WO2021102741A1 - Image analysis method and system for immunochromatographic detection - Google Patents
Image analysis method and system for immunochromatographic detection Download PDFInfo
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- WO2021102741A1 WO2021102741A1 PCT/CN2019/121284 CN2019121284W WO2021102741A1 WO 2021102741 A1 WO2021102741 A1 WO 2021102741A1 CN 2019121284 W CN2019121284 W CN 2019121284W WO 2021102741 A1 WO2021102741 A1 WO 2021102741A1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
- G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
- G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/22—Matching criteria, e.g. proximity measures
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/13—Edge detection
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/90—Determination of colour characteristics
Definitions
- This application relates to the technical field of reagent detection, and in particular to an image analysis method and system for immunochromatographic detection.
- the reagent card In in vitro diagnostic point-of-care testing (POCT), the reagent card is used to carry the test sample as a whole blood quantitative detection immunochromatographic device, which can effectively filter whole blood and quickly separate red blood cells and plasma.
- POCT diagnostic point-of-care testing
- the quality control strip and the test strip will be displayed in the detection window of the reagent card, and the result is generally judged by visual judgment to compare quality control The difference and change of the strips and the test strips can be used to obtain the test results.
- this kind of visual inspection result judgment method has a large judgment error, and manual judgment makes the detection process longer, time-consuming and low detection efficiency.
- the present application provides an image recognition reagent card to determine an accurate and efficient image analysis method and system for immunochromatographic detection.
- the technical solution provided by the embodiments of the present application is to provide an image analysis method for immunochromatographic detection, which includes the following steps:
- the tomographic image including a quality control strip and at least one test strip
- the preprocessed tomographic image to obtain a tomographic image matrix, the tomographic image matrix including a quality control strip matrix and a test strip matrix;
- the image analysis method of immunochromatographic detection while performing the step of establishing the standard concentration corresponding to the test item, further includes an edge positioning step, and the edge positioning step includes:
- the edge matrix data model is used to scan the tomographic image matrix to obtain the edge position coordinates of the detection window image of the reagent card in the tomographic image matrix.
- the step of performing image preprocessing on the tomographic image includes:
- Image edge detection and image edge enhancement processing are performed on the cropped tomographic image based on Sobel operator.
- the image analysis method for immunochromatographic detection further includes a background abnormality processing step on the tomographic image matrix, and the background abnormality processing step includes:
- the foreign body shape or color change difference data model includes an average threshold, a maximum threshold, a minimum threshold, and a standard square deviation threshold.
- the reagent card includes a main body, a hand-held part, a sample adding window, a detection window and a labeling part arranged on the main body, and an identification code is affixed on the labeling part.
- the image analysis method for immunochromatographic detection further includes a reagent card image detection step before the step of obtaining the tomographic image of the test sample on the reagent card, and the reagent card image detection step includes:
- the current reagent card image including an image of the hand-held part and an image of the labeling part;
- the KNN nearest neighbor classification algorithm is used to compare the handheld image and the handheld image database to determine the correctness of the insertion direction and orientation of the reagent card.
- the step of determining the test concentration of the test sample according to the gray value ratio of the test strip matrix and the quality control strip matrix includes:
- a fluctuation threshold is set, and the first standard formula or the second standard formula is selected through the fluctuation threshold to calculate the test concentration.
- the technical solution provided by the embodiments of the present application is to provide an image analysis system for immunochromatographic detection, including a standard storage module, an image acquisition module, an image preprocessing module, an analysis module, and a judgment module,
- the standard storage module is used to store the standard concentration corresponding to the test item
- the image acquisition module is used to acquire a tomographic image of the test sample on the reagent card, and the tomographic image includes a quality control strip and at least one test strip;
- the image preprocessing module is used to perform image preprocessing on the tomographic image to obtain a preprocessed tomographic image; the image preprocessing module is also used to amplify the preprocessed tomographic image to obtain a tomographic image matrix, the tomographic image matrix Including quality control strip matrix and test strip matrix;
- the analysis module is used to determine the test concentration of the test sample according to the gray value ratio of the test strip matrix and the quality control strip matrix;
- the judgment module is used to compare the test concentration with the standard concentration to determine the test result of the test sample.
- the image analysis system for immunochromatographic detection further includes an edge positioning module, and the edge positioning module includes an edge data storage module and a position determination module:
- the edge data storage module is used to store the edge matrix data model
- the edge position determining module is used to scan the tomographic image matrix using the edge matrix data model to obtain the edge position coordinates of the detection window image of the reagent card in the tomographic image matrix.
- the preprocessing module includes an image cropping module and an edge processing module,
- the image cropping module is used to perform cropping processing on the tomographic image to obtain a cropped tomographic image containing the detection window of the reagent card;
- the edge processing module is used to perform image edge detection and image edge enhancement processing on the cropped tomographic image based on the Sobel operator.
- the image analysis system for immunochromatographic detection also includes an abnormality processing module for performing background abnormality processing on the tomographic image matrix;
- the abnormality processing module includes an abnormal model storage module, a scanning judgment module, and an abnormal processing module :
- the abnormal model storage module is used to store the foreign body shape or color change difference data model
- the scanning judgment module is used for scanning the tomographic image matrix according to the edge position coordinates and the foreign body shape or color change difference data model, and judging the abnormal values in the tomographic image matrix row by row;
- the abnormal processing module is used to mark or restore the abnormal value.
- the foreign body shape or color change difference data model includes an average threshold, a maximum threshold, a minimum threshold, and a standard square deviation threshold.
- the reagent card includes a main body, a hand-held part, a sample adding window, a detection window, and a labeling part arranged on the main body, and an identification code is affixed on the labeling part.
- the image analysis system for immunochromatographic detection also includes a reagent card image detection module.
- the reagent card image detection module includes a reagent card image feature database, a reagent card image acquisition module, a two-dimensional code recognition module, and an orientation judgment module.
- the card image feature database includes the handheld image database and the labeling department database;
- the reagent card image acquisition module is used to acquire the current reagent card image when the reagent card is inserted, and the current reagent card image includes the image of the hand-held part and the image of the labeling part;
- the two-dimensional code recognition module is used for recognizing the reagent card information and item detection information based on the image of the labeling part;
- the orientation judgment module is used for comparing the handheld image with the handheld image database based on the handheld image and using the KNN nearest neighbor classification algorithm to determine the correctness of the inserting direction and orientation of the reagent card.
- an embodiment of the present application also provides an image analysis system for immunochromatographic detection, including at least one processor, a memory communicatively connected with the at least one processor, and an image acquisition device;
- the memory stores instructions that can be executed by the at least one processor, and when the instructions are executed by the at least one processor, the image acquisition device acquires image data and the at least one processor executes the method described above.
- the embodiments of the present application also provide a computer program product, the computer program product includes a computer program stored on a non-volatile computer-readable storage medium, the computer program includes program instructions, when the When the program instructions are executed by a computer, the computer executes the method described above.
- the beneficial effects of the implementation of the present application are: the image analysis method and system for immunochromatographic detection of this embodiment, by pre-establishing the standard concentration corresponding to the test item, based on the tomographic image of the test sample on the reagent card, by calculating the tomographic image
- the gray-scale ratio of the image quality control band and the detection band is used to determine the test concentration of the test sample, and then by comparing the test concentration with the standard concentration, the test result of the test sample is determined. Only the tomographic image is acquired during the test. It can quickly and accurately analyze the detection results of pattern samples, improving detection efficiency and detection accuracy.
- FIG. 1 is a schematic diagram of a three-dimensional structure of an immunochromatographic detection device according to an embodiment of the present application
- FIG. 2 is a structural diagram of a reagent card of an image analysis method for immunochromatographic detection according to an embodiment of the present application
- FIG. 3 is a schematic diagram of a tomographic image of a detection window of an image analysis method for immunochromatographic detection according to an embodiment of the present application
- FIG. 5 is a schematic diagram of the overall flow of an image analysis method for immunochromatographic detection according to an embodiment of the present application.
- FIG. 6 is a flowchart of edge positioning of an image analysis method for immunochromatographic detection according to an embodiment of the present application
- FIG. 7 is a flowchart of reagent card image detection in an image analysis method for immunochromatographic detection according to an embodiment of the present application
- FIG. 8 is a background abnormality processing flowchart of the image analysis method for immunochromatographic detection according to an embodiment of the present application.
- FIG. 9 is a schematic diagram of the module structure of an image analysis system for immunochromatographic detection according to an embodiment of the present application.
- FIG. 10 is a schematic diagram of the device hardware architecture of the image analysis system for immunochromatographic detection according to an embodiment of the present application.
- FIG. 11 is a schematic diagram of image processing of the labeling part of the reagent card of the image analysis method for immunochromatographic detection according to an embodiment of the present application.
- FIG. 12 is a schematic diagram of image processing of the reagent card holding part of the image analysis method for immunochromatographic detection according to an embodiment of the present application.
- the present application relates to an image analysis method and system for immunochromatographic detection.
- the image analysis method for immunochromatographic detection in this embodiment mainly includes the following steps:
- the image analysis system for immunochromatographic detection involved in this application includes at least one processor, a memory that is communicatively connected with the at least one processor, and an image acquisition device; the memory stores data that can be used by the at least one processor.
- An instruction executed by a processor when the instruction is executed by the at least one processor, causes the image acquisition device to acquire image data and causes the at least one processor to execute the aforementioned method.
- the image analysis system for immunochromatographic detection includes a standard storage module, an image acquisition module, an image preprocessing module, an analysis module, and a judgment module.
- the standard storage module is used to store the standard concentration corresponding to the test item.
- the image acquisition module is used to acquire a tomographic image of the test sample on the reagent card, and the tomographic image includes a quality control strip C and at least one test strip T.
- the image preprocessing module is used to perform image preprocessing on the tomographic image to obtain a preprocessed tomographic image; the image preprocessing module is also used to amplify the preprocessed tomographic image to obtain a tomographic image matrix, the tomographic image matrix Including quality control strip matrix and test strip matrix.
- the analysis module is used to determine the test concentration of the test sample according to the gray value ratio of the test strip matrix and the quality control strip matrix.
- the judgment module is used to compare the test concentration with the standard concentration to determine the test result of the test sample.
- the image analysis method and system for immunochromatographic detection of this embodiment establishes a standard concentration corresponding to the test item in advance, and calculates the quality control strip C and the test strip of the tomographic image based on the tomographic image of the sample
- the gray scale ratio with T indirectly infers the test concentration of the test sample, and then compares the difference between the test concentration and the standard concentration, and uses the standard concentration as the threshold to determine the test result of the test sample. Only by acquiring the tomographic image during detection, the detection result of the pattern sample can be quickly and accurately analyzed, thereby improving detection efficiency and detection accuracy.
- FIG. 9 shows the software module design diagram of the image analysis system for immunochromatographic detection of the present application.
- the image analysis method of immunochromatographic detection is executed by the system.
- the image analysis system 1 for immunochromatographic detection includes a standard storage module 2, an image acquisition module 3, an image preprocessing module 4, an analysis module 5, a judgment module 6, an abnormality processing module 7, a reagent card image detection module 8 and an edge positioning module 9.
- the standard storage module 2 is arranged on the memory, and stores the standard concentration corresponding to the test item.
- the image acquisition module 3 is connected to the output of the second camera to acquire the tomographic image of the test sample on the reagent card.
- the reagent card includes a main body 10, a handle 11, a sample adding window 12, a detection window 13, and a labeling portion 14 provided on the main body 10.
- An identification code is affixed to the labeling part 14.
- the identification code is a two-dimensional code.
- the two-dimensional code includes the correction curve parameters of the reagent card, project name, reagent batch number and expiration date and other related information.
- the tomographic image is an image displayed on the detection window 13 of the reagent card.
- the tomographic image includes a quality control strip C and at least one test strip.
- the tomographic image includes a quality control strip C, a first test strip T1, and a second test strip T2.
- the image preprocessing module 4 performs image preprocessing on the tomographic image to obtain a preprocessed tomographic image.
