Method, system and device for positioning all magnetic head spotting points of a microfluidic biochip

Abstract

The present invention belongs to the technical field of image processing, and discloses a method, a system and a device for locating all the spotting points of a microfluidic biochip. The method includes: acquiring the grayscale source image after spotting on the biochip, storing it in a specified directory on the disk, loading it into the memory for subsequent processing, obtaining a first processed image with the spotting area of the magnetic head separated from the circuit background, and generating a template grayscale image; performing template matching on the first processed image by using the normalized correlation matching method to obtain a result matrix dstmat; then through multi-target positioning processing, obtaining the positions of the centers of each magnetic head, and recording them in a list or an array; circularly acquiring the coordinates of the centers of each magnetic head in the list or the array, judging whether there are abnormal coordinate points, and determining whether the chip performs subsequent defect detection. The present invention can perform subsequent defect detection on the microfluidic biochip faster and more accurately, and provides an effective method for realizing the automatic detection of the quality of the spotting chip.

Description

Technical Field

[0001] The present invention belongs to the technical field of image processing, and particularly relates to a method, a system and a device for positioning all the spotting points of magnetic heads of a microfluidic biochip. Background Art

[0002] The microfluidic biochip technology has a bright future and is also the main direction of technological competition in the field of in vitro diagnosis in the future. Under the existing process and equipment conditions, generally, a microscope is used manually to check whether the spotting quality of each magnetic head on the chip meets the expected effect. This method is time-consuming and laborious, and it is very easy to cause missed inspections and misjudgments. The efficiency is relatively low, the labor intensity is high, and the high-intensity multi-batch detection work is likely to damage the human eyes and seriously endanger the health of workers. Even the detection of some indicators cannot be completed by manual visual inspection. Machine vision can complete this task efficiently and accurately.

[0003] When using machine vision to detect the spotting quality of a microfluidic biochip, it is necessary to locate the spotting center area of each magnetic head in the chip. However, the background of the chip image captured by the camera is complex, and there are various interferences such as chip circuits, contaminants, and bubbles. Moreover, due to the differences in light, chip placement position, spotting point shape, and spotting point position in each magnetic head spotting area, the accuracy of the center area of each magnetic head located by using the general template matching method is low, which affects the subsequent defect detection of the microfluidic biochip.

[0004] Therefore, it is necessary to provide a method for positioning all the spotting points of magnetic heads of a microfluidic biochip to solve the above problems.

[0005] Through the above analysis, the problems and defects existing in the prior art are as follows: there are differences in light, chip placement position, spotting point shape, and spotting point position in each magnetic head spotting area. The prior art cannot quickly and accurately identify all the center points of magnetic heads without serious defects for the biochip with a complex background after spotting. It cannot provide an effective guarantee for the automated detection of the quality of the spotted chip. Summary of the Invention

[0006] To overcome the problems existing in the related art, the disclosed embodiments of the present invention provide a method, a system and a device for positioning all the spotting points of magnetic heads of a microfluidic biochip.

[0007] The technical solution is as follows: A method for positioning all the spotting points of magnetic heads of a microfluidic biochip includes the following steps:

[0008] S1, driving the high-definition fixed-focus macro wide-angle lens and the camera fixed on the robotic arm to obtain the grayscale source image of the biochip after spotting, storing it in the specified directory of the disk, loading it into the memory for subsequent processing, obtaining the first processed image in which the spotting area of the magnetic head is separated from the circuit background, and generating a template grayscale image;

[0009] S2. Use the normalized correlation matching method to perform template grayscale image matching on the first processed image to obtain the result matrix dstmat;

[0010] S3. Based on the obtained result matrix dstmat, through multi-target localization processing, obtain the positions of the centers of each magnetic head, and record them in a list or array;

[0011] S4. Loop to obtain the coordinates of the centers of each magnetic head in the list or array, determine whether there are abnormal coordinate points, and determine whether the chip performs subsequent defect detection.

[0012] In one embodiment, in step S1, after loading into memory for subsequent processing, the first processed image obtained by separating the magnetic head spotting area and the circuit background includes:

[0013] Use the Threshold method in the OpenCV module to perform binary processing on the grayscale source image to obtain a binary image. After using the FindContours method in the OpenCV module to find the edges in the binary image, use the DrawContours method to draw white edge points with a line width of 2-3 pixels to obtain the first processed image of the separated magnetic head spotting area and the circuit background.

