CN106127738B - agglutination test interpretation method - Google Patents
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- CN106127738B CN106127738B CN201610427645.2A CN201610427645A CN106127738B CN 106127738 B CN106127738 B CN 106127738B CN 201610427645 A CN201610427645 A CN 201610427645A CN 106127738 B CN106127738 B CN 106127738B
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- 238000012360 testing method Methods 0.000 title claims abstract description 80
- 230000004520 agglutination Effects 0.000 title claims abstract description 63
- 238000000034 method Methods 0.000 title claims abstract description 33
- 238000006243 chemical reaction Methods 0.000 claims abstract description 27
- 230000004523 agglutinating effect Effects 0.000 claims abstract description 23
- 238000000605 extraction Methods 0.000 claims abstract description 14
- 230000009466 transformation Effects 0.000 claims abstract description 10
- 239000000284 extract Substances 0.000 claims abstract description 7
- 238000007781 pre-processing Methods 0.000 claims abstract description 7
- 239000000203 mixture Substances 0.000 claims abstract description 4
- 238000001914 filtration Methods 0.000 claims description 14
- 238000013519 translation Methods 0.000 claims description 4
- 230000003628 erosive effect Effects 0.000 claims description 3
- 230000000877 morphologic effect Effects 0.000 claims description 2
- 238000012545 processing Methods 0.000 abstract description 3
- 238000001514 detection method Methods 0.000 description 13
- 238000003556 assay Methods 0.000 description 5
- 230000003044 adaptive effect Effects 0.000 description 4
- 230000011218 segmentation Effects 0.000 description 4
- 239000000427 antigen Substances 0.000 description 3
- 102000036639 antigens Human genes 0.000 description 3
- 108091007433 antigens Proteins 0.000 description 3
- ZLFVRXUOSPRRKQ-UHFFFAOYSA-N chembl2138372 Chemical compound [O-][N+](=O)C1=CC(C)=CC=C1N=NC1=C(O)C=CC2=CC=CC=C12 ZLFVRXUOSPRRKQ-UHFFFAOYSA-N 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 238000012549 training Methods 0.000 description 3
- 241000589884 Treponema pallidum Species 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 239000011521 glass Substances 0.000 description 2
- 238000009589 serological test Methods 0.000 description 2
- 208000006379 syphilis Diseases 0.000 description 2
- 241000894006 Bacteria Species 0.000 description 1
- RZZPDXZPRHQOCG-OJAKKHQRSA-O CDP-choline(1+) Chemical compound O[C@@H]1[C@H](O)[C@@H](COP(O)(=O)OP(O)(=O)OCC[N+](C)(C)C)O[C@H]1N1C(=O)N=C(N)C=C1 RZZPDXZPRHQOCG-OJAKKHQRSA-O 0.000 description 1
- 238000002965 ELISA Methods 0.000 description 1
- 238000010521 absorption reaction Methods 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000023555 blood coagulation Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 239000003153 chemical reaction reagent Substances 0.000 description 1
- 150000001875 compounds Chemical class 0.000 description 1
- 239000006185 dispersion Substances 0.000 description 1
- 239000003792 electrolyte Substances 0.000 description 1
- 230000008030 elimination Effects 0.000 description 1
- 238000003379 elimination reaction Methods 0.000 description 1
- 238000009499 grossing Methods 0.000 description 1
- 230000035931 haemagglutination Effects 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 238000012128 rapid plasma reagin Methods 0.000 description 1
- 230000035484 reaction time Effects 0.000 description 1
- 230000003014 reinforcing effect Effects 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 210000002966 serum Anatomy 0.000 description 1
- 150000004992 toluidines Chemical class 0.000 description 1
Classifications
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/70—Denoising; Smoothing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10024—Color image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
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- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Investigating Or Analysing Materials By Optical Means (AREA)
Abstract
A kind of agglutination test interpretation method first carries out image preprocessing, obtains the digitized image of agglutination test result, while handling the brightness and contrast level parameter of picture；Then " Hough transformation " extraction algorithm is used, the boundary in the agglutination test region of picture is detected on Computer Vision Platform, after extracting boundary, the extraction on boundary is further carried out according to COLOR COMPOSITION THROUGH DISTRIBUTION, obtains the histogram for extracting picture；Extract the sample in the boundary in agglutination test region；Sample picture is extracted according to gained threshold value；The picture noise of filtered sample picture obtains Sample Image；Agglutinating reaction spot in Sample Image is extracted, agglutinating reaction spot number and area are calculated；Finally, the agglutinating reaction spot number and area according to acquisition provide rank belonging to sample.Agglutination test interpretation method provided by the invention, uses iconography and Computer Image Processing for means, substantially increases the reliability of agglutination test interpretation, reduces the randomness of eye-observation.
