Western blot imaging method

Abstract

The invention provides an immunoblotting imaging method which comprises the following steps: acquiring a target image which is a chemiluminescence image or a fluorescence image of an imprinting membrane in a darkroom, acquiring a selection signal, selecting a target region comprising target protein according to the selection signal, and calculating the signal intensity of the target protein according to a gray value in the target region. The signal intensity of the target protein can be calculated by combining the gray value of the target area and the distribution of the gray value. Besides, by acquiring the selection signal, the target area to be analyzed can be accurately analyzed, so that the analysis efficiency is improved, and the analysis difficulty is reduced.

Description

Technical Field

[0001] The present invention relates to imaging technology, and in particular to immunoblotting imaging methods. Background Art

[0002] Immunoblotting, also known as protein blotting (western blot), is often used for qualitative analysis of proteins. The method for preparing immunoblots / protein blots is usually to label the proteins separated by electrophoresis with fluorescent dyes or chemiluminescent dyes, and then transfer them to a solid phase carrier (such as NC membrane or PVDF membrane). Finally, the corresponding imaging device is used to collect the corresponding optical signal for imaging, so that users can analyze the expression of proteins. Summary of the invention

[0003] The present application provides an immunoblotting imaging method, comprising: acquiring a target image, which is a chemiluminescent image or a fluorescent image of a blot membrane in a dark room, acquiring a selection signal, selecting a target area including a target protein according to the selection signal, and calculating the signal intensity of the target protein according to the grayscale value in the target area.

[0004] In the immunoblotting test, the signal intensity of the target protein can characterize the content information of the target protein to a certain extent. The greater the signal intensity of the target protein, the greater the corresponding target protein content. The signal intensity can be reflected in the image through the grayscale value. The larger the grayscale value, the greater the signal intensity of the acquisition. In addition, the distribution of the grayscale value can also reflect the distribution of the target protein to a certain extent. Therefore, the target protein signal intensity can be calculated by combining the grayscale value and its distribution of the target area. In addition, by obtaining the selected signal, the target area to be analyzed can be accurately analyzed, which not only improves the analysis efficiency but also reduces the analysis difficulty.

[0005] Among them, acquiring the target image includes: acquiring an exposure time corresponding to the target image, continuously acquiring a first number of long exposure images and a second number of short exposure images so that the sum of the first number and the second number is minimized, and the sum of the total exposure time of the first number of long exposure images and the second number of short exposure images is equal to the exposure time, and generating the target image according to the first number of long exposure images and the second number of short exposure images.

[0006] Among them, generating a target image according to a first number of long-exposure images and a second number of short-exposure images includes: using a first pre-correction image to perform dark field correction on the long-exposure images respectively, the first pre-correction image is obtained by averaging at least one pre-collected first background image, and the acquisition time of each first background image is equal to the exposure time of the long-exposure image; using a second pre-correction image to perform dark field correction on the short-exposure images respectively, the second pre-correction image is obtained by averaging at least one pre-collected second background image, and the acquisition time of each second background image is equal to the exposure time of the short-exposure image; superimposing the first number of long-exposure images and the second number of short-exposure images after dark field correction to generate the target image.

[0007] Among them, obtaining a selection signal, and selecting a target area including a target protein according to the selection signal includes: obtaining a protein selection signal and a background selection signal, obtaining a protein area covering the target protein according to the protein selection signal, obtaining a background area including a non-protein area according to the background selection signal, the target area includes a protein area and a background area, obtaining the total grayscale value and pixel area of ​​the protein area and the grayscale mean of the background area, subtracting the product of its pixel area and the grayscale mean of the background area from the total grayscale value of the protein area, and calculating the signal intensity of the target protein.

[0008] Among them, obtaining a selection signal and selecting a target area including a target protein according to the selection signal includes: selecting a target area including a target protein and a background area according to the selection signal, obtaining a background area and a protein area by grayscale comparison, obtaining a total grayscale value and a pixel area of ​​the protein area and a grayscale mean of the background area, subtracting the product of its pixel area and the grayscale mean of the background area from the total grayscale value of the protein area, and calculating the signal intensity of the target protein.

