CN101943663B - Measuring analytical system and measuring analytical method for distinguishing diffraction image of particles automatically - Google Patents
Measuring analytical system and measuring analytical method for distinguishing diffraction image of particles automatically Download PDFInfo
- Publication number
- CN101943663B CN101943663B CN2010102217147A CN201010221714A CN101943663B CN 101943663 B CN101943663 B CN 101943663B CN 2010102217147 A CN2010102217147 A CN 2010102217147A CN 201010221714 A CN201010221714 A CN 201010221714A CN 101943663 B CN101943663 B CN 101943663B
- Authority
- CN
- China
- Prior art keywords
- particulate
- diffraction image
- data
- diffraction
- image
- 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
Images
Landscapes
- Investigating Or Analysing Materials By Optical Means (AREA)
- Investigating, Analyzing Materials By Fluorescence Or Luminescence (AREA)
Abstract
The invention relates to a measuring analytical system and a measuring analytical method for distinguishing a diffraction image of particles automatically. The system comprises a sample stream formed by flow particles, a coherent excitation light beam intersectant with the sample stream, a first scattered light receiving objective lens part with a first central scattering angle of a coherent scattering light beam, a first light split and filtering part, a first imaging measurement and data output part, an image processing circuit, a computer part and a display part connected with the image processing circuit and the computer part. The method comprises the following steps of: acquiring the corresponding adjustable wavelength and polarizing diffraction image data; storing the data; performing the conversion of image space coordinates; performing characteristic screening and selecting a characteristic area; selecting the characteristic of a diffraction image mode; and determining the position of the measured particles in the characteristic parameter vector sample space of the diffraction image. The measuring analytical system and the measuring analytical method haves the advantage that a large number of particles can be analyzed and distinguished quickly according to the characteristic of a three-dimensional structure in the particles, and the particles are not needed to be dyed.
Description
Technical field
The present invention relates to a kind of diffraction image measuring and analysis system and method.Particularly relate to wavelength and the adjustable diffraction image signal of polarization that a kind of measurement is formed by particulate coherent scattering light; Calculate to extract itself and the characteristics of image of particulate interior three-dimensional architectural feature height correlation, the diffraction image measuring and analysis system and the method for distinguishing particulate automatically of other particulate are debated in analysis quick and precisely automatically.
Background technology
In RESEARCH ON CELL-BIOLOGY; Biotechnology research; Medicament research and development, environmental pollution monitoring, the researchist needs quick and precisely to analyze to distinguish that a large amount of linear-scale are the method and the instrument system of one micron to hundreds of microns single particulate in many fields such as atmospheric science.Under a lot of situation, comprise with the cell be representative biological particle particulate function or its to the interaction in the external world usually with its three-dimensional structure form tight association.Therefore characteristic and the difference through contrast particulate three-dimensional structure form is to analyze to distinguish one of best approach of particulate.For example optical microscope is that to be used to observe the microgranular texture form also be one of at present the most frequently used instrument to the mankind the earliest.But, be difficult to that the particulate group that comprises a large amount of particulates is carried out express-analysis and distinguish because such reasons uses the optical microscope analysis to distinguish that the method for particulate has limitation.First; Optical microscope commonly used is (like fluorescent microscope; Bright field or dark ground microscope etc.) be based on the incoherent imaging principle design; Its image is to form through the two-dimensional projection to the particulate three-dimensional structure, and the microgranular texture that utilizes this image to survey is characterized as structure two-dimensional projection characteristic, can't truly reflect the three-dimensional structure form and the characteristic of particulate.The second, because micro-image is the two-dimensional projection to the particulate three-dimensional structure, graphical analysis distinguishes that particulate needs very complex image analytical approach usually in view of the above; It is all the more so when analysis has the cell of complex three-dimensional structural form; Generally need manual analysis, thereby be difficult to robotization, and the operation of relevant optical microscope also needs manually-operated with image measurement based on the image analysis method of optical microscope; Time-consuming, it is extremely low to be prone to generation error and analysis speed.The 3rd; The many particulates that comprise cell do not contain characteristic absorption or can fluorescigenic molecule in visible light and near-infrared wavelength scope; Therefore must could under optical microscope, observe its structural form after the dyeing; Dyeing often needs expensive reagent and complicated time-consuming operation, and might produce disturbing effect to observed biological particle such as cell etc.In recent years, light microscope technique has obtained new development, for example uses the burnt technology of copolymerization, can obtain the very short two dimensional image of several depth of field, through the three-dimensional structure form of two dimensional image stack reconstruction particulate.Copolymerization confocal optical microscope technology has only solved above-mentioned first problem, but needs the longer time, and other problems is still unresolved.
