Quantitative assessment method and device for immunoblot detection membrane strips, readable storage medium

Abstract

The application provides a quantitative evaluation method and device of an immunoblotting detection film strip, and a readable storage medium. The quantitative evaluation method comprises the following steps: acquiring a test paper image of the immunoblotting detection film strip; converting the test paper image into a test paper gray scale image; according to the number and position of detection lines on the immunoblotting detection film strip and the gray scale value of pixel points on the test paper gray scale image, segmenting the test paper gray scale image into a plurality of target regions and obtaining a target data set of the target regions; acquiring the gray scale value of the detection lines in the corresponding target regions according to the target data set; and quantitatively evaluating the characteristics of the protein according to the gray scale value of the detection lines. The gray scale value of the detection lines obtained by the quantitative evaluation method is relatively accurate, and thus when the protein detected by the immunoblotting detection film strip is quantitatively analyzed based on the gray scale value, the accuracy and reliability of the analysis result can be improved.

Description

Technical Field

[0001] This invention belongs to the field of immunoblotting detection technology, specifically relating to a quantitative evaluation method and device for immunoblotting detection membrane strips, and a readable storage medium. Background Technology

[0002] Immunoblotting, also known as protein blotting, is a method for detecting a specific protein in a complex sample based on the specific binding of antigens and antibodies. Due to its advantages such as large analytical capacity, high sensitivity, and strong specificity, it has become one of the most commonly used methods for detecting protein characteristics, expression, and distribution.

[0003] In the medical field, both manual and automated immunoblotting are widely used. Manual testing can only provide qualitative analysis, and the results are subject to subjectivity, low efficiency, and susceptibility to errors. Existing automated testing technologies mostly output qualitative or semi-quantitative results with low accuracy. In practical applications, doctors often find it difficult to provide more reasonable, effective, and precise treatment plans based on qualitative or semi-quantitative results.

[0004] Currently, some methods have emerged for quantitative analysis of immunoblot detection strips using grayscale values. These methods directly employ existing grayscale acquisition methods to segment the image and determine the position of the detection lines, thereby obtaining their grayscale values. However, most of these methods fail to consider the impact of factors such as light source attenuation, camera lifespan, and strip placement on the image quality of the immunoblot detection strip. This leads to inaccurate detection line positions, directly affecting the accuracy of the obtained grayscale values ​​and consequently the accuracy of the quantitative analysis results. Summary of the Invention

[0005] The purpose of this invention is to provide a quantitative evaluation method and apparatus for immunoblotting detection membrane strips, as well as a readable storage medium, which aims to improve the accuracy of the position of the acquired detection line, thereby improving the accuracy of the gray value of the acquired detection line and the accuracy of the quantitative evaluation results.

[0006] To achieve the above objectives, the present invention provides a quantitative evaluation method for immunoblotting detection membrane strips, comprising the following steps:

[0007] Obtain a colored test strip image of the immunoblot detection membrane strip based on the original colored image;

[0008] Convert the color test strip image to a grayscale test strip image;

[0009] Based on the number and position of the detection lines on the immunoblot detection membrane strip and the grayscale values ​​of the pixels in the grayscale image of the test strip, the grayscale image of the test strip is partitioned to obtain multiple target regions and a target dataset for each target region; the number of target regions is equal to the number of detection lines, the multiple target regions are arranged sequentially along a first direction, and each target region has one detection line; and,

[0010] The grayscale values ​​of the detection lines within the target region are obtained based on the target dataset.

[0011] Optionally, the step of partitioning the grayscale image of the test strip according to the number and position of the detection lines on the immunoblot detection membrane strip and the grayscale values ​​of the pixels on the grayscale image of the test strip to obtain multiple target regions and a target dataset for each target region includes:

[0012] The grayscale image of the test strip is initially partitioned according to the number and position of the detection lines on the immunoblot detection membrane strip to obtain multiple primary regions arranged sequentially along the first direction. The number of primary regions is equal to the number of detection lines, and each primary region has one detection line.

[0013] Each primary region is divided into two sub-regions arranged along the first direction;

[0014] The average gray value of each pixel group on the grayscale image of the test strip is obtained, and a first dataset composed of the average gray values ​​of all the pixel groups is obtained; each pixel group includes multiple pixels, and the multiple pixels in the same pixel group are arranged sequentially along a second direction, which is perpendicular to the first direction;

[0015] The first dataset is normalized to compress all data within it to a preset compression range, thus obtaining the second dataset.

[0016] Obtain the data belonging to the second dataset within each sub-region, and filter out the largest data within it as the valid largest data;

[0017] The starting line and the ending line are determined based on the maximum effective data of two sub-regions within the same primary region. A target region is then defined within the corresponding primary region, and all data located within the same target region and belonging to the second dataset are used as the target dataset. Both the starting line and the ending line extend along the second direction.

[0018] Optionally, the step of obtaining the average gray value of each pixel group in the grayscale image of the test strip includes:

[0019] The gray values ​​of all pixels located in the same pixel group are summed to obtain the total gray value of the corresponding pixel group;

[0020] The ratio of the total gray value of the pixel group to the corresponding pixel value in the pixel group is obtained as the average gray value of the pixel group.

[0021] Optionally, the compression range is 0-d, where d is the difference between the maximum and minimum data in the first dataset.

[0022] Optionally, the step of obtaining the grayscale values ​​of the detection lines within the target region based on the target dataset includes:

[0023] The data in the target dataset are grouped according to a predetermined rule to obtain multiple data groups. The data in each data group are in continuous positions, and the data in two adjacent data groups partially overlap.