Description
Technical field
The present invention relates to a kind of video data processing method more particularly to the digitized images of a kind of pair of agglutination test result
It is handled and is distinguished, and the method that interpretation provides conclusion (of pressure testing) accordingly, to realize the automation of agglutination test detection.
Background technique
The serological test that agglutination is aggregated after testing particulate antigen in conjunction with corresponding antibodies.Antigen is compound with antibody
Object, by certain time, forms macroscopic agglutination agglomerate under electrolyte effect.Test can carry out in glass plate, referred to as
Glass plate agglutination test, test can carry out on card, referred to as card agglutination test, can be used for bacterium identification and antibody it is qualitative
Detection；Also it can carry out in test tube, claim tube agglutination test, be mainly used for antiserum titre measurement.
Serological test is the most widely used technological means of current Lues Assay, is divided into rapid plasma reagin examination again
Testing (RPR), toluidine red, heat run (TRUST), treponema pallidum hemagglutination test (TPHA), microspironema pallidum ELISA be not real
Test with fluorescent treponemal antibody-absorption test (FTA-ABS) etc..Wherein, TRUST test is using VDRL antigen in toluidines
Reagin present in syphilitic's serum is detected in red solution, macroscopic pink agglutination block is occurred, can be sentenced
It is fixed, in the extension rate for combining sample, it can achieve the purpose of sxemiquantitative.This method is due to spy quick, intuitive, easy to operate
Property is applied in most of hospitals.
The automatization level of agglutination test is lower, reaction time of blood coagulation is being carried out, usually in an enterprising pedestrian's work of detection paper
Operation and interpretation, thereby increase labour and the time of medical worker.
Summary of the invention
It is an object of the present invention to provide a kind of agglutination test interpretation methods, to the resulting number of agglutination test result
Change image to be handled, and carry out computer interpretation accordingly, to realize the automation of agglutination test detection.
It is another object of the present invention to provide a kind of agglutination test interpretation methods, to the resulting number of agglutination test result
Word image is handled, and the method for carrying out computer interpretation accordingly and providing conclusion (of pressure testing), realizes agglutination test detection
Automation.
A kind of agglutination test interpretation method provided by the invention, step include:
Image preprocessing is first carried out, obtains the digitized image of agglutination test result (such as: trying using digital camera agglutination
Plate is tested to be shot), while the brightness of picture (Brightness) and contrast (Contrast) parameter are handled, with
Strengthen picture effective information；
Then, the boundary (such as: black circle) for extracting agglutination test region, using " Hough transformation " extraction algorithm, in computer
The boundary in the agglutination test region of picture is detected on vision platform (such as: OpenCV platform), after extracting boundary, further
The extraction on boundary is carried out according to COLOR COMPOSITION THROUGH DISTRIBUTION, obtains the histogram for extracting picture.
Then, the sample in the boundary in agglutination test region is extracted；
Later, sample picture is extracted according to gained threshold value；
Then, the picture noise of filtered sample picture obtains Sample Image；
Later, agglutinating reaction spot in Sample Image is extracted, agglutinating reaction spot number and area are calculated；
Finally, the agglutinating reaction spot number and area according to acquisition provide rank belonging to sample, such as: 2+、3+With 4+
Deng.
Technical solution of the present invention realize the utility model has the advantages that
Agglutination test interpretation method provided by the invention, be suitable for agglutination test detector, and by sample sampling, detection,
Image capturing and result judgement etc. are integrated in one, so that the acquisition of sample, the addition of reagent, agglutinating reaction and result judgement are automatic
It completes.
Agglutination test interpretation method provided by the invention, uses iconography and Computer Image Processing for means, mentions significantly
The high reliability of agglutination test interpretation, reduces the randomness of eye-observation.