Since the sixties in last century to being that the particulate of representative is carrying out under the quick flow state of laminar flow on the research basis of optical measurement with the cell; Flow cytometer becomes a kind of collector mechanics; Laser technology, the instrument that can carry out quick Measurement and analysis of the great achievement of photoelectric measurement and Study on Data Processing achievement to a large amount of individual cells.Flow cytometer utilizes concentric nozzle and liquid pressure difference in the sample chamber, to form the laminar flow of being made up of sample flow and sheath stream.Ring wraps in the outer sheath stream of sample flow reduces to contain particulate through pressure difference sample flow diameter; Force particulate to flow and pass through excitation beam with single-row mode; The particulate of the light beam irradiates that is excited can produce the scattered light identical with excitation wavelength, and its intensity changes with scattering angle.The scattered light that this wavelength equates with excitation wavelength is also referred to as elastic scattering light, is the radiation that produces owing to molecule electric dipole that the inner light beam electromagnetic field inducing that is excited of particulate forms.The induction molecule electric dipole CONCENTRATION DISTRIBUTION of particulate inside is by its inner optical index distribution and expression, so particulate interior three-dimensional structure can be expressed through its optical index distributed in three dimensions.Inhomogeneous or different with its carrier material optical index that is suspended like the optical index distributed in three dimensions of particulate inside, scattered light promptly exists, and normally particulate by the strongest signal in the various light signals that produced under the condition of illumination.The particulate of the light beam irradiates that is excited also can produce the fluorescence of its wavelength greater than excitation wavelength as containing fluorescence molecule, and fluorescence is the radiant light that the back produces because the inner fluorescence molecule of particulate is excited.Many particulates of cell that comprise do not contain or contain fluorescence molecule seldom, so these particulates only just can produce the sufficient intensity fluorescence signal after dyeing.Flow cytometer can carry out express-analysis to particulate and distinguish that its processing speed can reach the thousands of particulates of per second through measuring fluorescence and the scattered light signal that dyeing back particulate produces.Compare with the optical micro analysis method, the group and the statistical that comprise a large amount of particulates in analysis are furnished with its special advantages.Since the last century the eighties, flow cytometer is used widely in pollution monitoring and the other field field in RESEARCH ON CELL-BIOLOGY.
At present the flow cytometer product can be divided into angle integral form and two kinds of incoherent image-types by its optical signalling metering system, and most to have flow cytometers now be the angle integral form.In this flow cytometer, the scattered light signal that the particulate that flows produces under the incident beam irradiation is accepted and the corresponding output of generation electric signal by different monomer photoelectric sensor (like photodiode, photomultiplier etc.) with fluorescence signal.Monomer sensor is for only exporting the sensor of 1 electric signal, its signal intensity be proportional to scattered light or fluorescence signal intensity sensor area with respect to light source the integrated value in the formed three-dimensional viewpoin, abbreviate scattered light or fluorescence signal as.Fluorescence signal whether exist with the specific molecular (like certain protein molecule that can combine in the cell) that particulate inside comprises with fluorescence molecule and quantity relevant; Scattered light signal behind the angle integration is that granularity is relevant with particulate volume and interior lights refractive index uniformity coefficient only then; Different with the optical index distribution, granularity is the angle integrated value that optical index distributes.Scattered light and fluorescence signal are combined, carry out data analysis, can analyze automatically the group that comprises a large amount of particulates and distinguish, reach the particulate in the group is carried out the purpose that quick kind is distinguished through computing machine.Angle integral form flow cytometer can be measured 2 to 10 fluorescence signals and 2 scattered light signals usually at present.Fluorescence signal does not comprise structural information; Though 2 scattered light signals (forward direction and lateral scattering light signal) can provide the information of volume and internal particle degree; But its structural information content is extremely limited, thereby angle integral form flow cytometer mainly relies on fluorescence signal that particulate is carried out express-analysis to distinguish.