[0014] In one embodiment, in step S1, the method for generating the template grayscale image includes:

[0015] Select a grayscale source image after spotting on a biochip, perform binary processing on it and save the binary image to disk. Open this binary image with a painting software according to the original size, select a defect-free magnetic head center area from it and copy it, and save it as the template grayscale image.

[0016] In one embodiment, in step S3, the method for multi-target localization processing includes:

[0017] Step S301. Create a mask matrix area. The mask area has the same height and width as the matrix dstmat, and the area is all white;

[0018] Step S302. Find the coordinates of the first matching point. Use the MinMaxLoc method in the OpenCV module to find the maximum and minimum values in the white area of the mask area of the matrix dstmat. The position where the maximum value is found is the starting coordinate of the magnetic head area where the template is first matched;

[0019] Step S303: Add the current head center point coordinates to a list or an array, and draw the currently matched template area in the binary image. The currently matched template area is a rectangular area, the center point coordinates of this rectangle are the current head center point coordinates, the width of the rectangle is the width of the template grayscale image, and the height is the height of the template grayscale image;

[0020] Step S304: Change the range of the white area within the mask area, calculate the rectangle that needs to be set as the black area within the mask area. This rectangle is centered on the current head center point obtained in Step S302, has a width twice that of the template grayscale image, and a height twice that of the template grayscale image, and set the color within this rectangular area to black;

[0021] Step S305: Find the coordinates of the next matching point. Use the MinMaxLoc method in the OpenCV module to find the maximum and minimum values within the white area of the mask area of the matrix dstmat. The position where the maximum value is found is the starting coordinates of the head area where the next template is matched;

[0022] Step S306: Loop Steps S303 to S305 until the number of loop iterations reaches the set number of chip heads minus 1.

[0023] In one embodiment, in Step S302, calculating the position of the current head center point includes:

[0024] The X coordinate of the current head center point = the X coordinate of the head area starting coordinate + the width of the template grayscale image / 2;

[0025] The Y coordinate of the current head center point = the Y coordinate of the head area starting coordinate + the height of the template grayscale image / 2.

[0026] In one embodiment, in Step S304, the calculation method for the upper left corner position of the black rectangular area includes:

[0027] The upper left corner X coordinate of the black rectangular area = the X coordinate of the current head center point - the width of the template grayscale image;

[0028] The upper left corner Y coordinate of the black rectangular area = the Y coordinate of the current head center point - the height of the template grayscale image;

[0029] The calculation method for the lower right corner position of this black rectangular area includes:

[0030] The lower right corner X coordinate of the black rectangular area = the X coordinate of the current head center point + the width of the template grayscale image;

[0031] The lower right corner Y coordinate of the black rectangular area = the Y coordinate of the current head center point + the height of the template grayscale image;

[0032] If the upper left corner X coordinate of the black rectangular area < 0 or the upper left corner Y coordinate of the black rectangular area < 0, then set both the upper left corner X and Y coordinates of the black rectangular area to 0. If the lower right corner X coordinate of the black rectangular area > the width of the mask area - 1, or the lower right corner Y coordinate of the black rectangular area > the height of the mask area - 1, then set the lower right corner X coordinate of the black rectangular area = the width of the mask area - 1, and the lower right corner Y coordinate of the black rectangular area = the height of the mask area - 1.

[0033] In one embodiment, in step S305, calculate the position of the center point of the current magnetic head:

[0034] In one embodiment, in step S4, if there are abnormal coordinate points and there are magnetic heads in the chip with incomplete spotting or obvious foreign objects in the spotting point area, then this chip does not perform subsequent defect detection and directly displays a defect; if there are no abnormal coordinate points, then the obtained first processed image and the grayscale source image need to be input into the subsequent defect detection module;

[0035] The method for determining whether there are abnormal coordinate points includes:

[0036] Among the magnetic heads arranged in three rows above or below the image, find the points in the list or array recording the center point coordinates of each magnetic head where the Y coordinate differs from the Y coordinates of other points by more than a set distance as abnormal points; first add the Y coordinates of the center points of each magnetic head to the second list, then sort the values in the second list in ascending order, calculate the difference between two adjacent values in the sorted second list, and according to the fact that the difference between the Y coordinates of the center points of the magnetic heads in the same row in the chip image does not exceed a certain value, and the difference between the Y coordinates of the center points of the magnetic heads between two adjacent rows does not exceed a certain value, if the difference is greater than the calibration value, then there are abnormal coordinate points; obtain the average value of the difference between the Y coordinates of the center points of the magnetic heads between two adjacent rows according to experiments on multiple chip images, and add 50 to this average value to obtain the calibration value.

