CN105974120A - Automatic detection device and method for C-reactive protein chromaticity - Google Patents
Automatic detection device and method for C-reactive protein chromaticity Download PDFInfo
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- 102100032752 C-reactive protein Human genes 0.000 title claims abstract description 78
- 238000001514 detection method Methods 0.000 title claims abstract description 47
- 238000000034 method Methods 0.000 title claims abstract description 32
- 108010074051 C-Reactive Protein Proteins 0.000 title claims abstract description 16
- 239000003153 chemical reaction reagent Substances 0.000 claims abstract description 62
- 238000012545 processing Methods 0.000 claims abstract description 13
- 238000012360 testing method Methods 0.000 claims description 26
- 230000008569 process Effects 0.000 claims description 17
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- 230000003595 spectral effect Effects 0.000 abstract description 3
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- 238000004816 paper chromatography Methods 0.000 description 7
- 238000005259 measurement Methods 0.000 description 4
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- 230000000875 corresponding effect Effects 0.000 description 3
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- 102000004169 proteins and genes Human genes 0.000 description 3
- 108090000623 proteins and genes Proteins 0.000 description 3
- 238000004879 turbidimetry Methods 0.000 description 3
- XAGFODPZIPBFFR-UHFFFAOYSA-N aluminium Chemical compound [Al] XAGFODPZIPBFFR-UHFFFAOYSA-N 0.000 description 2
- 229910052782 aluminium Inorganic materials 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 208000037816 tissue injury Diseases 0.000 description 2
- 101000942118 Homo sapiens C-reactive protein Proteins 0.000 description 1
- 206010061218 Inflammation Diseases 0.000 description 1
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Classifications
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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
- G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
- G01N33/6803—General methods of protein analysis not limited to specific proteins or families of proteins
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/8483—Investigating reagent band
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/8483—Investigating reagent band
- G01N2021/8494—Measuring or storing parameters of the band
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2333/00—Assays involving biological materials from specific organisms or of a specific nature
- G01N2333/435—Assays involving biological materials from specific organisms or of a specific nature from animals; from humans
- G01N2333/46—Assays involving biological materials from specific organisms or of a specific nature from animals; from humans from vertebrates
- G01N2333/47—Assays involving proteins of known structure or function as defined in the subgroups
- G01N2333/4701—Details
- G01N2333/4737—C-reactive protein
Abstract
The invention relates to a detection device and a detection method for automatically identifying chromaticity and color difference of a reagent strip, in particular to a C-reactive protein chromaticity automatic detection device and a method. The CRP concentration detection method uses an industrial camera which has a wider spectral range and is suitable for performing a high-quality image processing algorithm, and by a machine vision detection method, the key characteristics of the CRP reagent strip are identified and detected in real time, quickly and automatically, and color change data obtained by image processing are compared with standard sample values in a database, so that a CRP concentration detection result is obtained.
Description
Technical field
The invention belongs to Machine Vision Detection field, relate to a kind of detection device that reagent strip colourity aberration is identified automatically
With method, particularly relate to a kind of c reactive protein colourity automatic detection device and method.
Background technology
C reactive protein (C-reaction protein, CRP) is that a kind of body is infected or blood plasma during tissue injury
In the Acute reaction protein that steeply rises.CRP except as tissue injury, inflammatory reaction coherent detection index in addition to, real
Test room and CRP is inferred have as the written instructions index of system inflammatory disorders, diagnosis, discriminating and prognosis simultaneously for some diseases
Great meaning.
Existing CRP determination techniques is mainly turbidimetry and paper chromatography. and turbidimetry is also known as nephelometry, by measuring
Light intensity through particle in suspension medium determines suspended sediment concentration, is a kind of light scattering measurement technology.Paper chromatography, also known as
Paper chromatography or paper chromatography, use immunochromatography principle, sample and the mouse-anti Human C-reactiveprotein monoclonal antibody containing label
Reaction, is analyzed the change of reagent strip color after instilling CRP reagent strip, determines the concentration of CRP.