Image measurement technology begins to be applied at flow cytometer in recent years, forms incoherent image-type flow cytometer.This flow cytometer is based on the traditional optical microscopy; Utilize like imageing sensors such as charge-coupled device (CCD) cameras and measure the angle distribution of incoherent light signal in the space; Exportable fluorescence; View data such as bright field and dark field, but various image is the two-dimensional projection of particulate three-dimensional structure.Compare with angle integral form flow cytometer, incoherent image-type flow cytometer can and be exported multiple image to each mobile measuring fine particles, and its structural information that comprises obviously greatly increases, and therefore can carry out finer analysis to microgranular texture.But it is identical with the traditional optical microscopy; Utilize the image-type flow cytometer of incoherent light signal imaging to have similar limitation; As can't distinguish particulate according to particulate three-dimensional structure configuration state signature analysis, need could obtain fluoroscopic image etc. to particulate dyeing.What is more important; Because the relation between two-dimensional projection image and the three-dimensional structure is very complicated; Usually need manual analysis; Therefore be difficult to realize the group that comprises a large amount of particulates being carried out the automated graphics data analysis, can't reach the particulate in the group is carried out the purpose that quick kind is distinguished through computer software.Because picture signal type flow cytometer can be measured hundreds of to thousands of particulates by per second, its image signal data total amount is very big, and as realizing the automated graphics signal analysis, its application receives great restriction.
As previously mentioned, the particulate under the excitation beam irradiation can produce scattered light, and its wavelength is identical with excitation wavelength.If excitation beam is one to have height coherence's light beam, scattered light also has the height coherence under the condition that wavelength equates.The particulate that contains fluorescence molecule also can produce fluorescence simultaneously, and its wavelength is different with the excitation beam wavelength, does not have the coherence.As use have the height coherence laser beam as excitation beam; The scattered light electromagnetic field with height coherence that the inner induction molecule electric dipole of particulate produces can form in the space because the light intensity that phase differential causes distributes with the diffraction that angle changes; Distribution of coherent scattering diffraction of light and polarization state are by the optical index of excitation beam wavelength and polarization state and particulate inside and the distributed in three dimensions decision of its suspending medium refringence; Therefore diffraction distribution and the polarization state and the particulate interior three-dimensional structural form height correlation of coherent scattering light intensity are also relevant with excitation beam wavelength and polarization state.Utilize imageing sensor to measure the distribution of coherent scattering diffraction of light and be diffraction image.Through several diffraction image computational analysis particulate Three Dimensions Structure, can obtain particulate three-dimensional structure form or relevant information.The X-ray diffraction technology that is applied as the laser hologram imaging technique in the visible wavelength range the earliest and in the X-ray wavelength coverage, calculates the biomacromolecule three-dimensional structure of this method.Generally speaking, reckoning particulate three-dimensional structure need be done complicated three-dimensional structure again and rebuild calculating after obtaining enough several (5 to 10 width of cloth or more) diffraction images under the different excitation beam incident angles.In cell streaming appearance,, be difficult to obtain simultaneously the diffraction image data of enough several different angles,, also can not in several seconds or shorter time, accomplish three-dimensional reconstruction and calculate even can obtain enough several images because particulate flows fast.When particulate flows through incident beam in addition, can there be the minimum optical interface of radius-of-curvature it near in laminar flow, comprises that sheath flows with the different interfaces of causing of refractive index of fluid chamber's material such as glass etc. etc.The minimum optical interface of these radius-of-curvature can cause usually becomes the scattering of image noise light field, generally can greater than or distribute much larger than diffraction intensity that particulate produced, make that the measured diffraction image signal contrast relevant with microgranular texture is very little.Obtaining the high-quality optical diffraction image relevant with microgranular texture need reduce or eliminate because the image noise that these optical interfaces produced is a technical matters that is difficult to solution.How to utilize the diffraction image data that obtained in addition, obtain with the information of particulate Three Dimensions Structure height correlation and in view of the above express-analysis comprise the group and the classification of a large amount of particulates, also be a still unsolved difficult problem.Because these problems although present commercial flow cytometer uses laser beam as excitation beam mostly, all can't be distinguished particulate through measuring with the mode of analyzing diffraction image.In angle integral form flow cytometer; Its measured scattered light signal is the angle integration; Therefore because the diffraction that changes with angle that the scattered light coherence causes is distributed in disappearance basically in the signal behind the over-angle integration, resulting architectural feature includes only the simple feature of volume and internal particle degree class; And in incoherent picture signal type flow cytometer; Its fluoroscopic image is because wavelength of fluorescence is incoherent image with respect to the excitation beam wavelength change; Bright field or dark field image then generally obtain under incoherent white light condition, also belong to incoherent image.
