CN204536211U - Visual identification is analyzed and susceptibility identification systems - Google Patents
Visual identification is analyzed and susceptibility identification systems Download PDFInfo
- Publication number
- CN204536211U CN204536211U CN201520169886.2U CN201520169886U CN204536211U CN 204536211 U CN204536211 U CN 204536211U CN 201520169886 U CN201520169886 U CN 201520169886U CN 204536211 U CN204536211 U CN 204536211U
- Authority
- CN
- China
- Prior art keywords
- polarizer
- digital image
- light
- light source
- acquisition device
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Fee Related
Links
- 230000000007 visual effect Effects 0.000 title claims abstract description 19
- 238000004458 analytical method Methods 0.000 claims abstract description 28
- 239000003814 drug Substances 0.000 claims abstract description 24
- 229940079593 drug Drugs 0.000 claims abstract description 21
- 230000003287 optical effect Effects 0.000 claims abstract description 14
- 230000035945 sensitivity Effects 0.000 claims abstract description 11
- 230000004075 alteration Effects 0.000 claims abstract 3
- 230000010287 polarization Effects 0.000 claims description 11
- 230000000903 blocking effect Effects 0.000 claims 1
- 238000001514 detection method Methods 0.000 abstract description 16
- 241000894006 Bacteria Species 0.000 abstract description 8
- 230000000694 effects Effects 0.000 abstract description 7
- 238000003384 imaging method Methods 0.000 abstract description 4
- 238000012545 processing Methods 0.000 description 30
- 238000012360 testing method Methods 0.000 description 16
- 238000003909 pattern recognition Methods 0.000 description 14
- 239000013598 vector Substances 0.000 description 14
- 239000000523 sample Substances 0.000 description 10
- 238000007639 printing Methods 0.000 description 9
- 230000010365 information processing Effects 0.000 description 8
- 238000000034 method Methods 0.000 description 8
- 230000001580 bacterial effect Effects 0.000 description 7
- 238000005516 engineering process Methods 0.000 description 7
- 238000010801 machine learning Methods 0.000 description 6
- 238000012423 maintenance Methods 0.000 description 6
- 230000033228 biological regulation Effects 0.000 description 5
- 238000012937 correction Methods 0.000 description 5
- 238000000605 extraction Methods 0.000 description 5
- 239000000203 mixture Substances 0.000 description 5
- 238000012797 qualification Methods 0.000 description 5
- 230000008859 change Effects 0.000 description 4
- 238000001914 filtration Methods 0.000 description 4
- 244000005700 microbiome Species 0.000 description 4
- 230000011218 segmentation Effects 0.000 description 4
- 230000000844 anti-bacterial effect Effects 0.000 description 3
- 238000007621 cluster analysis Methods 0.000 description 3
- 238000003708 edge detection Methods 0.000 description 3
- 230000002708 enhancing effect Effects 0.000 description 3
- 238000009533 lab test Methods 0.000 description 3
- 238000011160 research Methods 0.000 description 3
- 238000010998 test method Methods 0.000 description 3
- 238000002835 absorbance Methods 0.000 description 2
- 229940088710 antibiotic agent Drugs 0.000 description 2
- 239000003429 antifungal agent Substances 0.000 description 2
- 238000013473 artificial intelligence Methods 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 2
- 238000009395 breeding Methods 0.000 description 2
- 230000001488 breeding effect Effects 0.000 description 2
- 238000003759 clinical diagnosis Methods 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000018109 developmental process Effects 0.000 description 2
- 235000021061 dietary behavior Nutrition 0.000 description 2
- 238000009792 diffusion process Methods 0.000 description 2
- 238000010790 dilution Methods 0.000 description 2
- 239000012895 dilution Substances 0.000 description 2
- 235000006694 eating habits Nutrition 0.000 description 2
- 238000002474 experimental method Methods 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 239000001963 growth medium Substances 0.000 description 2
- 230000002401 inhibitory effect Effects 0.000 description 2
- 230000005764 inhibitory process Effects 0.000 description 2
- 238000007689 inspection Methods 0.000 description 2
- 230000000813 microbial effect Effects 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 229920001817 Agar Polymers 0.000 description 1
- 240000004859 Gamochaeta purpurea Species 0.000 description 1
- 240000004808 Saccharomyces cerevisiae Species 0.000 description 1
- 239000008272 agar Substances 0.000 description 1
- 239000003242 anti bacterial agent Substances 0.000 description 1