In routine use, the single sample required time of turbidimetry protein analyzer detection is longer, and required antiserum amount is relatively big,
Its equipment production cost and maintenance cost are higher.And traditional paper chromatography detection mode due to rely on artificial vision, need with
The comparison analysis one by one of sample colourity, has the biggest defect in speed and stability, and relatively costly. and this type of aberration recognition methods is led to
Often based on the Color Appearance System representation that HSI color space (Meng Saier colour system) is representative, relatively big by subjective psychological impact, difficult
With accurate expression aberration, precision is relatively low.
Summary of the invention
The technical problem to be solved is for the above-mentioned technical deficiency of CRP detection method, it is provided that a kind of C is anti-
Answering albumen colourity automatic detection device and method. the present invention uses that spectral region is wider, be appropriate to high-quality image procossing
The industrial camera of algorithm, by machine vision detection method, identifies in real time, fast and automatically and detects the crucial special of CRP reagent strip
Levy, obtain color delta data by image procossing and compare with the standard sample performance number in data base, and then draw CRP concentration
Testing result.
For solving above-mentioned technical problem, the present invention realizes as follows: a kind of c reactive protein color of the present invention
Degree automatic detection device includes colored industrial camera, light source, special light source controller, fine setting displacement platform, L-shaped locating piece, work
Control all-in-one and platform to be measured；Described colored industrial camera is connected on position adjustments track by fine setting displacement platform, colored
The camera lens of industrial camera is vertical with the platform to be measured placing CRP reagent strip；The length of described CRP reagent strip is not less than colored work
The 4/5 of the width of industry camera acquisition picture；Described light source is fixed on the camera lens of colored industrial camera, and light source selects white
Diffuse-reflectance annular light source, and it is provided with special light source controller；Described CRP reagent strip is fixed on to be measured flat by L-shaped locating piece
On platform.
Described colored industrial camera uses 5,000,000 pixels, the colored work of exportable 2592x1944 resolution digital image
Industry video camera；Before the camera lens of described colored industrial camera, the ring contraction visual field of increment can be installed.
A kind of method that c reactive protein colourity detects automatically mainly comprises the steps:
Step 1:CRP reagent strip identifies and image procossing automatically:
1) colored industrial camera collects the RGB triple channel image of CRP reagent strip on platform to be measured, and is converted into three width
Single channel image, extracts the complementary color passage of reaction zone to be detected, i.e. G channel image Iamge_S；
2) for improving system effectiveness, reducing the follow-up operator operation time, having of Iamge_S is selected by presetting coordinate
Effect ROI region Iamge_ROI, calculates as gray value and the image source of data base querying, and automatic storage is in data base；
Iamge_ROI should comprise complete CRP reagent strip chromatographic film district, and this process can reduce the analysis taken in edge extraneous areas
With the time of calculating；
3) Iamge_ROI equal proportion, reduction image quality are compressed to 70%, obtain compressing image Iamge_ROI_C, as certainly
Dynamic identification district to be detected Algorithm Analysis and the image source of main interface display；The most not only reduced computing relate to pixel sum but also reduce
Image reads the spent time repeatedly, and Iamge_ROI_C is automatically releasable internal memory after completing the detection of this reagent strip and deletes；
4) average gray value of doing sums minimum treat district Iamge_ROI_C calculates, and averaging of income gray value deducts 10 works
For overall situation gray threshold, base area binary conversion treatment is obtained bianry image；
5) bianry image processing gained corrodes with 3x3 rectangle for structural element, delete target border extraneous areas,
Extract CRP reagent strip chromatographic film image Iamge_ROI_D1 in the most complete region to be detected；
6) for removing the noise produced in image transmitting process, image Iamge_ROI_D1 is carried out gaussian filtering denoising,
By each pixel and Gaussian kernel convolution, by convolution with as the output pixel value of this pixel, pixel neighborhood of a point takes
Different weights, and the maximum weight of this pixel position, this step processes and obtains image Iamge_ROI_D_Gauss；
7) carry out image Iamge_ROI_D_Gauss continuing expansion process, with 3x3 rectangle for structural element to image