Recently study on the basis for many years with experiment in theory to the particulate light scattering that comprises cell; A kind of novel diffraction image type flow cytometer method is announced; Go through visible list of references (X.H.Hu for example; K.M.Jacobs; J.Q.Lu, " Flow cytometer apparatus for three dimensional diffraction imaging and related methods ", PCT Application No.WO 2009/151610 by East Carolina University).This novel diffraction image type flow cytometer has proposed laminar flow is placed mainly the design concept of the fluid chamber that is formed by liquid; Use as imageing sensors such as charge-coupled device camera note down particulate the angle distribution of generation coherent scattering light, can obtain the diffraction image signal of high-contrast.Experimental result shows that this novel diffraction image signal type flow cytometer can distinguish the particulate with different three-dimensional structures according to the signal analysis of particulate diffraction image, goes through visible list of references (K.M.Jacobs for example, L.V.Yang; J.Ding, A.E.Ekpenyong, R.Castellone; J.Q.Lu; X.H.Hu, " Diffraction imaging of spheres and melanoma cells with a microscope objective ", Journal of Biophotonics; Vol.2, pp.521-527 (2009); K.M.Jacobs, J.Q.Lu, X.H.Hu, " Development of a diffraction imaging flow cytometer ", Optics Letters, vol.34, pp.2985-2987 (2009)).Through particulate photon diffusion models and extensive numerical evaluation based on the classical electrodynamics theory; Proved the particulate two dimension diffraction image and its three-dimensional structure height correlation that are obtained by diffraction image type flow cytometer at present, can therefrom extract the many characteristics relevant, gone through it is thus clear that list of references (J.Q.Lu for example with the particulate Three Dimensions Structure; P.Yang; X.H.Hu, " Simulations of Light scattering from a biconcave red blood cell using the FDTD method ", Journal of Biomedical Optics; Vol.10,024022 (2005); R.S.Brock, X.H.Hu, D.A.Weidner; J.R.Mourant; J.Q.Lu, " Effect of detailed cell structure on light scattering distribution:FDTD study of a B-cell with 3D structure constructed from confocal images ", Journal of Quantitative Spectroscopy&Radiative Transfer; Vol.102, pp.25-36 (2006)).Although above-mentioned research provides System and method for how under flow state, to measure particulate two dimension diffraction image signal fast and through proved the high correlation of two-dimentional diffraction image characteristic and particulate Three Dimensions Structure based on the theoretical particulate photon diffusion models of classical electrodynamics; But from the diffraction image DATA DISTRIBUTION, extract single particulate interior three-dimensional structural parameters through particulate photon diffusion models and numerical evaluation and need carry out great amount of calculation; Even also normal needs several hours or longer time could obtain the reliable parameter data of the three-dimensional structure of single particulate on mainframe computer, can't analyze a large amount of particulates.How to measure several diffraction images simultaneously, therefrom calculate and obtain the image model characteristic relevant, reach and quick and precisely analyze the purpose of distinguishing particulate, still do not have efficient system design and analytical approach with particulate interior three-dimensional structure height.
