CN110007068A - A kind of urine drip detection method - Google Patents
A kind of urine drip detection method Download PDFInfo
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- CN110007068A CN110007068A CN201910226761.1A CN201910226761A CN110007068A CN 110007068 A CN110007068 A CN 110007068A CN 201910226761 A CN201910226761 A CN 201910226761A CN 110007068 A CN110007068 A CN 110007068A
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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
- G01N33/52—Use of compounds or compositions for colorimetric, spectrophotometric or fluorometric investigation, e.g. use of reagent paper and including single- and multilayer analytical elements
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N35/00—Automatic analysis not limited to methods or materials provided for in any single one of groups G01N1/00 - G01N33/00; Handling materials therefor
- G01N35/00584—Control arrangements for automatic analysers
- G01N35/00594—Quality control, including calibration or testing of components of the analyser
- G01N35/00613—Quality control
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Abstract
The present invention provides a kind of urine drip detection methods, execute drop sample to each medicine block of the Test paper, wherein state the original image of Test paper described in acquisition and carry out limb recognition to obtain detection image；The center of each medicine block in the detection image is determined according to the actual size of the Test paper；The detection image is cut according to the actual size of each medicine block on the center of medicine block each in the detection image and the Test paper, to obtain the segmented image of corresponding single medicine block；Processing calculating is carried out to the corresponding segmented image of each drop sample medicine block using adjustment cosine similarity algorithm or high-frequency signal distribution statistics algorithm, to obtain the characteristic parameter of each segmented image；According to the characteristic parameter of each segmented image by image comparison judge each drop sample medicine block whether the information of drip.
Description
Technical field
The present invention relates to the field of medical instrument technology more particularly to a kind of urine drip detection methods.
Background technique
Urine test is one of three big conventional projects of modern medicine clinical examination, is played in the diagnosis of disease very heavy
The effect wanted.Full automatic urine analyzer is by liquid channel system, sampling system, choosing system, detection system, electronic system and software
System composition.Liquid channel system is responsible for sample to drip on Test paper, to allow drop sample medicine on urine specimen and Test paper
Deblocking reaction realizes the detection and analysis of urinary fractions through detection system.However liquid channel system is there are many pipelines, pump valve, when use
Between it is long there may be pipeline aging, phenomena such as urinary fractions remain, cause that liquid channel system is blocked or air-tightness is bad,
The case where causing liquid channel system to be likely to occur drip when dripping sample.
Summary of the invention
The purpose of the present invention is to provide a kind of urine drip detection methods, timely to find the drop sample on Test paper
Whether medicine block has occurred drip.
In order to achieve the above object, the present invention provides a kind of urine drip detection methods, for detecting a Test paper
Each medicine block on whether dripped urine specimen characterized by comprising
Drop sample is executed to each medicine block of the Test paper；
It obtains the original image of the Test paper and carries out limb recognition to obtain detection image；
The center of each medicine block in the detection image is determined according to the actual size of the Test paper；
According to the actual size pair of each medicine block on the center of medicine block each in the detection image and the Test paper
The detection image is cut, to obtain the segmented image of corresponding each medicine block；
Using adjustment cosine similarity algorithm or high-frequency signal distribution statistics algorithm to the corresponding segmentation figure of each drop sample medicine block
As carrying out processing calculating, to obtain the characteristic parameter of the corresponding segmented image of each drop sample medicine block；
According to the characteristic parameter of each segmented image judge it is each drop sample medicine block whether drip and drip drop sample medicine block position.
Urine drip detection method as described in claim 1, which is characterized in that obtained using adjustment cosine similarity algorithm
The step of taking the characteristic parameter of each segmented image include:
Under HSV color space, the characteristics of image of the corresponding segmented image of each drop sample medicine block is extracted, and is fitted to one-dimensional spy
Vector is levied, the one-dimensional characteristic vector of the corresponding segmented image of each drop sample medicine block is obtained；
Obtain the image feature vector for not dripping sample medicine block；
Each each corresponding one-dimensional characteristic vector of drop sample medicine block and the image feature vector for not dripping sample medicine block are carried out
It adjusts cosine similarity to calculate, to obtain the characteristic parameter of each segmented image, the characteristic parameter is cosine similarity comparison ginseng
Number.
Optionally, when the corresponding cosine similarity reduced parameter of the drop sample medicine block is in one first setting range, then
Determine the drop sample medicine block drip；When the corresponding cosine similarity reduced parameter of the drop sample medicine block is outside first setting range
When, then determine the non-drip of drop sample medicine block.
Optionally, acquire it is multiple do not drip the corresponding image feature vector of sample medicine block and recycle be adjusted cosine similarity meter
It calculates, not dripped the waving interval of the cosine similarity reduced parameter of sample medicine block, first setting range is the cosine
The waving interval of similarity comparison parameter.