Iamge_ROI_D_Gauss expands, and coordinates the corrosion of above-mentioned 5th step by generation in overall situation gray threshold or subsequent treatment
Discrete point, line or burr filter, and export complete CRP reagent strip chromatographic film area image Iamge_ROI_D2；
8) complete CRP reagent strip chromatographic film area image Iamge_ROI_D2 is used at local auto-adaptive threshold binarization
Reason, obtains two detections of the most complete Iamge_test and Iamge_control and estimates district's image；
9) two detections of Iamge_test and Iamge_control are estimated district's image and again repeats the 5th step and the 7th step
Burn into expansion process；
10) district's image reference area feature is estimated in two detections after processing the 9th step, divides and intercepts out at Iamge_test
The rectangular area maximum with Iamge_control inner area, is denoted as test and control, treats as actual test and control
Detection zone, and indicate in main interface；
11) test and control is taken common factor with Iamge_ROI respectively, obtain Iamge_ROI relative to test and
The source images that the image of two rectangles of control needs as chrominance distortion, is denoted as T district and C district；
Step 2: area colorimetric calculates:
12) arithmetic average gray value algorithm: the T district obtained using the 11st step and the image in C district need as chrominance distortion
Source images；With single pixel (pixel) as unit, the width of traversal CRP reagent strip is M, and height is the image-region T district of N,
Each pixel gray value g (m, n) ∈ [0,255]；Individual divided by the effective pixel points in T district by pixel gray value sum G in T district
Number s, obtains the arithmetic average gray value in T districtIt is shown below:
Wherein, s ∈ (0, m × n],
In like manner, the arithmetic average gray value in C district is calculated；
13) weighted average gray value algorithm: the T district obtained using the 11st step and the image in C district need as chrominance distortion
Source images；4 row 4 are become to arrange totally 16 congruent matrixes the T district equal area partition of CRP reagent strip, by the arithmetic average gray value in T district
Average again after being multiplied by experience weight coefficient, i.e. can get weighted average gray valueIt is shown below:
Wherein, KijFor weight coefficient and Kij∈ (0,2], typically K in CRP detectsn1=1.3, Kn2=1, Kn3=1, Kn4
=1.15, (n=1,2,3,4)
In like manner, the weighted average gray value in C district is calculated；
Step 3: connection database processing data:
14) effective ROI region Iamge_ROI of by Access data base, the 2nd step in step one being obtained and corresponding
The T district of reagent strip, the weighted average gray value association in C district and preservation, and according to the master sample gray scale of the storage in data base
Value, matches the most reasonably CRP content reference value output.
The positive effect of the present invention: c reactive protein colourity automatic detection device of the present invention can complete with method
To CRP reagent strip test and the colorimetric detection of control reaction zone, showing through reality application, the method and device can not only be accurate
The true visual information that quantifies completes the mensuration of reagent strip, and is equally applicable to similar paper chromatography reagent strip, has simple to operate, inspection
Degree of testing the speed is fast, low cost and other advantages. and this invention has a following practical significance:
1. automatically identify region to be detected, reduce single reagent card detection required time, simplify CRP testing process, save people
Time needed for the visual comparison of work and wage cost, reduce and measure the error that operation is too much and causes, and improves detection efficiency；
The most single reagent strip detection time completes in caning be controlled in 2 seconds, and the operation result of measurement time controls at Millisecond, detection effect
Rate is greatly improved；
3. operating environment requirements is low, can long-time stable work, can abnormal self diagnosis, there is good robustness；
4. being applicable to any paper chromatography similar with CRP reaction reagent bar or colloid gold reagent bar, weight coefficient can basis
Different product demand is artificially changed, and makes that measurement is the most scientific and reasonable, the suitability is wider；
The most more data statistics of science, carries Access data base, automatically storage image and detection data, and data can be fast
Speed storage, and export appointment excel file.
6. there is expansibility, it is possible to be widely used in similar projects or the colorimetric detection of related reagent card.
Accompanying drawing explanation
The present invention is further detailed explanation with detailed description of the invention below in conjunction with the accompanying drawings.