- the image preprocessing module 4 also amplifies the preprocessed tomographic image to obtain a tomographic image matrix.
- the tomographic image matrix includes a quality control strip matrix and a test strip matrix.
- the analysis module 5 determines the test concentration of the test sample according to the ratio of the gray value of the test strip matrix to the quality control strip matrix.
- the judgment module 6 compares the test concentration with the standard concentration to determine the test result of the test sample. The above is the detection and analysis process of the image analysis system 1 for immunochromatographic detection.
- test concentration calculation formulas are set according to the experience of the detection concentration calculation.
- first standard formula and the second standard formula are set:
- first test concentration T1/C, where T1 represents the image gray value of the first test strip, and C represents the image gray value of the quality control strip;
- the image analysis system for immunochromatographic detection can select the first standard formula or the second standard formula to calculate the test concentration.
- the fluctuation threshold V is set in this embodiment, and the average value of the first test concentration of multiple reagent cards is calculated when the equipment needs it.
- the first test concentration is selected.
- the second standard formula calculates the test concentration; when the average value is less than the fluctuation threshold V, the first standard formula is selected to calculate the test concentration. That is, one of the first standard formula or the second standard formula is selected and determined by the fluctuation threshold V to calculate the test concentration.
- the image preprocessing module 4 includes an image cropping module 41 and an edge processing module 42.
- the image cropping module 41 performs cropping processing on the tomographic image to obtain a cropped tomographic image containing the detection window 13 of the reagent card.
- the edge processing module 42 performs image edge detection and image edge enhancement processing on the cropped tomographic image based on the Sobel operator.
- the image analysis system 1 for immunochromatographic detection can be applied to a high-throughput immunodetection device or a single-channel immunodetection device.
- the high-throughput immunodetection device 20 is taken as an example to introduce the immunity.
- the high-throughput immunoassay device 20 is provided with a reaction incubation chamber inside, and an interactive operation interface 22 and a collection device 24 for used reagent cards are provided outside. The user inserts the test sample reagent card 10 into the high-throughput immunoassay device, and then automatically enters the reaction incubation and detection process.
- the system processing reagent card detection Judging from the sequence of the system processing reagent card detection, it includes the reagent card image detection process, the edge positioning process, the detection analysis process, and the abnormal handling process.
- the detection and analysis process has been introduced.
- the reagent card image detection module 8 includes a reagent card image feature database 81, a reagent card image acquisition module 82, a two-dimensional code recognition module 83, and an orientation judgment module 84 .
- the reagent card image feature database 81 includes a handheld image database 111 and a labeling portion database 141.
- the reagent card image feature database 81 is an image detection model established after image feature extraction of reagent cards inserted in different directions and orientations.
- the reagent card image acquisition module 82 acquires the current reagent card image taken by the first camera when the reagent card is inserted, and the current reagent card image includes a handheld image 111 and a labeling image 141.
- the two-dimensional code recognition module 83 recognizes the reagent card information and item detection information based on the labeling part image 141.
- the orientation determination module 84 uses the KNN nearest neighbor classification algorithm to compare the handheld image 111 and the handheld image database 141 based on the handheld image 111 to determine the correctness of the reagent card insertion direction and orientation.
- the high-throughput immunodetection device 20 when the high-throughput immunodetection device 20 detects that the reagent card 10 is inserted, the high-throughput immunodetection device 20 first takes an image of the reagent card. Perform format conversion on the handheld image 111 or the labeling image 141; preprocess the handheld image 111 or the labeling image 141; perform image detection and analysis on the reagent card image, and each of the reagent card image feature database 81 The models are compared to draw conclusions. The judgment conclusion includes whether the wrong reagent card is inserted. When the wrong reagent card is inserted, the interactive operation interface 22 displays prompt information to the operating user.
- the image captured by the CCD of the first camera is a 24-bit BMP image, which needs to be converted into an 8-bit BMP image first.
- the preprocessing includes image edge processing and normalization processing based on the Sobel operator, which enlarges or smoothes the features of the hand-held image 111 or the labeling image 141.
- the reagent card image feature database 81 is used as the judgment threshold, and the currently photographed handheld image 111 or the labeling image 141 is normalized.
- the KNN nearest neighbor classification algorithm is used to compare the images, calculate the distance between the model and the current data, and draw the image detection judgment conclusion.
- the edge positioning module 9 includes an edge data storage module 91 and a position determination module 92.
- the edge data storage module 91 stores an edge matrix data model.
- the edge position determining module 92 scans the tomographic image matrix using the edge matrix data model to obtain the edge position coordinates of the image of the detection window 13 of the reagent card in the tomographic image matrix.
- the high-throughput immunoassay device 20 uses the CCD lens of the second camera to capture the reagent card detection window 13 Tomographic image.
- the reagent card enters the card slot of the high-throughput immune detection device 20 or the rotating motor is positioned, there is an uncontrollable slight deviation. Therefore, when the image is captured by the CCD lens, the non-image calculation part will be included in the tomographic image. Increase the image noise of image analysis. Therefore, in the actual detection and analysis process, it is necessary to position the actual position of the detection window 13 of the reagent card to ensure the accuracy of the final calculation result.
- the edge matrix data model is an edge matrix data model established according to the image size of the detection window 13.
- the edge position determination module 92 scans the tomographic image using the edge matrix data model, compares the image data extracted in real time with the rectangular model, and obtains the edge position coordinates of the image of the detection window 13 in the tomographic image.
- the process of edge positioning is as follows: in order to speed up the image processing, the tomographic image is cropped first to obtain the cropped tomographic image containing the detection window 13 of the reagent card, that is, the portion of the image within the range of the detection window 13 is obtained.
- Image edge detection and image edge enhancement processing are performed on the cropped tomographic image based on Sobel operator.
- the preprocessed tomographic image is enlarged to obtain a tomographic image matrix, and the tomographic image matrix includes a quality control strip matrix and a test strip matrix.
- the zero value method is used to remove tomographic image data that does not have a continuous relationship.
- the abnormal processing flow of the image background is established by setting the abnormal processing module 7, thereby improving the accuracy of analysis.
- the abnormality processing module 7 includes an abnormality model storage module 71, a scanning judgment module 72 and an abnormality processing module 73.
- the abnormal model storage module 71 stores a data model of the shape or color change difference of the foreign object.
- the scanning judgment module 72 scans the tomographic image matrix according to the edge position coordinates and the foreign body shape or color change difference data model, and judges abnormal values in the tomographic image matrix row by row.
- the abnormality processing module 73 marks or restores the abnormal value.
- the foreign body shape or color change difference data model includes an average threshold, a maximum threshold, a minimum threshold, and a standard square deviation threshold.
- the abnormality processing module 73 is used for judging the image characteristics of irregular shapes, combining the difference of the color change to establish a data model of the shape of the foreign body or the difference of the color change and the corresponding adjustment threshold.
- the abnormal handling process is as follows: firstly, based on a large amount of test data in the early stage, establish a data model of the difference in the shape or color of the foreign body. Calculate and count each column of test data based on the standard edge coordinate system, and determine the corresponding mean threshold, maximum threshold, minimum threshold, and standard square deviation threshold. Scan the current tomographic image matrix, and make judgments and comparisons based on the pre-stored data model of foreign body shape or color change difference. This scan needs to calculate the value of each pixel, and when determining the occurrence of abnormal image features, calculate the neighboring value of the corresponding pixel at the same time. At the edge of the color difference, the adjacent value is used for calculation to determine whether it is an abnormal value. When an abnormal value occurs, the abnormality processing module 7 marks or restores the abnormal value.
- FIG. 4 and FIG. 5 Please also refer to FIG. 4 and FIG. 5 for the specific software processing flow of the image analysis method for shooting immunochromatographic detection in this embodiment.
- the image analysis method of immunochromatographic detection mainly includes the following steps:
- Step 101 Establish a standard concentration corresponding to the test item
- Step 103 Obtain a tomographic image of the test sample on the reagent card, the tomographic image including a quality control strip and at least one test strip;
- Step 104 Perform image preprocessing on the tomographic image to obtain a preprocessed tomographic image
- Step 105 Enlarge the preprocessed tomographic image to obtain a tomographic image matrix, the tomographic image matrix including a quality control strip matrix and a test strip matrix;
- Step 108 Determine the test concentration of the test sample according to the gray value ratio of the test strip matrix and the quality control strip matrix;
- Step 109 Compare the test concentration with the standard concentration to determine the test result of the test sample.
- Step 102 a reagent card image detection step, which is started when the reagent card is inserted into the high-throughput immunodetection device 20.
- Step 106 an edge positioning step, which is started when the tomographic image matrix is reviewed after the preprocessing is completed.
- Step 107 Background anomaly processing step, which is started when the tomographic image matrix is reviewed after the preprocessing is completed.
- the step of performing image preprocessing on the tomographic image includes:
- Image edge detection and image edge enhancement processing are performed on the cropped tomographic image based on Sobel operator.
- the image detection step of the reagent card is set before the step of acquiring the tomographic image of the test sample on the reagent card.
- the image detection steps of the reagent card include:
- Step 1021 Establish a reagent card image feature database, including a handheld image database and a labeling department database;
- Step 1022 Acquire a current reagent card image, the current reagent card image includes an image of the hand-held part and an image of the labeling part;
- Step 1023 Recognize the reagent card information and item detection information based on the image of the labeling part
- Step 1024 Based on the hand-held image, the KNN nearest neighbor classification algorithm is used to compare the hand-held image and the hand-held image database to determine the correctness of the insertion direction and orientation of the reagent card.
- the edge positioning steps include:
- Step 1061 Establish an edge matrix data model
- Step 1062 Scan the tomographic image matrix using the edge matrix data model to obtain the edge position coordinates of the detection window image of the reagent card in the tomographic image matrix.
- the background exception handling steps include:
- Step 1071 Establish a data model for the difference in the shape or color of the foreign body
- Step 1072 Scan the tomographic image matrix according to the edge position coordinates and the foreign body shape or color change difference data model, and judge the abnormal value in the tomographic image matrix row by row;
- Step 1073 Mark or restore the abnormal value.
- the image analysis method and system for immunochromatographic detection of this embodiment establishes the standard concentration corresponding to the test item in advance, and calculates the tomographic image quality control band and the detection band based on the tomographic image of the test sample on the reagent card. Determine the test concentration of the test sample, and then compare the test concentration with the standard concentration to determine the test result of the test sample. Only the tomographic image needs to be obtained during the test to quickly and accurately analyze the detection of the pattern sample As a result, the detection efficiency and detection accuracy are improved.
- FIG. 10 is a device of an image analysis system for immunochromatographic detection provided by an embodiment of the present application, such as a schematic diagram of the hardware structure of a device 600. As shown in FIG. 10, the device 600 of the system includes:
- One or more processors 610 are taken as an example in FIG. 10.
- the memory 620 stores instructions that can be executed by the at least one processor 610, that is, the computer program 640.
- the image data of the detection window 13 is acquired through the image acquisition component 650, so that the At least one processor can execute the image analysis method of immunochromatographic detection.
- the processor 610, the memory 620, and the communication component 650 may be connected through a bus or in other ways. In FIG. 10, the connection through a bus is taken as an example.
- the memory 620 can be used to store non-volatile software programs, non-volatile computer-executable programs and modules, such as the image analysis of immunochromatographic detection in the embodiment of the present application.
- the program instruction/module corresponding to the method.
- the processor 610 executes various functional applications and data processing of the server by running the non-volatile software programs, instructions, and modules stored in the memory 620, that is, realizing the image analysis method of immunochromatographic detection in the above method embodiment .
- the memory 620 may include a storage program area and a storage data area.
- the storage program area can store an operating system and at least one application program required by a function; the storage data area can store data created based on the use of an image analysis system for immunochromatographic detection. Data etc.
- the memory 620 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other non-volatile solid-state storage devices.
- the memory 620 may optionally include memories remotely provided with respect to the processor 610, and these remote memories may be connected to the robot through a network. Examples of the aforementioned networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.