Agglutination test interpretation method provided by the invention, further improves the automatization level of agglutination test, makes total
According to the whole processes of the links such as acquisition, storage, reading and transmission realize automation.
Agglutination test interpretation method provided by the invention can further realize agglutination test using digitlization means
The short range of quantitative detection and test data and long-range transmitting, reduce artificial participation, reduce labor intensity.
Detailed description of the invention
Fig. 1 is the structural schematic diagram of one embodiment of agglutination test analyzer of the present invention；
Fig. 2 is the structural schematic diagram of one embodiment of agglutination test analyzer housing of the present invention；
Fig. 3 is the green channel grey level histogram obtained on Computer Vision Platform；
Fig. 4 is black circle Detection and Extraction effect picture；
Fig. 5 is assay maps of the sample 1 after black circle is extracted；
The Otsu algorithm adaptive threshold and the picture after segmentation that Fig. 6 is sample 1；
Fig. 7 is sample 1 according to Otsu threshold value extraction image information；
Fig. 8 is that sample 1 filters image section noise；
Fig. 9 is assay maps of the sample 2 after black circle is extracted；
The Otsu algorithm adaptive threshold and the picture after segmentation that Figure 10 is sample 2；
Figure 11 is sample 2 according to Otsu threshold value extraction image information；
Figure 12 is that sample 2 filters image section noise；
Figure 13 is assay maps of the sample 3 after black circle is extracted；
The Otsu algorithm adaptive threshold and the picture after segmentation that Figure 14 is sample 3；
Figure 15 is sample 3 according to Otsu threshold value extraction image information；
Figure 16 is that sample 3 filters image section noise；
Figure 17 is assay maps of the sample 4 after black circle is extracted；
The Otsu algorithm adaptive threshold and the picture after segmentation that Figure 18 is sample 4；
Figure 19 is sample 4 according to Otsu threshold value extraction image information；
Figure 20 is that sample 4 filters image section noise.
Specific embodiment
Below in conjunction with attached drawing, the technical schemes of the invention are described in detail.The embodiment of the present invention is only to illustrate skill of the invention
Art scheme rather than limit, although being described the invention in detail referring to preferred embodiment, those skilled in the art
It should be appreciated that can be with modification or equivalent replacement of the invented technical scheme, without departing from the essence of technical solution of the present invention
Mind and range, should all cover within the scope of the claims of the present invention.
The present invention illustrates to interpretation method and is illustrated by taking toluidine red not heat run as an example.
First by the working specification of toluidine red not heat run on testing inspection paper to various syphilis positive samples into
Row agglutinating reaction.
Resulting test result is subjected to image preprocessing, obtains the digitized image of agglutination test result (such as: using number
Code camera shoots agglutination test plate), while to the brightness of picture (Brightness) and contrast (Contrast)
Parameter is handled.
By carrying out the reinforcing that the modes such as gray scale translation and gray scale stretching realize picture effective information to image.Gray scale translation
It is that the overall gray value of image is subjected to linear translation, can be used to the brightness for adjusting image.Gray scale stretching is the gray scale to image
Histogram carries out linear or nonlinear transformation, facilitates the gray scale dynamic range for improving image, by original low contrast
Image stretch is the image of high contrast.Image difference before and after the processing is as depicted in figs. 1 and 2.
" Hough transformation " can detecte straight line and line segment, the detection of rectangle can be used the combination of straight line or line segment
It completes.For the OpenCV platform of image detection, rectangle is supported without direct function library (Hough transformation), needs to call
Hough transformation straight line or Hough transformation line segment reprogram and complete hough transform.Hough transform is the application of Hough transformation detection.
In the present embodiment, for the region in black circle as the region for completing agglutination test, black circle is agglutination test region
Boundary.Using " Hough transformation-circle " extraction algorithm, the black circle in picture is examined on OpenCV Computer Vision Platform
It surveys, after extracting circular boundary, the extraction of black circles is further carried out according to COLOR COMPOSITION THROUGH DISTRIBUTION, extracts the green channel gray scale of picture
Histogram (referring to Fig. 3).In Fig. 3, three peaks are presented in histogram, and leftmost side height is lower, and the biggish wave crest of width is black circle
Branch, and with regard to further screening black circle information in the branch.Black circle Detection and Extraction effect is as shown in figure 4, blue portion is in figure
The black circle detected, remaining RED sector are residual image part.
