Protein immunoblotting image automatic analysis method, device, equipment and medium

Abstract

The invention provides a western blot image automatic analysis method, device, equipment and medium, and relates to the technical field of image understanding, the method comprises the following steps: obtaining a western blot image to be analyzed, converting the western blot image to be analyzed into a gray level image, and carrying out background noise reduction processing on the gray level image; generating a gray scale curve based on the gray scale values of the pixel points in the gray scale image; carrying out trough identification on the gray curve to obtain each trough point in the gray curve; and based on the position of the trough point in the gray curve, adding a segmentation line in the gray image to obtain a lane division result of the to-be-analyzed western blot image, and determining gray value data of each lane based on the lane division result. According to the method, lane automatic division and gray value extraction of the western blot image can be realized, and the analysis efficiency of the western blot image is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of image understanding, and in particular to a Western Blot image automatic analysis method, device, equipment and medium. BACKGROUND

[0002] Western Blot is a common molecular biology experiment in basic scientific research, and is used to detect the presence and relative content of specific proteins in a sample. After the Western Blot experiment, a Western Blot image is generated, and gray scale analysis of the image can determine the presence and relative content of specific proteins in the sample.

[0003] The same Western Blot image shows the relative content of a specific protein in different samples. Since different lanes in the Western Blot image correspond to different samples, lane division is an important step in Western Blot image analysis. Through lane division, the specific protein in different samples can be quantitatively analyzed. In the prior art, different lanes are often divided by manually observing the Western Blot image with the naked eye, which is inefficient. SUMMARY

[0004] The present application provides a Western Blot image automatic analysis method, device, equipment and medium to solve the problem of low efficiency of lane division in the prior art and improve the analysis efficiency of Western Blot images.

[0005] The present application provides a Western Blot image automatic analysis method, comprising: Obtaining a Western Blot image to be analyzed, converting the Western Blot image to be analyzed into a gray scale image, and performing background noise reduction processing on the gray scale image; Generating a gray scale curve based on the gray scale values of the pixel points in the gray scale image; Identifying the valleys of the gray scale curve to obtain each valley point in the gray scale curve; Based on the position of the valley point in the gray scale curve, adding a division line in the gray scale image to obtain the lane division result of the Western Blot image to be analyzed, and determining the gray scale value data corresponding to each lane based on the lane division result.

[0006] According to the Western Blot image automatic analysis method provided by the present application, the valleys of the gray scale curve are identified to obtain each valley point in the gray scale curve, comprising: Based on the points corresponding to each local minimum value in the gray scale curve, a preliminary identification result of the valley points is obtained, and the preliminary identification result of the valley points includes a plurality of valley candidate points; screening the wave trough candidate points based on a constraint condition to obtain the wave trough points; The constraint condition comprises a first constraint condition, and the first constraint condition is used to constrain the uniformity of the lateral distance between adjacent two points in the screened wave trough points.

[0007] According to the protein immunoblot image automatic analysis method provided by the application, the constraint condition further comprises a second constraint condition, and the second constraint condition is used to constrain the significance of the wave peak existing between adjacent two points in the screened wave trough points.

[0008] According to the protein immunoblot image automatic analysis method provided by the application, the constraint condition further comprises a third constraint condition, and the third constraint condition is used to constrain the longitudinal coordinate stability between adjacent two points in the screened wave trough points.

[0009] According to the protein immunoblot image automatic analysis method provided by the application, the generation of the gray scale curve based on the gray scale value of the pixel point in the gray scale image comprises: a plurality of sampling lines are selected according to a preset interval along the length direction of the gray scale image; the average value of the gray scale of a plurality of pixel points located on the same sampling line is obtained as the gray scale value corresponding to the sampling line; a pair of two-dimensional coordinates is formed by the position of the sampling line in the length direction of the gray scale image and the gray scale value corresponding to the sampling line, and a broken line graph is generated based on each two-dimensional coordinate; the broken line graph is smoothed to obtain the gray scale curve.

[0010] According to the protein immunoblot image automatic analysis method provided by the application, after obtaining the lane division result of the to-be-analyzed protein immunoblot image, the method comprises: statistical data of each lane in the to-be-analyzed protein immunoblot image is determined based on the lane division result to obtain a statistical result, and the statistical result is stored in a preset file; the next image meeting the preset format requirement is accessed from a preset database as a new to-be-analyzed protein immunoblot image, and the step of converting the to-be-analyzed protein immunoblot image into a gray scale image is repeatedly executed until there is no image meeting the preset format requirement in the preset database.

