current values, accurately adjusts the fusion of multi-exposure and multi-brightness images, and reduces the influence of over-exposure and under-exposure areas in the image on the fitting accuracy of the camera 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 at different exposure levels and different current values, effectively overcoming the limitation of the loss of highlights or dark details in a single exposure or single brightness image, and can significantly improve the accuracy of HDR image synthesis. Effectively reduce the influence of image noise and quantization errors, so that the synthesized immunochromatographic image is closer to the real scene, and the analysis and detection results based on the immunochromatographic test paper card are 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.

[0188] Embodiment 2

[0189] On the basis of the above embodiments, the present invention further provides an analysis and detection device based on multi-exposure and multi-brightness dual parameters, characterized in that it is used to perform the analysis and detection method as described in any one of the embodiments, including:

[0190] An image acquisition module is used to photograph the paper card to be tested under multiple exposure time sequences and different light intensities to obtain a multi-exposure and multi-brightness image set of the paper card to be tested;

[0191] A concentration analysis and detection module, used to obtain the actual concentration value of the paper card to be tested according to the standard concentration quantitative curve, the multi-exposure and multi-brightness image set of the paper card to be tested, the corresponding exposure time and the corresponding current value;

[0192] The method for generating the standard concentration quantitative curve comprises:

[0193] Taking images of X standard immunochromatographic test strips with different concentrations in P multi-exposure time sequences and L light intensities to obtain a multi-exposure multi-brightness image set; dividing the multi-exposure multi-brightness image set into a first multi-exposure multi-brightness image set including X1 images of standard immunochromatographic test strips and a second multi-exposure multi-brightness image set including X2 images of standard immunochromatographic test strips; wherein P≥3, L≥3, X=X1+X2;

[0194] Obtain grayscale value changes of N pixels in the T-line area of ​​each image in the first multi-exposure multi-brightness image set to obtain a grayscale value matrix of N×P×L pixels;

[0195] Constructing an initial two-parameter camera response function based on the gray value matrix, exposure time and current value;

[0196] Construct the following piecewise weight function:

[0197]

[0198] 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;

[0199] Determine the function form of f1(z) and f2(z) and the segmentation points z1 and z2 that minimize the error between the average pixel value of the T-line area and the true pixel value, and obtain the target segmentation weight function; the calculation method of the average pixel value of the T-line area includes: optimizing the initial two-parameter camera response function by using the segmentation weight function; acquiring an HDR image according to the optimized camera response function, the second multi-exposure multi-brightness image set, the corresponding exposure time and the corresponding current value; and performing tone mapping on the HDR image to obtain the average pixel value of the T-line area;

[0200] The target segmented weight function is used to optimize the initial two-parameter camera response function to obtain the target two-parameter camera response function;

[0201] According to the target dual-parameter camera response function, the multi-exposure and multi-brightness image set of the standard immunochromatographic test paper card, the corresponding exposure time and the corresponding current value, the average pixel value of the T-line area of ​​the standard immunochromatographic test paper card is obtained, and the average pixel 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.

[0202] An analysis and detection device based on multi-exposure and multi-brightness dual parameters provided in an embodiment of the present invention is used to execute an analysis and detection method based on multi-exposure and multi-brightness dual parameters provided in any embodiment 1 of the present invention, and has corresponding functional modules and beneficial effects.