[0009] Among them, obtaining a selection signal and selecting a target area including a target protein according to the selection signal includes: selecting a target area including a target protein and a background area according to the selection signal, obtaining the background area by grayscale comparison, and calculating the grayscale mean of the background area, or selecting a grayscale mode value that is closest to a preset background grayscale and appears most frequently in the target area, obtaining the total grayscale value and pixel area of ​​the target area, subtracting the product of the pixel area and the grayscale mean or the grayscale mode value from the total grayscale value of the target area, and calculating the signal intensity of the target protein.

[0010] The target area is in the shape of a rectangle, a circle or an ellipse.

[0011] Before obtaining the selection signal, the method further includes performing band detection on the target image to identify the target protein.

[0012] Among them, the image detection method also includes: obtaining a colorimetric image of the blot membrane including the standard strips under bright field, the colorimetric image is a grayscale image or a color image, synthesizing the colorimetric image and the target image, and outputting an image with both the standard strips and the target protein.

[0013] Wherein, obtaining a colorimetric image of the blot membrane including the standard strips under bright field further comprises: performing flat field correction on the colorimetric image. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 A schematic diagram of a method for immunoblotting imaging according to one or more embodiments of the present application; Figure 2 is a schematic diagram of a sub-process of step S10 according to one or more embodiments of the present application; Figure 3 is a schematic diagram of a sub-process of step S103 according to one or more embodiments of the present application; Figure 4 is a schematic diagram of a first sub-process of step S20 according to one or more embodiments of the present application; Figure 5 is a schematic diagram of a second sub-process of step S20 according to one or more embodiments of the present application; Figure 6 is a schematic diagram of a third sub-process of step S20 according to one or more embodiments of the present application; Figure 7 Schematic diagram of another process of the immunoblotting imaging method according to one or more embodiments of the present application. DETAILED DESCRIPTION

[0015] The present application will be further described below in conjunction with the accompanying drawings and some embodiments. The following embodiments are mainly used to exemplarily illustrate the technical solution of the present application, and therefore cannot be used to limit the protection scope of the present application.

[0016] Unless otherwise defined, the technical and scientific terms used herein have the same meanings as those commonly understood by technicians in the technical field to which this application belongs; the terms used herein are mainly used to describe specific embodiments and are not intended to limit this application; the terms "include", "have", "comprise" and other synonyms with the same or similar meanings in the specification and claims of this application and the above-mentioned figure descriptions are intended to cover non-exclusive inclusions.

[0017] In the description of the embodiments of the present application, the technical terms "first", "second", etc. are mainly used to distinguish different objects, and should not be understood as explicitly or implicitly indicating the relative importance or implicitly indicating the number, specific order or primary and secondary relationship of the indicated technical features. In the description of the embodiments of the present application, the meaning of "multiple" is more than two, unless otherwise clearly and specifically defined.

[0018] In this article, the specific features, structures or characteristics described in any embodiment may be included in at least one embodiment of the present application or a combination of at least two embodiments. It will be understood by those skilled in the art that the embodiments described herein may be combined with other embodiments in this article or other embodiments outside this article.

[0019] In the description of the embodiments of the present application, technical terms used to indicate orientation or positional relationships, such as "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc., are mainly used to facilitate the description of the embodiments of the present application and simplify the description, and are not considered to mean that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0020] In the various embodiments of the present application, unless otherwise clearly specified and limited, technical terms such as "set", "install", "assemble", "connect", "connect", "fix" and the like should be understood in a broad sense. Taking connection as an example, it may include fixed connection, detachable connection or integral molding; it may also include at least one of mechanical connection and electrical connection; it may include direct connection or indirect connection through an intermediate medium. A person of ordinary skill in the art can understand the specific meanings of the above terms in the embodiments of the present application according to the specific circumstances.