[0024] The total value of each data group is obtained by summing all the data in the same data group.

[0025] The ratio of the total value of each data group to the number of data in the corresponding data group is obtained to obtain the average value of the corresponding data group.

[0026] The average values ​​of multiple data groups are compared, and the smallest average value is obtained as the gray value of the corresponding target region. The gray value of the target region is then used as the gray value of the detection line located within the corresponding target region.

[0027] Optionally, the target dataset includes m data points, the number of data groups is n, each data group includes p data points, and there are q data points overlapping between two adjacent data groups;

[0028] The predetermined rule is that m, n, p, and q satisfy the following relationship: n = mq, and p = q + 1.

[0029] Optionally, the step of acquiring the test strip image of the immunoblot detection membrane strip from the original image includes:

[0030] Obtain the membrane strip outline on the original image;

[0031] A colored immunoblot detection membrane image is extracted from the original image based on the membrane strip border.

[0032] The test strip border is obtained from the image of the immunoblot detection membrane strip;

[0033] A colored primary test strip image is extracted from the immunoblot detection membrane strip image based on the test strip border;

[0034] The upper and lower edge regions of the primary test strip image are removed to obtain the test strip image; the upper and lower edge regions are arranged opposite each other in a second direction, which is perpendicular to the first direction.

[0035] Optionally, the step of obtaining the membrane strip border on the original image includes:

[0036] Convert the original color image into a first black and white image;

[0037] The first black and white image is subjected to dilation, erosion and dilation operations in sequence, and multiple closed and mutually separated first rectangular contour lines are obtained by edge detection;

[0038] Obtain the area of ​​the first rectangular region enclosed by each of the first rectangular outlines;

[0039] Compare the areas of all the first rectangular regions, select the first rectangular region with the largest area, and use the outline of the first rectangle corresponding to the first rectangular region with the largest area as a membrane strip border.

[0040] Optionally, the step of acquiring the test strip border on the immunoblot detection membrane strip image includes:

[0041] The color image of the immunoblot detection membrane strip is converted into a second black and white image;

[0042] The second black and white image is subjected to dilation, erosion and dilation operations in sequence, and multiple closed and mutually separated second rectangular contour lines are obtained by edge detection;

[0043] Obtain the area of ​​the second rectangular region enclosed by each of the second rectangular outlines;

[0044] Compare the areas of all the second rectangular regions, select the second rectangular region with the largest area, and use the outline of the second rectangle corresponding to the second rectangular region with the largest area as the border of the test strip.

[0045] Optionally, the step of quantitatively evaluating the characteristics of the protein detected by the immunoblotting detection strip based on the gray value of the detection line includes:

[0046] Based on the gray value of the detection line and the preset conversion relationship between gray value and reflectance, the reflectance corresponding to each detection line is obtained;

[0047] The characteristics of the proteins detected by the immunoblotting strip are quantitatively evaluated based on the reflectance corresponding to each detection line.

[0048] To achieve the above objectives, the present invention also provides a quantitative evaluation device for immunoblotting detection membrane strips, including an image acquisition device and a control unit. The image acquisition device is used to acquire the original image, and the control unit is communicatively connected to the image acquisition device and is used to receive the original image. The control unit is also configured to perform the quantitative evaluation method for immunoblotting detection membrane strips as described above.

[0049] To achieve the above objectives, the present invention also provides a computer-readable storage medium having a program stored thereon, which, when executed, performs the quantitative evaluation method for immunoblotting detection membrane strips as described above.

[0050] To achieve the above objectives, the present invention also provides an electronic device, including a processor and a computer-readable storage medium as described above, the processor being configured to execute a program stored on the computer-readable storage medium.

[0051] Compared with the prior art, the quantitative evaluation method and apparatus for immunoblotting detection membrane strips and the readable storage medium of the present invention have the following advantages:

[0052] The aforementioned quantitative evaluation method for an immunoblot detection membrane strip includes the following steps: acquiring a color test strip image from a color original image; converting the color test strip image into a grayscale image; partitioning the grayscale image of the test strip according to the grayscale values ​​of the pixels on the grayscale image and the number of detection lines on the immunoblot detection membrane strip to obtain multiple target regions and a target dataset for each target region; the number of target regions is equal to the number of detection lines, the multiple target regions are arranged sequentially along a first direction, and each target region contains one detection line; acquiring the grayscale value of the detection line within the corresponding target region according to the target dataset; and quantitatively evaluating the characteristics of the protein detected by the immunoblot detection membrane strip based on the grayscale value of the detection line. By partitioning the grayscale image of the test strip, the position of each detection line can be accurately obtained, and thus the grayscale value of the detection line can be accurately obtained. Therefore, the quantitative evaluation result of the protein detected by the immunoblot detection membrane strip based on the grayscale value is more accurate.