- 229940121375 antifungal agent Drugs 0.000 description 1
- 238000000149 argon plasma sintering Methods 0.000 description 1
- 230000003115 biocidal effect Effects 0.000 description 1
- 239000003283 colorimetric indicator Substances 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000004069 differentiation Effects 0.000 description 1
- 238000001035 drying Methods 0.000 description 1
- 239000003205 fragrance Substances 0.000 description 1
- 238000005286 illumination Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 230000035800 maturation Effects 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 230000003923 mental ability Effects 0.000 description 1
- 230000006855 networking Effects 0.000 description 1
- 235000015097 nutrients Nutrition 0.000 description 1
- 239000013610 patient sample Substances 0.000 description 1
- 239000000243 solution Substances 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 238000012549 training Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
- 238000011179 visual inspection Methods 0.000 description 1
Landscapes
- Investigating Or Analysing Materials By Optical Means (AREA)
Abstract
本实用新型公开了一种视觉鉴定分析及药敏鉴定系统，包括：光学及数字图像采集装置，用于提供光源并采集数字图像，利用其中设置的起偏器和检偏器增强被测物的数字图像，利用透镜修正数字图像的立体像差；光学及数字图像采集装置包括：导光板、面光源、上补光灯、下补光灯、起偏器、CCD数字相机、检偏器、载物台、光源控制开关和亮度调节旋钮，光学及数字图像采集装置为顺时针旋转90°的凹形体，该凹形体具有一凹槽。本实用新型达到以下效果：(1)视觉鉴定分析及药敏鉴定系统设置了起偏器和检偏器，成像更清楚；(2)光学及数字图像采集装置中利用起偏器和检偏器增强被测物图像，利用透镜修正立体像差；(3)能够明显区分细菌生长情况。
The utility model discloses a system for visual identification analysis and drug sensitivity identification, which comprises: an optical and digital image acquisition device, which is used to provide a light source and collect digital images, and use a polarizer and a polarizer arranged therein to enhance the detection of the measured object. Digital image, use lens to correct stereoscopic aberration of digital image; optical and digital image acquisition device includes: light guide plate, surface light source, upper fill light, lower fill light, polarizer, CCD digital camera, analyzer, carrier The object table, the light source control switch and the brightness adjustment knob, and the optical and digital image acquisition device are concave bodies rotated 90° clockwise, and the concave bodies have a groove. The utility model achieves the following effects: (1) the visual identification analysis and drug sensitivity identification system are equipped with a polarizer and a polarizer, and the imaging is clearer; (2) the polarizer and the polarizer are used in the optical and digital image acquisition device Enhance the image of the measured object, and use the lens to correct the stereoscopic aberration; (3) It can clearly distinguish the growth of bacteria.
Description
Technical field
The utility model relates to medical instruments field, specifically, relates to a kind of visual identification analysis based on XMVision and susceptibility identification systems.
Background technology
Disk diffusion method (Kindy-Bauer, KB method) is the conventional antibiotics susceptibility test method of the most widespread use of medical circle, is placed on the agar plate inoculating certain bacterium a certain amount of by the filter paper being soaked with finite concentration antibacterials of drying.After cultivating, can occur without bacterial growth district around the scraps of paper.Measure without bacterial growth district size (inhibition zone), the sensitivity of this bacterium to certain medicine can be judged.KB method is easy, economical, from 1976 just be applied to and have accumulated a large amount of data now, becoming the Main Basis of microbiotic medication, is the most frequently used method of Surveillance on antibiotic resistance.
yeast drug sensitive test is a Colorimetric dilution test.Appropriate dilution doses antifungal agent and a colorimetric indicator is all with the addition of in each plate.Obtain drug sensitivity tests by visual inspection antifungal drug minimum inhibitory concentration (showing without color change), it belongs to the current rare hospital of novel method and uses.
These two kinds of methods are not high for the equipment requirement in laboratory, and the basic hospital with microorganism detection ability can carry out this experiment completely, do not need to carry out large-scale equipment and introduce and laboratory transformation.And the drug sensitive test paper producer of domestic existing maturation, along with the increase of application units, experimental cost also can reduce greatly, reduces the economy expenditure of patient.