[0011] The application further provides a protein immunoblot image automatic analysis device, comprising: an image reading module, used for acquiring a to-be-analyzed protein immunoblot image, converting the to-be-analyzed protein immunoblot image into a gray scale image, and performing background noise reduction processing on the gray scale image; a gray scale curve generation module configured to generate a gray scale curve based on the gray scale values of the pixels in the gray scale image; a valley identification module configured to identify valleys in the gray scale curve to obtain each valley point in the gray scale curve; an analysis result determination module configured to add a segmentation line in the gray scale image based on the position of the valley point in the gray scale curve to obtain a lane division result of the protein immunoblot image to be analyzed, and determine the gray scale value data corresponding to each lane based on the lane division result.

[0012] The present application also provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements any of the above protein immunoblot image automatic analysis methods when executing the program.

[0013] The present application also provides a non-transitory computer readable storage medium having a computer program stored thereon, wherein the computer program is executable by a processor to implement any of the above protein immunoblot image automatic analysis methods.

[0014] The present application also provides a computer program product comprising a computer program, wherein the computer program is executable by a processor to implement any of the above protein immunoblot image automatic analysis methods.

[0015] The protein immunoblot image automatic analysis method, device, equipment and medium provided by the present application can convert the gray scale values of each pixel point in the protein immunoblot image to be analyzed into a gray scale curve, and then identify the valley points in the gray scale curve to achieve automatic lane division. Since the gray scale values in the gray scale curve can correspond to the pixel points, the accuracy of the lane division result can be improved. Compared with the manual visual division method, the analysis efficiency of the protein immunoblot image can be effectively improved while ensuring the reliability of the lane division result of the protein immunoblot image. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the present application or prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without creative labor.

[0017] Figure 1 is a flowchart of the protein immunoblot image automatic analysis method provided by the present application.

[0018] Figure 2This is a schematic diagram of a grayscale image in the automatic analysis method for protein immunoblotting images provided by the present invention.

[0019] Figure 3 This is a schematic diagram of the grayscale curve in the automatic analysis method for protein immunoblotting images provided by the present invention.

[0020] Figure 4 This is a schematic diagram of the lane division results in the automatic analysis method for protein immunoblotting images provided by this invention.

[0021] Figure 5 This is a verification of the effectiveness of the automated protein immunoblotting image analysis method provided by the present invention. Figure 1 .

[0022] Figure 6 This is a verification of the effectiveness of the automated protein immunoblotting image analysis method provided by the present invention. Figure 2 .

[0023] Figure 7 This is a schematic diagram of the structure of the automated protein immunoblotting image analysis device provided by the present invention.

[0024] Figure 8 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0026] The following is combined with Figures 1-6 The automatic analysis method for protein immunoblotting images provided by this invention is described below. Figure 1 As shown, the method includes the following steps: S110. Obtain the immunoblot image of the protein to be analyzed, convert the immunoblot image of the protein to be analyzed into a grayscale image, and perform background noise reduction processing on the grayscale image. S120. Generate a grayscale curve based on the grayscale values ​​of pixels in the grayscale image; S130. Perform valley identification on the grayscale curve to obtain each valley point in the grayscale curve; S140. Based on the position of the trough point in the grayscale curve, add segmentation lines to the grayscale image to obtain the lane division result of the immunoblot image of the protein to be analyzed, and determine the grayscale value data corresponding to each lane based on the lane division result.

[0027] The protein immunoblot image automatic analysis method provided by the present application realizes automatic lane division by converting the gray value of each pixel point in the protein immunoblot image to be analyzed into a gray curve, and then identifying the valley points in the gray curve. Since the gray value in the gray curve can correspond to the pixel point, the accuracy of the lane division result can be improved. Compared with the manual naked-eye division method, the analysis efficiency of the protein immunoblot image can be effectively improved on the basis of ensuring the reliability of the lane division result of the protein immunoblot image.

[0028] In one possible implementation of the protein immunoblot image automatic analysis method provided by the present application, a database can be pre-set, for example, a folder is set as the database, and the protein immunoblot images that need to be analyzed are stored in the database. The protein immunoblot image to be analyzed can be automatically read from the database as an image meeting the preset format requirement (such as tif format, jpg format, png format, etc.) as the protein immunoblot image to be analyzed. Further, in one possible implementation of the method provided by the present application, all images meeting the preset format requirement in the database can be traversed and pre-ordered, for example, ordered according to the time of storage in the database or ordered according to the image file name, etc. The ordering result is stored, and when a new protein immunoblot image is obtained from the database as the protein immunoblot image to be analyzed, the new protein immunoblot image is selected based on the ordering result.