[0053] The step of quantitatively evaluating the characteristics of the protein detected by the immunoblot detection membrane strip based on the gray values ​​of the detection lines specifically includes: obtaining the reflectance corresponding to each detection line based on the gray values ​​of the detection lines and a preset conversion relationship between gray values ​​and reflectance; and quantitatively evaluating the characteristics of the protein detected by the immunoblot detection membrane strip based on the reflectance corresponding to each detection line. Quantitative analysis of the immunoblot detection membrane strip using reflectance overcomes the problem of lack of traceability caused by gray values ​​existing only in the image processing stage, meeting the requirement for traceable medical data in the medical field. Attached Figure Description

[0054] The accompanying drawings are provided to better understand the invention and are not intended to unduly limit the scope of the invention. Wherein:

[0055] Figure 1 This is a schematic diagram of the original image provided according to an embodiment of the present invention, in which color is not shown;

[0056] Figure 2 This is an overall flowchart of a quantitative evaluation method for immunoblotting detection membrane strips provided by the present invention according to an embodiment;

[0057] Figure 3 This is a schematic diagram of the first black and white image obtained from the original image in a quantitative evaluation method for immunoblotting detection membrane strips provided by an embodiment of the present invention;

[0058] Figure 4 This is a schematic diagram of the third black and white image obtained after dilution, erosion, and dilation processing of the first black and white image in a quantitative evaluation method for immunoblotting detection membrane strips provided by an embodiment of the present invention.

[0059] Figure 5 This is a schematic diagram of edge detection and obtaining a first rectangular outline in a quantitative evaluation method for immunoblotting detection membrane strips provided by an embodiment of the present invention;

[0060] Figure 6 This is a schematic diagram showing the border of the membrane strip in the original image in a quantitative evaluation method for immunoblotting detection membrane strips provided by an embodiment of the present invention.

[0061] Figure 7 This is a schematic diagram of an immunoblot detection membrane strip image extracted from an original image based on the membrane strip border in a quantitative evaluation method for immunoblot detection membrane strips according to an embodiment of the present invention.

[0062] Figure 8This is a schematic diagram of a second black-and-white image obtained from an immunoblot detection membrane strip image in a quantitative evaluation method for immunoblot detection membrane strips provided according to an embodiment of the present invention;

[0063] Figure 9 This is a schematic diagram of the fourth black and white image obtained after dilution, erosion, and dilation processing of the second black and white image in a quantitative evaluation method for immunoblotting detection membrane strips provided by an embodiment of the present invention.

[0064] Figure 10 This is a schematic diagram of edge detection and obtaining a second rectangular outline in a quantitative evaluation method for immunoblotting detection membrane strips provided by an embodiment of the present invention;

[0065] Figure 11 This is a schematic diagram showing the test strip border marked in an image of an immunoblot detection membrane strip in a quantitative evaluation method for immunoblot detection membrane strips provided according to an embodiment of the present invention.

[0066] Figure 12 This is a flowchart illustrating the quantitative evaluation method for immunoblotting detection membrane strips according to an embodiment of the present invention, which involves partitioning the grayscale image of the test strip to obtain target data, and obtaining the grayscale value of the detection line of the corresponding target area based on the target data.

[0067] Figure 13 This is a schematic diagram of obtaining a primary region by performing primary partitioning on the grayscale image of the test strip in a quantitative evaluation method for immunoblotting detection membrane strips according to an embodiment of the present invention.

[0068] Figure 14 This is a schematic diagram illustrating the process of dividing the primary region of the grayscale image of the test strip into sub-regions in a quantitative evaluation method for immunoblotting detection membrane strips according to an embodiment of the present invention.

[0069] Figure 15 This is a flowchart of a quantitative evaluation method for immunoblotting detection membrane strips provided by an embodiment of the present invention, illustrating the specific steps for quantitative evaluation of proteins based on the gray value of the detection line. Detailed Implementation

[0070] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in this embodiment are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show components related to the present invention and are not drawn according to the actual number, shape, and size of components in the actual implementation. In the actual implementation, the type, quantity, and proportion of each component can be arbitrarily changed, and the component layout may also be more complex.

[0071] Furthermore, while each embodiment described below possesses one or more technical features, this does not imply that users of the present invention must simultaneously implement all technical features in any embodiment, or can only separately implement some or all technical features in different embodiments. In other words, provided it is feasible, those skilled in the art can, based on the disclosure of the present invention and depending on design specifications or implementation requirements, selectively implement some or all technical features in any embodiment, or selectively implement a combination of some or all technical features in multiple embodiments, thereby increasing the flexibility in implementing the present invention.

[0072] As used herein, the singular forms “a,” “an,” and “the” include plural objects, and the plural form “multiple” includes two or more objects, unless otherwise expressly indicated. As used herein, the term “or” is generally used to include the meaning of “and / or,” unless otherwise expressly indicated, and the terms “installed,” “connected,” and “linked” should be interpreted broadly, for example, as a fixed connection, a detachable connection, or an integral connection. Connections can be mechanical or electrical. Connections can be direct or indirect through an intermediate medium, and can be internal communication between two elements or an interaction between two elements. Relational terms such as “first,” “second,” etc., are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations, nor do they indicate or imply relative importance or implicitly specify the number of indicated technical features. It should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "axial," "radial," and "circumferential," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. Those skilled in the art can understand the specific meaning of the above terms in the present invention according to the specific circumstances.

[0073] To make the objectives, advantages, and features of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings. It should be noted that the drawings are all in a very simplified form and use non-precise proportions, and are only used to facilitate and clearly illustrate the objectives of the embodiments of the present invention. The same or similar reference numerals in the drawings represent the same or similar parts.

[0074] Those skilled in the art will understand that, in the production of immunoblot detection membrane strips, the membrane strips are provided with multiple detection lines arranged sequentially along a first direction, each detection line extending along a second direction perpendicular to the first direction. The detection lines are used to react with proteins. Before reacting with proteins, the detection lines are not visible. After incubation, the detection lines that have reacted with proteins develop color and become visible. Typically, the immunoblot detection membrane strip to be analyzed includes at least one already developed detection line.