Summary of the invention
Technical matters to be solved by this invention is; Provide a kind of and can measure several wavelength and the adjustable diffraction image of polarization; And, analyze and to distinguish the group that comprises a large amount of particulates and it the diffraction image measuring and analysis system and method for distinguishing particulate automatically of classifying automatically according to these view data express-analysis extractions and the relevant image model characteristic of particulate interior three-dimensional structural form feature height.
The technical scheme that the present invention adopted is: a kind of diffraction image measuring and analysis system and method for distinguishing particulate automatically; Wherein, Automatically distinguish the diffraction image measuring and analysis system of particulate; Include the sample flow of forming by the particulate that flows, also be provided with the coherent excitation light beam that intersects with sample flow, measure the display part that first scattered light of the coherent scattering light beam with first center scattering angle that the particulate that excited by the coherent excitation light beam penetrates is accepted object lens part, first beam split and filtering part, first imaging measurement and data output section branch, image processing circuit and computing machine part and linked to each other with image processing circuit and computer department branch; Wherein,
Described first beam split and filtering partly are used for the scattered light of the particulate emission that is received is carried out beam split and filtering;
Described first imaging measurement and data output section branch are used for imaging measurement and data output are carried out in beam split and filtered scattered light; Thereby the diffraction image that acquisition is formed by the coherent scattering light beam; Include respectively first condenser lens and second condenser lens that the scattered light of the different directions that beam split and filtering part are penetrated focuses on; First imageing sensor that is positioned at the first condenser lens back be positioned at second imageing sensor of the second condenser lens back, and the back that is connected first imageing sensor is carried out the first data I/O mouth of data output input and is connected the second data I/O mouth of the second imageing sensor back;
Described image processing circuit and computing machine partly are used to receive the output information that data output section is divided, and extraction different wave length and polarizing diffraction view data characteristic also calculated, analyzed and distinguish.
Described display part is with calculating, analyze and distinguishing that result's statistics shows.
Second scattered light that also is provided with the coherent scattering light beam with second center scattering angle that is used to measure the particulate ejaculation that is excited by the coherent excitation light beam is accepted the object lens part; The scattered light that is received is carried out second beam split and the filtering part of beam split and filtering; Beam split and filtered scattered light are carried out second imaging measurement and the data output section branch of imaging measurement and output scattered light diffractogram; Described second imaging measurement and data output section are divided with described image processing circuit and computer department branch and are linked to each other; Wherein, described second scattered light is accepted object lens parts, second beam split and filtering part and second imaging measurement and data output section and is divided that to accept object lens part, first beam split and filtering part and first imaging measurement and data output section separation structure identical with described first scattered light.
The three-dimensional viewpoin scope of the coherent scattering light beam that the measured particulate that is excited by the coherent excitation light beam penetrates is at 0 to π sterad.
Described first scattered light is accepted object lens and partly is arranged in order the microcobjective of forming by a plurality of lens.
Described first beam split and filtering partly include light splitting piece; Scattered light and first narrow band pass filter that set gradually and first polarizing filter that reception transmits from light splitting piece, and receive scattered light and second narrow band pass filter that set gradually and second polarizing filter that reflects from light splitting piece.
Described first beam split and filtering partly include light splitting piece and first narrow band filter slice and second narrow band pass filter that receive from the different directions scattered light that light splitting piece penetrated, and first polarizing filter and second polarizing filter that is positioned at the second narrow band pass filter back that are positioned at the first narrow band filter slice back; Perhaps described beam split and filtering partly are made up of polarization arrowband light splitting piece, or are made up of light splitting piece and prism or diffraction grating.
The back of described first polarizing filter and second polarizing filter is provided with the light intensity attenuation sheet.
The described first data I/O mouth and the second data I/O mouth structure are identical; The supply voltage of required bias voltage of imageing sensor work and imageing sensor cooling power supply is provided; The clock signal of imageing sensor control signal and sensor output analog pulse data-signal is provided, and the data-signal of exporting after the digitizing of Sensor Analog Relay System pulse data signal is provided.