[0029] By setting a database and reading an image meeting the preset format from the database as the protein immunoblot image to be analyzed, batch automatic analysis of the protein immunoblot image can be realized, and the analysis efficiency of the protein immunoblot image can be improved.

[0030] After obtaining the protein immunoblot image to be analyzed, it is converted into a gray image, as shown in Figure 2 The gray image can be set to have a uniform size and a uniform format, for example, 8bit, jpg format. In one possible implementation, the gray image can be further processed to remove background noise to improve data quality, thereby improving the accuracy of the lane division result obtained by subsequent analysis. The existing denoising method can be used to achieve this.

[0031] After obtaining the gray image, a gray curve is generated based on the gray value of the pixel point in the gray image, specifically including: A plurality of sampling lines are selected along the length direction of the gray image according to a preset interval; The average gray value of a plurality of pixel points located on the same sampling line is obtained as the gray value corresponding to the sampling line; The position of the sampling line along the length of the grayscale image and the grayscale value corresponding to the sampling line are combined to form a pair of two-dimensional coordinates, and a line chart is generated based on each two-dimensional coordinate. Smooth the line chart to obtain a grayscale curve.

[0032] The immunoblot image of the protein to be analyzed is a strip image with obvious differences in length and width. After converting the immunoblot image of the protein to be analyzed into a grayscale image, multiple sampling lines can be selected along the length direction of the grayscale image at a preset interval. The preset interval can be n pixels. It can be understood that the larger the preset interval, the lower the sampling accuracy, and the smaller the preset interval, the higher the sampling accuracy. The size of the preset interval affects the sampling accuracy and thus the accuracy of the lane division result. An appropriate preset interval can be selected based on the accuracy requirements of the actual scenario.

[0033] Multiple pixels are selected along each sampling line. In Western blot images, only the darker detection lines reflect the protein response. To improve the accuracy of analysis results for Western blot images, pixels with gray values ​​greater than a threshold are selected along each sampling line. This eliminates interference from pixels not on the detection lines. For pixels selected along a sampling line, the average gray value is taken as the corresponding gray value for that sampling line.

[0034] The position of the sampling line along the length of the grayscale image and the grayscale value corresponding to the sampling line are combined to form a pair of two-dimensional coordinates. Specifically, the position of the sampling line along the length of the grayscale image is taken as the x-axis coordinate in the two-dimensional coordinate system, and the grayscale value corresponding to the sampling line is taken as the y-axis coordinate in the two-dimensional coordinate system. Then, each sampling line can correspond to a point in the two-dimensional coordinate system. Connecting these points will produce a line graph.

[0035] In practice, the color of the detection line produced by the protein reaction changes smoothly. In the method provided by this invention, the obtained line graph is smoothed to obtain a grayscale curve, such as... Figure 3 As shown, by smoothing the line graph to obtain a grayscale curve, it is equivalent to interpolating based on some existing grayscale values ​​along the length of the grayscale image, thus obtaining the grayscale values ​​at all positions along the length of the grayscale image. This can effectively improve the accuracy of the analysis results of the immunoblot images of the proteins to be analyzed.

[0036] After obtaining the grayscale curve, the valleys of the grayscale curve are identified to obtain each valley point in the grayscale curve, including: Based on the points corresponding to each local minimum in the grayscale curve, the preliminary identification results of the valley points are obtained, which include multiple valley candidate points. Based on the constraints, candidate trough points are selected to obtain trough points; The constraints include a first constraint, which is used to constrain the uniformity of the lateral distance between two adjacent points in the selected trough points.

[0037] Since different lanes in the immunoblot image of the protein to be analyzed correspond to different protein reactions, there will be a process of grayscale decrease followed by an increase between lanes. The method provided by this invention identifies the troughs in the grayscale curve, thereby accurately identifying the dividing lines between adjacent lanes.

[0038] First, the points corresponding to the local minima in the grayscale curve are used as candidate troughs. The local minima can be determined by the slope of the points in the curve. These candidate troughs are indeed troughs in the local part of the curve, but they may be due to local abrupt changes in the depth of the detection line in a single lane caused by instability during the experiment. Therefore, further screening of the candidate troughs is required.

