A film line positioning and film strip identification method

Abstract

The present invention provides a membrane line positioning and membrane strip identification method, which locates the membrane line, including determining the quality control membrane line and other membrane lines to be selected, comparing the other membrane lines to be selected with a preliminary membrane line set obtained according to the quality control membrane line, selecting suitable membrane lines to be selected as membrane lines in a final membrane line set, facilitating subsequent analysis of the membrane lines; and correcting the preliminary membrane line set in real time, ensuring that the positions of the membrane lines in the final membrane line set are accurate.

Description

Technical Field

[0001] The invention relates to the field of image recognition, and in particular to a film line positioning and film strip recognition method. Background Art

[0002] With the development of the medical field, allergic symptoms have changed from being helpless at first to later allergen detection. At present, conventional allergen detection includes prick test for allergens, spot test, total IgE test, food intolerance test and immunoblotting test. Among them, immunoblotting is a hybridization technology that combines high-resolution gel electrophoresis and immunochemical analysis technology. The membrane strip detected by immunoblotting of allergen-specific antibodies IgG is pasted on a standard format reading plate and scanned into an electronic image by a flatbed scanner. This algorithm recognizes the position of the membrane strip on the electronic image and identifies the specificity of the membrane strip band. Immunoblotting is not only widely used in the analysis of antigen components and their immune activity, but also can be used for the diagnosis of diseases.

[0003] In the traditional immunoblotting detection process, the membrane strip is attached to the result reading plate, and the test result is judged and identified by manual comparison with the colorimetric plate. The traditional method requires a lot of manpower. In addition, the manual comparison process is easily affected by personal vision, which leads to inaccurate comparison and has certain subjective factors. Therefore, a method for immunoblotting image recognition is needed. Summary of the invention

[0004] The purpose of the present invention is to solve the deficiencies of the prior art and provide a method for film line positioning and film strip identification.

[0005] In order to solve the above problems, the present invention adopts the following technical solutions:

[0006] A membrane line positioning method comprises the following steps:

[0007] Step 71: Determine the positions of the quality control membrane line and the first membrane line to be selected that meets the color requirements according to the membrane strip images in the membrane strip list;

[0008] Step 72: obtaining a grayscale image according to the film strip image, and determining the position of the second film line to be selected on the film strip according to the grayscale change rate of the grayscale image;

[0009] Step 73: According to the quality control membrane line and the set membrane line spacing, a membrane line set is selected from the candidate membrane line 1 and the candidate membrane line 2.

[0010] Furthermore, in step 71, obtaining the quality control membrane line and the first membrane line to be selected includes the following steps:

[0011] Step 711: performing multi-scale enhancement on the film strip images in the film strip list to obtain a single-channel enhanced image;

[0012] Step 712: convert the film strip image into a grayscale image; and perform a difference between the single-channel enhanced image and the grayscale image to obtain a difference image;

[0013] Step 713: according to the difference image, search for a quality control film line within a set distance range of the film strip image; and search for a candidate film line 1 that meets the color requirements.

[0014] Furthermore, in step 711, the method for obtaining the enhanced image is to first perform Gaussian blur processing on the film strip image to obtain a Gaussian blurred image; then perform weighted fusion of the Gaussian blurred image and the film strip image to obtain a single-channel enhanced image.

[0015] Furthermore, in step 712, the difference image is obtained by the following formula:

[0016]

[0017] Among them, dst represents the grayscale value of the corresponding pixel in the difference image; en_s represents the value of the corresponding pixel in the single-channel enhanced image; gr_s represents the grayscale value of the corresponding pixel in the grayscale image.

[0018] Furthermore, in step 713, firstly, a projection histogram is made for the binary difference image in the Y direction, and a mean histogram is made in the Y direction according to the grayscale image of the membrane strip image, wherein the mean histogram is the mean of each column of pixels in the membrane strip image; then, in a set distance area on the membrane strip image, a quality control membrane line is searched according to a judgment criterion; finally, according to the peak position in the projection histogram, a peak position that meets the set threshold requirement is searched as a candidate membrane line 1; wherein, the judgment criterion for the quality control membrane line is that the peak in the projection histogram exceeds the peak set threshold, and at the same time, the mean histogram also exceeds the mean set threshold;

[0019] If no quality control membrane line that meets the judgment criteria is found in the set distance area on the membrane strip image, the value with the largest peak in the projection histogram is selected in the set distance area, and the corresponding position is used as the quality control membrane line.

[0020] Furthermore, in step 72, the process of determining the second film line to be selected on the film strip includes the following steps:

[0021] Step 721: performing a gamma enhancement algorithm on the grayscale image of the film strip image to obtain an enhanced image gamma_strip;

[0022] Step 722: obtain a mean histogram gamma_mean_hist in the Y direction for the enhanced image gamma_strip, and perform Gaussian filtering; obtain a slope histogram slop_hist in the Y direction according to the mean histogram gamma_mean_hist; wherein the slope histogram slop_hist represents the slope of the mean histogram;

[0023] Step 723: Count the points in the slope histogram slop_hist that are less than a set value 1 and the points that are greater than or equal to a set value 2; wherein the set value 1 is less than the set value 2;

[0024] Step 724: Count the number of consecutive points less than a set value 1, and the number of consecutive points greater than or equal to a set value 2; and find a set of consecutive points 1 and a set of consecutive points 2 that meet the set point quantity requirement.

[0025] Step 725: In the statistical slope histogram slop_hist, select adjacent continuous point set 1 and continuous point set 2, and calculate the zero-crossing point between the two point sets where the value changes from negative to positive, to obtain the second model line to be selected.

[0026] Furthermore, in step 722, the slope histogram is obtained by the following formula:

[0027]

[0028] Among them, slop hist represents the value in the slope histogram slop_hist; gamma mean hist[i] represents the value of position i in the mean histogram gamma_mean_hist; bin represents the value of the maximum position in the mean histogram gamma_mean_hist.