- the one or more modules are stored in the memory 620, and when executed by the one or more processors 610, the above-mentioned immunochromatographic detection image analysis method is executed, for example, the above-described image analysis method in FIG. 4 is executed.
- the embodiment of the present application provides a non-volatile computer-readable storage medium, the computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are executed by one or more processors, for example, execute the above Steps 101 to 109 of the method in Fig. 4 described, or steps 1021 to 1024 of the method in Fig. 7, or steps 1071 to 1073 of the method in Fig. 8 are performed; the edge positioning module 9 and image preview in Fig. 6 are implemented
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Abstract
The present invention relates to an image analysis method for immunochromatographic detection, comprising the following steps: establishing a standard concentration corresponding to a test item; acquiring a chromatographic image of an assay sample on a reagent card, the chromatographic image comprising a quality control strip and at least one test strip; performing image pre-processing on the chromatographic image to obtain a pre-processed chromatographic image; amplifying the pre-processed chromatographic image to obtain a chromatographic image matrix, the chromatographic image matrix comprising a quality control strip matrix and a test strip matrix; on the basis of the greyscale value ratio of the test strip matrix and the quality control strip matrix, determining the test concentration of the assay sample; and comparing the test concentration with the standard concentration to determine the assay result of the assay sample.
Description
本申请涉及试剂检测技术领域,特别是涉及一种免疫层析检测的图像分析方法以及系统。This application relates to the technical field of reagent detection, and in particular to an image analysis method and system for immunochromatographic detection.
在体外诊断即时检测(point-of-care testing, POCT)中,使用试剂卡承载检测样品作为全血全定量检测免疫层析装置,可有效过滤全血,快速分离红细胞和血浆。In in vitro diagnostic point-of-care testing (POCT), the reagent card is used to carry the test sample as a whole blood quantitative detection immunochromatographic device, which can effectively filter whole blood and quickly separate red blood cells and plasma.
现有的及时检测,在试剂卡上的检测样品孵化时与试剂充分反应后,会在试剂卡的检测窗口显示出质控条带以及测试条带,结果判定时一般采用目测判断方式比较质控条带以及测试条带的差异和变化,得出检测结果。Existing timely detection, after the test sample on the reagent card fully reacts with the reagent during incubation, the quality control strip and the test strip will be displayed in the detection window of the reagent card, and the result is generally judged by visual judgment to compare quality control The difference and change of the strips and the test strips can be used to obtain the test results.
但是,该种目测检测结果判断方式,存在较大的判断误差,并且人工判断使得检测过程较长,耗时且检测效率不高。However, this kind of visual inspection result judgment method has a large judgment error, and manual judgment makes the detection process longer, time-consuming and low detection efficiency.
因此,现有的免疫层析检测判断识别技术还有待于改进。Therefore, the existing immunochromatographic detection and identification technology needs to be improved.
本申请针对以上存在的技术问题,提供一种图像识别试剂卡,判断准确高效的免疫层析检测的图像分析方法以及系统。In view of the above existing technical problems, the present application provides an image recognition reagent card to determine an accurate and efficient image analysis method and system for immunochromatographic detection.
第一方面,本申请实施方式提供的技术方案是:提供一种免疫层析检测的图像分析方法,包括以下步骤:In the first aspect, the technical solution provided by the embodiments of the present application is to provide an image analysis method for immunochromatographic detection, which includes the following steps:
建立与测试项目对应的标准浓度;Establish the standard concentration corresponding to the test item;
获取化验样本在试剂卡的层析图像,该层析图像包括质控条带以及至少一测试条带;Acquiring a tomographic image of the test sample on the reagent card, the tomographic image including a quality control strip and at least one test strip;
对该层析图像进行图像预处理,得到预处理层析图像;Perform image preprocessing on the tomographic image to obtain a preprocessed tomographic image;
放大该预处理层析图像得到层析图像矩阵,该层析图像矩阵包括质控条带矩阵以及测试条带矩阵;Enlarging the preprocessed tomographic image to obtain a tomographic image matrix, the tomographic image matrix including a quality control strip matrix and a test strip matrix;
根据该测试条带矩阵与该质控条带矩阵的灰度值比值,确定化验样本的测试浓度;Determine the test concentration of the test sample according to the ratio of the gray value of the test strip matrix to the quality control strip matrix;
比对该测试浓度与该标准浓度,确定化验样本的化验结果。Compare the test concentration with the standard concentration to determine the test result of the test sample.
优选地,为了确定图形边缘位置信息,该免疫层析检测的图像分析方法在建立与测试项目对应的标准浓度的步骤进行同时,还包括边缘定位步骤,该边缘定位步骤包括:Preferably, in order to determine the edge position information of the graphics, the image analysis method of immunochromatographic detection, while performing the step of establishing the standard concentration corresponding to the test item, further includes an edge positioning step, and the edge positioning step includes:
建立边缘矩阵数据模型;Establish an edge matrix data model;
使用该边缘矩阵数据模型扫描该层析图像矩阵,得到该层析图像矩阵中该试剂卡的检测窗口图像的边缘位置坐标。The edge matrix data model is used to scan the tomographic image matrix to obtain the edge position coordinates of the detection window image of the reagent card in the tomographic image matrix.
其中,该对该层析图像进行图像预处理的步骤包括:Wherein, the step of performing image preprocessing on the tomographic image includes:
对该层析图像进行裁剪处理,得到包含该试剂卡的检测窗口的裁剪层析图像;Performing cropping processing on the tomographic image to obtain a cropped tomographic image containing the detection window of the reagent card;
对该裁剪层析图像基于索贝尔算子进行图像边缘检测以及图像边缘增强处理。Image edge detection and image edge enhancement processing are performed on the cropped tomographic image based on Sobel operator.
该免疫层析检测的图像分析方法还包括对该层析图像矩阵进行背景异常处理步骤,该背景异常处理步骤包括:The image analysis method for immunochromatographic detection further includes a background abnormality processing step on the tomographic image matrix, and the background abnormality processing step includes:
建立异物形状或颜色变化差异数据模型;Establish a data model for the difference of foreign body shape or color change;
根据该边缘位置坐标以及该异物形状或颜色变化差异数据模型扫描该层析图像矩阵,逐行判断该层析图像矩阵中的异常值;Scan the tomographic image matrix according to the edge position coordinates and the foreign body shape or color change difference data model, and judge the abnormal value in the tomographic image matrix row by row;
对该异常值进行标记或者还原。Mark or restore the abnormal value.
进一步地,该异物形状或颜色变化差异数据模型包括平均值阈值、最大阈值、最小阈值以及标准平方差阈值。Further, the foreign body shape or color change difference data model includes an average threshold, a maximum threshold, a minimum threshold, and a standard square deviation threshold.
其中,该试剂卡包括本体以及设置在该本体上的手持部、加样窗口、检测窗口以及贴标部,该贴标部上贴有识别码。Wherein, the reagent card includes a main body, a hand-held part, a sample adding window, a detection window and a labeling part arranged on the main body, and an identification code is affixed on the labeling part.
该免疫层析检测的图像分析方法在该获取化验样本在试剂卡的层析图像的步骤之前,还包括试剂卡图像检测步骤,该试剂卡图像检测步骤包括:The image analysis method for immunochromatographic detection further includes a reagent card image detection step before the step of obtaining the tomographic image of the test sample on the reagent card, and the reagent card image detection step includes:
建立试剂卡图像特征数据库,包括手持部图像数据库以及贴标部数据库;Establish a database of image characteristics of reagent cards, including handheld image database and labeling department database;
获取当前试剂卡图像,该当前试剂卡图像包括手持部图像以及贴标部图像;Acquiring a current reagent card image, the current reagent card image including an image of the hand-held part and an image of the labeling part;
基于该贴标部图像识别出该试剂卡信息以及项目检测信息;Identify the reagent card information and item detection information based on the image of the labeling part;
基于该手持部图像,采用KNN最邻近分类算法对比该手持部图像以及手持部图像数据库,以确定该试剂卡插入方向和朝向的正确性。Based on the handheld image, the KNN nearest neighbor classification algorithm is used to compare the handheld image and the handheld image database to determine the correctness of the insertion direction and orientation of the reagent card.
其中,该根据该测试条带矩阵与该质控条带矩阵的灰度值比值,确定化验样本的测试浓度的步骤包括:Wherein, the step of determining the test concentration of the test sample according to the gray value ratio of the test strip matrix and the quality control strip matrix includes:
设定第一标准公式与第二标准公式;Set the first standard formula and the second standard formula;
设定波动阈值,通过该波动阈值选择该第一标准公式或者第二标准公式计算该测试浓度。A fluctuation threshold is set, and the first standard formula or the second standard formula is selected through the fluctuation threshold to calculate the test concentration.
第二方面,本申请实施方式提供的技术方案是:提供一种免疫层析检测的图像分析系统,包括标准存储模块、图像获取模块、图像预处理模块、分析模块以及判断模块,In the second aspect, the technical solution provided by the embodiments of the present application is to provide an image analysis system for immunochromatographic detection, including a standard storage module, an image acquisition module, an image preprocessing module, an analysis module, and a judgment module,
该标准存储模块用于存储与测试项目对应的标准浓度;The standard storage module is used to store the standard concentration corresponding to the test item;
该图像获取模块用于获取化验样本在试剂卡的层析图像,该层析图像包括质控条带以及至少一测试条带;The image acquisition module is used to acquire a tomographic image of the test sample on the reagent card, and the tomographic image includes a quality control strip and at least one test strip;
该图像预处理模块用于对该层析图像进行图像预处理,得到预处理层析图像;该图像预处理模块还用于放大该预处理层析图像得到层析图像矩阵,该层析图像矩阵包括质控条带矩阵以及测试条带矩阵;The image preprocessing module is used to perform image preprocessing on the tomographic image to obtain a preprocessed tomographic image; the image preprocessing module is also used to amplify the preprocessed tomographic image to obtain a tomographic image matrix, the tomographic image matrix Including quality control strip matrix and test strip matrix;
该分析模块用于根据该测试条带矩阵与该质控条带矩阵的灰度值比值,确定化验样本的测试浓度;以及The analysis module is used to determine the test concentration of the test sample according to the gray value ratio of the test strip matrix and the quality control strip matrix; and
该判断模块用于比对该测试浓度与该标准浓度,确定化验样本的化验结果。The judgment module is used to compare the test concentration with the standard concentration to determine the test result of the test sample.
该免疫层析检测的图像分析系统还包括边缘定位模块,该边缘定位模块包括边缘数据存储模块以及位置确定模块:The image analysis system for immunochromatographic detection further includes an edge positioning module, and the edge positioning module includes an edge data storage module and a position determination module:
该边缘数据存储模块用于存储边缘矩阵数据模型;The edge data storage module is used to store the edge matrix data model;
该边缘位置确定模块用于使用该边缘矩阵数据模型扫描该层析图像矩阵,得到该层析图像矩阵中该试剂卡的检测窗口图像的边缘位置坐标。The edge position determining module is used to scan the tomographic image matrix using the edge matrix data model to obtain the edge position coordinates of the detection window image of the reagent card in the tomographic image matrix.
其中,该预处理模块包括图像裁剪模块以及边缘处理模块,Among them, the preprocessing module includes an image cropping module and an edge processing module,
该图像裁剪模块用于对该层析图像进行裁剪处理,得到包含该试剂卡的检测窗口的裁剪层析图像;The image cropping module is used to perform cropping processing on the tomographic image to obtain a cropped tomographic image containing the detection window of the reagent card;
该边缘处理模块用于对该裁剪层析图像基于索贝尔算子进行图像边缘检测以及图像边缘增强处理。The edge processing module is used to perform image edge detection and image edge enhancement processing on the cropped tomographic image based on the Sobel operator.