But in the interpretation of drug sensitivity tests, mostly by the artificial interpretation of experienced doctor.Computer vision brings dawn with the application of (computing machine) mode identification technology to the machine interpretable aspect of drug sensitivity tests: pattern-recognition is a primary mental ability of the mankind, and in daily life, people carry out " pattern-recognition " through being everlasting.Along with the forties in 20th century computing machine appearance and the fifties artificial intelligence rise, people certainly also wish general-purpose computers to replace or expand the part brainwork of the mankind.(computing machine) pattern-recognition develops rapidly in early 1960s and becomes a new disciplines.21 century is century of intellectuality, informationization, calculatingization, networking, in the century that this take digital computation as feature, as computer vision and the mode identification technology of artificial intelligence technology basic subject, will obtain huge development space.In the world, each large authoritative research institution and each major company start computer vision and mode identification technology to be paid attention to as strategic Research Emphasis all one after another.
Along with the development of science and technology, computer vision and computer pattern recognition application demand in the interpretation of drug sensitivity tests strengthens, especially, how gathered accurately and efficiently by machine and identify that microbial growth breeding situation becomes the technical matters of the most important thing.At present, in prior art, usually by optical microscope or optical amplifier ccd image acquisition system, the direct collection of image is carried out to microbial growth situation, cannot strengthen further or depolarized process image, often there is gather not fogging clear, make machine be difficult to distinguish growth of microorganism situation breeding situation.
Therefore, how to research and develop a kind of visual identification analysis based on XMVision and susceptibility identification systems, just become technical matters urgently to be resolved hurrily.
Summary of the invention
Unintelligible for solving the imaging existed in prior art, be difficult to the technical matters distinguishing bacterial growth situation, the utility model provides a kind of visual identification analysis based on XMVision and susceptibility identification systems, it is characterized in that, comprise: optics and digital image acquisition device, wherein
Described optics and digital image acquisition device, for providing light source and capturing digital image, utilizing the digital picture of the polarizer and the analyzer enhancing measured object wherein arranged, utilizing the stereoscopic disparity of lens correction digital picture;
Described optics and digital image acquisition device, comprise: light guide plate, area source, upper light compensating lamp, lower light compensating lamp, the polarizer, CCD digital camera, analyzer, objective table, light control switch and brightness regulation knob, described optics and digital image acquisition device are the concave body of 90 ° of turning clockwise, this concave body has a groove, wherein
Described light guide plate, is positioned at the top of described groove, and this light guide plate is fixedly connected with area source, is positioned at directly over described objective table;
The described polarizer, is fixed on below described light guide plate and area source, for generation of linearly polarized light;
The described upper light compensating lamp of at least one, is positioned at below the described polarizer, is distributed in around the described polarizer, for generation of macroscopic light;
Described objective table, is positioned at the bottom of described groove, and for culture dish holding or 96 orifice plates, this objective table is provided with adapter draw-in groove, for replacing the detection thing adapter of different size;
Three described light control switch, are positioned at described concave body side, for controlling the Kai Heguan of described area source, upper light compensating lamp and lower light compensating lamp respectively;
Three described brightness regulation knobs, are positioned at described concave body side, for controlling the brightness of described area source, upper light compensating lamp and lower light compensating lamp respectively;
Described CCD digital camera, is positioned at the lower left of described concave body inside, for capturing digital image;
Described analyzer is linear polarizer further, is positioned at described CCD digital camera front, for stop without scattering polarized light through, polarization direction is vertical with the polarization direction of the described polarizer.
Further, described optics and digital image acquisition device, also comprise Fei Nier lens, is positioned at below objective table, and during for revising collection 96 orifice plate image, the three-dimensional position, hole of causing that differs is blocked;
Described optics and digital image acquisition device, also comprise front surface mirror, be arranged on the bottom of described concave body, be positioned at the dead ahead with CCD digital camera immediately below described lower light compensating lamp, be miter angle with horizontal direction, for extending light path and stoping catoptron ghost image.
Further, described area source, is further, 5500K LED cold light source, and described light guide plate, is further, and be the light guide plate of 250mm × 180mm, described CCD digital camera, is further, the industrial digital camera of 3,000,000-1,000 ten thousand pixels.
Compared with prior art, the visual identification analysis based on XMVision described in the utility model and susceptibility identification systems, reach following effect:
(1) visual identification analysis of the present utility model and susceptibility identification systems are provided with the polarizer and analyzer, and imaging is clearer.
(2) utilize the polarizer and analyzer to strengthen measured object image in optics of the present utility model and digital image acquisition device, utilize lens correction stereoscopic disparity.
(3) visual identification analysis of the present utility model and susceptibility identification systems can obviously distinguish bacterial growth situation.