[0029] Furthermore, in step 73, screening the membrane line set includes the following steps:

[0030] Step 731: determining a preliminary membrane line set according to the position of the quality control membrane line and the set membrane line interval; wherein the preliminary membrane line set includes the position of the quality control membrane line, and the position of the subsequent membrane line obtained by calculation according to the membrane line interval and the corresponding position serial number;

[0031] Step 732: traverse the first film line to be selected and the second film line to be selected, calculate the shortest distance between each film line and the position recorded in the preliminary film line set; select the film line with the smallest shortest distance, use the corresponding film line as the film line in the final film line set, record the position information of the film line, and remove the film line from the first film line to be selected and the second film line to be selected, and remove the position number from the preliminary film line set;

[0032] Step 733: updating the positions of other membrane lines in the preliminary membrane line set according to the distance between the model line and the quality control membrane line and the position sequence number corresponding to the model line;

[0033] Step 734: Determine whether all the position numbers in the preliminary membrane line set have been eliminated; if all have been eliminated, it means that the final membrane line set has been obtained, and the step ends; otherwise, return to step 732.

[0034] A film strip identification method based on film line positioning comprises the following steps:

[0035] Step 1: Use a scanning device to complete the image scanning of the film strip reading plate and obtain the scanned image;

[0036] Step 2: Preprocess the scanned image, including image enhancement, filtering, connected domain analysis, rotation and trimming, and scaling to obtain the image to be processed;

[0037] Step 3: extract the film strip area according to the image to be processed, obtain the shadow area of ​​the film strip area; and remove the shadow according to the shadow area;

[0038] Step 4: Determine whether there is adhesion in the film strip area; if there is adhesion, segment the adhered film strip area and proceed to the next step; if there is no adhesion, directly proceed to the next step;

[0039] Step 5: Perform tilt correction on the film strip area to make the film strip area face the set direction;

[0040] Step 6: Count the film strip areas after tilt correction, sort them into a film strip list, and identify the labels;

[0041] Step 7: According to the above-mentioned film line positioning method, the film lines on the film strips in the film strip list are identified and positioned to obtain a film line set;

[0042] Step 8: Perform stain removal operation on the film strips in the film strip list;

[0043] Step 9: Calculate the optical density value of each film line, and obtain the concentration value corresponding to each film line based on the optical density value; end step.

[0044] Furthermore, the calculation of the film line concentration value in step 9 includes the following steps:

[0045] Step 91: Obtain a histogram of film lines according to the decontamination image obtained in step 8;

[0046] Step 92: Calculate the optical density value on each film line according to the histogram of the film line, and obtain the average optical density value;

[0047] Step 93: Calculate the correction factor correction_factor according to the mean optical density value of the quality control line on the film strip and the set quality control line optical density, and correct the mean optical density values ​​of other film line positions;

[0048] Step 94: Substitute the corrected mean optical density value of the film line into the calibration curve equation; wherein the calibration curve equation is a cubic spline equation or a four-parameter fitting equation;

[0049] The quality control line in step 93 is the membrane line set on the membrane strip.

[0050] The beneficial effects of the present invention are:

[0051] By determining the quality control membrane lines and the membrane lines to be selected, and according to the set membrane line spacing, the membrane line set is screened and determined to achieve accurate positioning of the membrane lines on the membrane strip;

[0052] By positioning the membrane line, including determining the quality control membrane line and other candidate membrane lines, comparing the other candidate membrane lines with the preliminary membrane line set obtained based on the quality control membrane line, selecting the appropriate candidate membrane line as the membrane line in the final membrane line set, facilitating the subsequent analysis of the membrane line; and by correcting the preliminary membrane line set in real time, ensuring the accuracy of the position of the membrane line in the final membrane line set;

[0053] In the process of film line positioning, the positions of the film lines to be selected are determined by the projection histogram obtained by the difference image of the grayscale image and the enhanced image and the mean histogram obtained by the grayscale image, respectively, to ensure accurate film line positioning and avoid omissions;

[0054] By screening out stains and replacing the stain pixels with the grayscale mean of the non-stain pixels in the same column, the film strip decontamination process is completed to prevent the stains from interfering with the subsequent film line concentration value detection;

[0055] By quality controlling the mean optical density of the membrane line, the mean density of other membrane lines can be corrected to eliminate the scanning brightness deviation generated by the scanner during the image scanning process;

[0056] The conversion between the mean optical density value of the film line and the concentration value of the film line is realized through a four-parameter fitting equation;

[0057] By segmenting the adhered film strips, correcting the tilt of the film strips, and identifying and counting the film strip areas, the randomly pasted film strips on the film strip reading plate are sorted into a uniformly arranged film strip list, and the list is identified. By identifying the film lines on the film strips, the optical density values ​​of the film line identification are converted into concentration values, thereby realizing automatic detection and identification of the film strips. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 This is an overall flow chart of Embodiment 1 of the present invention;

[0059] Figure 2 This is a schematic diagram of a membrane strip reading plate after pretreatment according to Embodiment 1 of the present invention;

[0060] Figure 3 It is a schematic diagram of the scanned film strip and shadow according to the first embodiment of the present invention;

[0061] Figure 4 The image is obtained by performing an opening operation on the grayscale image in step 33 of the first embodiment of the present invention;

[0062] Figure 5 The shadow binarization image of the first embodiment of the present invention;

[0063] Figure 6 For the first embodiment of the present invention Figure 4 and Figure 5 Perform the AND operation to obtain the shadow image;

[0064] Figure 7 It is a binary image of the film strip area in the first embodiment of the present invention;

[0065] Figure 8 This is a schematic diagram of the film strip adhesion in the first embodiment of the present invention;

[0066] Fig. 9 This is a schematic diagram of an adhesion area image according to the first embodiment of the present invention;

[0067] Fig.10 The first membrane strip skeleton image of the first embodiment of the present invention;

[0068] Fig.11 The left and right adhesion film strips and the position of the dividing line of the first embodiment of the present invention;

[0069] Fig.12 It is an image diagram of the adhesion area where the film strips are adhered up and down according to the first embodiment of the present invention;

[0070] Fig.13 The basis of the first embodiment of the present invention Fig.12 The obtained membrane strip skeleton image 2;

[0071] Fig.14 The upper and lower adhesive film strips of the first embodiment of the present invention and the Fig.13 Schematic diagram of the obtained segmentation line;

[0072] Fig.15 The second image diagram of the adhesion area where the film strips are adhered to each other up and down according to the first embodiment of the present invention;

[0073] Fig.16 The basis of the first embodiment of the present invention Fig.15 The obtained membrane strip skeleton three;

[0074] Fig.17The upper and lower adhesive film strips of the first embodiment of the present invention and the Fig.16 Schematic diagram of the obtained segmentation line;

[0075] Fig.18 Schematic diagram of a single film strip area and a film strip deflection angle according to the first embodiment of the present invention;