By taking sample 1 as an example, then, extract the sample in the boundary in agglutination test region (referring to Fig. 5).As shown in fig. 6, its
First row is respectively that " original noisy image ", " histogram " and " global threshold ", second row are respectively from left to right from left to right
" original noisy image ", " histogram " and " OTSU threshold value ", it is respectively " image after gaussian filtering " that third is arranged from left to right, " straight
Side's figure " and " OTSU threshold value ".Have these figures as it can be seen that after gaussian filtering the OTSU algorithm threshold value of image it is more excellent (eliminate it is different
Constant value interference).When not using OTSU algorithm, when picture signal edge or feature strong enough, global threshold can get reason
Think interesting image regions (ROI), but when picture signal edge or not strong enough feature, global threshold can not be managed
Think interesting image regions.And OTSU algorithm can obtain interesting image regions ideal enough in both cases.In addition,
For our practical situations, the edge of noisy image interferes excessive (the visible burr in histogram), to OTSU
Threshold calculations are not accurate enough, include excessive annular side information.Therefore, it is calculated according to Otsu algorithm (having carried out gaussian filtering)
After the grey level histogram differential threshold of sample, binary conversion treatment is carried out according to image of the gained threshold value to sample；
Then, sample picture (referring to Fig. 7) is extracted according to gained threshold value,
Later, pass through image space filtering (such as: gaussian filtering) and morphologic filtering (such as: the two-value erosion algorithm of image)
Etc. modes filtered sample picture picture noise, obtain Sample Image (referring to Fig. 8)；
Then, agglutinating reaction spot in Sample Image is extracted, agglutinating reaction spot number and area are calculated；
Finally, the agglutinating reaction spot number (numerical value 121.98) and area (numerical value 425) according to acquisition provide examination
The rank tested is 4+。
The gaussian filtering that the present embodiment uses is a kind of linear smoothing filtering, is suitable for eliminating Gaussian noise, be widely applied
In the noise abatement process of image procossing.Gaussian filtering is exactly the process being weighted and averaged to entire image, each pixel
Value obtains after being all weighted averagely by other pixel values in itself and field.It is mainly used to eliminate the additivity on image
Random noise.
The two-value erosion algorithm for the image that the present embodiment uses is a kind of elimination boundary point, the mistake for shrinking boundary internally
Journey.It can be used to eliminate small and meaningless object, be mainly used to eliminate edge interference and Discrete Stochastic grain noise etc..
The rank judgement that the present embodiment uses be according to the size of " spot " extracted on image and the number of spot how much
Carry out grade classification.For 4+, the sample of 3+, 2+, it is believed that positive stronger sample, spot is bigger and number is relatively fewer.
But in the sample of 2+ or more, the main number according to spot distribution determines rank, because still having on strong positive sample image tiny
Red dot, consistent regularity no for the calculating of spot size, spatial distribution number and its dispersion degree confidence level are higher.For
Negative sample and positive sample are divided according to the number size of spot first.The numberical range of rank judgement is from calculation
Method training set as a result, being directly compared with the result of the algorithm training set when rank judges.With detection sample size
It improves, the result of algorithm training set then more levels off to true testing result, and the result of rank judgement is also more accurate.