Accompanying drawing explanation
Accompanying drawing described herein is used to provide further understanding of the present utility model, forms a part of the present utility model, and schematic description and description of the present utility model, for explaining the utility model, is not formed improper restriction of the present utility model.In the accompanying drawings:
The visual identification analysis based on XMVision that Fig. 1 provides for the utility model and susceptibility identification systems structural drawing;
The optics that Fig. 2 provides for the utility model and digital image acquisition device structural drawing;
The digital image processing apparatus structural drawing that Fig. 3 provides for the utility model;
The susceptibility identification and analysis structure drawing of device that Fig. 4 provides for the utility model.
Embodiment
As employed some vocabulary to censure specific components in the middle of instructions and claim.Those skilled in the art should understand, and hardware manufacturer may call same assembly with different noun.This specification and claims are not used as with the difference of title the mode distinguishing assembly, but are used as the criterion of differentiation with assembly difference functionally." comprising " as mentioned in the middle of instructions and claim is in the whole text an open language, therefore should be construed to " comprise but be not limited to "." roughly " refer to that in receivable error range, those skilled in the art can solve the technical problem within the scope of certain error, reach described technique effect substantially.In addition, " couple " word and comprise directly any and indirectly electric property coupling means at this.Therefore, if describe a first device in literary composition to be coupled to one second device, then represent described first device and directly can be electrically coupled to described second device, or be indirectly electrically coupled to described second device by other devices or the means that couple.Instructions subsequent descriptions is for implementing better embodiment of the present utility model, and right described description is to illustrate for the purpose of rule of the present utility model, and is not used to limit scope of the present utility model.Protection domain of the present utility model is when being as the criterion depending on the claims person of defining.
Below in conjunction with accompanying drawing, the utility model is described in further detail, but not as to restriction of the present utility model.
Embodiment one:
Based on visual identification analysis and the susceptibility identification systems of XMVision, as shown in Figure 1, comprising: optics and digital image acquisition device 101, digital image processing apparatus 102 and susceptibility identification and analysis device 103, wherein,
Described optics and digital image acquisition device 101, couple mutually with described digital image processing apparatus 102, for providing light source and capturing digital image, be sent to described digital image processing apparatus 102, utilize the digital picture of the polarizer and the analyzer enhancing measured object wherein arranged, utilize the stereoscopic disparity of lens correction digital picture;
Described digital image processing apparatus 102, couple mutually with described optics and digital image acquisition device 101 and susceptibility identification and analysis device 103 respectively, for receiving described digital picture, carry out motion-captured, pile up complete when perceiving object, filtering and feature enhancing are carried out to image, according to target sample characteristic sum bar code information determination target specimen types after acquisition target sample image, choose detection method, obtain testing result, after processing, be sent to described susceptibility identification and analysis device 103;
Described susceptibility identification and analysis device 103, couples mutually with described digital image processing apparatus 102, also combines wherein preset CLIS standard of perfection and carries out susceptibility qualification, and provide expert's conclusion by algorithm for pattern recognition.
Composition graphs 2, described optics and digital image acquisition device 101 are described in detail, comprise: light guide plate 201, area source 202, upper light compensating lamp 203, lower light compensating lamp 204, the polarizer 205, CCD digital camera 206, analyzer 207, objective table 208, light control switch 209 and brightness regulation knob 210, clearly can find out that from Fig. 2 described optics and digital image acquisition device 101 are for turning clockwise the concave body of 90 °, this concave body has a groove, wherein
Described light guide plate 201, is positioned at the top of described groove, and this light guide plate 202 is fixedly connected with area source 202, is positioned at directly over described objective table 208.Described area source 202, is further, 5500K LED cold light source, and described light guide plate 201, is further, is the light guide plate of 250mm × 180mm.
The described polarizer 205, is fixedly covered in below described light guide plate 201 and area source 202, for generation of linearly polarized light;
The described upper light compensating lamp 203 of at least one, is positioned at below the described polarizer 205, is distributed in around the described polarizer, for generation of macroscopic light;
Described objective table 208, is positioned at the bottom of described groove, and for culture dish holding or 96 orifice plates, this objective table is provided with adapter draw-in groove, for replacing the detection thing adapter of different size;
Three described light control switch 209, are positioned at described concave body side, for controlling the Kai Heguan of described area source 202, upper light compensating lamp 203 and lower light compensating lamp 204 respectively;
Three described brightness regulation knobs 210, are positioned at described concave body side, for controlling the brightness of described area source 202, upper light compensating lamp 203 and lower light compensating lamp 204 respectively;
Described CCD digital camera 206, is positioned at the lower left of described concave body inside, and for capturing digital image, described CCD digital camera 206, is further, the industrial digital camera of 3,000,000-1,000 ten thousand pixels.