Fig. 1 is the image overhaul flow chart of the present invention
Fig. 2 is the structural representation of the present invention
Fig. 3 is the automatic identification process figure of the present invention
Fig. 4 is the L-shaped locating piece schematic diagram with CRP reagent strip location and installation of the present invention
Fig. 5 is the schematic diagram that congruence matrix divides
In figure, the colored industrial camera 3 annular light source 4 platform to be checked 5L shape locating piece 6 of 1 light source controller 2 is finely tuned
Displacement platform 7 industrial control all-in-one machine 8CRP reagent strip
Detailed description of the invention
As it is shown in figure 1, a kind of c reactive protein colourity automatic detection device of the present invention includes colored industrial camera
(2), light source (3), special light source controller (1), fine setting displacement platform (6), L-shaped locating piece (5), industrial control all-in-one machine (7) and to be measured
Platform (4)；Described colored industrial camera uses 5,000,000 pixels, the colored work of exportable 2592x1944 resolution digital image
Industry video camera, its spectral region is higher than general camera, is appropriate to high-quality image processing algorithm；Described colored industry shooting
Can install the ring contraction visual field of increment before the camera lens of machine, described colored industrial camera is connected to position adjustments by fine setting displacement platform
On track, the camera lens of colored industrial camera is vertical with the platform to be measured placing CRP reagent strip, has the Precision trimming of micrometer
Displacement platform, can make colored industrial camera realize quick, high precision fine tuning, it is ensured that native system certainty of measurement and efficiency；
For reducing image sliced time and improving utilization ratio of visual field, it is to be ensured that CRP reagent strip length is adopted not less than video camera
The 4/5 of collection picture width；And the lens focus of industrial camera, operating distance, image resolution ratio are all close phase with Pixel Dimensions
Close, the vertical drop of best effort distance, i.e. video camera and CRP reagent strip can be calculated by correlation formula.Taking the photograph
Camera setting height(from bottom) cannot one step joint time, increment ring can be installed before camera lens additional, reduce visual field, make the picture that camera acquisition arrives
Reach requirement.
Described light source is fixed on the camera lens of colored industrial camera, and light source selects white diffuse-reflectance annular light source, and sets
It is equipped with special light source controller；Use white diffuse-reflectance annular light source can greatly reduce shade phenomenon, improve contrast, so prominent
Aobvious CRP reagent strip conversion zone imaging features.It is noted that annular reflective phenomenon can be produced when improper.
Described CRP reagent strip is fixed on platform to be measured by L-shaped locating piece.Can be by CRP reagent strip along arrow during detection
Guide signing pushes platform to be measured, and as shown in Figure 4, be close to, inside L-shaped locating piece, fit i.e. complete completely in CRP reagent strip both sides
Location.
This crust of the device uses 2020 lightweight aluminium section bars to build, and is fixed with machining aluminum plate by bolt link, before
Customization industrial control all-in-one machine installed by plate, can realize multiple point touching.
The method that a kind of c reactive protein colourity of the present invention detects automatically is by collecting colored industrial camera
Original image segmentation, denoising, corrode and the image processing algorithm such as expansion realizes automatically identifying and extract region to be determined, in pole
High accuracy data is provided for follow-up step in short time.Experiment and actual application all show, this identifies serial algorithm mistake automatically
Journey can quickly, accurately split, extract the characteristic area of CRP reagent strip, effectively filter out exterior light according to (including that daylight and indoor are white
Vehement lamp light), CRP differences between samples (different serum sample primary colours differences, the skew of reagent reacting district excessive), CRP reagent strip chromatography
Film defects etc. affect.