[0076] Fig.19 The film strip set obtained in step 64 of the first embodiment of the present invention;

[0077] Fig. 20 is a list of film strips obtained in step 65 of the first embodiment of the present invention;

[0078] Fig.21 Schematic diagram of the location of the concentrated membrane lines in the first embodiment of the present invention;

[0079] Fig. 22 is the CV histogram of the first embodiment of the present invention;

[0080] Fig.23 is the slope histogram obtained in step 913 of the first embodiment of the present invention;

[0081] Fig.24 It is a combination of the grayscale mean histogram obtained in step 911 and the baseline histogram obtained in step 915 of the first embodiment of the present invention;

[0082] Fig.25 is a mean optical density histogram of Embodiment 1 of the present invention;

[0083] Fig.26 is the four-parameter fitting curve of the first embodiment of the present invention;

[0084] Fig. 27 Schematic diagram of the corresponding output of the concentration value and the film line position in the first embodiment of the present invention. DETAILED DESCRIPTION

[0085] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0086] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and thus the drawings only show components related to the present invention rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0087] Embodiment 1:

[0088] like Figure 1 As shown, a method for recognizing an immunoblot image comprises the following steps:

[0089] Step 1: Use a scanning device to complete the image scanning of the film strip reading plate and obtain the scanned image;

[0090] Step 2: Preprocess the scanned image, including image enhancement, filtering, connected domain analysis, rotation and edge trimming, to obtain the image to be processed;

[0091] Step 3: extract the film strip area according to the image to be processed, obtain the shadow area of ​​the film strip area; and remove the shadow according to the shadow area;

[0092] Step 4: Determine whether there is adhesion in the film strip area; if there is adhesion, segment the adhered film strip area and proceed to the next step; if there is no adhesion, directly proceed to the next step;

[0093] Step 5: Perform tilt correction on the film strip area to make the film strip area face the set direction;

[0094] Step 6: Count the film strip areas after tilt correction, sort them into a film strip list, and identify the labels;

[0095] Step 7: Identify and locate the membrane lines on the membrane strips in the membrane strip list to obtain a membrane line set;

[0096] Step 8: Perform stain removal operation on the film strips in the film strip list;

[0097] Step 9: Calculate the optical density value of each film line, and obtain the concentration value corresponding to each film line based on the optical density value; end step.

[0098] It should be noted that in some other implementations, the processes of step 3 and step 8 may also be removed.

[0099] The film strip reading board in step 1 is a green paper in this example, and a film strip with a test result is pasted on the film strip reading board; it should be noted that in some other embodiments, the film strip reading board can be paper or cardboard of other colors. The scanned image obtained is a RGBA4 channel true color image, and the resolution of the scanned image is greater than or equal to 2000×3000dpi. After obtaining the scanned image, the scanned image is stored and marked, and in this example, the scanned image is marked as img_original.

[0100] like Figure 2 As shown, in this example, after completing the image preprocessing in step 2, the image needs to be reduced to obtain the image to be processed; in this example, the area of ​​the image is reduced by one third, because the scanned image is large and the time complexity is high, which will reduce the speed of subsequent processes. The preprocessing process includes the following steps:

[0101] Step 21: Scanned image enhancement, the scanned image is enhanced according to the brightness of the scanned image; in this example, the image enhancement is completed by gamma transformation for the brighter or darker scanned image;

[0102] Step 22: Scan the image and filter it using bilateral filtering. In this example, the parameters of the bilateral filtering include setting the window size to 5, the pixel variance to 11, and the spatial variance to 11.

[0103] Step 23: Performing connected domain analysis on the scanned image, wherein the connected domain with the largest area is the background region, and the background region refers to the region other than the film strip in the scanned image;

[0104] Step 24: Rotate the scanned image, select the connected domain with the largest area according to the result of the connected domain analysis, calculate the deflection angle according to the edge of the connected domain, and rotate the scanned image according to the deflection angle;

[0105] Step 25: trim the scanned image. According to the connected domain with the largest area, determine the circumscribed rectangular frame in the rotated scanned image, and trim the scanned image according to the range of the circumscribed rectangular frame to obtain the image to be processed.

[0106] The scanning image enhancement processing in step 21 and the scanning image filtering processing in step 22 are both for making the scanning image clearer.

[0107] In step 23, the connected domain analysis of the image includes the following steps:

[0108] Step 231: converting the RGB scanned image into a Lab image; the Lab image includes three channels, namely, channel l, channel a, and channel b;

[0109] Step 232: performing adaptive binarization processing on the a channel image of the Lab image;

[0110] Step 233: performing morphological processing on the binarized image;

[0111] Step 234: Assign the boundary of the morphologically processed image to 0 and find the connected domain.

[0112] In step 232, adaptive binarization is performed on the a channel image. In this example, green pixels or regions are assigned a value of 255, and non-green pixels or regions are assigned a value of 0.

[0113] The morphological processing in step 233 is an opening operation in this example, that is, the image is first eroded and then expanded to achieve the purpose of removing noise blocks and filling holes.

[0114] The circumscribed regular rectangular frame in step 25 represents the circumscribed minimum rectangular frame of the target area, and the rectangular frame is arranged upright.

[0115] like Figure 3-7 As shown, in step 3, since the film strip has a certain thickness, a shadow area will be formed during the image scanning of the film strip reading plate. The shadow area will affect the subsequent tilt correction, optical density calculation and other processes, so the shadow area needs to be removed first. To extract the film strip area, first convert the image to be processed obtained in step 2 from RGB space to LAB space; secondly, perform adaptive binarization on the A channel image of the LAB space to obtain a binary image binary. In this example, the green area or pixel point is assigned a value of 0, and the non-green area or pixel point is assigned a value of 255, where the white area is the film strip area. Removing the shadow of the film strip area includes the following steps:

[0116] Step 31: converting the image to be processed obtained in step 2 from a color image to a grayscale image;

[0117] Step 32: Binarize the grayscale image to obtain a grayscale binary image gray_binary; in this example, the area or pixel point greater than the grayscale threshold is assigned a value of 0, and the area or pixel point less than or equal to the grayscale threshold is assigned a value of 255; the grayscale threshold is obtained according to the average grayscale value of the grayscale image;

[0118] Step 33: Perform morphological opening operation on the grayscale binary image gray_binary;

[0119] Step 34: convert the image to be processed obtained in step 2 from the RGB space to the HSV space to obtain an HSV image;

[0120] Step 35: Binarize the HSV image, assigning 255 to the pixels within the set HSV range and 0 to the other pixels, to obtain a binary image of the shadow of the film strip, which is expressed as:

[0121]

[0122] In this example, [h1, s1, v1] is [0, 0, 0], and [h2, s2, v2] is [180, 255, 50]; black represents the pixel in the shadow binary image; h, s, v represent the hue H, saturation S, and lightness V of the pixel in the HSV image;

[0123] Step 36: Perform an AND operation on the grayscale binary image gray_binary and the shadow binary image black to obtain an AND operation image, recorded as and_gray_black;

[0124] Step 37: Subtract the operation image and_gray_black from the shadow binary image black to obtain the shadow area image;

[0125] Step 38: Subtract the shadow area image from the binary image to obtain a binary image of the membrane stripe area.