Described analyzer 207 is linear polarizer further, is positioned at described CCD digital camera 206 front, for stop without scattering polarized light through, polarization direction is vertical with the polarization direction of the described polarizer.
Described optics and digital image acquisition device 101, also comprise Fei Nier lens 211, is positioned at below objective table 208, and during for revising collection 96 orifice plate image, the three-dimensional position, hole of causing that differs is blocked;
Described optics and digital image acquisition device 101, also comprise front surface mirror 212, be arranged on the bottom of described concave body, be positioned at the dead ahead with CCD digital camera 206 immediately below described lower light compensating lamp 204, be miter angle with horizontal direction, for extending light path and stoping catoptron ghost image.
Composition graphs 3, described digital image processing apparatus 102, comprising: digital signal processing module 301, machine learning control module 303 and pattern-recognition control module 302, wherein,
Described digital signal processing module 301, couple mutually with described optics and digital image acquisition device 101 and described pattern-recognition control module 302 respectively, for receiving the digital picture of described optics and digital image acquisition device 101 transmission, filtering, image enhaucament, edge detection and region segmentation operation are carried out to described digital picture, also obtain barcode data for transferring bar-code identification interface, the digital image information according to detecting selects method of testing automatically;
Described pattern-recognition control module 302, couple mutually with described digital signal processing module 301 and susceptibility identification and analysis device 103 respectively, receive the digital picture of described digital signal processing module 301, carry out feature extraction and obtain proper vector, compare with the proper vector in feature database, after obtaining characteristic results, machine interpretable is carried out to object content;
Described machine learning control module 303, couple mutually with described digital signal processing module 101 and susceptibility identification and analysis device 103 respectively, receive the digital picture of described digital signal processing module 101, carry out feature extraction and obtain proper vector, when there is not this proper vector in described feature database, cluster analysis being carried out to proper vector and adds result to described feature database.
Composition graphs 4, described susceptibility identification and analysis device 103, comprises further: expert's conclusion and patient information processing module 401, reporting printing control module 402 and detection data upload download interface 403, wherein,
Described expert's conclusion and patient information processing module 401, respectively with described digital image processing apparatus 102, detect data upload download interface 403 and reporting printing control module 402 couples mutually, for the testing result according to described digital image processing apparatus 102, in conjunction with CLIS standard and identification of strains information experimental results and medication comment, be sent to described detection data upload download interface 403 and reporting printing control module 402 respectively;
Described reporting printing control module 402, couples mutually with described expert's conclusion and patient information processing module 401, for editing reporting format, printout result and transferring report data;
Described detection data office uploads download interface 403, couple mutually with described expert's conclusion and patient information processing module 401, be connected with LIS system by ASTM or H17 consensus standard, the laboratory test report outputed for downloading LIS system maybe will detect data upload to LIS system.LIS (Laboratory Information Management System) in the utility model is a set of Laboratory Information Management System aiming at hospital laboratory design, can by experimental apparatus and computing machine network consisting, make patient samples login, experimental data access, report examination & verification, print and distribute, numerous and diverse operating process such as experimental data statistical study achieve intellectuality, robotization and standardized management.Contribute to the holistic management level improving laboratory, reduce leak, improve quality inspection.The workflow of LIS: the inspection request proposed by clinician and workstation of being in hospital, generates the chemical examination bar-code label of respective patient, by corresponding with testing instruments for the essential information of patient while generation laboratory test report; After testing instruments generate result, system can according to corresponding relation, checked and approved by data-interface and result and should be able to realize checking information electronization, laboratory information management automated network system by automatically relative with patient information for check data, possess with doctor's advice two-way communication, adopt the critical function features such as bar code management means, financial automatic charging, instrument double-direction control.
Described expert's conclusion and patient information processing module 401, comprising: control of authority unit, data dictionary maintenance unit, standard of perfection maintenance unit and susceptibility qualification unit, wherein,
Described control of authority unit, logs in for responsible user and exits;
Described data dictionary maintenance unit, for configuring section office, clinical diagnosis and specimen types;
Described standard of perfection maintenance unit, for configuring microorganism classification, microbiotic information, being suitable for microbiotic, criterion, medication comment and Expert Rules;
Described susceptibility qualification unit, for providing expert's conclusion according to result, criterion and Expert Rules, also upload download interface acquisition patient information for calling described detection data office and upload report information, the interface calling reporting printing control module exports papery report.
Embodiment two:
The visual identification analysis that the utility model provides and susceptibility identification systems are applicable to disk diffusion method susceptibility, microwell plate drug sensitive experiment, mainly comprise: optics and digital image acquisition device 101, digital image processing apparatus 102, susceptibility identification and analysis device 103 3 parts.