为了提高检测准确率,该免疫层析检测的图像分析系统还包括异常处理模块用于对该层析图像矩阵进行背景异常处理;该异常处理模块包括异常模型存储模块、扫描判断模块以及异常处理模块:In order to improve the detection accuracy, the image analysis system for immunochromatographic detection also includes an abnormality processing module for performing background abnormality processing on the tomographic image matrix; the abnormality processing module includes an abnormal model storage module, a scanning judgment module, and an abnormal processing module :
该异常模型存储模块用于存储异物形状或颜色变化差异数据模型;The abnormal model storage module is used to store the foreign body shape or color change difference data model;
该扫描判断模块用于根据该边缘位置坐标以及该异物形状或颜色变化差异数据模型扫描该层析图像矩阵,逐行判断该层析图像矩阵中的异常值;The scanning judgment module is used for scanning the tomographic image matrix according to the edge position coordinates and the foreign body shape or color change difference data model, and judging the abnormal values in the tomographic image matrix row by row;
该异常处理模块用于对该异常值进行标记或者还原。The abnormal processing module is used to mark or restore the abnormal value.
其中,该异物形状或颜色变化差异数据模型包括平均值阈值、最大阈值、最小阈值以及标准平方差阈值。Wherein, the foreign body shape or color change difference data model includes an average threshold, a maximum threshold, a minimum threshold, and a standard square deviation threshold.
该试剂卡包括本体以及设置在该本体上的手持部、加样窗口、检测窗口以及贴标部,该贴标部上贴有识别码。The reagent card includes a main body, a hand-held part, a sample adding window, a detection window, and a labeling part arranged on the main body, and an identification code is affixed on the labeling part.
该免疫层析检测的图像分析系统还包括试剂卡图像检测模块,该试剂卡图像检测模块包括试剂卡图像特征数据库、试剂卡图像获取模块、二维码识别模块以及方位判断模块,其中,该试剂卡图像特征数据库包括手持部图像数据库以及贴标部数据库;The image analysis system for immunochromatographic detection also includes a reagent card image detection module. The reagent card image detection module includes a reagent card image feature database, a reagent card image acquisition module, a two-dimensional code recognition module, and an orientation judgment module. The card image feature database includes the handheld image database and the labeling department database;
该试剂卡图像获取模块用于在试剂卡插入时获取当前试剂卡图像,该当前试剂卡图像包括手持部图像以及贴标部图像;The reagent card image acquisition module is used to acquire the current reagent card image when the reagent card is inserted, and the current reagent card image includes the image of the hand-held part and the image of the labeling part;
该二维码识别模块用于基于该贴标部图像识别出该试剂卡信息以及项目检测信息;The two-dimensional code recognition module is used for recognizing the reagent card information and item detection information based on the image of the labeling part;
该方位判断模块用于基于该手持部图像,采用KNN最邻近分类算法对比该手持部图像以及手持部图像数据库,以确定该试剂卡插入方向和朝向的正确性。The orientation judgment module is used for comparing the handheld image with the handheld image database based on the handheld image and using the KNN nearest neighbor classification algorithm to determine the correctness of the inserting direction and orientation of the reagent card.
第三方面,本申请实施例还提供了一种免疫层析检测的图像分析系统,包括至少一个处理器、与该至少一个处理器通信连接的存储器以及图像获取装置;In a third aspect, an embodiment of the present application also provides an image analysis system for immunochromatographic detection, including at least one processor, a memory communicatively connected with the at least one processor, and an image acquisition device;
该存储器存储有可被该至少一个处理器执行的指令,该指令被该至少一个处理器执行时,使该图像获取装置获取图像数据以及使该至少一个处理器执行如上所述的方法。The memory stores instructions that can be executed by the at least one processor, and when the instructions are executed by the at least one processor, the image acquisition device acquires image data and the at least one processor executes the method described above.
第四方面,本申请实施例还提供了一种计算机程序产品,所述计算机程序产品包括存储在非易失性计算机可读存储介质上的计算机程序,所述计算机程序包括程序指令,当所述程序指令被计算机执行时,使所述计算机执行如上所述的方法。In a fourth aspect, the embodiments of the present application also provide a computer program product, the computer program product includes a computer program stored on a non-volatile computer-readable storage medium, the computer program includes program instructions, when the When the program instructions are executed by a computer, the computer executes the method described above.
本申请实施方式的有益效果是:本实施例的免疫层析检测的图像分析方法以及系统,通过预先建立与测试项目对应的标准浓度,基于化验样本在试剂卡的层析图像,通过计算层析图像质控条带与检测条带的灰度比值,确定化验样本的测试浓度,再通过比对该测试浓度与该标准浓度,确定化验样本的化验结果,检测时只需获取该层析图像即可快速准确分析出花样样本的检测结果,提高检测效率以及检测准确度。The beneficial effects of the implementation of the present application are: the image analysis method and system for immunochromatographic detection of this embodiment, by pre-establishing the standard concentration corresponding to the test item, based on the tomographic image of the test sample on the reagent card, by calculating the tomographic image The gray-scale ratio of the image quality control band and the detection band is used to determine the test concentration of the test sample, and then by comparing the test concentration with the standard concentration, the test result of the test sample is determined. Only the tomographic image is acquired during the test. It can quickly and accurately analyze the detection results of pattern samples, improving detection efficiency and detection accuracy.
一个或多个实施例通过与之对应的附图中的图片进行示例性说明,这些示例性说明并不构成对实施例的限定,附图中具有相同参考数字标号的元件表示为类似的元件,除非有特别申明,附图中的图不构成比例限制。One or more embodiments are exemplified by the pictures in the accompanying drawings. These exemplified descriptions do not constitute a limitation on the embodiments. The elements with the same reference numerals in the drawings are denoted as similar elements. Unless otherwise stated, the figures in the attached drawings do not constitute a scale limitation.
图1是本申请实施例的免疫层析检测装置的立体结构示意图;FIG. 1 is a schematic diagram of a three-dimensional structure of an immunochromatographic detection device according to an embodiment of the present application;
图2是本申请实施例的免疫层析检测的图像分析方法的试剂卡结构图;FIG. 2 is a structural diagram of a reagent card of an image analysis method for immunochromatographic detection according to an embodiment of the present application;
图3是本申请实施例的免疫层析检测的图像分析方法的检测窗口的层析图像示意图; 3 is a schematic diagram of a tomographic image of a detection window of an image analysis method for immunochromatographic detection according to an embodiment of the present application;
图4是本申请实施例的免疫层析检测的图像分析方法的主要处理流程图;4 is a main processing flowchart of the image analysis method for immunochromatographic detection according to an embodiment of the present application;
图5是本申请实施例的免疫层析检测的图像分析方法的整体流程示意图;5 is a schematic diagram of the overall flow of an image analysis method for immunochromatographic detection according to an embodiment of the present application;
图6是本申请实施例的免疫层析检测的图像分析方法的边缘定位流程图;6 is a flowchart of edge positioning of an image analysis method for immunochromatographic detection according to an embodiment of the present application;
图7是本申请实施例的免疫层析检测的图像分析方法的试剂卡图像检测流程图;FIG. 7 is a flowchart of reagent card image detection in an image analysis method for immunochromatographic detection according to an embodiment of the present application;
图8是本申请实施例的免疫层析检测的图像分析方法的背景异常处理流程图;FIG. 8 is a background abnormality processing flowchart of the image analysis method for immunochromatographic detection according to an embodiment of the present application;
图9是本申请实施例的免疫层析检测的图像分析系统的模块结构示意图;9 is a schematic diagram of the module structure of an image analysis system for immunochromatographic detection according to an embodiment of the present application;
图10是本申请实施例的免疫层析检测的图像分析系统的设备硬件架构简图;FIG. 10 is a schematic diagram of the device hardware architecture of the image analysis system for immunochromatographic detection according to an embodiment of the present application;
图11是本申请实施例的免疫层析检测的图像分析方法的试剂卡贴标部的图像处理示意图;以及11 is a schematic diagram of image processing of the labeling part of the reagent card of the image analysis method for immunochromatographic detection according to an embodiment of the present application; and
图12是本申请实施例的免疫层析检测的图像分析方法的试剂卡手持部的图像处理示意图。FIG. 12 is a schematic diagram of image processing of the reagent card holding part of the image analysis method for immunochromatographic detection according to an embodiment of the present application.
为使本申请实施例的目的、技术方案和优点更加清楚明白,下面结合附图对本申请实施例作进一步详细说明。在此,本申请的示意性实施例及其说明用于解释本申请,但并不作为对本申请的限定。In order to make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the following further describes the embodiments of the present application in detail with reference to the accompanying drawings. Here, the exemplary embodiments of the present application and the description thereof are used to explain the present application, but are not intended to limit the present application.
如图1以及图4所示,本申请涉及免疫层析检测的图像分析方法以及系统。As shown in Fig. 1 and Fig. 4, the present application relates to an image analysis method and system for immunochromatographic detection.
从软件设计上来看,本实施例的免疫层析检测的图像分析方法主要包括以下步骤:From the perspective of software design, the image analysis method for immunochromatographic detection in this embodiment mainly includes the following steps:
建立与测试项目对应的标准浓度;获取化验样本在试剂卡的层析图像,该层析图像包括质控条带以及至少一测试条带;对该层析图像进行图像预处理,得到预处理层析图像;放大该预处理层析图像得到层析图像矩阵,该层析图像矩阵包括质控条带矩阵以及测试条带矩阵;根据该测试条带矩阵与该质控条带矩阵的灰度值比值,确定化验样本的测试浓度;比对该测试浓度与该标准浓度,确定化验样本的化验结果。Establish a standard concentration corresponding to the test item; obtain a tomographic image of the test sample on the reagent card, the tomographic image includes a quality control strip and at least one test strip; perform image preprocessing on the tomographic image to obtain a preprocessing layer Analyze the image; enlarge the preprocessed tomographic image to obtain a tomographic image matrix, the tomographic image matrix includes a quality control strip matrix and a test strip matrix; according to the gray value of the test strip matrix and the quality control strip matrix The ratio determines the test concentration of the test sample; compares the test concentration with the standard concentration to determine the test result of the test sample.
对应地,从硬件系统上来看,本申请涉及的免疫层析检测的图像分析系统包括至少一个处理器、与该至少一个处理器通信连接的存储器以及图像获取装置;该存储器存储有可被该至少一个处理器执行的指令,该指令被该至少一个处理器执行时,使该图像获取装置获取图像数据以及使该至少一个处理器执行前述的方法。Correspondingly, from the perspective of the hardware system, the image analysis system for immunochromatographic detection involved in this application includes at least one processor, a memory that is communicatively connected with the at least one processor, and an image acquisition device; the memory stores data that can be used by the at least one processor. An instruction executed by a processor, when the instruction is executed by the at least one processor, causes the image acquisition device to acquire image data and causes the at least one processor to execute the aforementioned method.
从前述方法涉及的软件模块来看,该免疫层析检测的图像分析系统包括标准存储模块、图像获取模块、图像预处理模块、分析模块以及判断模块。From the perspective of the software modules involved in the foregoing method, the image analysis system for immunochromatographic detection includes a standard storage module, an image acquisition module, an image preprocessing module, an analysis module, and a judgment module.
该标准存储模块用于存储与测试项目对应的标准浓度。该图像获取模块用于获取化验样本在试剂卡的层析图像,该层析图像包括质控条带C以及至少一测试条带T。该图像预处理模块用于对该层析图像进行图像预处理,得到预处理层析图像;该图像预处理模块还用于放大该预处理层析图像得到层析图像矩阵,该层析图像矩阵包括质控条带矩阵以及测试条带矩阵。该分析模块用于根据该测试条带矩阵与该质控条带矩阵的灰度值比值,确定化验样本的测试浓度。该判断模块用于比对该测试浓度与该标准浓度,确定化验样本的化验结果。The standard storage module is used to store the standard concentration corresponding to the test item. The image acquisition module is used to acquire a tomographic image of the test sample on the reagent card, and the tomographic image includes a quality control strip C and at least one test strip T. The image preprocessing module is used to perform image preprocessing on the tomographic image to obtain a preprocessed tomographic image; the image preprocessing module is also used to amplify the preprocessed tomographic image to obtain a tomographic image matrix, the tomographic image matrix Including quality control strip matrix and test strip matrix. The analysis module is used to determine the test concentration of the test sample according to the gray value ratio of the test strip matrix and the quality control strip matrix. The judgment module is used to compare the test concentration with the standard concentration to determine the test result of the test sample.