[0075] One objective of this invention is to provide a quantitative evaluation method for immunoblotting detection membrane strips. This method involves analyzing images of the immunoblotting membrane strips 200 (e.g., ...). Figure 1 The original image 100 (shown) is processed in a series of steps to obtain the gray values ​​of the detection lines in the immunoblot detection membrane strip image 200. Then, the characteristics of the protein detected by the immunoblot detection membrane strip are quantitatively evaluated based on the gray values ​​of the detection lines in the image.

[0076] Please refer to Figure 1 The original image 100 is typically a color image, captured in a standard imaging environment. The setup of this standard imaging environment will be detailed later. The original image 100 includes a background area 300 and at least one of the immunoblot detection strip images 200. Each immunoblot detection strip image 200 includes a test strip image 400; therefore, the area of ​​the test strip image 400 is smaller than the area of ​​the immunoblot detection strip image 200. The test strip image 400 includes a detection line image 500 of the developed detection line.

[0077] Based on this, the quantitative evaluation method for the immunoblotting detection membrane strip includes, for example: Figure 2 The steps S100, S300, S300, S400, and S500 are shown. Step S100 includes acquiring a color test strip image 400 based on the original image 100. Step S200 includes converting the color test strip image 400 into a grayscale test strip image. Step S300 includes partitioning the grayscale test strip image into multiple target regions and a target dataset for each target region based on the number and position of the detection lines on the immunoblot detection membrane strip and the grayscale values ​​of the pixels in the grayscale test strip image. The number of target regions is equal to the number of detection lines, and the multiple target regions are arranged sequentially along the first direction, with each target region containing one detection line. Step S400 includes acquiring the grayscale values ​​of the detection lines within the corresponding target regions based on the target dataset. Step S500 includes quantitatively evaluating the characteristics of the protein detected by the immunoblot detection membrane strip based on the grayscale values ​​of the detection lines.

[0078] It is worth noting that although the undeveloped detection line is not displayed on the test strip image 400, the test strip image 400 still has a positional area corresponding to the undeveloped detection line. Therefore, "each target area has one detection line" means that each target area covers the area where one detection line is located. In other words, the size of each target area in the first direction is larger than the size of the detection line in the first direction, and the start line and end line of the target area are located on opposite sides of the area where the detection line is located in the first direction.

[0079] Next, steps S100 to S500 will be described in detail.

[0080] Step S100 specifically includes the following steps S110 to S150:

[0081] Step S110 includes: obtaining the membrane strip border 101 (e.g., on the colored original image 100) Figure 6 (As shown).

[0082] Step S120 includes: extracting a colored image 200 of the immunoblot detection membrane strip from the original image 100 according to the membrane strip border 101.

[0083] Step S130 includes: acquiring the test strip border 102 (e.g., on the immunoblot detection membrane strip image 200) on the test strip image 200. Figure 11 (As shown).

[0084] Step S140 includes: extracting a colored primary test strip image (not shown in the figure) from the immunoblot detection membrane strip image 200 according to the test strip border 102.

[0085] Step S150 includes: removing the upper and lower edge regions from the primary test strip image to obtain a colored test strip image 400. In this step, the upper and lower edge regions are arranged opposite each other in the second direction. The lengths of the upper and lower edge regions are the same as the length of the test strip image 400. The widths of the upper and lower edge regions can be preset or set during actual operation. The setting principle is to ensure that the obtained test strip image 400 has no residual membrane strip edges and that the upper and lower edges of the test strip image 400 are free of dirt.

[0086] The operation of obtaining the membrane strip border 101 on the original image 100 may include steps S111, S112, S113, S114 and S115.

[0087] Specifically, step S111 includes converting the original image 100 into a format such as... Figure 3 The first black and white image 110 is shown. This step can be performed based on the HSV color model. The HSV color model classifies black in the original image 100 as black and other colors as white. Depending on the actual situation, the area of ​​the detection line image 500 in the first black and white image 110 may be equal to or smaller than the area of ​​the detection line.

[0088] Step S112 includes performing dilation, erosion, and dilation operations sequentially on the first black and white image 110 to obtain the image as shown below. Figure 4The third black and white image 120 is shown. Typically, the area of ​​the detection line image 500 in the third black and white image 120 is smaller than the area of ​​the detection line image 500 in the first black and white image 110.

[0089] Step S113 includes performing edge detection on the third black and white image 120 to detect the positions where black and white lines intersect, and to obtain multiple closed and mutually separated first rectangular outlines 10 (e.g., Figure 5 (As shown). It can be understood that on the third black-and-white image 120, the intersection of each detection line image 500 with its surrounding area forms a black-and-white intersection point, and the intersection of the immunoblot detection membrane strip image 200 with the background area 300 also forms a black-and-white intersection point. That is, on the third black-and-white image 120, the edge of each detection line image 500 forms a first rectangular outline 10, and the edge of each immunoblot detection membrane strip image 200 forms a first rectangular outline 10. Correspondingly, the area of ​​the first rectangular region enclosed by the first rectangular outline 10 formed by the edges of the detection line images 500 is smaller. The area of ​​the first rectangular region enclosed by the first rectangular outline 10 formed by the edges of the immunoblot detection membrane strip image 200 is larger. It should also be noted that... Figure 5 The first rectangular outline 10 shown should actually coincide with the intersection of black and white. However, in order to clearly show the first rectangular outline 10, it is slightly offset from the intersection of black and white.