Utilize the polarizer 205 and analyzer 207 to strengthen measured object image in optics and digital image acquisition device 101, utilize lens correction stereoscopic disparity; Digital image processing apparatus 102, comprise: Digital Image Processing module 301, the pattern-recognition control module 302 of drug sensitive test paper and type of culture medium, the machine learning control module 303 of the new scraps of paper, new nutrient culture media, the clustering algorithm of image modality and drug sensitive test paper type; Susceptibility identification and analysis device 103: by the learning training of machine to standards of perfection such as CLIS, produces the control module of qualification result.
Optics and digital image acquisition device 101: in optical system, light guide plate 201 and area source 202 play with the combination of polaroid and be the effect of the area source polarizer, and area source 202 exports the linearly polarized light of particular polarization.The sample of growth bacterium has depolarized effect for the polarized light of transmission or reflection, and the sample not growing bacterium can not change polarisation of light state.There is a polariscope before camera lens, polarization direction is vertical with polaroid, is not stopped by after depolarized light therethrough polariscope, and depolarized light can be normal through.The image gathered through CCD digital camera 206 presents following character: the sample for growth bacterium has bright background; The sample not growing bacterium is dark background.Bacterial growth situation can be obviously distinguished in target image.
The composition of optics and digital image acquisition device 101:
Light guide plate 201 and area source 202: adopt 5500K LED cold light source, parcel 250 × 180mm light guide plate and light scattering sheet material;
The polarizer 205: select linear polarizer as the polarizer in the present embodiment, concrete, the bottom of light guide plate and area source covers linear polarizer, produces linear polarization area source;
CCD digital camera 206: adopt 3,000,000 ~ 1,000 ten thousand pixel industrial digital camera and Fine-adjustment support;
Analyzer 207: select linear polarizer in the present embodiment, plays analyzer effect, stop without scattering polarized light through, polarization direction is vertical with the polarization direction of the line polarizer;
Light control switch 209 and brightness regulation knob 210: totally 3 groups, the open and close of difference chain of command light source 202, upper light compensating lamp 203, lower light compensating lamp 204 and brightness;
Upper light compensating lamp 203: for generation of macroscopic light;
Objective table 208: for culture dish holding or 96 orifice plates, above have adapter draw-in groove, for replacing the detection thing adapter of different size;
Luxuriant and rich with fragrance Neil lens 211: during for revising collection 96 orifice plate image, the three-dimensional position, hole of causing that differs is blocked;
Lower light compensating lamp 204: for the illumination to scraps of paper word, be convenient to pattern-recognition;
Front surface mirror 212: for extending the ghost problems of light path and solution normal mirror.
Digital image processing apparatus 102, comprising: digital signal processing module 301, machine learning control module 303 and pattern-recognition control module 302, wherein,
Digital signal processing module 301: after obtaining image, moving image is followed the tracks of, when recognize object pile up appropriate after automatic acquisition target image, and the operations such as filtering, image enhaucament, edge detection, region segmentation are carried out to image, be also responsible for calling bar-code identification interface acquisition barcode data if comprise this module of bar code information.According to the above-mentioned information instrument detected automatically according to the corresponding method of testing of feature selecting.
Machine learning control module 303: carried out feature extraction by pattern-recognition control module 302 pairs of images and obtained proper vector after digital signal processing module 301 pairs of image procossing, if proper vector no longer in feature database, is then re-started cluster analysis by machine learning control module 303 pairs of proper vectors and is added result to feature database.If feature database is imperfect or identification error can carry out greatly this operation raising accuracy of identification repeatedly.
Pattern-recognition control module 302: carry out feature extraction to providing the image of segmentation at digital signal processing module 301, obtain proper vector, compare with the feature in feature database, after obtaining a result, machine interpretable is carried out, as scraps of paper word, antibacterial circle diameter, searching 96 orifice bore position, interpretation hole position OD, position, interpretation hole CV value to object content.
Concrete, as shown in Figure 3, after digital picture is sent to digital signal processing module 301, digital signal processing module 301 carries out motion-captured, pile up complete when perceiving object, instrument completes shooting operation automatically, and filtering is carried out to image, image enhaucament, edge detection, the operations such as region segmentation, when comprising bar code information in image, then digital signal processing module 301 is also responsible for calling bar-code identification interface and then obtaining barcode data, after acquisition target sample image, digital signal processing module 301 is according to target sample feature, first the information such as bar code determine target sample type, then automatically choose detection method.After determining detection method, whether systems axiol-ogy " sets up proper vector ".