[0039] Based on constraints, candidate trough points are screened. The first constraint is that different lanes should have a consistent width, that is, the distance between two adjacent trough points should be uniform. According to this constraint, some candidate trough points can be deleted.

[0040] Furthermore, in another possible implementation of the method provided by the present invention, the constraint condition may also include a second constraint condition, which is used to constrain the significance of the peaks between two adjacent points in the selected trough points.

[0041] The peak between two adjacent points refers to the point with the largest gray value between two adjacent points. Since different lanes correspond to different protein detection lines, there must be a unique point with the largest gray value in the same protein detection line. There is a large difference in gray value between this point and the start and end points of the detection line. Therefore, by using the second constraint condition, the candidate points of the valleys can be identified by further eliminating local gray value fluctuations.

[0042] Furthermore, in another possible implementation of the method provided by the present invention, the constraint conditions may also include a third constraint condition, which is used to constrain the longitudinal coordinate stability between two adjacent points in the selected trough points.

[0043] The stability of the longitudinal coordinate between two adjacent troughs refers to the existence of only one peak between two adjacent troughs. That is, in the interval between two adjacent troughs in the grayscale curve, there is only one point where the longitudinal coordinate to the left of the point gradually increases and the longitudinal coordinate to the right of the point gradually decreases.

[0044] By constructing a triple trough identification and correction mechanism that includes the first constraint, the second constraint, and the third constraint, the influence of local gray value fluctuations or background interference caused by unstable factors in the experiment on the trough identification results can be effectively eliminated, thereby improving the accuracy of the trough points and ensuring the accuracy and robustness of the lane division results. This solves the key pain points of lane fusion, drifting, or misclassification when dividing lanes by eye.

[0045] Based on the position of the trough points in the grayscale curve, segmentation lines are added to the grayscale image. Specifically, based on the x-axis coordinate of the trough point in the grayscale curve, the corresponding position of the trough point along the length of the grayscale image can be determined. A line segment perpendicular to the length of the grayscale image is then generated at this corresponding position as the segmentation line. Figure 4 As shown.

[0046] After obtaining the lane division results of the immunoblot image of the protein to be analyzed, the following is included: Based on the lane division results, the grayscale data of each lane in the immunoblot image of the protein to be analyzed are determined and statistically analyzed to obtain the statistical results, which are then stored in a preset file. The system retrieves the next image that meets the preset format requirements from the preset database as a new immunoblot image of the protein to be analyzed. The process of converting the immunoblot image of the protein to be analyzed into a grayscale image is repeated until there are no images that meet the preset format requirements in the preset database.

[0047] Adding dividing lines to a grayscale image allows for the output of swimlane-divided images. Furthermore, the grayscale values ​​of the swimlanes can be statistically analyzed and output. Specifically, based on the swimlane division results, portions of each swimlane are extracted, and the statistical results of the grayscale values ​​of each swimlane are automatically output, as shown in Table 1.

[0048] Table 1

[0049] Furthermore, statistical results can be stored in a preset file, which users can copy and download. The preset file can be in CSV format for easy downloading. This preset file records the protein immunoblotting analysis data of all images in the preset database, facilitating downstream statistical analysis or report generation.

[0050] Specifically, a preset file can be created for the preset database. When performing automatic batch analysis on the protein immunoblot images in the preset database, the analysis results of each protein immunoblot image are stored in the preset file. This allows users to import multiple protein immunoblot image files at once, automatically complete the analysis tasks of each image in sequence, and generate corresponding result files, which significantly improves data processing efficiency and consistency.

[0051] Furthermore, if issues such as swimlane recognition failure, insufficient image resolution, or extremely uneven background occur during processing, the system can automatically record the image's number or filename and prompt the user for manual review, ensuring the stability and controllability of the batch processing process.

[0052] The automated protein immunoblot image analysis method provided by this invention can effectively improve the efficiency of protein immunoblot image analysis. Traditionally, analyzing a single protein immunoblot image requires 10-20 minutes after visually dividing the lanes. However, the automated method provided by this invention can complete the analysis of a protein immunoblot image within 1-2 seconds. Furthermore, manually dividing lanes is easily influenced by the subjectivity of different operators, and even different analyses by the same analyst may yield different results. This is especially true when the protein differences between adjacent lanes are small, leading to low reliability of manually divided lanes. The method provided by this invention, based on automated data analysis, provides stable, reliable, highly consistent, and reproducible results. Additionally, the method provided in this application can simultaneously output a visual image of the lane division, facilitating user monitoring of the reliability of the results.