[0090] Optionally, step S114 includes obtaining the area of ​​the first rectangular region enclosed by each of the first rectangular outlines 20. And step S115 includes comparing the areas of all the first rectangular regions, selecting the first rectangular region with the largest area, and using the first rectangular outline 10 corresponding to the first rectangular region with the largest area as one of the membrane strip borders 101 (e.g., ...). Figure 6 (As shown). When multiple first rectangular regions have equal areas and are all the largest, it indicates that multiple membrane strip borders 101 exist.

[0091] When performing step S120, the membrane strip border 101 is used as a dividing line to cut out the image from the original image 100. Figure 7 The image 200 of the immunoblot detection membrane strip is shown. Since the immunoblot detection membrane strip image 200 is directly extracted from the original image 100, it is also a color image.

[0092] The operation of step S130 is basically the same as that of step S110. Specifically, the execution process of step S130 is as follows:

[0093] First, step S131 is executed: the immunoblotting detection membrane strip image 200 is converted into... Figure 8 The second black and white image 210 is shown. This step can be performed based on the HSV color model. Depending on the actual situation, the area of ​​the detection line image 500 on the second black and white image 210 may be equal to or smaller than the area of ​​the detection line.

[0094] Next, step S132 is executed: the second black and white image 210 is subjected to dilation, erosion, and dilation operations in sequence to obtain the image as shown below. Figure 9 The fourth black and white image 220 is shown. Typically, the area of ​​the detection line image 500 in the fourth black and white image 220 is smaller than the area of ​​the detection line image 500 in the second black and white image 210.

[0095] Then, step S133 is executed: edge detection is performed on the fourth black and white image 220 to detect the positions where black and white lines intersect, resulting in multiple closed and mutually separated second rectangular contour lines 20 (e.g., Figure 10 (As shown). A portion of the plurality of second rectangular outlines 20 described here is formed by the edges of the test strip image 400, and another portion is formed by the edges of the detection line image 500. The area of ​​the second rectangular region enclosed by the second rectangular outlines 20 formed by the edges of the detection line image 500 is smaller. The area of ​​the second rectangular region enclosed by the second rectangular outlines 20 formed by the edges of the test strip image 400 is larger. Similar to... Figure 5 , Figure 10 The second rectangular outline 20 shown should actually coincide with the intersection of black and white. However, in order to clearly show the second rectangular outline 20, it is slightly offset from the intersection of black and white.

[0096] Next, step S134 is executed: obtain the area of ​​the second rectangular region enclosed by each of the second rectangular outlines 20.

[0097] Finally, step S135 is executed: the areas of all the second rectangular regions are compared, the second rectangular region with the largest area is selected, and the outline 20 of the second rectangle corresponding to the second rectangular region with the largest area is used as the test paper border 102 (e.g., ...). Figure 11 (As shown).

[0098] Figure 12 The specific flow of steps S300 and S400 is shown. For example... Figure 12 As shown, the operation of step S300 includes the following steps S310 to S360.

[0099] Step S310 includes obtaining the average gray value of each pixel group in the grayscale image of the test strip, and obtaining a first dataset composed of the average gray values ​​of all the pixel groups. Each pixel group includes multiple pixels, and the multiple pixels in the same pixel group are arranged sequentially along the second direction. Therefore, the position of each pixel group in the first direction can be used as the position of the average gray value of the corresponding pixel group in the grayscale image of the test strip, so that each data in the first dataset has a definite position in the grayscale image of the test strip.

[0100] In a non-limiting implementation, step S310 specifically includes sequentially executing steps S311 and S312 (not shown in the figure). Step S311 includes accumulating the grayscale values ​​of all pixels located in the same pixel group to obtain the total grayscale value of the corresponding pixel group. Step S312 includes obtaining the ratio of the total grayscale value of the pixel group to the number of pixels in the corresponding pixel group, and using the ratio of the total grayscale value of the pixel group to the number of pixels in the corresponding pixel group as the average grayscale value of the corresponding pixel group. It should be understood that steps S311 and S312 are repeatedly executed, the number of times equal to the number of pixel groups, to obtain the average grayscale value of all pixel groups.

[0101] Step S320 includes normalizing the first dataset to compress the data within it to a preset compression range, and using all the compressed data as the second dataset. This step reduces the space complexity of subsequent steps. Optionally, the compression range is 0-d, where d is the difference between the maximum and minimum data in the first dataset. It should be understood that each data point in the second dataset corresponds one-to-one with a data point in the first dataset; therefore, the data in the second dataset also has a definite position in the grayscale image of the test strip.

[0102] Step S330 includes initially partitioning the grayscale image of the test strip according to the number and position of the detection lines on the immunoblot detection membrane strip, obtaining multiple primary regions 30 arranged sequentially along the first direction (e.g., Figure 13 (As shown). The number of primary regions 30 is equal to the number of detection lines, and each primary region 30 has one detection line, that is, each primary region 30 covers the location area of ​​one detection line.