[0203] 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. A dual-parameter analysis and detection method based on multi-exposure and multi-brightness, characterized in that: include: The paper card to be tested is photographed in a multi-exposure time sequence and at different light intensities to obtain a multi-exposure and multi-brightness image set of the paper card to be tested; According to the standard concentration quantitative curve, the multi-exposure and multi-brightness image set of the paper card to be tested, the corresponding exposure time and the corresponding current value, the actual concentration value of the paper card to be tested is obtained; The method for generating the standard concentration quantitative curve comprises: S1, photographing X images of standard immunochromatographic test strips with different concentrations in P multi-exposure time sequences and L light intensities to obtain a multi-exposure multi-brightness image set; dividing the multi-exposure multi-brightness image set into a first multi-exposure multi-brightness image set including X1 images of standard immunochromatographic test strips and a second multi-exposure multi-brightness image set including X2 images of standard immunochromatographic test strips; wherein P≥3, L≥3, X=X1+X2; S2, obtaining gray value changes of N pixels in the T-line area of ​​each image in the first multi-exposure multi-brightness image set to obtain a gray value matrix of N×P×L pixels; S3, constructing an initial dual-parameter camera response function based on the gray value matrix, exposure time and current value; S4. 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; S5, determining the function forms of f1(z) and f2(z) and the segmentation points z1 and z2 that minimize the error between the average pixel value of the T-line area and the true pixel value, and obtaining a target segmentation weight function; the calculation method of the average pixel value of the T-line area includes: optimizing the initial two-parameter camera response function by using the segmentation weight function; acquiring an HDR image according to the optimized camera response function, the second multi-exposure multi-brightness image set, the corresponding exposure time and the corresponding current value; performing tone mapping on the HDR image to obtain the average pixel value of the T-line area; S6, optimizing the initial dual-parameter camera response function using the target segmented weight function to obtain the target dual-parameter camera response function; S7. According to the target dual-parameter camera response function, the multi-exposure and multi-brightness image set of the standard immunochromatographic test paper card, the corresponding exposure time and the corresponding current value, the average pixel value of the T-line area of ​​the standard immunochromatographic test paper card is obtained, and the average pixel 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 concentrations.

3. The analysis and detection method according to claim 1, characterized in that: S2 specifically includes: S21, extracting a T-line region of each image in the first multi-exposure 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 grayscale value changes of N / X1 pixels in each T-line region in the guide image to obtain a grayscale value matrix of N×P×L pixels.

4. The analysis and detection method according to claim 1, characterized in that: The error between the average pixel value and the true pixel value is the root mean square error RMSE and the mean absolute percentage error MAPE of the average pixel value of the T-line area and the true pixel 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 pixel value of the T-line area of ​​the standard immunochromatographic test paper card according to the target dual-parameter camera response function, the multi-exposure and multi-brightness image set of the standard immunochromatographic test paper card, the corresponding exposure time, and the corresponding current value; 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.

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.

7. The analysis and detection method according to claim 1, characterized in that: The multi-exposure multi-brightness image set covers 0-255 pixel values ​​under different exposure time sequences and light intensities.

8. The analysis and detection method according to claim 1, characterized in that: After obtaining the multi-exposure multi-brightness image set, the method further includes: preprocessing the multi-exposure 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 multi-exposure and multi-brightness dual parameters, 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 multiple exposure time sequences and different light intensities to obtain a multi-exposure and multi-brightness image set of the paper card to be tested; A concentration analysis and detection module, used to obtain the actual concentration value of the paper card to be tested according to the standard concentration quantitative curve, the multi-exposure and multi-brightness image set of the paper card to be tested, the corresponding exposure time and the corresponding current value; The method for generating the standard concentration quantitative curve comprises: Taking images of X standard immunochromatographic test strips with different concentrations in P multi-exposure time sequences and L light intensities to obtain a multi-exposure multi-brightness image set; dividing the multi-exposure multi-brightness image set into a first multi-exposure multi-brightness image set including X1 images of standard immunochromatographic test strips and a second multi-exposure multi-brightness image set including X2 images of standard immunochromatographic test strips; wherein P≥3, L≥3, X=X1+X2; Obtain grayscale value changes of N pixels in the T-line area of ​​each image in the first multi-exposure multi-brightness image set to obtain a grayscale value matrix of N×P×L pixels; Constructing an initial two-parameter camera response function based on the gray value matrix, exposure time and current value; 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; Determine the function form of f1(z) and f2(z) and the segmentation points z1 and z2 that minimize the error between the average pixel value of the T-line area and the true pixel value, and obtain the target segmentation weight function; the calculation method of the average pixel value of the T-line area includes: optimizing the initial two-parameter camera response function by using the segmentation weight function; acquiring an HDR image according to the optimized camera response function, the second multi-exposure multi-brightness image set, the corresponding exposure time and the corresponding current value; and performing tone mapping on the HDR image to obtain the average pixel value of the T-line area; The target segmented weight function is used to optimize the initial two-parameter camera response function to obtain the target two-parameter camera response function; According to the target dual-parameter camera response function, the multi-exposure and multi-brightness image set of the standard immunochromatographic test paper card, the corresponding exposure time and the corresponding current value, the average pixel value of the T-line area of ​​the standard immunochromatographic test paper card is obtained, and the average pixel 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.

Images