本实施例的免疫层析检测的图像分析方法以及系统,预先建立与测试项目对应的标准浓度,基于拍摄的化验样本的层析图像,通过计算该层析图像的质控条带C与检测条带T的灰度比值,间接推断化验样本的测试浓度,再通过比对测试浓度与该标准浓度的差异,以标准浓度作为阈值,确定化验样本的化验结果。检测时只需获取该层析图像即可快速准确分析出花样样本的检测结果,提高检测效率以及检测准确度。The image analysis method and system for immunochromatographic detection of this embodiment establishes a standard concentration corresponding to the test item in advance, and calculates the quality control strip C and the test strip of the tomographic image based on the tomographic image of the sample The gray scale ratio with T indirectly infers the test concentration of the test sample, and then compares the difference between the test concentration and the standard concentration, and uses the standard concentration as the threshold to determine the test result of the test sample. Only by acquiring the tomographic image during detection, the detection result of the pattern sample can be quickly and accurately analyzed, thereby improving detection efficiency and detection accuracy.
实施例1Example 1
请参考图9,所示为本申请免疫层析检测的图像分析系统的软件模块设计图。该免疫层析检测的图像分析方法由该系统执行完成。Please refer to FIG. 9, which shows the software module design diagram of the image analysis system for immunochromatographic detection of the present application. The image analysis method of immunochromatographic detection is executed by the system.
该免疫层析检测的图像分析系统1包括标准存储模块2、图像获取模块3、图像预处理模块4、分析模块5、判断模块6、异常处理模块7、试剂卡图像检测模块8以及边缘定位模块9。The image analysis system 1 for immunochromatographic detection includes a standard storage module 2, an image acquisition module 3, an image preprocessing module 4, an analysis module 5, a judgment module 6, an abnormality processing module 7, a reagent card image detection module 8 and an edge positioning module 9.
该标准存储模块2设置在存储器上,存储与测试项目对应的标准浓度。The standard storage module 2 is arranged on the memory, and stores the standard concentration corresponding to the test item.
该图像获取模块3连接第二摄像头的输出,获取化验样本在试剂卡的层析图像。The image acquisition module 3 is connected to the output of the second camera to acquire the tomographic image of the test sample on the reagent card.
请参考图2,该试剂卡包括本体10以及设置在该本体10上的手持部11、加样窗口12、检测窗口13以及贴标部14。该贴标部14上贴有识别码。本实施例中该识别码为二维码。该二维码包括了试剂卡的矫正曲线参数、项目名称、试剂批号以及有效日期等相关信息。Please refer to FIG. 2, the reagent card includes a main body 10, a handle 11, a sample adding window 12, a detection window 13, and a labeling portion 14 provided on the main body 10. An identification code is affixed to the labeling part 14. In this embodiment, the identification code is a two-dimensional code. The two-dimensional code includes the correction curve parameters of the reagent card, project name, reagent batch number and expiration date and other related information.
请参考图3,本实施例中,该层析图像为展示在试剂卡检测窗口13的图像。该层析图像包括质控条带C以及至少一测试条带。本实施例中,该层析图像包括质控条带C、第一测试条带T1以及第二测试条带T2。Please refer to FIG. 3. In this embodiment, the tomographic image is an image displayed on the detection window 13 of the reagent card. The tomographic image includes a quality control strip C and at least one test strip. In this embodiment, the tomographic image includes a quality control strip C, a first test strip T1, and a second test strip T2.
该图像预处理模块4对该层析图像进行图像预处理,得到预处理层析图像。该图像预处理模块4还放大该预处理层析图像得到层析图像矩阵,该层析图像矩阵包括质控条带矩阵以及测试条带矩阵。该分析模块5根据该测试条带矩阵与该质控条带矩阵的灰度值比值,确定化验样本的测试浓度。该判断模块6比对该测试浓度与该标准浓度,确定化验样本的化验结果。以上为免疫层析检测的图像分析系统1的检测分析流程。The image preprocessing module 4 performs image preprocessing on the tomographic image to obtain a preprocessed tomographic image. The image preprocessing module 4 also amplifies the preprocessed tomographic image to obtain a tomographic image matrix. The tomographic image matrix includes a quality control strip matrix and a test strip matrix. The analysis module 5 determines the test concentration of the test sample according to the ratio of the gray value of the test strip matrix to the quality control strip matrix. The judgment module 6 compares the test concentration with the standard concentration to determine the test result of the test sample. The above is the detection and analysis process of the image analysis system 1 for immunochromatographic detection.
本实施例中,在根据该测试条带矩阵与该质控条带矩阵的灰度值比值,计算化验样本的测试浓度时,根据检测浓度计算的经验设置不同测试浓度计算公式。本实施例中,设定第一标准公式与第二标准公式:In this embodiment, when calculating the test concentration of the test sample according to the gray value ratio of the test strip matrix and the quality control strip matrix, different test concentration calculation formulas are set according to the experience of the detection concentration calculation. In this embodiment, the first standard formula and the second standard formula are set:
该第一标准公式为:第一测试浓度= T1/C,此处T1表示第一测试条带的图像灰度值,C表示质控条带的图像灰度值;The first standard formula is: first test concentration = T1/C, where T1 represents the image gray value of the first test strip, and C represents the image gray value of the quality control strip;
该第二标准公式为;第二测试浓度=(T1+T2)/C, 此处T1表示第一测试条带的图像灰度值,T2表示第二测试条带的图像灰度值,C表示质控条带的图像灰度值。The second standard formula is; second test density=(T1+T2)/C, where T1 represents the image gray value of the first test strip, T2 represents the image gray value of the second test strip, and C represents The image gray value of the quality control strip.
该免疫层析检测的图像分析系统可以选择第一标准公式或者第二标准公式来计算测试浓度。The image analysis system for immunochromatographic detection can select the first standard formula or the second standard formula to calculate the test concentration.
或者为了适应不同设备精度要求,本实施例中设定波动阈值V,在设备需要时计算多个试剂卡的第一测试浓度的平均值,在该平均值大于该波动阈值V时,选择该第二标准公式计算测试浓度;在该平均值小于该波动阈值V时,选择该第一标准公式计算测试浓度。亦即通过该波动阈值V选择确定该第一标准公式或者第二标准公式其中之一来计算测试浓度。Or in order to meet the accuracy requirements of different equipment, the fluctuation threshold V is set in this embodiment, and the average value of the first test concentration of multiple reagent cards is calculated when the equipment needs it. When the average value is greater than the fluctuation threshold V, the first test concentration is selected. The second standard formula calculates the test concentration; when the average value is less than the fluctuation threshold V, the first standard formula is selected to calculate the test concentration. That is, one of the first standard formula or the second standard formula is selected and determined by the fluctuation threshold V to calculate the test concentration.
本实施例中,该图像预处理模块4包括图像裁剪模块41以及边缘处理模块42。In this embodiment, the image preprocessing module 4 includes an image cropping module 41 and an edge processing module 42.
该图像裁剪模块41对该层析图像进行裁剪处理,得到包含该试剂卡的检测窗口13的裁剪层析图像。该边缘处理模块42对该裁剪层析图像基于索贝尔算子进行图像边缘检测以及图像边缘增强处理。The image cropping module 41 performs cropping processing on the tomographic image to obtain a cropped tomographic image containing the detection window 13 of the reagent card. The edge processing module 42 performs image edge detection and image edge enhancement processing on the cropped tomographic image based on the Sobel operator.
请参考图1,该免疫层析检测的图像分析系统1可应用在高通量免疫检测装置上或者单通道免疫检测装置上,本实施例以高通量免疫检测装置20为例加以介绍该免疫层析检测的图像分析方法和系统。该高通量免疫检测装置20内部设置反应孵化腔室,外部设置交互操作界面22以及已使用试剂卡的收集装置24。用户将加入化验样本试剂卡10插入该高通量免疫检测装置,即可自动进入反应孵化和检测流程。Please refer to FIG. 1. The image analysis system 1 for immunochromatographic detection can be applied to a high-throughput immunodetection device or a single-channel immunodetection device. In this embodiment, the high-throughput immunodetection device 20 is taken as an example to introduce the immunity. Image analysis method and system for tomographic detection. The high-throughput immunoassay device 20 is provided with a reaction incubation chamber inside, and an interactive operation interface 22 and a collection device 24 for used reagent cards are provided outside. The user inserts the test sample reagent card 10 into the high-throughput immunoassay device, and then automatically enters the reaction incubation and detection process.
从系统处理试剂卡检测的先后顺序来看,包括试剂卡图像检测流程、边缘定位流程、检测分析流程以及异常处理流程。该检测分析流程已做了介绍。Judging from the sequence of the system processing reagent card detection, it includes the reagent card image detection process, the edge positioning process, the detection analysis process, and the abnormal handling process. The detection and analysis process has been introduced.
请参考图11以及图12,为了保证化验样本准确无误地孵化,该试剂卡图像检测模块8包括试剂卡图像特征数据库81、试剂卡图像获取模块82、二维码识别模块83以及方位判断模块84。其中,该试剂卡图像特征数据库81包括手持部图像数据库111以及贴标部数据库141。该试剂卡图像特征数据库81是对不同方向和朝向插入的试剂卡进行图像特征提取后建立的图像检测模型。Please refer to Figure 11 and Figure 12, in order to ensure the accurate incubation of the test sample, the reagent card image detection module 8 includes a reagent card image feature database 81, a reagent card image acquisition module 82, a two-dimensional code recognition module 83, and an orientation judgment module 84 . Among them, the reagent card image feature database 81 includes a handheld image database 111 and a labeling portion database 141. The reagent card image feature database 81 is an image detection model established after image feature extraction of reagent cards inserted in different directions and orientations.
该试剂卡图像获取模块82在试剂卡插入时获取第一摄像头拍摄的当前试剂卡图像,该当前试剂卡图像包括手持部图像111以及贴标部图像141。The reagent card image acquisition module 82 acquires the current reagent card image taken by the first camera when the reagent card is inserted, and the current reagent card image includes a handheld image 111 and a labeling image 141.
该二维码识别模块83基于该贴标部图像141识别出该试剂卡信息以及项目检测信息。The two-dimensional code recognition module 83 recognizes the reagent card information and item detection information based on the labeling part image 141.
该方位判断模块84基于该手持部图像111,采用KNN最邻近分类算法对比该手持部图像111以及手持部图像数据库141,以确定该试剂卡插入方向和朝向的正确性。The orientation determination module 84 uses the KNN nearest neighbor classification algorithm to compare the handheld image 111 and the handheld image database 141 based on the handheld image 111 to determine the correctness of the reagent card insertion direction and orientation.
在具体操作时,当高通量免疫检测装置20检测到试剂卡10插入时,高通量免疫检测装置20先拍摄该试剂卡的图像。对手持部图像111或者贴标部图像141进行格式转换;对手持部图像111或者贴标部图像141进行预处理;对该试剂卡图像进行图像检测分析,与该试剂卡图像特征数据库81的各个模型进行比较得出判断结论。该判断结论包括是否属于错误的试剂卡插入,在判断为错误的试剂卡插入时,在该交互操作界面22上显示对操作用户的提示信息。In a specific operation, when the high-throughput immunodetection device 20 detects that the reagent card 10 is inserted, the high-throughput immunodetection device 20 first takes an image of the reagent card. Perform format conversion on the handheld image 111 or the labeling image 141; preprocess the handheld image 111 or the labeling image 141; perform image detection and analysis on the reagent card image, and each of the reagent card image feature database 81 The models are compared to draw conclusions. The judgment conclusion includes whether the wrong reagent card is inserted. When the wrong reagent card is inserted, the interactive operation interface 22 displays prompt information to the operating user.