Mainly comprise the steps:
Step 1:CRP reagent strip identifies and image procossing automatically, as shown in Figure 3:
1) colored industrial camera collects the RGB triple channel image of CRP reagent strip on platform to be measured, and is converted into three width
Single channel image, extracts the complementary color passage of reaction zone to be detected, i.e. G channel image Iamge_S；
2) for improving system effectiveness, reducing the follow-up operator operation time, having of Iamge_S is selected by presetting coordinate
Effect ROI region Iamge_ROI, calculates as gray value and the image source of data base querying, and automatic storage is in data base；
Iamge_ROI should comprise complete CRP reagent strip chromatographic film district, and this process can reduce the analysis taken in edge extraneous areas
With the time of calculating；
3) Iamge_ROI equal proportion, reduction image quality are compressed to 70%, obtain compressing image Iamge_ROI_C, as certainly
Dynamic identification district to be detected Algorithm Analysis and the image source of main interface display；The most not only reduced computing relate to pixel sum but also reduce
Image reads the spent time repeatedly, and Iamge_ROI_C is automatically releasable internal memory after completing the detection of this reagent strip and deletes；
4) average gray value of doing sums minimum treat district Iamge_ROI_C calculates, and averaging of income gray value deducts 10 works
For overall situation gray threshold, base area binary conversion treatment is obtained bianry image；
5) bianry image processing gained corrodes with 3x3 rectangle for structural element, delete target border extraneous areas,
Extract CRP reagent strip chromatographic film image Iamge_ROI_D1 in the most complete region to be detected；
6) for removing the noise produced in image transmitting process, image Iamge_ROI_D1 is carried out gaussian filtering denoising,
By each pixel and Gaussian kernel convolution, by convolution with as the output pixel value of this pixel, pixel neighborhood of a point takes
Different weights, and the maximum weight of this pixel position, this step processes and obtains image Iamge_ROI_D_Gauss；
7) carry out image Iamge_ROI_D_Gauss continuing expansion process, with 3x3 rectangle for structural element to image
Iamge_ROI_D_Gauss expands, and coordinates the corrosion of above-mentioned 5th step by generation in overall situation gray threshold or subsequent treatment
Discrete point, line or burr filter, and export complete CRP reagent strip chromatographic film area image Iamge_ROI_D2；
8) complete CRP reagent strip chromatographic film area image Iamge_ROI_D2 is used at local auto-adaptive threshold binarization
Reason, obtains two detections of the most complete Iamge_test and Iamge_control and estimates district's image；
9) two detections of Iamge_test and Iamge_control are estimated district's image and again repeats the 5th step and the 7th step
Burn into expansion process；
10) district's image reference area feature is estimated in two detections after processing the 9th step, divides and intercepts out at Iamge_test
The rectangular area maximum with Iamge_control inner area, is denoted as test and control, treats as actual test and control
Detection zone, and indicate in main interface；
11) test and control is taken common factor with Iamge_ROI respectively, obtain Iamge_ROI relative to test and
The source images that the image of two rectangles of control needs as chrominance distortion, is denoted as T district and C district；
Step 2: area colorimetric calculates:
12) arithmetic average gray value algorithm: the T district obtained using the 11st step and the image in C district need as chrominance distortion
Source images；With single pixel (pixel) as unit, the width of traversal CRP reagent strip is M, and height is the image-region T district of N,
Each pixel gray value g (m, n) ∈ [0,255]；Individual divided by the effective pixel points in T district by pixel gray value sum G in T district
Number s, obtains the arithmetic average gray value in T districtIt is shown below:
Wherein, s ∈ (0, m × n],
In like manner, the arithmetic average gray value in C district is calculated；
13) weighted average gray value algorithm: the T district obtained using the 11st step and the image in C district need as chrominance distortion
Source images；4 row 4 are become to arrange totally 16 congruent matrixes the T district equal area partition of CRP reagent strip, by the arithmetic average gray value in T district
Average again after being multiplied by experience weight coefficient, i.e. can get weighted average gray valueIt is shown below:
Wherein, KijFor weight coefficient and Kij∈ (0,2], typically K in CRP detectsn1=1.3, Kn2=1, Kn3=1, Kn4
=1.15, (n=1,2,3,4)
In like manner, the weighted average gray value in C district is calculated；
Step 3: connection database processing data:
Effective ROI region Iamge_ROI 2nd step in step one obtained by Access data base and corresponding reagent
The T district of bar, the weighted average gray value association in C district and preservation, and according to the master sample gray value of the storage in data base,
Allot the most reasonably CRP content reference value output.