[0103] It should be noted that the number of detection lines on the immunoblot detection membrane strip can be obtained from the instructions for use of the immunoblot detection membrane strip. The position of the detection lines can be determined based on factors such as the size of the immunoblot detection membrane strip in the first direction, the number of detection lines, and the size of the detection lines in the first direction, which is readily available to those skilled in the art. Furthermore, when the immunoblot detection membrane strip is a sensitive screening membrane strip, the sensitive screening membrane strip includes a membrane strip body and a shell housing the membrane strip body. The color of the shell may vary depending on the number of detection lines. For example, when the number of detection lines is 30, the shell is yellow; when the number of detection lines is 21, the shell is any one of red, green, or blue; when the number of detection lines is 11, the shell is gray or orange; and when the number of detection lines is 16, the shell is color 16. Therefore, for the sensitive screening membrane strip, the color of the area between the membrane strip border 101 and the test strip border 102 in the immunoblot detection membrane strip image 200 is the same as the color of the outer shell, and the corresponding color has a definite correspondence with the number and position of the detection lines. In this case, step S330 can be considered as partitioning the grayscale image of the test strip according to the color of the outer shell. Therefore, the grayscale value acquisition method may further include step S600 (not shown in the figure): acquiring the color of the outer shell. Step S600 should be performed before step S330.

[0104] In an optional implementation, step S600 includes: converting the immunoblot detection membrane strip image 200 extracted from the original image 100 into an HSV color model image. Then, the hue (H value), saturation (S value), and luminance (V value) of each pixel in the HSV color model image corresponding to the region between the membrane strip border 101 and the test strip border 102 are obtained. Next, the color of each pixel is determined based on its hue, saturation, and luminance. Then, the number of pixels belonging to each color is counted, and the color with the most pixels is selected and used as the color of the outer shell.

[0105] Step S340 includes dividing each of the primary regions 30 into two sub-regions 31 arranged along the first direction (e.g., ...). Figure 14 (As shown).

[0106] Step S350 includes acquiring data located within each of the sub-regions 31 and belonging to the second dataset, and filtering out the largest data among them as the valid largest data for each sub-region 31. The positions of the valid largest data of two sub-regions 31 within the same primary region 30 are arranged along the first direction.

[0107] Step S360 includes determining a start line and an end line based on the location of the maximum effective data in two sub-regions 31 within the same primary region 30, dividing the target region within the corresponding primary region 30, and using all data located within the same target region and belonging to the second dataset as the target dataset. Let m represent the number of data in the target dataset, with m data points arranged sequentially within the target region, where m is an integer greater than 1. Furthermore, the start line and the end line extend along the second direction.

[0108] The execution of steps S340 to S360 can reduce the amount of computation. Furthermore, the execution of these three steps also minimizes the adverse effects of residual dirt on the test strip image 400 and potential inaccuracies in the initial partitioning, thereby improving the accuracy of the grayscale values ​​obtained in subsequent steps.

[0109] In practice, the colors of all pixels on the detection line are often not entirely the same; for example, some pixels may be white. This will affect the accuracy of the grayscale values ​​obtained from the detection line. (Continue to refer to...) Figure 12 In a non-limiting embodiment, step S400 of this embodiment includes the following steps S410, S420, S430 and S440. By executing these steps, the grayscale values ​​on the detection line are segmented and superimposed, which can reduce the adverse effects on the accuracy of the obtained grayscale values ​​caused by the different colors of some pixels.

[0110] Step S410 includes grouping the m data points in the target dataset according to a predetermined rule to obtain n data groups. The data points in each data group are consecutive, and the data in adjacent data groups partially overlap. Optionally, each data group includes p data points, and the number of repeated data points between adjacent data groups is q. n, p, and q are all integers greater than 1. n, p, and q are all less than m, and q is less than q.

[0111] In one optional implementation, the predetermined rule can be that m, n, p, and q satisfy the relation: n = mq, and p = q + 1. For example, in one embodiment, the target dataset includes ten data points, which are divided into seven data groups. The first data group includes the first, second, third, and fourth data points; the second data group includes the second, third, fourth, and fifth data points; the third data group includes the third, fourth, fifth, and sixth data points; the fourth data group includes the fourth, fifth, sixth, and seventh data points; the fifth data group includes the fifth, sixth, seventh, and eighth data points; the sixth data group includes the sixth, seventh, eighth, and ninth data points; and the seventh data group includes the seventh, eighth, ninth, and tenth data points.

[0112] Step S420 includes summing all the data in the same data group to obtain the total value of each data group.

[0113] Step S430 includes obtaining the ratio of the total value of each data group to the number of data in the corresponding data group, and obtaining the average value of the corresponding data group.

[0114] Step S440 includes comparing the average values ​​of multiple data groups and obtaining the minimum average value. The minimum average value is used as the grayscale value of the corresponding target region, and also as the grayscale value of the detection line located within the corresponding target region.

[0115] Figure 15 The specific process of step S500 is shown. For example... Figure 15 As shown, step S500 includes the following steps S510 and S520.

[0116] Step S510 includes obtaining the reflectance corresponding to the detection line based on the gray value of the detection line on the immunoblot detection membrane strip image 200 and a preset conversion relationship between gray value and reflectance.

[0117] Step S520 includes quantitatively evaluating the characteristics of the protein detected by the immunoblot detection strip based on the reflectance corresponding to the detection line.

[0118] The conversion relationship between grayscale value and reflectance is obtained using a quality control test strip. The quality control test strip can be a commercially available product or it can be designed independently; in this embodiment of the invention, a self-designed quality control test strip is used.