其中,在上述格式转换中,第一摄像头的CCD捕获的图像为24位的BMP图像,需先将其转换为8位的BMP图像。在上述图像预处理中,该预处理包括基于索贝尔算子(Sobeloperator)的图像边缘处理以及归一化处理等预处理,将对手持部图像111或者贴标部图像141的特征放大或抹平,再将该试剂卡图像特征数据库81作为判断阈值,将当前拍摄的手持部图像111或者贴标部图像141进行归一化处理。对实时手持部图像111或者贴标部图像141完成预处理后,使用KNN最邻近分类算法对图像进行比较,计算模型与当前数据之间的距离,得出图像检测判断结论。Among them, in the above format conversion, the image captured by the CCD of the first camera is a 24-bit BMP image, which needs to be converted into an 8-bit BMP image first. In the above image preprocessing, the preprocessing includes image edge processing and normalization processing based on the Sobel operator, which enlarges or smoothes the features of the hand-held image 111 or the labeling image 141 Then, the reagent card image feature database 81 is used as the judgment threshold, and the currently photographed handheld image 111 or the labeling image 141 is normalized. After the real-time handheld image 111 or the labeling image 141 is preprocessed, the KNN nearest neighbor classification algorithm is used to compare the images, calculate the distance between the model and the current data, and draw the image detection judgment conclusion.
请一并参考图6,该边缘定位模块9包括边缘数据存储模块91以及位置确定模块92。Please also refer to FIG. 6, the edge positioning module 9 includes an edge data storage module 91 and a position determination module 92.
该边缘数据存储模块91存储边缘矩阵数据模型。该边缘位置确定模块92使用该边缘矩阵数据模型扫描该层析图像矩阵,得到该层析图像矩阵中该试剂卡的检测窗口13图像的边缘位置坐标。The edge data storage module 91 stores an edge matrix data model. The edge position determining module 92 scans the tomographic image matrix using the edge matrix data model to obtain the edge position coordinates of the image of the detection window 13 of the reagent card in the tomographic image matrix.
在具体操作时,当滴入化验样本的试剂卡在该高通量免疫检测装置20内部孵化完成后,该高通量免疫检测装置20使用第二摄像头的CCD镜头获取该试剂卡检测窗口13内的层析图像。In a specific operation, after the reagent card dropped into the test sample is incubated inside the high-throughput immunoassay device 20, the high-throughput immunoassay device 20 uses the CCD lens of the second camera to capture the reagent card detection window 13 Tomographic image.
由于试剂卡进入该高通量免疫检测装置20的卡槽或旋转电机在定位时,存在不可控的微小偏差,因此,对CCD镜头捕获图像时,会出现非图像计算部分算入层析图像中,增加图像分析的图像噪音。因此,在实际检测分析流程中,需要对试剂卡的检测窗口13实际位置进行位置定位,以保证最终计算结果的准确性。Since the reagent card enters the card slot of the high-throughput immune detection device 20 or the rotating motor is positioned, there is an uncontrollable slight deviation. Therefore, when the image is captured by the CCD lens, the non-image calculation part will be included in the tomographic image. Increase the image noise of image analysis. Therefore, in the actual detection and analysis process, it is necessary to position the actual position of the detection window 13 of the reagent card to ensure the accuracy of the final calculation result.
如图3所示,试剂卡的检测窗口13四周存在明显边框,对检测窗口13的层析图像基于索贝尔算子进行边缘处理。然后,对层析图像的非边缘部分进行零值处理,得到大于零值的边缘数据。As shown in FIG. 3, there is an obvious border around the detection window 13 of the reagent card, and the tomographic image of the detection window 13 is edge-processed based on the Sobel operator. Then, zero-value processing is performed on the non-edge part of the tomographic image to obtain edge data greater than zero.
该边缘矩阵数据模型是根据该检测窗口13的图像大小建立的边缘矩阵数据模型。该边缘位置确定模块92使用该边缘矩阵数据模型扫描该层析图像,将实时提取的图像数据与矩形模型进行比较,得到该层析图像中该检测窗口13图像的边缘位置坐标。The edge matrix data model is an edge matrix data model established according to the image size of the detection window 13. The edge position determination module 92 scans the tomographic image using the edge matrix data model, compares the image data extracted in real time with the rectangular model, and obtains the edge position coordinates of the image of the detection window 13 in the tomographic image.
边缘定位的过程为:为加快图像处理速度,首先对该层析图像进行裁剪处理,得到包含该试剂卡的检测窗口13的裁剪层析图像,亦即包含检测窗口13的范围内图像部分。The process of edge positioning is as follows: in order to speed up the image processing, the tomographic image is cropped first to obtain the cropped tomographic image containing the detection window 13 of the reagent card, that is, the portion of the image within the range of the detection window 13 is obtained.
对该裁剪层析图像基于索贝尔算子进行图像边缘检测以及图像边缘增强处理。Image edge detection and image edge enhancement processing are performed on the cropped tomographic image based on Sobel operator.
放大该预处理层析图像得到层析图像矩阵,该层析图像矩阵包括质控条带矩阵以及测试条带矩阵。对不存在连续关系的层析图像数据使用零值法去除。The preprocessed tomographic image is enlarged to obtain a tomographic image matrix, and the tomographic image matrix includes a quality control strip matrix and a test strip matrix. The zero value method is used to remove tomographic image data that does not have a continuous relationship.
得到层析图像矩阵后,使用已建立的边缘矩阵数据模型进行扫描、平移等计算处理,得到实际的准确边缘位置坐标系。After obtaining the tomographic image matrix, use the established edge matrix data model to perform calculation processing such as scanning and translation to obtain the actual accurate edge position coordinate system.
请参考图8,试剂卡在加样窗口12加入化验样本后,在层析过程中可能存在分布不均匀或出现块状物或外界掉落异物到试剂卡检测窗口13上。该些异常状况将会影响图像分析结果的准确性。本实施例通过设置异常处理模块7建立图像背景异常处理流程,从而提高分析准确度。Please refer to FIG. 8, after the reagent card is added to the test sample in the sample loading window 12, there may be uneven distribution or lumps or foreign objects falling onto the detection window 13 of the reagent card during the chromatography process. These abnormal conditions will affect the accuracy of the image analysis results. In this embodiment, the abnormal processing flow of the image background is established by setting the abnormal processing module 7, thereby improving the accuracy of analysis.
该异常处理模块7包括异常模型存储模块71、扫描判断模块72以及异常处理模块73。The abnormality processing module 7 includes an abnormality model storage module 71, a scanning judgment module 72 and an abnormality processing module 73.
该异常模型存储模块71存储异物形状或颜色变化差异数据模型。该扫描判断模块72根据该边缘位置坐标以及该异物形状或颜色变化差异数据模型扫描该层析图像矩阵,逐行判断该层析图像矩阵中的异常值。该异常处理模块73对该异常值进行标记或者还原。The abnormal model storage module 71 stores a data model of the shape or color change difference of the foreign object. The scanning judgment module 72 scans the tomographic image matrix according to the edge position coordinates and the foreign body shape or color change difference data model, and judges abnormal values in the tomographic image matrix row by row. The abnormality processing module 73 marks or restores the abnormal value.
其中,该异物形状或颜色变化差异数据模型包括平均值阈值、最大阈值、最小阈值以及标准平方差阈值。Wherein, the foreign body shape or color change difference data model includes an average threshold, a maximum threshold, a minimum threshold, and a standard square deviation threshold.
在检测窗口13的图像识别中,检测窗口13的边框及各种条带的形状均为规则形状。该异常处理模块73用于判断不规则形状的图像特征,结合颜色变化的差异建立异物形状或颜色变化差异数据模型以及对应的调节阀值。In the image recognition of the detection window 13, the shape of the frame and various strips of the detection window 13 are all regular shapes. The abnormality processing module 73 is used for judging the image characteristics of irregular shapes, combining the difference of the color change to establish a data model of the shape of the foreign body or the difference of the color change and the corresponding adjustment threshold.
异常处理过程为:首先基于前期大量测试数据,建立异物形状或颜色变化差异数据模型。基于标准边缘坐标系对每一列测试数据进行计算和统计,确定对应均值阈值、最大阈值、最小阈值以及标准平方差阈值。对当前的层析图像矩阵进行扫描,根据预存的异物形状或颜色变化差异数据模型进行判断及对比。该扫描需要计算每个像素点值,在确定出现异常图像特征时,同时计算对应像素点的临近值。在颜色差异处边缘使用临近值进行计算,判断是否为异常值。出现异常值时,该异常处理模块7对异常值进行标记或还原。The abnormal handling process is as follows: firstly, based on a large amount of test data in the early stage, establish a data model of the difference in the shape or color of the foreign body. Calculate and count each column of test data based on the standard edge coordinate system, and determine the corresponding mean threshold, maximum threshold, minimum threshold, and standard square deviation threshold. Scan the current tomographic image matrix, and make judgments and comparisons based on the pre-stored data model of foreign body shape or color change difference. This scan needs to calculate the value of each pixel, and when determining the occurrence of abnormal image features, calculate the neighboring value of the corresponding pixel at the same time. At the edge of the color difference, the adjacent value is used for calculation to determine whether it is an abnormal value. When an abnormal value occurs, the abnormality processing module 7 marks or restores the abnormal value.
实施例2Example 2
请一并参考图4以及图5,本实施例射击免疫层析检测的图像分析方法的具体软件处理流程。Please also refer to FIG. 4 and FIG. 5 for the specific software processing flow of the image analysis method for shooting immunochromatographic detection in this embodiment.
该免疫层析检测的图像分析方法主要包括以下步骤:The image analysis method of immunochromatographic detection mainly includes the following steps:
步骤101:建立与测试项目对应的标准浓度;Step 101: Establish a standard concentration corresponding to the test item;
步骤103:获取化验样本在试剂卡的层析图像,该层析图像包括质控条带以及至少一测试条带;Step 103: Obtain a tomographic image of the test sample on the reagent card, the tomographic image including a quality control strip and at least one test strip;
步骤104:对该层析图像进行图像预处理,得到预处理层析图像;Step 104: Perform image preprocessing on the tomographic image to obtain a preprocessed tomographic image;
步骤105:放大该预处理层析图像得到层析图像矩阵,该层析图像矩阵包括质控条带矩阵以及测试条带矩阵;Step 105: Enlarge the preprocessed tomographic image to obtain a tomographic image matrix, the tomographic image matrix including a quality control strip matrix and a test strip matrix;
步骤108:根据该测试条带矩阵与该质控条带矩阵的灰度值比值,确定化验样本的测试浓度;Step 108: Determine the test concentration of the test sample according to the gray value ratio of the test strip matrix and the quality control strip matrix;
步骤109:比对该测试浓度与该标准浓度,确定化验样本的化验结果。Step 109: Compare the test concentration with the standard concentration to determine the test result of the test sample.
在完整的图像分析方法中,还包括:In the complete image analysis method, it also includes:
步骤102:试剂卡图像检测步骤,该步骤在试剂卡插入高通量免疫检测装置20时启动。Step 102: a reagent card image detection step, which is started when the reagent card is inserted into the high-throughput immunodetection device 20.
步骤106:边缘定位步骤,该步骤在预处理完成后,审查层析图像矩阵时启动。Step 106: an edge positioning step, which is started when the tomographic image matrix is reviewed after the preprocessing is completed.
步骤107:背景异常处理步骤,该步骤在预处理完成后,审查层析图像矩阵时启动。Step 107: Background anomaly processing step, which is started when the tomographic image matrix is reviewed after the preprocessing is completed.
其中,该对该层析图像进行图像预处理的步骤包括:Wherein, the step of performing image preprocessing on the tomographic image includes:
对该层析图像进行裁剪处理,得到包含该试剂卡的检测窗口13的裁剪层析图像;Performing cropping processing on the tomographic image to obtain a cropped tomographic image containing the detection window 13 of the reagent card;
对该裁剪层析图像基于索贝尔算子进行图像边缘检测以及图像边缘增强处理。Image edge detection and image edge enhancement processing are performed on the cropped tomographic image based on Sobel operator.