[0021] The immunoblot imager may include a base and an upper cover, wherein the upper cover can form a darkroom space when closed, the upper cover is provided with a light source, and can provide backlight to the darkroom space, and the base is provided with a photosensitive chip and a processing module. The photosensitive chip may include a CMOS sensor, which can collect optical signals carrying immunoblot membrane information in the darkroom space and convert them into electrical signals. The processing module can receive electrical signals and process them to generate digital images. In order to further improve the efficiency of collecting optical signals, the immunoblot membrane can be attached to the photosensitive chip, and the pixel size of the photosensitive chip can be 80 mm to 120 mm. This is conducive to reducing the loss of optical signals during propagation, and can therefore also be used to detect some extremely weak optical signals (proteins with extremely low concentrations / contents).

[0022] Generally, since proteins are usually labeled with chemiluminescent dyes or fluorescent dyes, in the immunoblotting imaging method, at least a chemiluminescent image or a fluorescent image of the blotting membrane needs to be collected.

[0023] In order to further determine the molecular weight of the target protein, the blotting membrane can also have a standard band. The standard band is usually obtained by electrophoresis and transfer of several proteins of known molecular weight. In the electrophoresis test, proteins of different molecular weights migrate at different speeds, so in the standard band, different molecular weights will be distributed at different positions. Based on the above principle, by comparing the positions of the target protein and the standard band, the molecular weight of the target protein can be roughly estimated.

[0024] Since the standard band only serves as a reference, the requirements for the dyes used are lower. For example, chemiluminescent dyes, fluorescent dyes, or even ordinary dyes (such as blue dyes or color dyes) can be used. If the standard band and the target protein use chemiluminescent dyes at the same time, only the chemiluminescent image is obtained, and the chemiluminescent image of the blot membrane can be obtained for analysis. However, the cost of chemiluminescent dyes is relatively high, so ordinary dyes are more economical. After the standard band is labeled with ordinary dyes, the color difference between it and the unstained area is visible to the naked eye. This means that by turning on the light source and collecting the light that passes through the blot membrane or the light reflected by the blot membrane, the required colorimetric image can be obtained. After the colorimetric image and the chemiluminescent image are synthesized, the molecular weight of the target protein can be estimated with the assistance of the standard band.

[0025] The immunoblotting imaging method involved in the present application can be stored in the form of a program or other readable medium, and read and executed by a processor. The processor can be a part of the processing module, or a processor in a computer connected to the above-mentioned immunoblotting imaging device.

[0026] See also Figure 1 The immunoblotting imaging method may include: obtaining a target image, which is a chemiluminescent image or a fluorescent image of the blotting membrane in a dark room (step S10); obtaining a selection signal, and selecting a target area including a target protein according to the selection signal (step S20); and calculating the signal intensity of the target protein according to the gray value in the target area (step S30).

[0027] In step S20, the selection signal may be a signal input by the user. For example, the user may manually select a frame through an interactive device of a computer or an operating screen of an immunoblot imager. In this way, the target area that the user wants to analyze can be accurately analyzed, which not only improves the analysis efficiency but also reduces the analysis difficulty. Of course, the selection signal may also be generated by an algorithm by a processor. For example, the processor may identify the edge of a target protein and output a selection signal including the edge.

[0028] In the immunoblotting test, the signal intensity of the target protein can characterize the content information of the target protein to a certain extent. The greater the signal intensity of the target protein, the greater the corresponding target protein content. The signal intensity can be reflected by the gray value on the image. The larger the gray value, the greater the signal intensity collected. In addition, the distribution of gray values ​​can also reflect the distribution of the target protein to a certain extent. Therefore, the signal intensity of the target protein can be calculated by combining the gray value and distribution of the target area.

[0029] Since the chemiluminescent signal or fluorescent signal is very weak in the immunoblotting test, it is often necessary to obtain a long exposure image. However, this long exposure image also has some problems, such as the easy introduction of thermal noise, and the image may become blurred. In order to solve this problem, the inventor of the present application adopted the use of multiple short exposure images for superposition to improve the above problem. That is, continuously collecting 18 short exposure images with an exposure time of 0.1 seconds for synthesis can replace the exposure image with an exposure time of 1.8 seconds, and the synthesized image has the advantages of being clearer and less noisy.