[0037] Another object of the present invention is to provide a system for positioning the spotting points of all magnetic heads of a microfluidic biochip, including:

[0038] The first processed image acquisition and template grayscale image loading module is used to drive the high-definition fixed-focus macro wide-angle lens and camera fixed on the robotic arm to acquire the grayscale source image after spotting of the biochip and store it in the specified directory on the disk, load it into the memory for subsequent processing, obtain the first processed image with the spotting area of the magnetic head and the circuit background separated, and load the template grayscale image;

[0039] The result matrix acquisition module is used to perform template matching on the first processed image by the normalized correlation matching method using the MatchTemplate method to obtain the result matrix dstmat;

[0040] Each head center point position acquisition module is used to obtain the positions of the centers of each head through multi-target positioning processing and record them in a list or array;

[0041] The abnormal coordinate point judgment module is used to cyclically obtain the coordinates of the centers of each head in the list or array and judge whether there are abnormal coordinate points.

[0042] Another object of the present invention is to provide a computer device, which includes a memory and a processor. The memory stores a computer program. When the computer program is executed by the processor, the processor executes the method for positioning all the head spotting points of the microfluidic biochip.

[0043] Combined with all the above technical solutions, the advantages and positive effects of the present invention are as follows:

[0044] First, aiming at the technical problems existing in the above-mentioned prior art and the difficulty of solving the problems, closely combining the technical solutions to be protected by the present invention and the results and data in the R & D process, etc., analyze in detail and profoundly how the technical solutions of the present invention solve the technical problems and the creative technical effects brought after solving the problems. The specific description is as follows:

[0045] The present invention is different from the general method of only performing grayscale processing or binary processing on the image. Instead, after performing binary processing on the grayscale source image, edge searching and edge line drawing are performed, which is beneficial to obtaining the first processed image with the head spotting area separated from the circuit background, reducing the interference of the background, and improving the accuracy of subsequent template matching. The present invention is different from the general method of only using the target object in the grayscale source image as the template image. Instead, in the Figure 2 image after thresholding, a defect-free head center area is intercepted as the target object in the template image, which is beneficial to accurately matching with the head center areas in the aforementioned first processed image. The multi-target positioning processing method of the present invention is based on the characteristics of the image and uses the neighborhood search method to completely find multiple matching targets in the image at a relatively small time cost. The method for judging abnormal coordinate points of the present invention is a simple and effective method obtained based on the characteristics of the image and can quickly judge abnormal points.

[0046] Second, regarding the technical solution as a whole or from the perspective of the product, the technical effects and advantages of the technical solution to be protected by the present invention are as follows:

[0047] Even if there are differences in light, chip placement position, spotting point shape, and spotting point position in each head spotting area, the method can quickly and accurately identify all the head center points without serious defects on a biochip with a complex background (such as various interferences like circuits, contaminants, bubbles, etc.) after spotting. It has a higher accuracy than the center area of each head located by the general template matching method, so that the subsequent defect detection of the microfluidic biochip can be carried out faster and more accurately, providing an effective method for realizing the automated detection of the quality of the spotted chip.

[0048] Thirdly, as the creative auxiliary evidence of the claims of the present invention, it is also reflected in the following important aspects:

[0049] (1) The present invention can effectively locate each head area of the microfluidic biochip and identify abnormal spotting points, so that the subsequent defect detection of the microfluidic biochip can be carried out faster and more accurately, making it possible to realize the automated detection of the quality of the spotted chip, reducing the manual detection cost and the risk of manual detection errors of the manufacturer, improving the detection accuracy and speed, and reducing the risk of scrapping the entire batch of chips, thus reducing the after-sales cost.