[0053] The method provided by this invention can be integrated into platform software or web tools, facilitating cross-laboratory sharing and promotion. Batch processing functionality utilizes the `os` module in Python for directory traversal and image reading, and iterates through the `analyze_wb_image()` function in a loop. Output results are automatically named and saved, supporting both image visualization (.png) and data table (.csv) formats. The front-end interface simplifies user operation through web applications (such as Streamlit) or desktop GUIs (such as PyQt). The method provided by this invention can be published as a web version in conjunction with Shiny or Streamlit, enabling online analysis with zero installation.

[0054] To verify the reliability of the automated protein immunoblotting image analysis method provided by this invention, a comparative experiment was conducted using both the method provided by this invention and manual lane division analysis on the same protein immunoblotting images. The comparison results are as follows: Figure 5 As shown.

[0055] The Blond-Altman analysis method was further used to analyze the results of multiple protein immunoblot images analyzed using the method provided in this invention and the manual lane division analysis method, such as... Figure 6 As shown, the average difference between the two is close to 0, and all data points fall within the 95% consistency limit (±1.96SD), indicating that the method provided in this application has a high degree of accuracy and reliability in swimlane integral calculation that is consistent with traditional manual methods.

[0056] It is worth noting that, for the results of the manual lane division analysis method used in the comparative experiment, multiple groups of people can analyze the same protein immunoblot image, and the average or the most reliable value can be taken as the analysis result of the manual lane division analysis method for that protein immunoblot image.

[0057] The automatic protein immunoblotting image analysis device provided by the present invention is described below. The automatic protein immunoblotting image analysis device described below can be referred to in correspondence with the automatic protein immunoblotting image analysis method described above. For example... Figure 7 As shown, the automated protein immunoblotting image analysis device provided by the present invention includes: The image reading module 710 is used to acquire the immunoblot image of the protein to be analyzed, convert the immunoblot image of the protein to be analyzed into a grayscale image, and perform background noise reduction processing on the grayscale image. The grayscale curve generation module 720 is used to generate a grayscale curve based on the grayscale values ​​of pixels in a grayscale image. Valley recognition module 730 is used to identify valleys in grayscale curves to obtain each valley point in the grayscale curve; The analysis result determination module 740 is used to add segmentation lines to the grayscale image based on the position of the trough point in the grayscale curve, obtain the lane division result of the immunoblot image of the protein to be analyzed, and determine the grayscale value data corresponding to each lane based on the lane division result.

[0058] The automatic analysis device for protein immunoblot images provided by this invention automatically divides lanes by converting the gray values ​​of each pixel in the protein immunoblot image to be analyzed into gray-scale curves and then identifying the troughs in the gray-scale curves. Since the gray values ​​in the gray-scale curves can correspond to the pixels, the accuracy of the lane division results can be improved. Compared with manual eye division, it can effectively improve the efficiency of lane division while ensuring the reliability of the lane division results for protein immunoblot images.

[0059] Figure 8 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 8As shown, the electronic device may include a processor 810, a communication interface 820, a memory 830, and a communication bus 840. The processor 810, communication interface 820, and memory 830 communicate with each other via the communication bus 840. The processor 810 can call logical instructions in the memory 830 to execute an automatic protein immunoblot image analysis method. This automatic protein immunoblot image analysis method includes: acquiring a protein immunoblot image to be analyzed; converting the protein immunoblot image to be analyzed into a grayscale image and performing background noise reduction processing on the grayscale image; generating a grayscale curve based on the grayscale values ​​of pixels in the grayscale image; identifying valleys in the grayscale curve to obtain each valley point; adding segmentation lines to the grayscale image based on the position of the valley points in the grayscale curve to obtain the swimlane division result of the protein immunoblot image to be analyzed; and determining the grayscale value data corresponding to each swimlane based on the swimlane division result.

[0060] Furthermore, the logical instructions in the aforementioned memory 830 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0061] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the automatic protein immunoblot image analysis method provided by the above methods. The automatic protein immunoblot image analysis method includes: acquiring a protein immunoblot image to be analyzed; converting the protein immunoblot image to be analyzed into a grayscale image and performing background noise reduction processing on the grayscale image; generating a grayscale curve based on the grayscale values ​​of the pixels in the grayscale image; identifying the valleys of the grayscale curve to obtain each valley point in the grayscale curve; adding dividing lines to the grayscale image based on the position of the valley points in the grayscale curve to obtain the lane division result of the protein immunoblot image to be analyzed; and determining the grayscale value data corresponding to each lane based on the lane division result.