[0126] In step 38, after obtaining the binary image of the membrane stripe area, a morphological closing operation is performed on the binary image of the membrane stripe area, that is, dilation is first performed and then erosion is performed to fill the holes in the membrane stripe area image; then a morphological opening operation is performed to remove noise blocks to obtain a membrane stripe area mask image morph, wherein in the membrane stripe area mask image, a grayscale value of 0 represents a non-membrane stripe area, and 255 represents a membrane stripe area.

[0127] like Figure 8-17 As shown, in step 4, since the film strips are pasted one by one on the film strip reading plate, it is inevitable that the film strips will stick together, and the sticking of the film strips will greatly interfere with the identification of the film strips, so it is necessary to identify the sticking film strips and segment them. The segmentation of the sticking film strip area includes the following steps:

[0128] Step 401: according to the film strip area mask morph, a connected domain search is performed on the film strip area mask morph to determine the area of ​​the film strip connected domain; the film strip connected domain represents the connected domain of the film strip area; if the area of ​​the film strip connected domain meets the set single film strip area range, it is considered to be a single film strip, and the step ends; if the area of ​​the film strip connected domain meets the set double film strip area range, it is considered to be double film strip adhesion, and the process proceeds to step 402; if the area of ​​the film strip connected domain meets the set multi-film strip area range, it is considered to be multi-film strip adhesion, the film strip area is discarded, and a mark is made at the corresponding position of the scanned image or on the film strip area mask morph, and the step ends;

[0129] Step 402: setting a circumscribed rectangular frame according to the membrane strip connected domain;

[0130] Step 403: cutting out the adhesion film strip rectangular frame image cut_image according to the circumscribed regular rectangular frame;

[0131] Step 404: performing distance transformation on the adhesion film strip rectangular frame image cut_image to obtain a distance transformation image distance_image; the distance transformation image is a grayscale image, and the grayscale value of a pixel in the distance transformation image represents the distance between the pixel and the nearest background pixel;

[0132] Step 405: Calculate the maximum distance value max_dis in the distance transformation image distance_image;

[0133] Step 406: binarize the distance transformation image distance_image to obtain the adhesion region binarized image adjoin_image; wherein the binarization segmentation threshold is 0.8*max_dis;

[0134] Step 407: Calculate and obtain the centroid coordinates center of the image based on the binary image adjoin_image of the adhesion region;

[0135] Step 408: performing morphological dilation processing on the adhesion region binary image adjoin_image to obtain an image dilate_image;

[0136] Step 409: subtract the adhesion film strip rectangular frame image cut_image from the image dilate_image to obtain the non-adhesion area image diff;

[0137] Step 410: Perform distance transformation processing on the non-adhesion area image diff, find the maximum value in the obtained distance transformation image; and perform binarization processing on the distance transformation image according to the maximum value to obtain the membrane strip skeleton binary; wherein the threshold value of the binarization processing is 0.7 times the maximum value;

[0138] Step 411: Calculate the area binary_area of ​​the film strip skeleton binary and the area center_area of ​​the adhesion region binary image adjoin_image;

[0139] Step 412: perform straight line fitting on the membrane strip skeleton binary, and judge the adhesion of the membrane strip according to the length of the fitted straight line; if the fitted straight line is longer than the set value, it is considered that the membrane strip is adhered to the left and right, and the process proceeds to step 413; if the fitted straight line is not longer than the set value, it is considered that the membrane strip is adhered to the top and bottom, and the process proceeds to step 414;

[0140] Step 413: Calculate the centroid of the membrane strip skeleton binary, set a vertical dividing line at the centroid, in this example, the Y-axis direction, and proceed to step 417;

[0141] Step 414: Determine the size of the area binary_area and the area center_area; if the area binary_area is less than the set value 1, and the area center_area is greater than the set value 2, it is considered that the upper and lower adhesion areas of the film strip are large, and the process proceeds to step 415; otherwise, it is considered that the upper and lower adhesion areas of the film strip are small, and the process proceeds to step 416;

[0142] Step 415: if the upper and lower adhesion areas of the film strip are relatively large, a straight line fitting is performed on the binary image adjoin_image of the adhesion area to obtain the slope of the fitting line and the centroid of the binary image adjoin_image of the adhesion area; a segmentation line is obtained according to the slope and centroid of the fitting line, and the process proceeds to step 417;

[0143] Step 416: if the upper and lower adhesion areas of the film strip are small, the slopes of the upper and lower adhesion film strips are calculated respectively, and the slope average is obtained; and the centroid of the binary image adjoin_image of the adhesion area is obtained; the segmentation line is obtained according to the slope average and the centroid, and the process proceeds to step 417;

[0144] Step 417: Divide the film strip according to the dividing line to obtain a single film strip area, and end the step.

[0145] In step 416, the calculation of the slope mean includes the following steps:

[0146] Step 4161: Performing connected domain analysis on the membrane strip skeleton binary and performing a straight line fitting operation on each connected domain;

[0147] Step 4162: Obtain the slope of the fitted straight line;

[0148] Step 4163: Merge connected domains with similar distances to obtain the mean slope of adjacent connected domains.