[0050] (2) There are only a few enterprises producing magnetic immunoassay analyzers, and there is no relevant literature on studying the problems that occur after mass production. The present invention provides the pre-technical support for the automated detection of the spotting quality of the microfluidic biochip after mass production, filling the technical gap in the automatic recognition of multiple head areas of the microfluidic biochip with a complex background.

[0051] (3) The present invention solves the problem that it is impossible to accurately identify each head spotting area of the microfluidic biochip under a complex background, and can quickly identify the spotting points with serious defects, so as to avoid spending more time on redundant defect detection.

[0052] (4) Starting from the characteristics of the image itself, the present invention uses a relatively simple and easy-to-understand method to achieve a better effect, and does not necessarily use complex and so-called cutting-edge artificial intelligence and other technologies for defect detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The accompanying drawings herein are incorporated into the specification and form a part of the specification, showing embodiments consistent with the present disclosure and used together with the specification to explain the principles of the present disclosure;

[0054] Figure 1 is a flowchart of the method for locating all the head spotting points of the microfluidic biochip provided by the embodiment of the present invention;

[0055] Figure 2 is a schematic diagram of the original gray-scale image of the biochip after spotting provided by the embodiment of the present invention;

[0056] Figure 3 It is a schematic diagram of the binarized image provided by the embodiment of the present invention;

[0057] Figure 4 It is a schematic diagram of the first processed image provided by the embodiment of the present invention;

[0058] Figure 5 It is a schematic diagram of the template grayscale image provided by the embodiment of the present invention;

[0059] Figure 6 It is a flowchart of the method for multi-target positioning processing provided by the embodiment of the present invention;

[0060] Figure 7 It is the effect diagram after positioning each head area provided by the embodiment of the present invention;

[0061] Figure 8 It is a schematic diagram of the system for positioning all the spotting points of the magnetic heads of the microfluidic biochip provided by the embodiment of the present invention;

[0062] In the figure: 1. Module for obtaining the first processed image and loading the template grayscale image; 2. Result matrix obtaining module; 3. Module for obtaining the position of the center point of each head; 4. Abnormal coordinate point judgment module. Detailed implementation manners

[0063] In order to make the above objects, features and advantages of the present invention more obvious and understandable, the following detailed description of the specific implementation manners of the present invention will be made with reference to the accompanying drawings. Many specific details are set forth in the following description in order to fully understand the present invention. However, the present invention can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific implementations disclosed below.

[0064] I. Explanation of the embodiment:

[0065] Embodiment 1

[0066] As Figure 1 shown, the method for positioning all the spotting points of the magnetic heads of the microfluidic biochip provided by the embodiment of the present invention includes the following steps:

[0067] S1. Drive the high-definition fixed-focus macro wide-angle lens and camera fixed on the robotic arm to obtain the grayscale source image after spotting on the biochip ( Figure 2 ) and store it in the specified directory on the disk, and load it into the memory for subsequent processing;

[0068] Use the Threshold method in the OpenCV module to Figure 2 perform binarization processing to obtain the binarized image ( Figure 3 ), and in Figure 3After using the FindContours method in the OpenCV module to find the edges, the DrawContours method is used to draw white edge points with a line width of 2 to 3 pixels, obtaining the first processed image where the head spotting area is separated from the circuit background ( Figure 4 ), and generating a template grayscale image ( Figure 5 );

[0069] S2. The MatchTemplate method is used to perform normalized correlation matching of the template grayscale image on the first processed image ( Figure 4 ) to obtain a result matrix (abbreviated as matrix dstmat);

[0070] Among them, the result matrix is a similarity result matrix with a specific size, and the value at each position is a similarity value between 0 and 1. If the elements of this result matrix have a large similarity value (closer to 1), then the object in the template is more likely to be at that position. Similarly, if the matrix has a small similarity value (closer to 0), then it is less likely that the target object exists at that position. The specific size includes width and height, where the height is equal to the height of the binary image minus the height of the template grayscale image, and the width is equal to the width of the binary image minus the width of the template grayscale image.