Optionally, the step of obtaining the characteristic parameter of each segmented image using high-frequency signal distribution statistics algorithm include:
Under rgb color space, the position distribution and number of the high-frequency signal of the corresponding segmented image of each drop sample medicine block are obtained
Amount, obtains the characteristic parameter of the corresponding segmented image of each drop sample medicine block, and the characteristic parameter is high-frequency information parameter.
Optionally, when the corresponding high-frequency information parameter of the drop sample medicine block is in one second setting range, then determining should
Drip sample medicine block drip；When the corresponding high-frequency information parameter of the drop sample medicine block is outside second setting range, then determining should
Drip the non-drip of sample medicine block.
Optionally, multiple position distributions and quantity for not dripping the corresponding high-frequency signal of sample medicine block are acquired, not dripped sample
The waving interval of the high-frequency information parameter of medicine block, second setting range are the waving interval of the high-frequency information parameter.
Beneficial effects of the present invention are as follows:
1, by way of image comparison, the characteristic parameter of the corresponding segmented image of drop sample drop sample medicine block is obtained, figure is passed through
As comparison mode obtain drop sample medicine block whether the information of drip；
2, by addition drip identification function, remind user that the position of the drop sample medicine block of drip and the drop sample medicine block of drip occurs
Set, the drop sample medicine block for avoiding not dripping sample generate examine inaccuracy as a result, keeping detection accuracy higher.
Detailed description of the invention
Fig. 1 is the flow chart of urine drip detection method provided in an embodiment of the present invention.
Specific embodiment
A specific embodiment of the invention is described in more detail below in conjunction with schematic diagram.According to following description and
Claims, advantages and features of the invention will become apparent from.It should be noted that attached drawing is all made of very simplified form and
Using non-accurate ratio, only for the purpose of facilitating and clarifying the purpose of the embodiments of the invention.
As shown in Figure 1, a kind of urine drip detection method is present embodiments provided, for detecting each medicine of a Test paper
Whether urine specimen has been dripped on block, comprising:
Step S1: drop sample is executed to each medicine block of the Test paper.
Step S2:, which obtaining the original image of the Test paper, and carries out limb recognition obtains edge coordinate, determines edge
Physical location of the coordinate in original image, available detection image only contain the detection examination in the detection image
The image information of paper.
Step S3: according to the ratio between the actual size and edge coordinate of the Test paper, from transverse and longitudinal both direction
Determine the center of each medicine block in the detection image.
Step S4: according to the reality of each medicine block on the center of medicine block each in the detection image and the Test paper
Size cuts the detection image, to obtain the segmented image of corresponding single medicine block, convenient for the segmentation figure to each medicine block
As being handled, and do not drip convenient for difference drop sample drop sample medicine block and sample medicine block.
Step S5: corresponding to each drop sample medicine block using adjustment cosine similarity algorithm or high-frequency signal distribution statistics algorithm
Segmented image carries out processing calculating, to obtain the characteristic parameter of each segmented image；
Specifically, the step of obtaining the characteristic parameter of each segmented image using adjustment cosine similarity algorithm includes:
Step S51: under HSV color space, the characteristics of image of the corresponding segmented image of each drop sample medicine block is extracted, and is fitted
At one-dimensional characteristic vector, the one-dimensional characteristic vector of the corresponding segmented image of each drop sample medicine block is obtained；
Step S52: the image feature vector that sample medicine block is not dripped in acquisition (does not drip the image feature vector of sample medicine block before factory
It has calibrated)；
Step S53: the one-dimensional characteristic vector of the corresponding segmented image of each drop sample medicine block and the image for not dripping sample medicine block is special
Sign vector is adjusted cosine similarity calculating, and to obtain the characteristic parameter of each segmented image, the characteristic parameter is cosine phase
Like degree reduced parameter X1.
Step S6: when the corresponding cosine similarity reduced parameter X1 of the drop sample medicine block is in one first setting range X2
When, then determine the drop sample medicine block drip；When the corresponding cosine similarity reduced parameter X1 of the drop sample medicine block is set described first
When determining outside range X2, then the non-drip of drop sample medicine block is determined.Wherein, acquire that multiple not drip sample medicine block (big-sample data) corresponding
Image feature vector and circulation are adjusted cosine similarity calculating, to obtain the cosine similarity comparison for not dripping sample medicine block
The waving interval of parameter sets the first setting range X2 to the waving interval of the cosine similarity reduced parameter.And
With drop sample medicine block be according to each segmented image it is corresponding, judge to drip sample medicine block whether after drip, can be according to its segmented image
Find the position of drop sample medicine block.
The step of obtaining the characteristic parameter of each segmented image using high-frequency signal distribution statistics algorithm include:
Step S51 ': under rgb color space, the position of the high-frequency signal of the corresponding segmented image of each drop sample medicine block is obtained
Distribution and quantity, obtain the characteristic parameter of the corresponding segmented image of each drop sample medicine block, and the characteristic parameter is high-frequency information parameter
Y1.Since the detection image high-frequency signal narrow distribution range and distributed quantity of drop sample drop sample medicine block significantly reduce and (do not drip sample medicine
The high-frequency signal of block has calibrated before being distributed in factory), the detection image high-frequency signal distribution of sample medicine block is not dripped extensively and quantity
Greatly, it can distinguish on this basis.