A kind of c reactive protein colourity automatic detection device that the present invention that simply explains through diagrams recited above is correlated with and side
A kind of concrete application example of method, some owing to being easy to carry out on this basis for the technical staff in constructed field
Amendment, therefore this specification is not really wanted a kind of c reactive protein colourity automatic detection device of the present invention and method office
Limit is in shown or described concrete mechanism and the scope of application, therefore every corresponding amendment that may be utilized and equivalent,
Belong to the protection domain of patent of the present invention.
Claims (2)
1. a c reactive protein colourity automatic detection device, it is characterised in that: include colored industrial camera, light source, dedicated optical
Source controller, fine setting displacement platform, L-shaped locating piece, industrial control all-in-one machine and platform to be measured；Described colored industrial camera is by fine setting
Displacement platform is connected on position adjustments track, and the camera lens of colored industrial camera is vertical with the platform to be measured placing CRP reagent strip；
The length of described CRP reagent strip gathers the 4/5 of the width of picture not less than colored industrial camera；Described light source is fixed on colour
On the camera lens of industrial camera, light source selects white diffuse-reflectance annular light source, and is provided with special light source controller；Described CRP
Reagent strip is fixed on platform to be measured by L-shaped locating piece；
A kind of realize the method that c reactive protein colourity detects automatically, mainly by above-mentioned c reactive protein colourity automatic detection device
Comprise the steps:
CRP reagent strip identifies and image procossing automatically:
1) colored industrial camera collects the RGB triple channel image of CRP reagent strip on platform to be measured, and is converted into three width single-passes
Road image, extracts the complementary color passage of reaction zone to be detected, i.e. G channel image Iamge_S；
2) for improving system effectiveness, reducing the follow-up operator operation time, the effective of Iamge_S is selected by presetting coordinate
ROI region Iamge_ROI, calculates as gray value and the image source of data base querying, and automatic storage is in data base；
Iamge_ROI should comprise complete CRP reagent strip chromatographic film district, and this process can reduce the analysis taken in edge extraneous areas
With the time of calculating；
3) Iamge_ROI equal proportion, reduction image quality are compressed to 70%, obtain compressing image Iamge_ROI_C, as automatically knowing
District the most to be detected Algorithm Analysis and the image source of main interface display；The most not only reduced computing relate to pixel sum but also reduce image
Repeatedly reading the time spent, Iamge_ROI_C is automatically releasable internal memory after completing the detection of this reagent strip and deletes；
4) average gray value of doing sums minimum treat district Iamge_ROI_C calculates, and averaging of income gray value deducts 10 as complete
Office's gray threshold obtains bianry image to base area binary conversion treatment；
5) bianry image processing gained corrodes with 3x3 rectangle for structural element, and delete target border extraneous areas is extracted
Go out CRP reagent strip chromatographic film image Iamge_ROI_D1 in the most complete region to be detected；
6) for removing the noise produced in image transmitting process, image Iamge_ROI_D1 is carried out gaussian filtering denoising, will be every
One pixel and Gaussian kernel convolution, by convolution with as the output pixel value of this pixel, pixel neighborhood of a point takes difference
Weights, and the maximum weight of this pixel position, this step processes and obtains image Iamge_ROI_D_Gauss；
7) carry out image Iamge_ROI_D_Gauss continuing expansion process, with 3x3 rectangle for structural element to image Iamge_