[0071] S3. After multi-target positioning processing, the positions of the centers of each head are obtained and recorded in a list (or array);

[0072] S4. The coordinates of the centers of each head in the list (or array) are obtained cyclically to determine whether there are abnormal coordinate points;

[0073] If there are abnormal coordinate points, it means that there are heads in the chip with incomplete spotting or obvious foreign objects in the spotting area. Then, this chip does not need to perform subsequent defect detection and directly shows defects. If there are no abnormal coordinate points, then the obtained first processed image ( Figure 4 ) and the grayscale source image ( Figure 2 ) need to be input into the subsequent defect detection module.

[0074] Embodiment 2

[0075] Based on the method for positioning all head spotting points of the microfluidic biochip recorded in Embodiment 1 of the present invention, further, in step S1, the method for generating the template grayscale image is as follows:

[0076] Select a grayscale source image after spotting on a biochip ( Figure 2 ), perform binaryzation processing to obtain a binary image ( Figure 3 ) and save it to disk. Open this binary image ( Figure 3 ) in the original size with a painting software, select a defect-free head center area and copy it, and save it as the template grayscale image (Figure 5 )。

[0077] Example 3

[0078] Based on the method for locating all the spotting points of the magnetic heads of the positioning microfluidic biochip described in Embodiment 1 of the present invention, further, in step S3, as Figure 6 shown, the method for multi-target positioning processing is as follows:

[0079] Step S301, create a mask matrix region. The mask region has the same height and width as the matrix dstmat, and the region is all white.

[0080] Step S302, find the coordinates of the first matching point. Use the MinMaxLoc method in the OpenCV module to find the maximum and minimum values in the white area of the mask region of the matrix dstmat. The position where the maximum value is found is the starting coordinate of the magnetic head region that first matches the template. Calculate the position of the center point of the current magnetic head: The X coordinate of the center point of the current magnetic head = the X coordinate of the starting coordinate of the magnetic head region + the width of the template grayscale image / 2; The Y coordinate of the center point of the current magnetic head = the Y coordinate of the starting coordinate of the magnetic head region + the height of the template grayscale image / 2;

[0081] Step S303, add the coordinates of the current magnetic head center point to a list (or array), and draw the currently matched template region in the binary image ( Figure 3 ). The currently matched template region is a rectangular region. The center point coordinates of this rectangle are the coordinates of the current magnetic head center point. The width of the rectangle is the width of the template grayscale image ( Figure 5 ), and the height is the height of the template grayscale image ( Figure 5 ).

[0082] Step S304, change the range of the white area in the mask region. Calculate the rectangle that needs to be set to black in the mask region. This rectangle is centered on the current magnetic head center point obtained in step S302, with a width twice the width of the template grayscale image and a height twice the height of the template grayscale image. Set the color of the rectangle area to black. The calculation method for the upper left corner position of this black rectangle area is as follows:

[0083] The upper left corner X coordinate of the black rectangle area = the X coordinate of the current magnetic head center point - the width of the template grayscale image;

[0084] The upper left corner Y coordinate of the black rectangle area = the Y coordinate of the current magnetic head center point - the height of the template grayscale image;

[0085] The calculation method for the lower right corner position of this black rectangle area is as follows:

[0086] The X coordinate of the lower right corner of the black rectangular area = the X coordinate of the current head center point + the width of the template grayscale image;

[0087] The Y coordinate of the lower right corner of the black rectangular area = the Y coordinate of the current head center point + the height of the template grayscale image;

[0088] Specifically, if the X coordinate of the upper left corner of the black rectangular area < 0 or the Y coordinate of the upper left corner of the black rectangular area < 0, then set both the X and Y coordinates of the upper left corner of the black rectangular area to 0. If the X coordinate of the lower right corner of the black rectangular area > the width of the mask area - 1, or the Y coordinate of the lower right corner of the black rectangular area > the height of the mask area - 1, then set the X coordinate of the lower right corner of the black rectangular area = the width of the mask area - 1, and the Y coordinate of the lower right corner of the black rectangular area = the height of the mask area - 1.