If result of determination is yes, then Dietary behavior identification control module 302, carry out target identification, and obtain recognition result, such as: if double dish then carries out Text region and inhibition zone measurement, if then experimentally board type selecting chromatic value detection (CV) or absorbance (OD) detection of 96 orifice plates.
If result of determination is no, then enter machine study control module 303, feature extraction carried out to digital picture and obtains proper vector, when there is not this proper vector in described feature database, then cluster analysis carried out to proper vector and result is added in described feature database.Then, whether systems axiol-ogy " sets up proper vector ": if testing result is "No", then repeat aforesaid operations; If testing result is "Yes", then Dietary behavior identification control module 302.
Susceptibility identification and analysis device 103, comprising: expert's conclusion and patient information processing module 401, reporting printing control module 402 and detection data upload download interface 403, wherein,
Expert's conclusion and patient information processing module 401: user obtains patient information by LIS LINK interface.And by digital image processing apparatus 102, detection target is detected, in conjunction with CLSI standard and identification of strains information experimental results and medication comment, Main functional units is as follows:
Control of authority unit: responsible user logins and publishes control and avoid maloperation;
Data dictionary maintenance unit: for configuring section office, clinical diagnosis, specimen types and other dictionary information;
Standard of perfection maintenance unit: for configuring microorganism classification, microbiotic information, being suitable for microbiotic and criterion, medication comment and Expert Rules;
Susceptibility qualification unit: for providing expert's conclusion according to testing result, criterion and Expert Rules.Be responsible for calling communication interface and obtain patient information and the task of uploading report information.Be responsible for calling Print Control interface and export papery report.
Reporting printing control module 402: to the printing control model of expert's conclusion, has reporting format editor, a function such as result printout, report data are transferred.
Detect data upload download interface 403: by consensus standards such as ASTM or HI7, be connected with LIS system by instrument, the laboratory test report that LIS system outputs can be downloaded and maybe will detect data upload to LIS system.
Concrete, as shown in Figure 4, after digital image processing apparatus 102 analyzes recognition image, the code (WHONET) of medicine, content (C), detect aperture position chromatic value (CV), detect aperture position absorbance (OD), detect aperture position decision content (N/P), minimal inhibitory concentration (MIC), antibacterial circle diameter (mm) etc. has been identified by algorithm for pattern recognition and has calculated, and obtain recognition result, described recognition result and the built-in standard of perfection of system, integrate through expert's conclusion and patient information processing module, expert's conclusion can be produced.Described standard of perfection includes but not limited to: the standards of perfection such as CLIS and industry standard of perfection, bacterium dictionary and classification, applicable microbiotic and judgement dividing value, medication comment and/or medicine guide; Described expert's conclusion includes but not limited to: the sensitivity of certain bacterial strain sample, the result of determination of resistance and medication guide and suggestion.User, after obtaining expert's conclusion, can be had two kinds of selection: A, is connected to the report of printer output papery by reporting printing control module 402; B, by detecting data upload download interface 403, report is uploaded to LIS system.
Compared with prior art, the visual identification analysis based on XMVision described in the utility model and susceptibility identification systems, reach following effect:
(1) visual identification analysis of the present utility model and susceptibility identification systems are provided with the polarizer and analyzer, and imaging is clearer.
(2) utilize the polarizer and analyzer to strengthen measured object image in optics of the present utility model and digital image acquisition device, utilize lens correction stereoscopic disparity.
(3) visual identification analysis of the present utility model and susceptibility identification systems can obviously distinguish bacterial growth situation.
Above-mentioned explanation illustrate and describes some preferred embodiments of the present utility model, but as previously mentioned, be to be understood that the utility model is not limited to the form disclosed by this paper, should not regard the eliminating to other embodiments as, and can be used for other combinations various, amendment and environment, and can in invention contemplated scope described herein, changed by the technology of above-mentioned instruction or association area or knowledge.And the change that those skilled in the art carry out and change do not depart from spirit and scope of the present utility model, then all should in the protection domain of the utility model claims.