ROI_D_Gauss expands, and the corrosion coordinating above-mentioned 5th step is discrete by produce in overall situation gray threshold or subsequent treatment
Point, line or burr filter, and export complete CRP reagent strip chromatographic film area image Iamge_ROI_D2；
8) complete CRP reagent strip chromatographic film area image Iamge_ROI_D2 is used local auto-adaptive threshold binarization treatment,
Obtain two detections of the most complete Iamge_test and Iamge_control and estimate district's image；
9) district's image is estimated in two detections of Iamge_test and Iamge_control and again repeat the 5th step and the corruption of the 7th step
Erosion, expansion process；
10) district's image reference area feature is estimated in two detections after processing the 9th step, point intercept out at Iamge_test and
The rectangular area that Iamge_control inner area is maximum, is denoted as test and control, to be checked as actual test and control
Survey district, and indicate in main interface；
11) test and control is taken common factor with Iamge_ROI respectively, obtain Iamge_ROI relative to test and control
The source images that the image of two rectangles needs as chrominance distortion, is denoted as T district and C district；
Area colorimetric calculates:
12) arithmetic average gray value algorithm: the T district obtained using the 11st step and the image in C district are as the source figure that chrominance distortion needs
Picture；With single pixel (pixel) as unit, the width of traversal CRP reagent strip is M, and height is the image-region T district of N, each
Pixel gray value g (m, n) ∈ [0,255]；By pixel gray value sum G in T district divided by effective pixel points number s in T district,
Obtain the arithmetic average gray value in T districtIt is shown below:
Wherein,
In like manner, the arithmetic average gray value in C district is calculated；
13) weighted average gray value algorithm: the T district obtained using the 11st step and the image in C district are as the source figure that chrominance distortion needs
Picture；Become 4 row 4 to arrange totally 16 congruent matrixes the T district equal area partition of CRP reagent strip, the arithmetic average gray value in T district is multiplied by
Average again after experience weight coefficient, i.e. can get weighted average gray valueIt is shown below:
Wherein, KijFor weight coefficient and Kij∈ (0,2], typically in CRP detects
Kn1=1.3, Kn2=1, Kn3=1, Kn4=1.15, (m=1,2,3,4)
In like manner, the weighted average gray value in C district is calculated；
Connection database processing data:
14) effective ROI region Iamge_ROI by Access data base, the 2nd step obtained and the T district of corresponding reagent strip, C
The weighted average gray value association in district and preservation, and according to the master sample gray value of the storage in data base, match the most
Reasonably CRP content reference value output.
A kind of c reactive protein colourity automatic detection device the most according to claim 1, it is characterised in that: described colored work
Industry video camera uses 5,000,000 pixels, the colored industrial camera of exportable 2592x1944 resolution digital image；Described colour
Increment ring contraction visual field can be installed before the camera lens of industrial camera.
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108051434A (en) * | 2017-12-13 | 2018-05-18 | 深圳市在田翊方科技有限公司 | A kind of prepare liquid concentration quantitative detection method based on color identification |
| CN108279229A (en) * | 2017-01-05 | 2018-07-13 | 深圳市帝迈生物技术有限公司 | A kind of whole blood CRP detection devices |
| CN110100198A (en) * | 2016-12-23 | 2019-08-06 | 生物辐射实验室股份有限公司 | The reduction of background signal in print image |
| CN110223290A (en) * | 2019-06-19 | 2019-09-10 | 深圳市亚辉龙生物科技股份有限公司 | Film appraisal procedure, device, computer equipment and storage medium |
| CN113466180A (en) * | 2021-09-02 | 2021-10-01 | 天津迈科隆生物科技有限公司 | Specific protein detection method, electronic equipment and computer readable storage medium |