请参考图7,该试剂卡图像检测步骤设置在获取化验样本在试剂卡的层析图像的步骤之前。该试剂卡图像检测步骤包括:Please refer to FIG. 7, the image detection step of the reagent card is set before the step of acquiring the tomographic image of the test sample on the reagent card. The image detection steps of the reagent card include:
步骤1021:建立试剂卡图像特征数据库,包括手持部图像数据库以及贴标部数据库;Step 1021: Establish a reagent card image feature database, including a handheld image database and a labeling department database;
步骤1022:获取当前试剂卡图像,该当前试剂卡图像包括手持部图像以及贴标部图像;Step 1022: Acquire a current reagent card image, the current reagent card image includes an image of the hand-held part and an image of the labeling part;
步骤1023:基于该贴标部图像识别出该试剂卡信息以及项目检测信息;Step 1023: Recognize the reagent card information and item detection information based on the image of the labeling part;
步骤1024:基于该手持部图像,采用KNN最邻近分类算法对比该手持部图像以及手持部图像数据库,以确定该试剂卡插入方向和朝向的正确性。Step 1024: Based on the hand-held image, the KNN nearest neighbor classification algorithm is used to compare the hand-held image and the hand-held image database to determine the correctness of the insertion direction and orientation of the reagent card.
请参考图7,该边缘定位步骤包括:Please refer to Figure 7, the edge positioning steps include:
步骤1061:建立边缘矩阵数据模型;Step 1061: Establish an edge matrix data model;
步骤1062:使用该边缘矩阵数据模型扫描该层析图像矩阵,得到该层析图像矩阵中该试剂卡的检测窗口图像的边缘位置坐标。Step 1062: Scan the tomographic image matrix using the edge matrix data model to obtain the edge position coordinates of the detection window image of the reagent card in the tomographic image matrix.
请参考图8,该背景异常处理步骤包括:Please refer to Figure 8. The background exception handling steps include:
步骤1071:建立异物形状或颜色变化差异数据模型;Step 1071: Establish a data model for the difference in the shape or color of the foreign body;
步骤1072:根据该边缘位置坐标以及该异物形状或颜色变化差异数据模型扫描该层析图像矩阵,逐行判断该层析图像矩阵中的异常值;Step 1072: Scan the tomographic image matrix according to the edge position coordinates and the foreign body shape or color change difference data model, and judge the abnormal value in the tomographic image matrix row by row;
步骤1073:对该异常值进行标记或者还原。Step 1073: Mark or restore the abnormal value.
本实施例的免疫层析检测的图像分析方法以及系统,通过预先建立与测试项目对应的标准浓度,基于化验样本在试剂卡的层析图像,通过计算层析图像质控条带与检测条带的灰度比值,确定化验样本的测试浓度,再通过比对该测试浓度与该标准浓度,确定化验样本的化验结果,检测时只需获取该层析图像即可快速准确分析出花样样本的检测结果,提高检测效率以及检测准确度。The image analysis method and system for immunochromatographic detection of this embodiment establishes the standard concentration corresponding to the test item in advance, and calculates the tomographic image quality control band and the detection band based on the tomographic image of the test sample on the reagent card. Determine the test concentration of the test sample, and then compare the test concentration with the standard concentration to determine the test result of the test sample. Only the tomographic image needs to be obtained during the test to quickly and accurately analyze the detection of the pattern sample As a result, the detection efficiency and detection accuracy are improved.
实施例3Example 3
图10是本申请实施例提供的免疫层析检测的图像分析系统的设备,比如设备600的硬件结构示意图,如图10所示,该系统的设备600包括:FIG. 10 is a device of an image analysis system for immunochromatographic detection provided by an embodiment of the present application, such as a schematic diagram of the hardware structure of a device 600. As shown in FIG. 10, the device 600 of the system includes:
一个或多个处理器610、存储器620以及图像获取组件650,图10中以一个处理器610为例。该存储器620存储有可被该至少一个处理器610执行的指令,亦即计算机程序640,该指令被该至少一个处理器执行时,通过图像获取组件650获取检测窗口13的图像数据,以使该至少一个处理器能够执行该免疫层析检测的图像分析方法。One or more processors 610, a memory 620, and an image acquisition component 650. One processor 610 is taken as an example in FIG. 10. The memory 620 stores instructions that can be executed by the at least one processor 610, that is, the computer program 640. When the instructions are executed by the at least one processor, the image data of the detection window 13 is acquired through the image acquisition component 650, so that the At least one processor can execute the image analysis method of immunochromatographic detection.
处理器610、存储器620以及通信组件650可以通过总线或者其他方式连接,图10中以通过总线连接为例。The processor 610, the memory 620, and the communication component 650 may be connected through a bus or in other ways. In FIG. 10, the connection through a bus is taken as an example.
存储器620作为一种非易失性计算机可读存储介质,可用于存储非易失性软件程序、非易失性计算机可执行程序以及模块,如本申请实施例中的免疫层析检测的图像分析方法对应的程序指令/模块。处理器610通过运行存储在存储器620中的非易失性软件程序、指令以及模块,从而执行服务器的各种功能应用以及数据处理,即实现上述方法实施例中的免疫层析检测的图像分析方法。As a non-volatile computer-readable storage medium, the memory 620 can be used to store non-volatile software programs, non-volatile computer-executable programs and modules, such as the image analysis of immunochromatographic detection in the embodiment of the present application. The program instruction/module corresponding to the method. The processor 610 executes various functional applications and data processing of the server by running the non-volatile software programs, instructions, and modules stored in the memory 620, that is, realizing the image analysis method of immunochromatographic detection in the above method embodiment .
存储器620可以包括存储程序区和存储数据区,其中,存储程序区可存储操作系统、至少一个功能所需要的应用程序;存储数据区可存储根据免疫层析检测的图像分析系统的使用所创建的数据等。此外,存储器620可以包括高速随机存取存储器,还可以包括非易失性存储器,例如至少一个磁盘存储器件、闪存器件、或其他非易失性固态存储器件。在一些实施例中,存储器620可选包括相对于处理器610远程设置的存储器,这些远程存储器可以通过网络连接至机器人。上述网络的实例包括但不限于互联网、企业内部网、局域网、移动通信网及其组合。The memory 620 may include a storage program area and a storage data area. The storage program area can store an operating system and at least one application program required by a function; the storage data area can store data created based on the use of an image analysis system for immunochromatographic detection. Data etc. In addition, the memory 620 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other non-volatile solid-state storage devices. In some embodiments, the memory 620 may optionally include memories remotely provided with respect to the processor 610, and these remote memories may be connected to the robot through a network. Examples of the aforementioned networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.
所述一个或者多个模块存储在所述存储器620中,当被所述一个或者多个处理器610执行时,执行上述免疫层析检测的图像分析方法,例如,执行以上描述的图4中的方法步骤101至步骤109,或者执行图7中的方法步骤1021至步骤1024,或者执行图8中的方法步骤1071至步骤1073;实现附图6边缘定位模块9、图像预处理模块4、分析模块5、判断模块6、异常处理模块7以及试剂卡图像检测模块8的功能。The one or more modules are stored in the memory 620, and when executed by the one or more processors 610, the above-mentioned immunochromatographic detection image analysis method is executed, for example, the above-described image analysis method in FIG. 4 is executed. Method step 101 to step 109, or execute method step 1021 to step 1024 in FIG. 7, or execute method step 1071 to step 1073 in FIG. 8; implement Figure 6 edge positioning module 9, image preprocessing module 4, analysis module 5. The functions of the judgment module 6, the abnormality processing module 7, and the reagent card image detection module 8.
上述产品可执行本申请实施例所提供的方法,具备执行方法相应的功能模块和有益效果。未在本实施例中详尽描述的技术细节,可参见本申请实施例所提供的方法。The above-mentioned products can execute the methods provided in the embodiments of the present application, and have functional modules and beneficial effects corresponding to the execution methods. For technical details not described in detail in this embodiment, please refer to the method provided in the embodiment of this application.
本申请实施例提供了一种非易失性计算机可读存储介质,所述计算机可读存储介质存储有计算机可执行指令,该计算机可执行指令被一个或多个处理器执行,例如,执行以上描述的图4中的方法步骤101至步骤109,或者执行图7中的方法步骤1021至步骤1024,或者执行图8中的方法步骤1071至步骤1073;实现附图6边缘定位模块9、图像预处理模块4、分析模块5、判断模块6、异常处理模块7以及试剂卡图像检测模块8的功能。The embodiment of the present application provides a non-volatile computer-readable storage medium, the computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are executed by one or more processors, for example, execute the above Steps 101 to 109 of the method in Fig. 4 described, or steps 1021 to 1024 of the method in Fig. 7, or steps 1071 to 1073 of the method in Fig. 8 are performed; the edge positioning module 9 and image preview in Fig. 6 are implemented The functions of the processing module 4, the analysis module 5, the judgment module 6, the abnormality processing module 7 and the reagent card image detection module 8.
最后应说明的是:以上实施例仅用以说明本申请的技术方案,而非对其限制;在本申请的思路下,以上实施例或者不同实施例中的技术特征之间也可以进行组合,步骤可以以任意顺序实现,并存在如上所述的本申请的不同方面的许多其它变化,为了简明,它们没有在细节中提供;尽管参照前述实施例对本申请进行了详细的说明,本领域的普通技术人员应当理解:其依然可以对前述各实施例所记载的技术方案进行修改,或者对其中部分技术特征进行等同替换;而这些修改或者替换,并不使相应技术方案的本质脱离本申请各实施例技术方案的范围。Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, not to limit them; under the idea of this application, the above embodiments or the technical features in different embodiments can also be combined. The steps can be implemented in any order, and there are many other variations in different aspects of the application as described above. For the sake of brevity, they are not provided in the details; although the application has been described in detail with reference to the foregoing embodiments, the ordinary in the field The technical personnel should understand that: they can still modify the technical solutions described in the foregoing embodiments, or equivalently replace some of the technical features; and these modifications or substitutions do not make the essence of the corresponding technical solutions deviate from the implementations of this application. Examples of the scope of technical solutions.
Claims (17)
- 一种免疫层析检测的图像分析方法,其特征在于,包括以下步骤:An image analysis method for immunochromatographic detection is characterized in that it comprises the following steps:建立与测试项目对应的标准浓度;Establish the standard concentration corresponding to the test item;获取化验样本在试剂卡的层析图像,所述层析图像包括质控条带以及至少一测试条带;Acquiring a tomographic image of the test sample on the reagent card, the tomographic image including a quality control strip and at least one test strip;对所述层析图像进行图像预处理,得到预处理层析图像;Performing image preprocessing on the tomographic image to obtain a preprocessed tomographic image;放大所述预处理层析图像得到层析图像矩阵,所述层析图像矩阵包括质控条带矩阵以及测试条带矩阵;Enlarging the preprocessed tomographic image to obtain a tomographic image matrix, the tomographic image matrix including a quality control strip matrix and a test strip matrix;根据所述测试条带矩阵与所述质控条带矩阵的灰度值比值,确定化验样本的测试浓度;Determine the test concentration of the test sample according to the ratio of the gray value of the test strip matrix to the quality control strip matrix;比对所述测试浓度与所述标准浓度,确定化验样本的化验结果。The test concentration is compared with the standard concentration to determine the test result of the test sample.
- 根据权利要求1所述的免疫层析检测的图像分析方法,其特征在于,在建立与测试项目对应的标准浓度的步骤进行同时,还包括边缘定位步骤,所述边缘定位步骤包括:The image analysis method for immunochromatographic detection according to claim 1, wherein while the step of establishing the standard concentration corresponding to the test item is carried out, it further comprises an edge positioning step, and the edge positioning step comprises:建立边缘矩阵数据模型;Establish an edge matrix data model;使用所述边缘矩阵数据模型扫描所述层析图像矩阵,得到所述层析图像矩阵中所述试剂卡的检测窗口图像的边缘位置坐标。The edge matrix data model is used to scan the tomographic image matrix to obtain the edge position coordinates of the detection window image of the reagent card in the tomographic image matrix.