[0062] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program implements the automatic protein immunoblot image analysis method provided by the above methods. The automatic protein immunoblot image analysis method includes: acquiring a protein immunoblot image to be analyzed; converting the protein immunoblot image to be analyzed into a grayscale image and performing background noise reduction processing on the grayscale image; generating a grayscale curve based on the grayscale values ​​of pixels in the grayscale image; identifying valleys in the grayscale curve to obtain each valley point in the grayscale curve; adding segmentation lines to the grayscale image based on the position of the valley points in the grayscale curve to obtain the swimlane division result of the protein immunoblot image to be analyzed; and determining the grayscale value data corresponding to each swimlane based on the swimlane division result.

[0063] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0064] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0065] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An automated method for analyzing protein immunoblot images, characterized in that, include: Acquire an immunoblot image of the protein to be analyzed, convert the immunoblot image of the protein to be analyzed into a grayscale image, and perform background noise reduction processing on the grayscale image; A grayscale curve is generated based on the grayscale values ​​of the pixels in the grayscale image; Valley identification is performed on the grayscale curve to obtain each valley point in the grayscale curve; Based on the position of the trough point in the grayscale curve, a segmentation line is added to the grayscale image to obtain the lane division result of the immunoblot image of the protein to be analyzed, and the grayscale value data corresponding to each lane is determined based on the lane division result.

2. The automatic analysis method for protein immunoblotting images according to claim 1, characterized in that, The step of identifying the valleys of the grayscale curve to obtain each valley point in the grayscale curve includes: Based on the points corresponding to each local minimum value in the grayscale curve, a preliminary valley point identification result is obtained, which includes multiple valley candidate points. The candidate valley points are selected based on the constraints to obtain the valley points; The constraints include a first constraint, which is used to constrain the uniformity of the lateral distance between two adjacent points in the selected trough points.

3. The automatic analysis method for protein immunoblotting images according to claim 2, characterized in that, The constraint condition also includes a second constraint condition, which is used to constrain the significance of the peaks between two adjacent points in the selected trough points.

4. The automatic analysis method for protein immunoblotting images according to claim 3, characterized in that, The constraints also include a third constraint, which is used to constrain the stability of the longitudinal coordinates between two adjacent points in the selected trough points.

5. The automatic analysis method for protein immunoblotting images according to claim 1, characterized in that, The step of generating a grayscale curve based on the grayscale values ​​of pixels in the grayscale image includes: Multiple sampling lines are selected along the length direction of the grayscale image at preset intervals; The average grayscale value of multiple pixels located on the same sampling line is obtained as the grayscale value corresponding to the sampling line; The position of the sampling line along the length of the grayscale image and the grayscale value corresponding to the sampling line are combined to form a pair of two-dimensional coordinates, and a line graph is generated based on each of the two-dimensional coordinates. The line graph is smoothed to obtain the grayscale curve.

6. The automatic analysis method for protein immunoblotting images according to claim 1, characterized in that, After obtaining the lane division results of the immunoblot image of the protein to be analyzed, the process includes: Based on the lane division results, the grayscale data of each lane in the immunoblot image of the protein to be analyzed are statistically analyzed to obtain statistical results, and the statistical results are stored in a preset file. The next image that meets the preset format requirements is retrieved from the preset database as the new immunoblot image of the protein to be analyzed. The step of converting the immunoblot image of the protein to be analyzed into a grayscale image is repeated until there is no image that meets the preset format requirements in the preset database.

7. An automated protein immunoblotting image analysis device, characterized in that, The device includes: The image reading module is used to acquire the immunoblot image of the protein to be analyzed, convert the immunoblot image of the protein to be analyzed into a grayscale image, and perform background noise reduction processing on the grayscale image. The grayscale curve generation module is used to generate a grayscale curve based on the grayscale values ​​of the pixels in the grayscale image. The valley identification module is used to identify the valleys of the grayscale curve and obtain each valley point in the grayscale curve. The analysis result determination module is used to add segmentation lines to the grayscale image based on the position of the valley point in the grayscale curve, to obtain the lane division result of the immunoblot image of the protein to be analyzed, and to determine the grayscale value data corresponding to each lane based on the lane division result.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the automatic analysis method for protein immunoblotting images as described in any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the automatic analysis method for protein immunoblotting images as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the automatic analysis method for protein immunoblotting images as described in any one of claims 1 to 6.

Images