[0030] However, there are still some problems. This solution of using multiple short-exposure images for synthesis will generate a large amount of image data. For example, if a 20-second exposure image needs to be replaced, 200 0.1 mm short-exposure images need to be obtained for synthesis. Therefore, the inventor of this application has made further improvements to this solution.

[0031] See also Figure 2 In step S10, the method may include: obtaining an exposure time corresponding to a target image (step S101), continuously obtaining a first number of long exposure images and a second number of short exposure images so that the sum of the first number and the second number is minimized, and the sum of the total exposure time of the first number of long exposure images and the second number of short exposure images is equal to the exposure time (step S102), and generating a target image according to the first number of long exposure images and the second number of short exposure images (step S103).

[0032] The exposure time corresponding to the target image can be customized by the user. For example, the user can set the exposure time to 1.8 seconds, 2.3 seconds, or 5.1 seconds. The exposure time of the long exposure image and the exposure time of the short exposure image can be set by the implementation. For example, the exposure time of the long exposure image can be 1 second, and the exposure time of the short exposure image can be 0.1 second. The first quantity or the second quantity can be 0.

[0033] The core principle of the above scheme is to obtain the target image by superimposing several long-exposure images and short-exposure images at the same time, which can not only make the target image clear and less noisy, but also reduce the total amount of image data due to the use of some long-exposure images. For example, if the exposure time corresponding to the target image is 2.3 seconds, the exposure time of the short-exposure image is set to 0.1 second, and the exposure time of the long-exposure image is set to 1 second, then collecting 2 long-exposure images and 3 short-exposure images can synthesize the required target image. In contrast, if only short-exposure images are collected, 23 images need to be collected, and the generated image data increases significantly.

[0034] In the above method, when the condition that the sum of the total exposure time of the first number and the long-exposure images and the second number and the short-exposure images is equal to the exposure time of the target image is met, the sum of the first number and the second number is the smallest, so that the generated image data can be minimized.

[0035] In addition, it should be noted that the order of collecting long-exposure images and short-exposure images can be arbitrary. A first number of long-exposure images can be collected first, and then a second number of short-exposure images; a second number of short-exposure images can be collected first, and then the first number of long-exposure images; or long-exposure images and short-exposure images can be collected alternately, and the method of the present application does not impose any specific limitations.

[0036] Usually, there will be noise information in the long exposure image or short exposure image collected by the photosensitive chip. The source of this noise information may be dark current, inherent defects of the photosensitive chip, stray light in the darkroom structure, etc. Therefore, this noise information needs to be processed.

[0037] See also Figure 3 In step S103, it can include: using the first pre-corrected image to perform dark field correction on the long exposure images respectively (step S104); using the second pre-corrected image to perform dark field correction on the short exposure images respectively (step S105); and superimposing the first number of long exposure images and the second number of short exposure images after dark field correction to generate a target image (step S106).

[0038] In step S104, the first pre-corrected image can be a pre-collected first background image, or can be obtained by averaging at least two first background images. The first background image can be an image collected by the photosensitive chip when the blotting membrane is not placed in the darkroom space. The acquisition time of the first background image can be equal to the exposure time of the long exposure image. For example, the exposure time of the long exposure image is 1 second, and the acquisition time of the first background image corresponds to 1 second. Furthermore, 30 1-second first background images can be collected, and then accumulated and averaged to obtain the first pre-corrected image.

[0039] When there are multiple long exposure images, each of the multiple long exposure images needs to be individually dark field corrected. Usually, dark field correction can reduce noise in the image. In step S104, it can include: comparing the long exposure image with the first pre-corrected image to remove abnormal points. The specific method can be: subtracting the long exposure image from the first pre-corrected image, and then performing median filtering to filter out abnormal points.

[0040] In step S105, the second pre-corrected image can be a pre-collected second background image, or at least two second background images can be averaged. The second background image can be an image collected by the photosensitive chip when the blotting membrane is not placed in the darkroom space. The acquisition time of each second background image is equal to the exposure time of the short exposure image. The principles and steps of obtaining the second pre-corrected image and using the second pre-corrected image for short exposure can refer to the above description and will not be repeated here.