- 根据权利要求2所述的免疫层析检测的图像分析方法,其特征在于,所述对所述层析图像进行图像预处理的步骤包括:The image analysis method for immunochromatographic detection according to claim 2, wherein the step of performing image preprocessing on the tomographic image comprises:对所述层析图像进行裁剪处理,得到包含所述试剂卡的检测窗口的裁剪层析图像;Performing cropping processing on the tomographic image to obtain a cropped tomographic image containing the detection window of the reagent card;对所述裁剪层析图像基于索贝尔算子进行图像边缘检测以及图像边缘增强处理。Image edge detection and image edge enhancement processing are performed on the cropped tomographic image based on the Sobel operator.
- 根据权利要求3所述的免疫层析检测的图像分析方法,其特征在于,还包括对所述裁剪层析图像进行背景异常处理步骤,所述背景异常处理步骤包括:The image analysis method for immunochromatographic detection according to claim 3, further comprising a background abnormality processing step on the cropped tomographic image, and the background abnormality processing step comprises:建立异物形状或颜色变化差异数据模型;Establish a data model for the difference of foreign body shape or color change;根据所述边缘位置坐标以及所述异物形状或颜色变化差异数据模型扫描所述层析图像矩阵,逐行判断所述层析图像矩阵中的异常值;Scanning the tomographic image matrix according to the edge position coordinates and the foreign body shape or color change difference data model, and judging the abnormal values in the tomographic image matrix row by row;对所述异常值进行标记或者还原。Mark or restore the abnormal value.
- 根据权利要求4所述的免疫层析检测的图像分析方法,其特征在于,所述异物形状或颜色变化差异数据模型包括平均值阈值、最大阈值、最小阈值以及标准平方差阈值。The image analysis method for immunochromatographic detection according to claim 4, wherein the foreign body shape or color change difference data model includes an average threshold, a maximum threshold, a minimum threshold, and a standard square deviation threshold.
- 根据权利要求1所述的免疫层析检测的图像分析方法,其特征在于,所述试剂卡包括本体以及设置在所述本体上的手持部、加样窗口、检测窗口以及贴标部,所述贴标部上贴有识别码。The image analysis method for immunochromatographic detection according to claim 1, wherein the reagent card comprises a main body, a hand-held part, a sample application window, a detection window, and a labeling part provided on the main body, and The identification code is affixed on the labeling section.
- 根据权利要求6所述的免疫层析检测的图像分析方法,其特征在于,在所述获取化验样本在试剂卡的层析图像的步骤之前,还包括试剂卡图像检测步骤,所述试剂卡图像检测步骤包括:The image analysis method for immunochromatographic detection according to claim 6, characterized in that, before the step of obtaining the tomographic image of the test sample on the reagent card, it further comprises a reagent card image detection step, and the reagent card image The detection steps include:建立试剂卡图像特征数据库,包括手持部图像数据库以及贴标部数据库;Establish a database of image characteristics of reagent cards, including handheld image database and labeling department database;获取当前试剂卡图像,所述当前试剂卡图像包括手持部图像以及贴标部图像;Acquiring a current reagent card image, the current reagent card image including an image of the hand-held part and an image of the labeling part;基于所述贴标部图像识别出所述试剂卡信息以及项目检测信息;Recognizing the reagent card information and item detection information based on the image of the labeling part;基于所述手持部图像,采用KNN最邻近分类算法对比所述手持部图像以及手持部图像数据库,以确定所述试剂卡插入方向和朝向的正确性。Based on the handheld image, the KNN nearest neighbor classification algorithm is used to compare the handheld image and the handheld image database to determine the correctness of the insertion direction and orientation of the reagent card.
- 根据权利要求1所述的免疫层析检测的图像分析方法,其特征在于,所述根据所述测试条带矩阵与所述质控条带矩阵的灰度值比值,确定化验样本的测试浓度的步骤包括:The image analysis method for immunochromatographic detection according to claim 1, wherein the ratio of the gray value of the test strip matrix and the quality control strip matrix is used to determine the test concentration of the test sample The steps include:设定第一标准公式与第二标准公式;Set the first standard formula and the second standard formula;设定波动阈值,通过所述波动阈值选择所述第一标准公式或者第二标准公式计算所述测试浓度。A fluctuation threshold is set, and the first standard formula or the second standard formula is selected through the fluctuation threshold to calculate the test concentration.
- 一种免疫层析检测的图像分析系统,其特征在于,包括标准存储模块、图像获取模块、图像预处理模块、分析模块以及判断模块,An image analysis system for immunochromatographic detection, which is characterized by comprising a standard storage module, an image acquisition module, an image preprocessing module, an analysis module, and a judgment module,所述标准存储模块用于存储与测试项目对应的标准浓度;The standard storage module is used to store the standard concentration corresponding to the test item;所述图像获取模块用于获取化验样本在试剂卡的层析图像,所述层析图像包括质控条带以及至少一测试条带;The image acquisition module is used to acquire a tomographic image of the test sample on the reagent card, and the tomographic image includes a quality control strip and at least one test strip;所述图像预处理模块用于对所述层析图像进行图像预处理,得到预处理层析图像;所述图像预处理模块还用于放大所述预处理层析图像得到层析图像矩阵,所述层析图像矩阵包括质控条带矩阵以及测试条带矩阵;The image preprocessing module is used to perform image preprocessing on the tomographic image to obtain a preprocessed tomographic image; the image preprocessing module is also used to amplify the preprocessed tomographic image to obtain a tomographic image matrix, so The tomographic image matrix includes a quality control strip matrix and a test strip matrix;所述分析模块用于根据所述测试条带矩阵与所述质控条带矩阵的灰度值比值,确定化验样本的测试浓度;以及The analysis module is used to determine the test concentration of the test sample according to the gray value ratio of the test strip matrix and the quality control strip matrix; and所述判断模块用于比对所述测试浓度与所述标准浓度,确定化验样本的化验结果。The judgment module is used to compare the test concentration with the standard concentration to determine the test result of the test sample.
- 根据权利要求9所述的免疫层析检测的图像分析系统,其特征在于,还包括边缘定位模块,所述边缘定位模块包括边缘数据存储模块以及位置确定模块:The image analysis system for immunochromatographic detection according to claim 9, further comprising an edge positioning module, the edge positioning module comprising an edge data storage module and a position determination module:所述边缘数据存储模块用于存储边缘矩阵数据模型;The edge data storage module is used to store an edge matrix data model;所述边缘位置确定模块用于使用所述边缘矩阵数据模型扫描所述层析图像矩阵,得到所述层析图像矩阵中所述试剂卡的检测窗口图像的边缘位置坐标。The edge position determination module is used to scan the tomographic image matrix using the edge matrix data model to obtain the edge position coordinates of the detection window image of the reagent card in the tomographic image matrix.
- 根据权利要求9所述的免疫层析检测的图像分析系统,其特征在于,所述预处理模块包括图像裁剪模块以及边缘处理模块,The image analysis system for immunochromatographic detection according to claim 9, wherein the preprocessing module includes an image cropping module and an edge processing module,所述图像裁剪模块用于对所述层析图像进行裁剪处理,得到包含所述试剂卡的检测窗口的裁剪层析图像;The image cropping module is configured to perform cropping processing on the tomographic image to obtain a cropped tomographic image containing the detection window of the reagent card;所述边缘处理模块用于对所述裁剪层析图像基于索贝尔算子进行图像边缘检测以及图像边缘增强处理。The edge processing module is used to perform image edge detection and image edge enhancement processing on the cropped tomographic image based on the Sobel operator.
- 根据权利要求11所述的免疫层析检测的图像分析系统,其特征在于,还包括异常处理模块用于对所述裁剪层析图像进行背景异常处理;所述异常处理模块包括异常模型存储模块、扫描判断模块以及异常处理模块:The image analysis system for immunochromatographic detection according to claim 11, further comprising an abnormality processing module for performing background abnormality processing on the cropped tomographic image; the abnormality processing module includes an abnormal model storage module, Scanning judgment module and exception handling module:所述异常模型存储模块用于存储异物形状或颜色变化差异数据模型;The abnormal model storage module is used to store the foreign body shape or color change difference data model;所述扫描判断模块用于根据所述边缘位置坐标以及所述异物形状或颜色变化差异数据模型扫描所述层析图像矩阵,逐行判断所述层析图像矩阵中的异常值;The scanning judgment module is configured to scan the tomographic image matrix according to the edge position coordinates and the foreign body shape or color change difference data model, and judge the abnormal value in the tomographic image matrix row by row;所述异常处理模块用于对所述异常值进行标记或者还原。The abnormality processing module is used to mark or restore the abnormal value.
- 根据权利要求12所述的免疫层析检测的图像分析系统,其特征在于,所述异物形状或颜色变化差异数据模型包括平均值阈值、最大阈值、最小阈值以及标准平方差阈值。The image analysis system for immunochromatographic detection according to claim 12, wherein the foreign body shape or color change difference data model includes an average threshold, a maximum threshold, a minimum threshold, and a standard square deviation threshold.
- 根据权利要求9所述的免疫层析检测的图像分析系统,其特征在于,所述试剂卡包括本体以及设置在所述本体上的手持部、加样窗口、检测窗口以及贴标部,所述贴标部上贴有识别码。The image analysis system for immunochromatographic detection according to claim 9, wherein the reagent card comprises a main body, a hand-held part, a sample addition window, a detection window, and a labeling part provided on the main body, and The identification code is affixed on the labeling section.
- 根据权利要求14所述的免疫层析检测的图像分析系统,其特征在于,还包括试剂卡图像检测模块,所述试剂卡图像检测模块包括试剂卡图像特征数据库、试剂卡图像获取模块、二维码识别模块以及方位判断模块,其中,所述试剂卡图像特征数据库包括手持部图像数据库以及贴标部数据库;The image analysis system for immunochromatographic detection according to claim 14, further comprising a reagent card image detection module, and the reagent card image detection module includes a reagent card image feature database, a reagent card image acquisition module, and a two-dimensional A code recognition module and an orientation judgment module, wherein the reagent card image feature database includes a handheld image database and a labeling portion database;所述试剂卡图像获取模块用于在试剂卡插入时获取当前试剂卡图像,所述当前试剂卡图像包括手持部图像以及贴标部图像;The reagent card image acquisition module is used to acquire the current reagent card image when the reagent card is inserted, and the current reagent card image includes the image of the handheld part and the image of the labeling part;所述二维码识别模块用于基于所述贴标部图像识别出所述试剂卡信息以及项目检测信息;The two-dimensional code recognition module is used for recognizing the reagent card information and item detection information based on the image of the labeling part;所述方位判断模块用于基于所述手持部图像,采用KNN最邻近分类算法对比所述手持部图像以及手持部图像数据库,以确定所述试剂卡插入方向和朝向的正确性。The orientation determination module is used for comparing the handheld image with the handheld image database based on the handheld image and using the KNN nearest neighbor classification algorithm to determine the correctness of the inserting direction and orientation of the reagent card.
- 一种免疫层析检测的图像分析系统,其特征在于,包括至少一个处理器、与所述至少一个处理器通信连接的存储器以及图像获取装置;An image analysis system for immunochromatographic detection, characterized in that it comprises at least one processor, a memory communicatively connected with the at least one processor, and an image acquisition device;所述存储器存储有可被所述至少一个处理器执行的指令,所述指令被所述至少一个处理器执行时,使所述图像获取装置获取图像数据以及使所述至少一个处理器执行权利要求1-8任一项所述的方法。The memory stores instructions that can be executed by the at least one processor, and when the instructions are executed by the at least one processor, the image acquisition device acquires image data and the at least one processor executes the claims The method described in any one of 1-8.
- 一种计算机程序产品,其特征在于,所述计算机程序产品包括存储在非易失性计算机可读存储介质上的计算机程序,所述计算机程序包括程序指令,当所述程序指令被计算机执行时,使所述计算机执行权利要求1-8任一项所述的方法。A computer program product, wherein the computer program product includes a computer program stored on a non-volatile computer-readable storage medium, the computer program includes program instructions, and when the program instructions are executed by a computer, The computer is caused to execute the method according to any one of claims 1-8.
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