[0119] Specifically, firstly, a rectangular frame with dimensions X mm x Y mm is drawn using drawing software. The specific values ​​of X and Y are determined as needed; this embodiment of the invention does not impose a limitation, but typically X is greater than Y. Next, s baselines are evenly spaced within the rectangular frame, where s is a positive integer greater than 1, and the specific value is set as needed. Then, the quality control test strip is sequentially filled with grayscale values ​​for each baseline. The filling principle is that the R, G, and B values ​​of each baseline are the same, and the difference in grayscale values ​​between two adjacent baselines is 1. That is, the first The R, G, and B values ​​of the baseline mentioned above are all set to... It should be noted that the R, G, and B values ​​for each of the aforementioned baselines are all integers. (If based on...) If the calculated result is not an integer, it will be rounded to the nearest integer. Table 1 shows the grayscale values ​​of the s baselines.

[0120] Table 1

[0121] serial number 1 2 3 4 5 …. s-1 s R value 0 255 / (s-1) 255*2 / (s-1) 255*3 / (s-1) 255*4 / (s-1) … 255*(s-2 / (s-1) 255 G value 0 255 / (s-1) 255*2 / (s-1) 255*3 / (s-1) 255*4 / (s-1) … 255*(s-2 / (s-1) 255 B value 0 255 / (s-1) 255*2 / (s-1) 255*3 / (s-1) 255*4 / (s-1) … 255*(s-2 / (s-1) 255

[0122] Next, the quality control test paper was printed using a high-resolution color photocopying device. The paper material was matte white photographic paper. Then, the reflectance of each baseline line on the printed quality control test paper was measured using a reflectance meter. The results are shown in Table 2. The measurement standard was JJG453-2002 "Standard Color Plate Verification Procedure".

[0123] Table 2

[0124] serial number grayscale value reflectivity 1 0 R1 2 255 / (s-1) R2 3 255*2 / (s-1) R3 4 255*3 / (s-1) R4 5 255*4 / (s-1) R5 … … … s-1 255*(s-2) / (s-1) Rs-1 n 255 Rs

[0125] The relationship curve between the gray value and the corresponding reflectance of the baseline in the quality control test paper is obtained by fitting the curve. Finally, MATLAB software and the Polynomial Regression function are used to perform polynomial fitting on the relationship curve to obtain the conversion relationship between gray value and reflectance, as shown in formula (1). Formula (1) is:

[0126] R=A1*G V +A2*G V 2 +A3*G V 3 +……A s *G V s ,

[0127] In the formula, R represents reflectivity, Gv represents grayscale value, and A1……A s Represents the polynomial coefficients.

[0128] In addition, the quality control test strip is also used to set up the standard shooting environment. The specific setup process includes the following steps S001, S002, S003, S004 and S005 (not shown in the figure).

[0129] Step S001 includes taking a picture of the quality control test strip using the camera to obtain a color image of the quality control test strip. Step S002 includes converting the color image of the quality control test strip into a grayscale image of the quality control test strip. Step S003 includes obtaining the grayscale value of each baseline on the grayscale image of the quality control test strip using any suitable method. Step S004 includes determining whether the grayscale value of each baseline is the same as the corresponding set grayscale value. If so, the current shooting environment is considered to be the standard shooting environment; otherwise, step S005 is executed. Step S005 includes adjusting at least one of the intensity of the light emitted by the light source, the incident angle of the light, and the exposure time of the camera. After step S005 is completed, the process returns to step S001.

[0130] Furthermore, embodiments of the present invention also provide a computer-readable storage medium storing a program that, when executed, performs a quantitative evaluation method for the immunoblot detection membrane strip.

[0131] Furthermore, embodiments of the present invention also provide an electronic device, including a processor and a computer-readable storage medium as described above, the processor being configured to execute a program stored on the computer-readable storage medium.

[0132] Furthermore, this embodiment of the invention also provides a quantitative evaluation device for immunoblotting detection membrane strips, which includes an image acquisition unit and a control unit. The image acquisition unit is used to acquire the original image. The control unit is communicatively connected to the image acquisition unit and is used to receive the original image. The control unit is also configured to execute the quantitative evaluation method for the immunoblotting detection membrane strips.

[0133] While the present invention has been disclosed above, it is not limited thereto. Those skilled in the art can make various modifications and variations to the present invention without departing from its spirit and scope. Therefore, if such modifications and variations fall within the scope of the claims and their equivalents, the present invention also intends to include such modifications and variations.

Claims

1. A quantitative evaluation method for immunoblotting detection membrane strips, characterized in that, Includes the following steps: Obtain a colored test strip image of the immunoblot detection membrane strip based on the original colored image; Convert the color test strip image to a grayscale test strip image; Based on the number and position of the detection lines on the immunoblot detection membrane strip and the grayscale values ​​of the pixels in the grayscale image of the test strip, the grayscale image of the test strip is partitioned to obtain multiple target regions and a target dataset for each target region; the number of target regions is equal to the number of detection lines, the multiple target regions are arranged sequentially along a first direction, and each target region has one detection line; Obtain the grayscale value of the detection line within the target area based on the target dataset; as well as, The characteristics of the proteins detected by the immunoblot detection membrane strip are quantitatively evaluated based on the gray value of the detection line. The step of partitioning the grayscale image of the test strip according to the number and position of the detection lines on the immunoblot detection membrane strip and the grayscale values ​​of the pixels in the grayscale image of the test strip to obtain multiple target regions and a target dataset for each target region includes: The grayscale image of the test strip is initially partitioned according to the number and position of the detection lines on the immunoblot detection membrane strip to obtain multiple primary regions arranged sequentially along the first direction. The number of primary regions is equal to the number of detection lines, and each primary region has one detection line. Each primary region is divided into two sub-regions arranged along the first direction; The average gray value of each pixel group on the grayscale image of the test strip is obtained, and a first dataset composed of the average gray values ​​of all the pixel groups is obtained; each pixel group includes multiple pixels, and the multiple pixels in the same pixel group are arranged sequentially along a second direction, which is perpendicular to the first direction; The first dataset is normalized to compress all data within it to a preset compression range, thus obtaining the second dataset. Obtain the data belonging to the second dataset within each sub-region, and filter out the largest data within it as the valid largest data; The starting line and the ending line are determined based on the maximum effective data of two sub-regions within the same primary region. A target region is then defined within the corresponding primary region, and all data located within the same target region and belonging to the second dataset are used as the target dataset. Both the starting line and the ending line extend along the second direction.