Step S6 ': when the corresponding high-frequency information parameter Y1 of the drop sample medicine block is in one second setting range Y2, then
Determine the drop sample medicine block drip；When the corresponding high-frequency information parameter Y1 of the drop sample medicine block is outside the second setting range Y2
When, then determine the non-drip of drop sample medicine block.Wherein, acquisition is multiple does not drip the corresponding high-frequency signal of sample medicine block (big-sample data)
Position distribution and quantity described second is set with obtaining the waving interval of the high-frequency information parameter for not dripping sample medicine block
Range Y2 is set as the waving interval of the high-frequency information parameter.
The above is only a preferred embodiment of the present invention, does not play the role of any restrictions to the present invention.Belonging to any
Those skilled in the art, in the range of not departing from technical solution of the present invention, to the invention discloses technical solution and
Technology contents make the variation such as any type of equivalent replacement or modification, belong to the content without departing from technical solution of the present invention, still
Within belonging to the scope of protection of the present invention.
Claims (7)
1. a kind of urine drip detection method, whether is dripped in each medicine block for detecting a Test paper and has gone up urine specimen,
It is characterized in that, comprising:
Drop sample is executed to each medicine block of the Test paper；
It obtains the original image of the Test paper and carries out limb recognition to obtain detection image；
The center of each medicine block in the detection image is determined according to the actual size of the Test paper；
According to the actual size of each medicine block on the center of medicine block each in the detection image and the Test paper to described
Detection image is cut, to obtain the segmented image of corresponding each medicine block；
Using adjustment cosine similarity algorithm or high-frequency signal distribution statistics algorithm to the corresponding segmented image of each drop sample medicine block into
Row processing calculates, to obtain the characteristic parameter of the corresponding segmented image of each drop sample medicine block；
According to the characteristic parameter of each segmented image judge it is each drop sample medicine block whether drip and drip drop sample medicine block position.
2. urine drip detection method as described in claim 1, which is characterized in that obtained using adjustment cosine similarity algorithm
The step of characteristic parameter of each segmented image includes:
Under HSV color space, extract the characteristics of image of the corresponding segmented image of each drop sample medicine block, and be fitted to one-dimensional characteristic to
Amount obtains the one-dimensional characteristic vector of the corresponding segmented image of each drop sample medicine block；
Obtain the image feature vector for not dripping sample medicine block；
The corresponding one-dimensional characteristic vector of each each drop sample medicine block is adjusted with the image feature vector for not dripping sample medicine block
Cosine similarity calculates, and to obtain the characteristic parameter of each segmented image, the characteristic parameter is cosine similarity reduced parameter.
3. urine drip detection method as claimed in claim 2, which is characterized in that when the corresponding cosine phase of the drop sample medicine block
When like degree reduced parameter in one first setting range, then the drop sample medicine block drip is determined；When the drop sample medicine block is corresponding remaining
When string similarity comparison parameter is outside first setting range, then the non-drip of drop sample medicine block is determined.
4. urine drip detection method as claimed in claim 3, which is characterized in that acquisition is multiple not to drip the corresponding figure of sample medicine block
It as feature vector and recycles and is adjusted cosine similarity and calculates, not dripped the cosine similarity reduced parameter of sample medicine block
Waving interval, first setting range are the waving interval of the cosine similarity reduced parameter.
5. urine drip detection method as described in claim 1, which is characterized in that obtained using high-frequency signal distribution statistics algorithm
The step of taking the characteristic parameter of each segmented image include:
Under rgb color space, the position distribution and quantity of the high-frequency signal of the corresponding segmented image of each drop sample medicine block are obtained, is obtained
To the characteristic parameter of the corresponding segmented image of each drop sample medicine block, the characteristic parameter is high-frequency information parameter.
6. urine drip detection method as claimed in claim 5, which is characterized in that when the corresponding high frequency letter of the drop sample medicine block
When ceasing parameter in one second setting range, then the drop sample medicine block drip is determined；When the corresponding high-frequency information of the drop sample medicine block
When parameter is outside second setting range, then the non-drip of drop sample medicine block is determined.
7. urine drip detection method as claimed in claim 6, which is characterized in that acquisition is multiple not to drip the corresponding height of sample medicine block
The position distribution and quantity of frequency signal, not dripped the waving interval of the high-frequency information parameter of sample medicine block, second setting
Range is the waving interval of the high-frequency information parameter.
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| CN201910226761.1A CN110007068B (en) | 2019-03-25 | 2019-03-25 | Urine leakage detection method |
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| CN201910226761.1A CN110007068B (en) | 2019-03-25 | 2019-03-25 | Urine leakage detection method |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN110412257A (en) * | 2019-07-22 | 2019-11-05 | 深圳市预防宝科技有限公司 | A kind of combination is manually demarcated and the indicator paper block localization method of astral ray algorithm |
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