By above-mentioned identical method, interpretation is carried out to sample 2.By resulting test result progress image preprocessing
Afterwards, the sample (referring to Fig. 9) in the boundary in agglutination test region is extracted, the grey level histogram for calculating sample according to Otsu algorithm is poor
After dividing threshold value, binary conversion treatment (referring to Figure 10) is carried out according to image of the gained threshold value to sample.It is extracted and is tried according to gained threshold value
Master drawing piece (referring to Figure 11), the picture noise of filtered sample picture obtain Sample Image (referring to Figure 12) and extract in Sample Image
Agglutinating reaction spot calculates agglutinating reaction spot number and area.Finally, the agglutinating reaction spot number (numerical value according to acquisition
It is 3 64.95) to provide the rank of test with area (numerical value 558)+。
By above-mentioned identical method, interpretation is carried out to sample 3.By resulting test result progress image preprocessing
Afterwards, the sample (referring to Figure 13) in the boundary in agglutination test region is extracted, the grey level histogram of sample is calculated according to Otsu algorithm
After differential threshold, binary conversion treatment (referring to Figure 14) is carried out according to image of the gained threshold value to sample.It is extracted according to gained threshold value
Sample picture (referring to Figure 15), the picture noise of filtered sample picture obtain Sample Image (referring to Figure 16) and extract Sample Image
Middle agglutinating reaction spot calculates agglutinating reaction spot number and area.Finally, the agglutinating reaction spot number (number according to acquisition
Value is 2 87.087) to provide the rank of test with area (numerical value 583)+。
By above-mentioned identical method, interpretation is carried out to sample 4.By resulting test result progress image preprocessing
Afterwards, the sample (referring to Figure 17) in the boundary in agglutination test region is extracted, the grey level histogram of sample is calculated according to Otsu algorithm
After differential threshold, binary conversion treatment (referring to Figure 18) is carried out according to image of the gained threshold value to sample.It is extracted according to gained threshold value
Sample picture (referring to Figure 19), the picture noise of filtered sample picture obtain Sample Image (referring to fig. 2 0) and extract Sample Image
Middle agglutinating reaction spot calculates agglutinating reaction spot number and area.Finally, the agglutinating reaction spot number (number according to acquisition
Value is 1 6.926) to provide the rank of test with area (numerical value 982)+。
Claims (10)
1. a kind of agglutination test interpretation method, characterized by comprising:
Image preprocessing is first carried out, the digitized image of agglutination test result, while brightness and contrast to picture are obtained
Parameter is handled, to strengthen picture effective information；
Then, the boundary for extracting agglutination test region, using " Hough transformation " extraction algorithm, to figure on Computer Vision Platform
The boundary in the agglutination test region of piece is detected, and after extracting boundary, the extraction on boundary is carried out again according to COLOR COMPOSITION THROUGH DISTRIBUTION, is obtained
Extract the histogram of picture；
Then, the sample in the boundary in agglutination test region is extracted；
Later, sample picture is extracted according to threshold value；
Then, the picture noise of filtered sample picture obtains Sample Image；
Later, agglutinating reaction spot in Sample Image is extracted, agglutinating reaction spot number and area are calculated；
Finally, the agglutinating reaction spot number and area according to acquisition provide rank belonging to sample.
2. agglutination test interpretation method according to claim 1, it is characterised in that the acquisition methods of the digitized image
To be shot using digital camera to agglutination test plate.
3. agglutination test interpretation method according to claim 1, it is characterised in that show agglutination test region using black circle
Boundary.
4. agglutination test interpretation method according to claim 1, it is characterised in that " Hough transformation " is that " Hough becomes
Change-round ".
5. agglutination test interpretation method according to claim 1, it is characterised in that the Computer Vision Platform is
OpenCV platform.
6. agglutination test interpretation method according to claim 1, it is characterised in that use gray scale translation and gray scale stretching
Mode strengthens picture effective information.
7. agglutination test interpretation method according to claim 1, it is characterised in that extracted using threshold value obtained by OTSU algorithm
Gaussian filtering has been carried out to image when sample picture.
8. agglutination test interpretation method according to claim 1, it is characterised in that pass through image space filtering and morphology
The picture noise of the filtered sample picture is implemented in filtering.
9. agglutination test interpretation method according to claim 8, it is characterised in that the image space is filtered into Gauss
Filtering.
10. agglutination test interpretation method according to claim 8, it is characterised in that the morphologic filtering is image
Two-value erosion algorithm.
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| CN106980860A (en) * | 2017-04-07 | 2017-07-25 | 广州誉康医药有限公司 | A kind of result automatic interpretation method for liquid medium cross matching |
| CN107064503B (en) * | 2017-05-16 | 2020-07-31 | 上海兰卫医学检验所股份有限公司 | Method and device for judging detection result of treponema pallidum antibody |
| CN107992851B (en) * | 2017-12-20 | 2020-05-01 | 闫鸿远 | Identification method of agglutination test |
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