[0041] In step S106, it may include: obtaining a defect map based on the third background image, obtaining the defect point position of the defect map, and using the defect point position to repair the bad points of the superimposed image. The third background image is also an image captured by the photosensitive chip when the imprinted membrane is not placed in a darkroom space. The acquisition time can be 0.1 seconds, 0.15 seconds, 0.2 seconds, or 1 second. The principle of obtaining a defect map based on the third background image is: calculating the grayscale difference between each pixel and its neighborhood. If the grayscale difference is greater than the threshold, the pixel point is marked as a defect point, thereby forming a defect map. The method for repairing bad points can be any commonly used method, such as: PINTO algorithm, median gradient filtering method, neighborhood averaging method, bilateral filtering method and other methods.

[0042] As mentioned above, although in theory the target protein content can be calculated by combining the distribution of the target protein in the target image and the corresponding grayscale value. However, since the shape of the target protein may be irregular and the density of different regions is different, the grayscale value will be larger when the density is high and smaller when the density is low. Therefore, it is necessary to improve this method to improve the efficiency of analyzing the target protein content. This application proposes three methods below (see Figure 4 , Figure 5 and Figure 6 ) can efficiently complete the target protein content analysis. These three methods can be used independently or in combination with each other.

[0043] See also Figure 4In step S20, the first method may include: obtaining a protein selection signal and a background selection signal (step S210), obtaining a protein region covering the target protein according to the protein selection signal, obtaining a background region including a non-protein region according to the background selection signal (step S211), obtaining a total grayscale value and a pixel area of ​​the protein region and a grayscale mean of the background region (step S212), subtracting the product of its pixel area and the grayscale mean of the background region from the total grayscale value of the protein region, and calculating the signal intensity of the target protein (step S213).

[0044] Wherein, the target area includes a protein area and a background area. The positional relationship between the background area and the protein area is not limited. Preferably, the background area and the protein area do not intersect, which is conducive to improving the efficiency of calculation. Of course, the background area and the protein area can also intersect, but need to be combined with at least one of the following two methods. In this method, the selection signal includes a protein selection signal and a background selection signal. The protein selection signal and the background selection signal can be manually input by the user. For example, manually select on the interactive interface of a computer or an immunoblot imager itself.

[0045] The total grayscale value of the protein area can be obtained by summing the grayscale values ​​of all pixels in the area. If the grayscale mean value of the background area is 0 (that is, the influence of environmental factors is not considered at all), then theoretically the total grayscale value of the protein area can be used to calculate the signal intensity of the target protein, and then calculate the content of the target protein. However, considering that environmental factors may have an impact, it is necessary to remove this impact (the product of the pixel area of ​​the protein area and the grayscale mean value of the background area).

[0046] See also Figure 5 In step S20, the second method may include: selecting a target area including a target protein and a background area according to a selection signal (step S221), obtaining a background area and a protein area by grayscale comparison, obtaining a total grayscale value and a pixel area of ​​the protein area and a grayscale mean of the background area (step S222), subtracting the product of its pixel area and the grayscale mean of the background area from the total grayscale value of the protein area, and calculating the signal intensity of the target protein (step S223).

[0047] The main difference between the second method and the first method is that the user only needs to input the selection signal once (select the target area including both the target protein and the background area), while in the first method, the user needs to select the protein area and the background area separately. The protein area extraction is completed by the algorithm itself, which is more accurate than the protein area manually selected by the user.

[0048] In step S221, the shape of the target area is preferably a regular shape, for example, a rectangle, a circle or an ellipse.