| US11307147B2 (en) | 2018-06-28 | 2022-04-19 | International Business Machines Corporation | Accurate colorimetric based test strip reader system |
| EP3980758A4 (en) * | 2019-06-05 | 2023-05-03 | Genprime | Substrate reader and method of reading a substrate |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4639117A (en) * | 1981-11-07 | 1987-01-27 | Ricoh Company, Ltd. | Recording image density control method for electrophotography |
| CN1134750A (en) * | 1993-11-12 | 1996-10-30 | 尤尼帕思有限公司 | Reading devices for teststrips |
| US6141120A (en) * | 1996-02-12 | 2000-10-31 | Splash Technology, Inc. | Color calibration method and system having independent color scanner profiles |
| US20060246574A1 (en) * | 2005-04-29 | 2006-11-02 | Sarah Rosenstein | Dispenser for making a lateral flow device |
| CN101539572A (en) * | 2009-01-06 | 2009-09-23 | 中国科学院上海光学精密机械研究所 | Up-converting phosphor bio-detector and method for detecting sampling test strips |
| CN101576562A (en) * | 2009-06-01 | 2009-11-11 | 无锡中德伯尔生物技术有限公司 | Fluorescence microballoon immunochromatography testing card for testing HIV and preparation method |
| CN102279263A (en) * | 2011-05-06 | 2011-12-14 | 杭州顿力医疗器械股份有限公司 | CCD-type quantitative analysis system of colloidal gold immunity chromatography diagnosis test papers |
| WO2013131057A1 (en) * | 2012-03-01 | 2013-09-06 | Quidel Corporation | System and apparatus for point-of-care diagnostics |
-
2016
- 2016-04-27 CN CN201610266296.0A patent/CN105974120B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4639117A (en) * | 1981-11-07 | 1987-01-27 | Ricoh Company, Ltd. | Recording image density control method for electrophotography |
| CN1134750A (en) * | 1993-11-12 | 1996-10-30 | 尤尼帕思有限公司 | Reading devices for teststrips |
| US6141120A (en) * | 1996-02-12 | 2000-10-31 | Splash Technology, Inc. | Color calibration method and system having independent color scanner profiles |
| US20060246574A1 (en) * | 2005-04-29 | 2006-11-02 | Sarah Rosenstein | Dispenser for making a lateral flow device |
| CN101539572A (en) * | 2009-01-06 | 2009-09-23 | 中国科学院上海光学精密机械研究所 | Up-converting phosphor bio-detector and method for detecting sampling test strips |
| CN101576562A (en) * | 2009-06-01 | 2009-11-11 | 无锡中德伯尔生物技术有限公司 | Fluorescence microballoon immunochromatography testing card for testing HIV and preparation method |
| CN102279263A (en) * | 2011-05-06 | 2011-12-14 | 杭州顿力医疗器械股份有限公司 | CCD-type quantitative analysis system of colloidal gold immunity chromatography diagnosis test papers |
| WO2013131057A1 (en) * | 2012-03-01 | 2013-09-06 | Quidel Corporation | System and apparatus for point-of-care diagnostics |
Non-Patent Citations (1)
| Title |
|---|
| 王艳华等: "盐酸克伦特罗试纸数字化检测仪的设计", 《电脑知识与技术》 * |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110100198A (en) * | 2016-12-23 | 2019-08-06 | 生物辐射实验室股份有限公司 | The reduction of background signal in print image |
| CN110100198B (en) * | 2016-12-23 | 2022-03-22 | 生物辐射实验室股份有限公司 | Reduction of background signal in print image |
| CN108279229A (en) * | 2017-01-05 | 2018-07-13 | 深圳市帝迈生物技术有限公司 | A kind of whole blood CRP detection devices |
| CN108279229B (en) * | 2017-01-05 | 2024-02-27 | 深圳市帝迈生物技术有限公司 | Whole blood CRP detection device |
| CN108051434A (en) * | 2017-12-13 | 2018-05-18 | 深圳市在田翊方科技有限公司 | A kind of prepare liquid concentration quantitative detection method based on color identification |
| CN108051434B (en) * | 2017-12-13 | 2021-03-19 | 深圳市在田翊方科技有限公司 | Color recognition-based quantitative detection method for concentration of liquid to be detected |
| US11307147B2 (en) | 2018-06-28 | 2022-04-19 | International Business Machines Corporation | Accurate colorimetric based test strip reader system |
| EP3980758A4 (en) * | 2019-06-05 | 2023-05-03 | Genprime | Substrate reader and method of reading a substrate |
| CN110223290A (en) * | 2019-06-19 | 2019-09-10 | 深圳市亚辉龙生物科技股份有限公司 | Film appraisal procedure, device, computer equipment and storage medium |
| CN113466180A (en) * | 2021-09-02 | 2021-10-01 | 天津迈科隆生物科技有限公司 | Specific protein detection method, electronic equipment and computer readable storage medium |
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