[0149] like Fig.18 As shown, in step 5, the film strip area obtained in step 4 is subjected to tilt correction, and the tilt correction includes the following steps:

[0150] Step 51: searching for a connected domain according to the film strip area mask morph to obtain the film strip connected domain; setting an outer rectangular frame rect according to the film strip connected domain, and recording the length, width, corner coordinates of the outer rectangular frame rect and the film strip deflection angle angle; in this example, the corner coordinates are the upper left corner coordinates;

[0151] Step 52: In the circumscribed regular rectangular frame rect, count the number of pixels with a pixel value of 255, and calculate the ratio of the number of pixels with a pixel value of 255 to the total number of pixels in the circumscribed regular rectangular frame rect, which is recorded as the white block ratio white_block_rate;

[0152] Step 53: determine the relationship between the white block ratio white_block_rate and the set threshold; if the white block ratio white_block_rate is greater than the set threshold, it is considered that the film strip deflection angle angle is calculated correctly, and the step ends; otherwise, it is considered that the film strip deflection angle angle is calculated incorrectly, and the film strip deflection angle angle is corrected, and the step ends.

[0153] The connected domain search in step 51 is implemented by the Seed-Filling method; the membrane strip deflection angle angle is the angle between the lower edge of the membrane strip connected domain and the set X-axis.

[0154] In step 52, in some other implementations, the number of pixels with a pixel value of 0 may also be counted, and its ratio to the total number of pixels in the circumscribed rectangular frame rect may be calculated, recorded as the black block ratio, and subsequent steps may be performed.

[0155] In step 53, due to the interference of factors such as stains, the film strip deflection angle angle may be calculated incorrectly, so it needs to be corrected; the correction of the film strip deflection angle angle includes the following steps:

[0156] Step 531: convert the image to be processed obtained in step 2 into a grayscale image, and segment the film strip grayscale image rect_strip according to the position of the circumscribed rectangular frame rect;

[0157] Step 532: Rotate the film strip grayscale image rect_strip within a set angle range and with a set step length, and calculate the nuclear norm of the image after each rotation; in this example, the angle range is set to -5° to +5°, and the step length is 0.1°;

[0158] Step 533: taking the rotation angle with the maximum nuclear norm as the corrected film strip deflection angle angle.

[0159] The calculation of the nuclear norm in step 532 is obtained by singular value decomposition method.

[0160] like Fig.19 , 20 As shown, the process of arranging the film strip table in step 6 includes the following steps:

[0161] Step 61: In the image to be processed obtained in step 2, the shadow in the processed image is removed according to the shadow area image;

[0162] Step 62: intercepting a single film strip image through the dividing line and the circumscribed rectangular frame;

[0163] Step 63: rotating the intercepted single film strip image according to the film strip deflection angle angle to obtain the film strip image big_strip;

[0164] Step 64: Arrange the film strip image to obtain a film strip set strip; the film strip set strip includes the length, width and upper left corner coordinates of the film strip area;

[0165] Step 65: Identify the strip set, obtain the strip list and count the strip serial numbers.

[0166] The film strip set in step 64 is similar to the image to be processed. In the film strip set, only the film strip position in the image to be processed is corrected, and the position information of each film strip is recorded; since the film strips in the image to be processed are divided into two columns, there are also two columns in the film strip table. It should be noted that in this example, the column represents the Y direction, that is, the vertical direction; the row represents the X direction, that is, the horizontal direction.

[0167] The steps of identifying the strip set, obtaining the strip list and counting the strip sequence numbers in step 65 include:

[0168] Step 651: converting the image to be processed obtained in step 2 into a grayscale image;

[0169] Step 652: Calculate the Laplacian edge LAP of the grayscale image;

[0170] Step 653: according to the film strip area mask image morph obtained in the previous step, remove the film strip area wrapped by the Laplace edge in the Laplace edge LAP; in this example, set the film strip area to 0;

[0171] Step 654: Project the Laplace edge LAP in the X-axis direction to obtain a projection histogram histogram in the horizontal direction; in this example, the projection histogram is obtained by a histogram projection algorithm;

[0172] Step 655: according to the set table interval space_table and table line projection accumulation peak_table, in the projection histogram hist, the peak position that meets the threshold of the table interval space_table and the table line projection accumulation peak_table is recorded as the table line, and the missing table lines are relatively completed, and a table position set box is obtained; wherein the table position set box records the position information of each table box segmented by the table line;

[0173] Step 656: according to the X-axis coordinate of the upper left corner coordinate of the film strip in the film strip set strip, the film strip set is divided into two columns, the left film strip set strip_left and the film strip set strip_right;

[0174] Step 657: sort the separated left film strip set strip_left and the separated film strip set strip_right from top to bottom along the Y axis direction;

[0175] Step 658: Find the center position of each table box in the table position set box, mark the corresponding sequence number from top to bottom for each center position, obtain the sequence number columns order_left and order_right; and check and update the sequence number columns order_left and order_right;

[0176] Step 659: Merge the sequence number columns order_left, order_right, the left film strip set strip_left and the right film strip set strip_right and the corresponding table position set box to obtain a film strip list; in this example, the sequence number column order_left is arranged in front of the sequence number column order_right.

[0177] In step 655, the table position set box records the position information of the table box set on the film strip reading board, wherein the film strip is pasted in the table box. The table position set box also contains the position data of two columns of tables, so the sequence numbers order_left and order_right can be obtained in step 658.

[0178] In step 659, the position of the table position set box corresponding to each serial number in the obtained film strip list corresponds to the position of the film strip in the film strip set strip, so it may happen that the film strip image corresponding to the serial number is empty.

[0179] like Fig.21 As shown, the process of identifying and locating the membrane line in step 7 includes the following steps:

[0180] Step 71: Determine the positions of the quality control membrane line and the darker membrane line to be selected according to the membrane strip images in the membrane strip list;

[0181] Step 72: obtaining a grayscale image according to the film strip image, and determining the position of the second film line to be selected on the film strip according to the grayscale change rate of the grayscale image;

[0182] Step 73: According to the quality control membrane line and the set membrane line spacing, a membrane line set is selected from the candidate membrane line 1 and the candidate membrane line 2.

[0183] In step 71, in some other embodiments, the membrane strip reading plate image can also be identified by an existing image processing method, and the membrane strip area on the image can be segmented to obtain a membrane strip list. The acquisition of the quality control membrane line and the candidate membrane line 1 includes the following steps:

[0184] Step 711: performing multi-scale enhancement on the film strip images in the film strip list to obtain a single-channel enhanced image;

[0185] Step 712: convert the film strip image into a grayscale image; and perform a difference between the single-channel enhanced image and the grayscale image to obtain a difference image;

[0186] Step 713: according to the difference image, search for a quality control membrane line within a set distance range of the membrane strip image; and search for a darker color candidate membrane line 1.