[0089] Step S305, find the coordinates of the next matching point. Use the MinMaxLoc method in the OpenCV module to find the maximum and minimum values within the white area of the mask area of the matrix dstmat. The position where the maximum value is found is the starting coordinate of the head area where the next template is matched. Calculate the position of the current head center point: The X coordinate of the current head center point = the X coordinate of the head area starting coordinate + the width of the template grayscale image / 2; The Y coordinate of the current head center point = the Y coordinate of the head area starting coordinate + the height of the template grayscale image / 2;

[0090] Step S306, loop through steps S303 to S305 until the number of loops reaches the number of chip heads set in advance minus 1.

[0091] Embodiment 4

[0092] Based on the method for positioning the spotting points of all heads of a microfluidic biochip recorded in Embodiment 1 of the present invention, further, in step S4, the method for determining whether there are abnormal coordinate points is as follows:

[0093] Because all the heads are concentrated in the upper three rows or the lower three rows of the image, in the list (or array) recording the coordinates of the center points of each head, find the points whose Y coordinates differ from the Y coordinates of other points by more than a set distance, which are the abnormal points. First, add the Y coordinates of the center points of each head to a new list (the second list), then sort the values in the second list in ascending order, calculate the difference between two adjacent values in the sorted second list. According to the arrangement characteristics of each head in the chip image (the difference in the Y coordinates of the head centers on the same row does not exceed a certain value, and the difference in the Y coordinates of the head centers between adjacent rows does not exceed a certain value), if the difference is greater than the calibration value, it indicates that there are abnormal coordinate points. According to experiments on multiple chip images, the average value of the difference in the Y coordinates of the head centers between adjacent rows is obtained, and 50 is added to this average value to obtain the calibration value. For example Figure 7as shown

[0094] Example 5

[0095] as Figure 8 as shown, the spotting point system of all the magnetic heads of the positioning microfluidic biochip provided by the embodiment of the present invention includes:

[0096] The first processing image acquisition and template grayscale image loading module 1 is used to drive the high-definition fixed-focus macro wide-angle lens and camera fixed on the robotic arm to acquire the grayscale source image of the biochip after spotting ( Figure 2 ), store it in the specified directory of the disk, and load it into the memory for post-processing to obtain the first processed image with the spotting area of the magnetic head and the circuit background separated ( Figure 4 ), and at this time, load the template grayscale image ( Figure 5 );

[0097] The result matrix acquisition module 2 is used to perform normalized correlation matching method template matching on the first processed image ( Figure 4 ) by using the MatchTemplate method to obtain a result matrix (abbreviated as matrix dstmat);

[0098] The position acquisition module 3 of each magnetic head center point is used to obtain the positions of the center points of each magnetic head through multi-target positioning processing and record them in a list (or array);

[0099] The abnormal coordinate point judgment module 4 is used to circularly obtain the coordinates of the center points of each magnetic head in the list (or array) and judge whether there are abnormal coordinate points.

[0100] In the above embodiments, the descriptions of each embodiment have their own emphases. For the parts not detailed or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0101] For the information interaction, execution process, etc. between the above devices / units, since they are based on the same concept as the method embodiment of the present invention, their specific functions and the technical effects brought can be specifically referred to in the method embodiment part, and will not be elaborated here.

[0102] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above division of each functional unit and module is used as an example. In actual applications, the above functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit. In addition, the specific names of each functional unit and module are only for the convenience of mutual distinction and do not limit the protection scope of the present invention. The specific working process of the units and modules in the above system can refer to the corresponding process in the foregoing method embodiments.

[0103] II. Application Embodiment:

[0104] Application Example

[0105] An embodiment of the present invention provides a computer device, which includes: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor. When the processor executes the computer program, the steps in any of the foregoing method embodiments are implemented.

[0106] An embodiment of the present invention further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps in each of the foregoing method embodiments can be implemented.

[0107] An embodiment of the present invention further provides an information data processing terminal, which is used to provide a user input interface to implement the steps in each of the foregoing method embodiments when executed on an electronic device. The information data processing terminal is not limited to mobile phones, computers, and switches.

[0108] An embodiment of the present invention further provides a server, which is used to provide a user input interface to implement the steps in each of the foregoing method embodiments when executed on an electronic device.

[0109] An embodiment of the present invention provides a computer program product. When the computer program product runs on an electronic device, the electronic device can implement the steps in each of the foregoing method embodiments when executed.