[0049] In step S222, the background area and the protein area can be identified by the Otsu method. The main idea of ​​the Otsu method is to find a threshold value to maximize the inter-class variance. The process of finding the threshold value is a traversal, that is, calculating the inter-class variance after using each grayscale segmentation in the entire grayscale range. Assuming that the total number of image pixels is M, the grayscale value of the current segmentation is k, the number of pixels less than or equal to k is recorded as m1, and the number greater than is recorded as m2, the overall grayscale mean of the image is recorded as w, the grayscale mean of pixels less than or equal to k is w1, and the grayscale mean greater than is w2, then the calculation formula for the inter-class variance is: After the traversal calculation is completed, the grayscale corresponding to the largest inter-class variance is used as the selected threshold.

[0050] See also Figure 6 In step S20, the third method may include: selecting a target area including a target protein and a background area according to a selection signal (step S231), obtaining the background area by grayscale comparison, and calculating the grayscale mean of the background area, or selecting the grayscale mode value that is closest to the preset background grayscale and has the highest frequency of occurrence in the target area (step S232), obtaining the total grayscale value and pixel area of ​​the target area, subtracting the product of its pixel area and the grayscale mean or grayscale mode value from the total grayscale value of the target area, and calculating the signal intensity of the target protein (step S233).

[0051] The main difference between the third method and the second method is that the protein region may not be identified, and the total gray value of the target region is used minus the influence of environmental factors (pixel area and gray mean or gray mode value of the target region).

[0052] In step S221, the shape of the target area is preferably a regular shape, for example, a rectangle, a circle or an ellipse.

[0053] Before step S20, the method may further include performing band detection on the target image to identify the target protein.

[0054] The purpose of band detection may include: identifying the target protein, locating the edge of the target protein, and removing background interference. The algorithm of band detection may be one or more of the Roll ball algorithm, clustering algorithm, and gradient detection algorithm.

[0055] See also Figure 7 In some immunoblotting imaging methods, the image detection method further includes: obtaining a colorimetric image of the blot membrane including the standard bands under bright field (step S12); synthesizing the colorimetric image and the target image, and outputting an image having both the standard bands and the target protein (step S13).

[0056] The main purpose of combining the colorimetric image and the target image is to facilitate the molecular weight analysis of the target protein (comparison with the standard band). Bright field means turning on the light source. Flat field correction of the colorimetric image is mainly to reduce the influence of uneven illumination of the light source (that is, the illumination intensity of each area is not always equal).

[0057] In step S12, the colorimetric image may be a grayscale image or a color image, which mainly depends on the light source and the color of the dye.

[0058] In step S12, the RGB three-color images can be collected separately, and the background outside the mark can be set as a white point, that is, their RGB values ​​are adjusted to be the same, and the RGB three-color images are merged to obtain a color image.

[0059] In step S12, flat field correction may also be performed on the colorimetric image.

[0060] For grayscale images, flat field correction can be achieved by the following method: C=m*(RD) / (FD), where C is the image after flat field correction, R is the colorimetric image without flat field correction, D is the dark field image collected when the light source is turned off (it can be the average image of multiple dark field images), F is the saturated image with the light source turned on (the bright field image just reaching overexposure), and m is the grayscale feature matrix, which can be a constant, such as a set value of 230. Of course, m can also be calculated by the following formula:, where N is the total number of pixels. F (saturated image) can be obtained by turning on the light source and collecting a series of pictures, such as 0.1 seconds, 0.2 seconds...N seconds, N+0.1 seconds, where the picture of N seconds is not overexposed, and the picture of N+0.1 seconds is overexposed, then the picture of N seconds is picture F.

[0061] For the three-color image, flat field correction can be performed on the three-color image according to the above method, which will not be described in detail here.

[0062] Finally, it should be noted that: the above embodiments are mainly used to illustrate the technical solutions of the present application, and should not be understood as limiting the present application; the above embodiments exemplarily illustrate the present application in detail and specifically, and ordinary technicians in this field can modify the technical solutions recorded in the above embodiments, or replace some or all of the technical features therein; and these modifications or replacements cannot make the corresponding technical solutions and the technical solutions of the present application constitute different inventions, and therefore should all be included in the scope of the claims and specification of the present application. In particular, in the absence of structural conflicts or combination obstacles, the various technical features mentioned in each embodiment can be combined in any way, and the technical solutions formed by these combinations should still not be considered to be out of the scope of the technical solutions of the present application in essence.