[0187] In step 711, the method for obtaining an enhanced image is to first perform Gaussian blur processing on the film strip image. In this example, three Gaussian blur scales are set, namely 5, 9, and 19, and three Gaussian blurred images are obtained according to the three scales; then, the three Gaussian blurred images are weightedly fused with the film strip image to obtain a single-channel enhanced image.

[0188] In step 712, the difference image is obtained by the following formula:

[0189]

[0190] Among them, dst represents the gray value of the corresponding pixel in the difference image; en_s represents the value of the corresponding pixel in the single-channel enhanced image; gr_s represents the gray value of the corresponding pixel in the grayscale image. After obtaining the difference image, the image is binarized, where pixels greater than the set threshold are assigned a value of 0, and pixels less than or equal to the set threshold are assigned a value of 255; then morphological processing is performed to remove noise and obtain a binary difference image.

[0191] In step 713, first, a projection histogram is made in the Y direction for the binary difference image, and a mean histogram is made in the Y direction based on the grayscale image of the membrane strip image. In this example, the mean histogram is the mean of each column of pixels in the membrane strip image, and the grayscale image is the grayscale image obtained in step 651; then, within the set distance area on the membrane strip image, the quality control membrane line is searched according to the judgment criteria; finally, according to the peak position in the projection histogram, the peak position that meets the threshold requirement is searched as the first membrane line to be selected. In this example, the judgment criterion for the quality control membrane line is that the peak in the projection histogram exceeds the peak setting threshold, and the mean histogram also exceeds the mean setting threshold, where the peak setting threshold is 0.8 times the maximum peak in the projection histogram of the membrane strip, and the mean setting threshold is a grayscale value of 180; it should be noted that if no quality control membrane line that meets the judgment criterion is found within the set distance area on the membrane strip image, the value with the largest peak in the projection histogram is selected within the set distance area, and its corresponding position is used as the position of the quality control membrane line.

[0192] In step 72, the process of determining the second film line to be selected on the film strip includes the following steps:

[0193] Step 721: performing a gamma enhancement algorithm on the grayscale image of the film strip image to obtain an enhanced image gamma_strip;

[0194] Step 722: obtain a mean histogram gamma_mean_hist in the Y direction for the enhanced image gamma_strip, and perform Gaussian filtering; obtain a slope histogram slop_hist in the Y direction according to the mean histogram gamma_mean_hist; wherein the slope histogram slop_hist represents the slope of the mean histogram;

[0195] Step 723: Count the points in the slope histogram slop_hist that are less than a set value 1 and the points that are greater than or equal to a set value 2; in this example, the set value 1 is -1.5 and the set value 2 is 0;

[0196] Step 724: Count the number of continuous points less than the set value 1, and the number of continuous points greater than or equal to the set value 2; find a continuous point set 1 and a continuous point set 2 that meet the set point quantity requirement; in this example, the set point quantity requirement is greater than 6 continuous points, including a continuous point set 1 consisting of more than six continuous points less than the set value 1, and a continuous point set 2 consisting of more than six continuous points greater than or equal to the set value 2; it should be noted that in some other implementations, other quantity thresholds can be set for the number of points less than the set value 1 and the number of points greater than or equal to the set value 2, respectively;

[0197] Step 725: In the statistical slope histogram slop_hist, select adjacent continuous point set 1 and continuous point set 2, and calculate the zero-crossing point between the two point sets where the value changes from negative to positive, to obtain the second model line to be selected.

[0198] In step 722, the slope histogram is obtained by the following formula:

[0199]

[0200] Among them, slop hist represents the value in the slope histogram slop_hist; gamma mean hist[i] represents the value of position i in the mean histogram gamma_mean_hist; bin represents the value of the maximum position in the mean histogram gamma_mean_hist.

[0201] In step 73, the film line set includes the position information of the film line; the process of selecting the film line set includes the following steps:

[0202] Step 731: Determine a preliminary membrane line set according to the position of the quality control membrane line and the set membrane line interval; wherein the preliminary membrane line set includes the position of the quality control membrane line, and the position of the subsequent membrane line obtained by calculation according to the membrane line interval and the corresponding position serial number; in this example, the position serial numbers of the subsequent membrane lines are 28 in total, plus the quality control membrane line, there are a total of 29 membrane lines on the membrane strip;

[0203] Step 732: traverse the first film line to be selected and the second film line to be selected, calculate the shortest distance between each film line and the position in the preliminary film line set; select the film line with the smallest shortest distance, use the corresponding film line as the film line in the final film line set, record the position information of the film line, and remove the film line from the first film line to be selected and the second film line to be selected, and remove the position number from the preliminary film line set;

[0204] Step 733: updating the positions of other membrane lines in the preliminary membrane line set according to the distance between the model line and the quality control membrane line and the position sequence number corresponding to the model line;

[0205] Step 734: Determine whether all the position numbers in the preliminary membrane line set have been eliminated; if all have been eliminated, it means that the final membrane line set has been obtained, and the step ends; otherwise, return to step 732.

[0206] In step 733, if the position number of the quality control membrane line is 1, the position number of the model line selected according to the position relationship is n, and the distance between the model line and the quality control membrane line is L, then the new membrane line interval is obtained. And the positions of the membrane lines corresponding to the remaining position numbers are updated according to the new membrane line interval.

[0207] In some other embodiments, the step 8 of removing stains on the film strip can also be arranged before the step 7, that is, the stains on the film strip are removed first, and then the film line identification on the film strip is completed; wherein the stain removal on the film strip includes the following steps:

[0208] Step 81: Perform a Gaussian blur operation on the film strip image in the film strip list to enhance the image;

[0209] Step 82: Making a difference image of the enhanced image; wherein the purpose of making the difference image is to remove noise on the film strip image;

[0210] Step 83: Identify and remove stains in the image to obtain a stain-free image.

[0211] In step 81, three Gaussian blur values ​​are set in this example, and Gaussian blur processing is performed on the membrane strip image according to the three Gaussian blur values, and the blurred image is weightedly fused with the original membrane strip image to obtain a single-channel enhanced image; in this example, the weight value is a set value.