[0110] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, to implement all or part of the processes in the above method embodiments of this application, a computer program can be used to instruct relevant hardware to complete. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium can at least include: any entity or device that can carry the computer program code to the photographing device / terminal device, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disc, etc.

[0111] In the above embodiments, the descriptions of the respective embodiments have their own emphases. For the parts not detailed or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0112] III. Evidence of the related effects of the embodiments:

[0113] Using the embodiments of the present invention to identify the head areas of 10 microfluidic biochip images with various serious defects, all normal or slightly defective head areas can be accurately identified by using the embodiments of the present invention, and chips with serious defects can be identified at the beginning.

[0114] There is no literature disclosing the positioning method for each head of the microfluidic biochip and the method for quickly identifying serious spotting defects based on this. In other fields, general positioning methods are used. Usually, after binarizing a grayscale image, template matching is directly performed. The present invention simulates the implementation of the general positioning method. Compared with the general positioning method, the method of the present invention is more accurate in positioning the head areas of the microfluidic biochip and faster in identifying chips with serious defects.

[0115] The above is only a relatively preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be covered by the protection scope of the present invention.

Claims

1. A method for locating all the spotting points of magnetic heads on a microfluidic biochip, characterized in that, The method includes the following steps: S1. Drive the high-definition fixed-focus macro wide-angle lens and camera fixed on the robotic arm to obtain the grayscale source image after spotting the biochip, store it in the specified directory of the disk, load it into the memory for post-processing, obtain the first processed image with the spotting area of the magnetic head separated from the circuit background, and generate a template grayscale image; S2. Use the normalized correlation matching method to match the template grayscale image with the first processed image to obtain the result matrix dstmat; S3. Based on the obtained result matrix dstmat, perform multi-target localization processing to obtain the positions of the center points of each magnetic head, and record them in a list or array; S4. Loop to obtain the coordinates of the center points of each magnetic head in the list or array, determine whether there are abnormal coordinate points, and determine whether the chip needs to perform subsequent defect detection; In step S3, the method of multi-target localization processing includes: Step S301. Create a mask matrix area. The mask area has the same height and width as the matrix dstmat, and the area is all white; Step S302. Find the coordinates of the first matching point. Use the MinMaxLoc method in the OpenCV module to find the maximum and minimum values in the white area of the mask area of the matrix dstmat. The position where the maximum value is found is the starting coordinate of the magnetic head area where the template is first matched; Step S303. Add the current magnetic head center point coordinates to the list or array, and draw the currently matched template area in the binary image. The currently matched template area is a rectangular area. The center point coordinates of the rectangle are the current magnetic head center point coordinates. The width of the rectangle is the width of the template grayscale image, and the height is the height of the template grayscale image; Step S304. Change the range of the white area in the mask area. Calculate the rectangle that needs to be set to the black area in the mask area. This rectangle is centered on the current magnetic head center point obtained in step S302, with a width twice the width of the template grayscale image and a height twice the height of the template grayscale image. Set the color of this rectangular area to black and set it as the black rectangular area; Step S305. Find the coordinates of the next matching point. Use the MinMaxLoc method in the OpenCV module to find the maximum and minimum values in the white area of the mask area of the matrix dstmat. The position where the maximum value is found is the starting coordinate of the magnetic head area where the next template is matched; Step S306. Loop steps S303 to S305 until the number of loop times reaches the set number of magnetic heads on the chip minus 1; In step S4, if there are abnormal coordinate points, that is, there are magnetic heads on the chip with incomplete spotting or obvious foreign objects in the spotting area, then the chip does not perform subsequent defect detection and directly displays a defect; if there are no abnormal coordinate points, then the obtained first processed image and the grayscale source image need to be input into the subsequent defect detection module.

2. The method for positioning all the spotting points of the magnetic heads on the microfluidic biochip according to claim 1, wherein In step S1, the process of loading into the memory for post-processing to obtain the first processed image with the spotting area of the magnetic head separated from the circuit background includes: The Threshold method in the OpenCV module is used to perform binarization processing on the grayscale source image to obtain a binary image. After using the FindContours method in the OpenCV module to find the edges in the binary image, the DrawContours method is used to draw white edge points with a line width of 2-3 pixels, obtaining the first processed image with the head spotting area separated from the circuit background.