Claims

1. A method for immunoblotting imaging, characterized in that include: Acquire a target image, wherein the target image is a chemiluminescent image or a fluorescent image of the blot membrane in a dark room, obtaining a selection signal, and selecting a target region including a target protein according to the selection signal, The signal intensity of the target protein is calculated according to the gray value in the target area.

2. The immunoblotting imaging method according to claim 1, characterized in that The acquiring of the target image comprises: Get the exposure time corresponding to the target image. continuously acquiring a first number of long-exposure images and a second number of short-exposure images so that a sum of the first number and the second number is minimized, and a sum of total exposure times of the first number of long-exposure images and the second number of short-exposure images is equal to the exposure duration, A target image is generated based on the first number of long-exposure images and the second number of short-exposure images.

3. The immunoblotting imaging method according to claim 2, characterized in that Generating a target image according to the first number of long-exposure images and the second number of short-exposure images comprises: Performing dark field correction on the long exposure images respectively using first pre-corrected images, wherein the first pre-corrected images are obtained by averaging at least one pre-collected first background image, and the collection time of each of the first background images is equal to the exposure time of the long exposure image; Performing dark field correction on the short exposure images respectively using second pre-corrected images, wherein the second pre-corrected images are obtained by averaging at least one pre-collected second background image, and the collection time of each second background image is equal to the exposure time of the short exposure image; The target image is generated by superimposing the first number of long-exposure images and the second number of short-exposure images after dark field correction.

4. The immunoblotting imaging method according to claim 1, characterized in that The obtaining of the selection signal and selecting the target region including the target protein according to the selection signal comprises: Get protein selection signal and background selection signal, Acquire a protein region covering the target protein according to the protein selection signal, Acquire a background region including a non-protein region according to the background selection signal, wherein the target region includes the protein region and the background region, The total grayscale value and pixel area of ​​the protein region and the grayscale mean of the background region are obtained, and the signal intensity of the target protein is calculated by subtracting the product of the pixel area and the grayscale mean of the background region from the total grayscale value of the protein region.

5. The immunoblotting imaging method according to claim 1, characterized in that The obtaining of the selection signal and selecting the target region including the target protein according to the selection signal comprises: Selecting a target region including a target protein and a background region according to the selection signal, The background area and the protein area are obtained by grayscale comparison, The total grayscale value and pixel area of ​​the protein region and the grayscale mean of the background region are obtained, and the signal intensity of the target protein is calculated by subtracting the product of the pixel area and the grayscale mean of the background region from the total grayscale value of the protein region.

6. The immunoblotting imaging method according to claim 1, characterized in that The obtaining of the selection signal and selecting the target region including the target protein according to the selection signal comprises: Selecting a target region including a target protein and a background region according to the selection signal, The background area is obtained by grayscale comparison, and the grayscale mean of the background area is calculated, or the grayscale mode value that is closest to the preset background grayscale and appears most frequently is selected in the target area. The total grayscale value and pixel area of ​​the target region are obtained, and the product of the pixel area and the grayscale mean or grayscale mode value is subtracted from the total grayscale value of the target region to calculate the signal intensity of the target protein.

7. The immunoblotting imaging method according to claim 5 or 6, characterized in that: The target area is in the shape of a rectangle, a circle or an ellipse.

8. The immunoblotting imaging method according to claim 1, characterized in that Before obtaining the selection signal, the method further includes: The target image is subjected to band detection to identify the target protein.

9. The immunoblotting imaging method according to claim 1, characterized in that The image detection method further comprises: Acquire a colorimetric image of the blot membrane including the standard strips under bright field, wherein the colorimetric image is a grayscale image or a color image, The colorimetric image and the target image are synthesized to output an image having both the standard band and the target protein.

10. The immunoblotting imaging method according to claim 9, characterized in that The method of acquiring a colorimetric image of the blot membrane including the standard strips under bright field also includes: Also included is performing a flat field correction on the colorimetric image.

Images