Claims (3)
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201520169886.2U CN204536211U (en) | 2015-03-25 | 2015-03-25 | Visual identification is analyzed and susceptibility identification systems |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201520169886.2U CN204536211U (en) | 2015-03-25 | 2015-03-25 | Visual identification is analyzed and susceptibility identification systems |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN204536211U true CN204536211U (en) | 2015-08-05 |
Family
ID=53750197
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201520169886.2U Expired - Fee Related CN204536211U (en) | 2015-03-25 | 2015-03-25 | Visual identification is analyzed and susceptibility identification systems |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN204536211U (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108680576A (en) * | 2018-07-16 | 2018-10-19 | 珠海美华医疗科技有限公司 | A kind of microbial identification Analysis of Drug Susceptibility device |
| CN113984763A (en) * | 2021-10-28 | 2022-01-28 | 内蒙古大学 | Visual identification-based insect repellent pesticide effect experimental device and method |
| CN114255334A (en) * | 2021-12-13 | 2022-03-29 | 中国中医科学院中药研究所 | Shape feature acquisition device, database and identification system for traditional Chinese medicine |
| JP2022543275A (en) * | 2019-08-09 | 2022-10-11 | インターナショナル・ビジネス・マシーンズ・コーポレーション | Bacterial classification |
| CN117286018A (en) * | 2023-11-27 | 2023-12-26 | 珠海美华医疗科技有限公司 | Drug sensitivity analysis system based on microorganism detection |
-
2015
- 2015-03-25 CN CN201520169886.2U patent/CN204536211U/en not_active Expired - Fee Related
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108680576A (en) * | 2018-07-16 | 2018-10-19 | 珠海美华医疗科技有限公司 | A kind of microbial identification Analysis of Drug Susceptibility device |
| JP2022543275A (en) * | 2019-08-09 | 2022-10-11 | インターナショナル・ビジネス・マシーンズ・コーポレーション | Bacterial classification |
| CN113984763A (en) * | 2021-10-28 | 2022-01-28 | 内蒙古大学 | Visual identification-based insect repellent pesticide effect experimental device and method |
| CN113984763B (en) * | 2021-10-28 | 2024-03-26 | 内蒙古大学 | Insect repellent efficacy experimental device and method based on visual recognition |
| CN114255334A (en) * | 2021-12-13 | 2022-03-29 | 中国中医科学院中药研究所 | Shape feature acquisition device, database and identification system for traditional Chinese medicine |
| CN117286018A (en) * | 2023-11-27 | 2023-12-26 | 珠海美华医疗科技有限公司 | Drug sensitivity analysis system based on microorganism detection |
| CN117286018B (en) * | 2023-11-27 | 2024-02-20 | 珠海美华医疗科技有限公司 | Drug sensitivity analysis system based on microorganism detection |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104749180B (en) | Machine Vision Identification Analysis and Drug Sensitivity Identification System | |
| CN204536211U (en) | Visual identification is analyzed and susceptibility identification systems | |
| CN101685060B (en) | Sample imaging apparatus | |
| Yagi | Color standardization and optimization in whole slide imaging | |
| EP0656938B1 (en) | A technique to count objects in a scanned image | |
| US9109194B2 (en) | Device for harvesting bacterial colony and method therefor | |
| CN105388147A (en) | Detection method for body fluid based on special test paper | |
| US20080310692A1 (en) | System and method of organism identification | |
| Bohnenkamp et al. | Hyperspectral signal decomposition and symptom detection of wheat rust disease at the leaf scale using pure fungal spore spectra as reference | |
| JP2017504307A5 (en) | ||
| US11697833B2 (en) | Antimicrobial susceptibility testing device and method for use with portable electronic device | |
| CN103257061B (en) | The preparation method of microscopic examination sample and the device for checking sample covering quality | |
| JP2010104301A (en) | Method, apparatus, and program for detecting microorganism | |
| JP7416136B2 (en) | Colony detection device | |
| CN109415753A (en) | Method and system for identifying gram type of bacteria | |
| Inoue et al. | Color standardization and optimization in whole slide imaging | |
| JP2016049055A (en) | Verification method of microorganism inspection apparatus, verification device in microorganism inspection apparatus, and program | |
| CN109661473A (en) | For determining the presence of microorganism and identifying the method, system and computer program product of the microorganism | |
| US20090317895A1 (en) | Observation device | |
| Dragotakes et al. | Automated measurement of cryptococcal species polysaccharide capsule and cell body | |
| Pomeranz et al. | High‐resolution computational imaging of leaf hair patterning using polarized light microscopy | |
| Peng et al. | Automatic monitoring system for seed germination test based on deep learning | |
| Gershenzon et al. | New software for lens retro‐illumination digital image analysis | |
| CN104359925A (en) | System for realizing automatic detection of electronic glass defects | |
| CN108235831B (en) | The control method and control system and electronic equipment of lamps and lanterns |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20150805 |