2. The quantitative evaluation method for an immunoblotting detection membrane strip according to claim 1, characterized in that, The step of obtaining the average gray value of each pixel group in the grayscale image of the test strip includes: The gray values ​​of all pixels located in the same pixel group are summed to obtain the total gray value of the corresponding pixel group; The ratio of the total gray value of the pixel group to the corresponding pixel value in the pixel group is obtained as the average gray value of the pixel group.

3. The quantitative evaluation method for an immunoblotting detection membrane strip according to claim 1, characterized in that, The compression range is 0-d, where d is the difference between the maximum and minimum data in the first dataset.

4. A quantitative evaluation method for an immunoblotting detection membrane strip according to any one of claims 1-3, characterized in that, The steps for obtaining the grayscale values ​​of the detection lines within the target region based on the target dataset include: The data in the target dataset are grouped according to a predetermined rule to obtain multiple data groups. The data in each data group are in continuous positions, and the data in two adjacent data groups partially overlap. The total value of each data group is obtained by summing all the data in the same data group. The ratio of the total value of each data group to the number of data in the corresponding data group is obtained to obtain the average value of the corresponding data group. The average values ​​of multiple data groups are compared, and the smallest average value is obtained as the gray value of the corresponding target region. The gray value of the target region is then used as the gray value of the detection line located within the corresponding target region.

5. The quantitative evaluation method for an immunoblotting detection membrane strip according to claim 4, characterized in that, The target dataset includes m data points, the number of data groups is n, each data group includes p data points, and there are q overlapping data points between two adjacent data groups; The predetermined rule is that m, n, p, and q satisfy the following relationship: n = mq, and p = q + 1.

6. The quantitative evaluation method for an immunoblotting detection membrane strip according to claim 1, characterized in that, The steps for obtaining the test strip image of the immunoblot detection membrane strip from the original image include: Obtain the membrane strip outline on the original image; A colored immunoblot detection membrane image is extracted from the original image based on the membrane strip border. The test strip border is obtained from the image of the immunoblot detection membrane strip; A colored primary test strip image is extracted from the immunoblot detection membrane strip image based on the test strip border; The upper and lower edge regions of the primary test strip image are removed to obtain the test strip image; the upper and lower edge regions are arranged opposite each other in a second direction, which is perpendicular to the first direction.

7. The quantitative evaluation method for an immunoblotting detection membrane strip according to claim 6, characterized in that, The step of obtaining the membrane strip border on the original image includes: Convert the original color image into a first black and white image; The first black and white image is subjected to dilation, erosion and dilation operations in sequence, and multiple closed and mutually separated first rectangular contour lines are obtained by edge detection; Obtain the area of ​​the first rectangular region enclosed by each of the first rectangular outlines; Compare the areas of all the first rectangular regions, select the first rectangular region with the largest area, and use the outline of the first rectangle corresponding to the first rectangular region with the largest area as a membrane strip border.

8. The quantitative evaluation method for an immunoblotting detection membrane strip according to claim 6, characterized in that, The step of acquiring the test strip border on the immunoblot detection membrane strip image includes: The color image of the immunoblot detection membrane strip is converted into a second black and white image; The second black and white image is subjected to dilation, erosion and dilation operations in sequence, and multiple closed and mutually separated second rectangular contour lines are obtained by edge detection; Obtain the area of ​​the second rectangular region enclosed by each of the second rectangular outlines; Compare the areas of all the second rectangular regions, select the second rectangular region with the largest area, and use the outline of the second rectangle corresponding to the second rectangular region with the largest area as the border of the test strip.

9. The quantitative evaluation method for immunoblotting detection membrane strips according to claim 1, characterized in that, The steps for quantitatively evaluating the characteristics of the protein detected by the immunoblotting detection strip based on the gray value of the detection line are as follows: Based on the gray value of the detection line and the preset conversion relationship between gray value and reflectance, the reflectance corresponding to each detection line is obtained; The characteristics of the proteins detected by the immunoblotting strip are quantitatively evaluated based on the reflectance corresponding to each detection line.

10. A quantitative evaluation device for immunoblotting detection membrane strips, characterized in that, The device includes an image acquisition device and a control unit. The image acquisition device is used to acquire the original image, and the control unit is communicatively connected to the image acquisition device and is used to receive the original image. The control unit is also configured to perform the quantitative evaluation method of the immunoblotting detection membrane strip as described in claim 9.

11. A computer-readable storage medium having a program stored thereon, characterized in that, When the procedure is executed, the quantitative evaluation method for the immunoblotting detection membrane strip as described in any one of claims 1-9 is performed.

12. An electronic device, characterized in that, It includes a processor and a computer-readable storage medium as claimed in claim 11, the processor being configured to execute a program stored on the computer-readable storage medium.

Images