[0212] The acquisition of the difference image in step 82 includes the following steps:

[0213] Step 821: converting the film strip image in the film strip list into a grayscale image;

[0214] Step 822: subtract the enhanced image obtained in step 81 from the grayscale image obtained in step 821 to obtain a difference image diff1;

[0215] Step 823: subtract the grayscale image obtained in step 821 from the enhanced image obtained in step 81 to obtain a difference image diff2;

[0216] Step 824: merge the difference image diff1 and the difference image diff2 to obtain image diff3;

[0217] Step 825: binarize and morphologically process the image diff3 to obtain a denoised difference image diff3_binary; pixels greater than a set threshold are assigned a value of 0, and pixels less than or equal to the set threshold are assigned a value of 255.

[0218] In step 83, identifying and removing stains in the image includes the following steps:

[0219] Step 831: Count the number of black pixels sum_black and the number of white pixels sum_white in each column of pixels in the denoised difference image diff3_binary;

[0220] Step 832: If the number of white pixels sum_white is less than a set threshold, the white pixels in the column of pixels are considered to be stains; in this example, the threshold is set to half the height of the film strip;

[0221] Step 833: Obtain the pixel column with stains, find the pixel column with stains in the grayscale image, and replace the stain area in the pixel column with the average grayscale value of the pixels except the stains; obtain the stain-removed image.

[0222] like Figure 22-27 As shown, the calculation of the membrane line concentration value in step 9 includes the following steps:

[0223] Step 91: Obtaining a histogram of film lines according to the decontamination image;

[0224] Step 92: Calculate the optical density value on each film line according to the histogram of the film line, and obtain the average optical density value;

[0225] Step 93: Calculate the correction factor correction_factor according to the mean optical density value of the quality control line and the set quality control line optical density, and correct the mean optical density values ​​of other film line positions;

[0226] Step 94: Substitute the corrected mean optical density value of the film line into the calibration curve equation; wherein the calibration curve equation may be a cubic spline equation or a four-parameter fitting equation; in this example, the calibration curve equation is a four-parameter fitting equation.

[0227] In step 91, a histogram of the film line is obtained, including the following steps:

[0228] Step 911: Calculate the grayscale mean histogram in the Y direction and the CV histogram in the Y direction according to the decontamination image;

[0229] Step 912: Perform Gaussian filtering on the mean histogram;

[0230] Step 913: taking a first-order derivative of the mean histogram after Gaussian filtering to obtain a slope histogram; wherein the method for obtaining the slope histogram is the same as that in step 722;

[0231] Step 914: Obtain a slope square wave histogram according to the film line set obtained in step 7 and the slope histogram obtained in step 913;

[0232] Step 915: Obtain a baseline histogram according to the slope square wave histogram.

[0233] In step 914, to obtain the slope square wave histogram, it is first necessary to obtain the position of each film line in the film line set, and select the film line area on the left and right sides of each film line according to the set range. In this example, the set range is -15 to +15 pixels; the position of the film line area is combined with the slope histogram obtained in step 913, and in each film line area in the slope histogram, the starting position start of the negative slope, the zero-crossing position trough from negative to positive, and the ending position end of the positive slope are counted, where if the starting position start of the negative slope exceeds the film line range, -15 is taken, and similarly, if the ending position end of the positive slope exceeds the film line range, +15 is taken; on the slope histogram, the points between the interval [start, trough] are assigned a value of -1, the points before the interval [trough, end] are assigned a value of +1, and the other points on the slope histogram are assigned a value of 0 to obtain a slope square wave histogram.

[0234] In step 915, for the non-0 point positions in the obtained slope square wave histogram, line segment interpolation is performed from the starting position start to the ending position end in the grayscale mean histogram; for the 0 point position, in the grayscale mean histogram, the grayscale mean value of the position is retained; and a baseline histogram is obtained.

[0235] In step 92, the average optical density value of the film line is calculated, including the following steps:

[0236] Step 921: Calculate the optical density value on each film line according to the film line set and the baseline histogram to obtain an optical density histogram; the calculation formula of the optical density is as follows:

[0237]

[0238] Among them, od represents optical density, base represents baseline value, and mean represents grayscale mean;

[0239] Step 922: Calculate the mean optical density on each film line according to the optical density histogram and the film line set; in this example, the mean optical density is the mean value obtained by statistically analyzing the optical density within 0.8 times the peak optical density.

[0240] In step 94, the four-parameter fitting equation is often used in the field of immunoassay to describe the law of absorbance changing with antigen concentration. In this example, the four-parameter fitting equation is as follows:

[0241]

[0242] Wherein, the X value represents the mean optical density value of the film line; Y represents the calculated concentration value of the film line; a, b, c, and d are the four curve parameters with set meanings in the four-parameter fitting equation, a represents the asymptote estimate on the curve, b represents the slope of the curve, c represents the dose corresponding to half the maximum binding, and d represents the asymptote estimate under the curve. In this example, the curve is the change curve of the mean optical density value.

[0243] In step 9, after the concentration value corresponding to each membrane line is calculated, the concentration value and the position of the membrane line are displayed in correspondence.

[0244] During the implementation process, the randomly pasted film strips on the film strip reading plate are sorted into a uniformly arranged film strip list by segmenting the adhered film strips, correcting the tilt of the film strips, and identifying and counting the film strip areas, and the multiple columns of film strips on the film strip reading plate are sorted into one column, so as to facilitate the subsequent identification of film lines on the film strips; by positioning the film lines, including determining the quality control film lines and other candidate film lines, the other candidate film lines are compared one by one with the preliminary film line set obtained according to the quality control film lines, and the appropriate candidate film lines are selected, and the preliminary film line set is corrected in real time to ensure that the positions of the film lines in the final determined film line set are accurate; by screening stains and replacing the stain pixels with the grayscale mean of the non-stain pixels in the same column of pixels, the film strip decontamination process is completed to avoid the stains interfering with the subsequent film line concentration value detection; by the mean optical density of the quality control film line, the mean density of other film lines is corrected to eliminate the scanning brightness deviation generated by the scanner during the image scanning process; by the four-parameter fitting equation, the conversion between the mean optical density value of the film line and the concentration value of the film line is realized.

[0245] The above description is only a specific example of the present invention and does not constitute any limitation to the present invention. It is obvious that for professionals in this field, after understanding the content and principle of the present invention, various modifications and changes in form and details may be made without departing from the principle and structure of the present invention, but these modifications and changes based on the idea of ​​the present invention are still within the scope of protection of the claims of the present invention.