3. The method for positioning all the spotting points of the magnetic heads on the microfluidic biochip according to claim 1, wherein In step S1, the method for generating the template grayscale image includes: Select a grayscale source image after spotting on a biochip, perform binarization processing on it, save the binary image to the disk, open this binary image in the original size using a painting software, select and copy a defect-free head center area among them, and save it as the template grayscale image.

4. The method for positioning all the spotting points of the magnetic heads on the microfluidic biochip according to claim 1, characterized in that In step S302, the calculation of the current head center point coordinates includes: The X coordinate of the current head center point = the X coordinate of the starting coordinate of the head area + the width of the template grayscale image / 2; The Y coordinate of the current head center point = the Y coordinate of the starting coordinate of the head area + the height of the template grayscale image / 2.

5. The method for positioning all the spotting points of the magnetic heads on the microfluidic biochip according to claim 1, characterized in that, In step S304, the calculation method of the black rectangular area position is: The calculation method of the upper left corner position of the black rectangular area includes: The upper left corner X coordinate of the black rectangular area = the X coordinate of the current head center point - the width of the template grayscale image; The upper left corner Y coordinate of the black rectangular area = the Y coordinate of the current head center point - the height of the template grayscale image; The calculation method of the lower right corner position of this black rectangular area includes: The lower right corner X coordinate of the black rectangular area = the X coordinate of the current head center point + the width of the template grayscale image; The lower right corner Y coordinate of the black rectangular area = the Y coordinate of the current head center point + the height of the template grayscale image; If the upper left corner X coordinate of the black rectangular area < 0 or the upper left corner Y coordinate of the black rectangular area < 0, then set both the upper left corner X and Y coordinates of the black rectangular area to 0; if the lower right corner X coordinate of the black rectangular area > the width of the mask area - 1, or the lower right corner Y coordinate of the black rectangular area > the height of the mask area - 1, then set the lower right corner X coordinate of the black rectangular area = the width of the mask area - 1, and the lower right corner Y coordinate of the black rectangular area = the height of the mask area - 1.

6. The method for positioning all the magnetic head spotting points of the microfluidic biochip according to claim 1, wherein In step S4, the method for determining whether there are abnormal coordinate points includes: Among the three rows where the heads are concentrated at the top or bottom of the image, in the list or array recording the coordinates of the head center points, find the points whose Y coordinates differ from the Y coordinates of other points by more than a set distance, which are abnormal points; first add the Y coordinates of the head center points to the second list, then sort the values in the second list in ascending order, calculate the difference between two adjacent values in the sorted second list, according to the fact that the difference between the Y coordinates of the head centers in the same row of the chip image does not exceed a certain value, and the difference between the Y coordinates of the head centers between adjacent rows does not exceed a certain value. If the difference is greater than the calibration value, there are abnormal coordinate points; according to experiments on multiple chip images, the average value of the difference between the Y coordinates of the head centers between adjacent rows is obtained, and 50 is added to this average value to obtain the calibration value.

7. A system for positioning all the spotting points of the magnetic heads on a microfluidic biochip, which adopts the method for positioning all the spotting points of the magnetic heads on a microfluidic biochip as described in any one of claims 1 - 6, characterized in that, The system includes: The first processing image acquisition and template grayscale image loading module (1) is used to drive the high-definition fixed-focus macro wide-angle lens and camera fixed on the robotic arm to acquire the grayscale source image after the biochip spotting, store it in a specified directory on the disk, load it into the memory for subsequent processing, obtain the first processing image with the magnetic head spotting area and the circuit background separated, and load the template grayscale image; The result matrix acquisition module (2) is used to perform template matching on the first processing image by the normalized correlation matching method using the MatchTemplate method to obtain the result matrix dstmat; The position acquisition module of each magnetic head center point (3) is used to obtain the position of each magnetic head center point through multi-target positioning processing and record it in a list or array; The abnormal coordinate point judgment module (4) is used to loop through the coordinates of each magnetic head center point in the list or array to judge whether there are abnormal coordinate points.

8. A computer device, characterized in that, The computer device includes a memory and a processor. When the computer program stored in the memory is executed by the processor, the processor executes the method for positioning all the magnetic head spotting points of the microfluidic biochip according to any one of claims 1 to 6.

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