Claims

1. A membrane line positioning method, characterized in that: The steps include: Step 71: Determine the positions of the quality control membrane line and the first membrane line to be selected that meets the color requirements according to the membrane strip images in the membrane strip list; Step 72: Obtain a grayscale image according to the film strip image, and determine the position of the second film line to be selected on the film strip according to the grayscale change rate of the grayscale image. The process of determining the second film line to be selected on the film strip includes the following steps: Step 721: performing a gamma enhancement algorithm on the grayscale image of the film strip image to obtain an enhanced image gamma_strip; Step 722: obtain a mean histogram gamma_mean_hist in the Y direction for the enhanced image gamma_strip, and perform Gaussian filtering; obtain a slope histogram slop_hist in the Y direction according to the mean histogram gamma_mean_hist; wherein the slope histogram slop_hist represents the slope of the mean histogram; Step 723: Count the points in the slope histogram slop_hist that are less than a set value 1 and the points that are greater than or equal to a set value 2; wherein the set value 1 is less than the set value 2; Step 724: Count the number of consecutive points less than a set value 1, and the number of consecutive points greater than or equal to a set value 2; and find a set of consecutive points 1 and a set of consecutive points 2 that meet the set point quantity requirement. Step 725: In the statistical slope histogram slop_hist, select adjacent continuous point set 1 and continuous point set 2, and calculate the zero-crossing point between the two point sets where the value changes from negative to positive, to obtain the second model line to be selected; Step 73: According to the quality control membrane line and the set membrane line spacing, a membrane line set is selected from the candidate membrane line 1 and the candidate membrane line 2. The selection of the membrane line set includes the following steps: Step 731: determining a preliminary membrane line set according to the position of the quality control membrane line and the set membrane line interval; wherein the preliminary membrane line set includes the position of the quality control membrane line, and the position of the subsequent membrane line obtained by calculation according to the membrane line interval and the corresponding position serial number; Step 732: traverse the first film line to be selected and the second film line to be selected, calculate the shortest distance between each film line and the position recorded in the preliminary film line set; select the film line with the smallest shortest distance, use the corresponding film line as the film line in the final film line set, record the position information of the film line, and remove the film line from the first film line to be selected and the second film line to be selected, and remove the position number from the preliminary film line set; Step 733: updating the positions of other membrane lines in the preliminary membrane line set according to the distance between the model line and the quality control membrane line and the position sequence number corresponding to the model line; Step 734: Determine whether all the position numbers in the preliminary membrane line set have been eliminated; if all have been eliminated, it means that the final membrane line set has been obtained, and the step ends; otherwise, return to step 732.

2. A film line positioning method according to claim 1, characterized in that: In step 71, obtaining the quality control membrane line and the first membrane line to be selected includes the following steps: Step 711: performing multi-scale enhancement on the film strip images in the film strip list to obtain a single-channel enhanced image; Step 712: converting the film strip image into a grayscale image; And make a difference between the single-channel enhanced image and the grayscale image to obtain a difference image; Step 713: searching for a quality control membrane line within a set distance range of the membrane strip image according to the difference image; And find a candidate film line that meets the color requirements.

3. A film line positioning method according to claim 2, characterized in that: In step 711, the method for obtaining the enhanced image is to first perform Gaussian blur processing on the film strip image to obtain a Gaussian blurred image; then perform weighted fusion of the Gaussian blurred image and the film strip image to obtain a single-channel enhanced image.

4. A film line positioning method according to claim 3, characterized in that: In step 712, the difference image is obtained by the following formula: ； in, Represents the grayscale value of the corresponding pixel in the difference image; Represents the value of the corresponding pixel in the single-channel enhanced image; Represents the grayscale value of the corresponding pixel in the grayscale image.

5. A film line positioning method according to claim 4, characterized in that: In step 713, firstly, a projection histogram in the Y direction is made for the binary difference image, and a mean histogram in the Y direction is made according to the grayscale image of the film strip image, wherein the mean histogram is the mean value of each column of pixels in the film strip image; Then, in the set distance area on the membrane strip image, the quality control membrane line is found according to the judgment criteria; finally, according to the peak position in the projection histogram, the peak position that meets the set threshold requirement is found as the first membrane line to be selected; wherein, the judgment criterion of the quality control membrane line is that the peak in the projection histogram exceeds the peak set threshold, and the mean histogram also exceeds the mean set threshold; If no quality control membrane line that meets the judgment criteria is found in the set distance area on the membrane strip image, the value with the largest peak in the projection histogram is selected in the set distance area, and the corresponding position is used as the quality control membrane line.

6. A film strip identification method based on film line positioning, characterized in that: The steps include: Step 1: Use a scanning device to complete the image scanning of the film strip reading plate and obtain the scanned image; Step 2: Preprocess the scanned image, including image enhancement, filtering, connected domain analysis, rotation and edge trimming; Finally, the image is reduced to obtain the image to be processed; Step 3: extract the film strip area according to the image to be processed, obtain the shadow area of ​​the film strip area; and remove the shadow according to the shadow area; Step 4: Determine whether there is adhesion in the film strip area; If there is adhesion, the adhesion film strip area is segmented and the next step is entered; if there is no adhesion, the next step is entered directly; Step 5: Perform tilt correction on the film strip area to make the film strip area face the set direction; Step 6: Count the film strip areas after tilt correction, sort them into a film strip list, and identify the labels; Step 7: According to the method described in any one of claims 1 to 5, the film lines on the film strips in the film strip list are identified and located to obtain a film line set; Step 8: Perform stain removal operation on the film strips in the film strip list; Step 9: Calculate the optical density value of each film line, and obtain the concentration value corresponding to each film line based on the optical density value; end step.

7. The film strip identification method based on film line positioning according to claim 6, characterized in that: The calculation of the film line concentration value in step 9 includes the following steps: Step 91: Obtain a histogram of film lines according to the decontamination image obtained in step 8; Step 92: Calculate the optical density value on each film line according to the histogram of the film line, and obtain the average optical density value; Step 93: Calculate the correction factor correction_factor according to the mean optical density value of the quality control line on the film strip and the set quality control line optical density, and correct the mean optical density values ​​of other film line positions; Step 94: Substitute the corrected mean optical density value of the film line into the calibration curve equation; The calibration curve equation is a cubic spline equation or a four-parameter fitting equation; The quality control line in step 93 is the membrane line set on the membrane strip.

Images