Described image processing circuit and computer department branch include respectively the first imageing sensor power supply and the second imageing sensor power supply that bias voltage and supply voltage are provided to first imageing sensor and second imageing sensor; Receive first image processing circuit and computing machine and second image processing circuit and the computing machine of picture signal respectively through the first data I/O mouth and the second data I/O mouth
Wherein, described first image processing circuit and computing machine and second image processing circuit and computing machine are two identical in structure computing machines, or are same computing machine; Image processing circuit and computing machine can carry out stores processor and calculating to the picture signal that is received, and extract different wave length and polarizing diffraction image model characteristic parameter and export display parameter data.
Automatically distinguish the analytical approach of the diffraction image measuring and analysis system of particulate, include following steps:
First step:, obtain corresponding wavelengthtunable and polarizing diffraction view data through imageing sensor and the particulate scattered light space distribution of corresponding light drive test amount under the multi-wavelength excitation light beam irradiates;
Second step: all particulates in the tested particulate group are differentiated criterion different wave length and polarizing diffraction view data and population be transferred to and supply the next step graphical analysis to use in the image processing circuit storer in the computing machine;
Third step: distribute the grey scale pixel value figure place by being 8 greater than 8 potential drop rank and carrying out the image space coordinate transform according to its image distribution pattern according to the grey scale pixel value of diffraction image;
The 4th step: carry out characteristic through different wave length after using the characteristic discriminating method to conversion and polarizing diffraction image and screen also selected characteristic zone;
The 5th step: in characteristic area, different wave length and polarizing diffraction image after the characteristic examination are chosen the diffraction image pattern feature.
The 6th step: form the diffraction image feature parameter vector and confirm the position of measured particulate at diffraction image feature parameter vector sample space according to this vector according to the pattern feature parameter of different wave length and polarizing diffraction image;
The 7th step: confirm all particulates in the tested particulate group behind the position of diffraction image feature parameter vector sample space, differentiate criterion according to particulate the population of the position distribution of feature parameter vector sample space and input tested particulate group is categorized into different populations automatically and exports corresponding data.
Described wavelengthtunable of first step and polarizing diffraction view data are: scattering light wavelength and the polarization of when measuring the diffractogram data, selecting the particulate under the multi-wavelength excitation light beam irradiates to penetrate through the light path element before the imageing sensor.
The described population of second step is differentiated criterion and is obtained by particulate group training data.
The described diffraction image pattern feature of the 5th step is based on the related diffraction image pattern feature that calculates of pixel grey scale.
The 5th step described based on the pixel grey scale coulometer at last based on input image data being divided into the parallel algorithm of calculating respectively after a plurality of data stream.
Automatically diffraction image measuring and analysis system and the method for distinguishing particulate of the present invention; Be utilized in the coherent scattering light that coherent excitation light beam irradiates current downflow particulate is produced; Measure the wavelength and the adjustable diffraction image data of polarization of its space distribution; Diffraction image pattern feature parameter is extracted in express-analysis, and the feature parameter vector of composition and particulate interior three-dimensional architectural feature height correlation is classified to the particulate in the micrometer grain group of institute in view of the above automatically.The present invention has and can distinguish a large amount of particulates and need not the advantage to particulate dyeing according to the express-analysis of particulate interior three-dimensional architectural feature, does not influence the inside Biochemical processes of institute's micrometer grain such as cell etc. and measure cost low.
Description of drawings
Fig. 1 is the structural representation that the present invention distinguishes diffraction image measuring and analysis system first embodiment of particulate automatically;
Fig. 2 is the structural representation that the present invention distinguishes diffraction image measuring and analysis system second embodiment of particulate automatically;
Fig. 3 is the process flow diagram that the present invention debates the diffraction image Measurement and analysis method of other particulate automatically;
Fig. 4 is the process flow diagram of the related software for calculation of diffractogram pixel gray scale of the present invention;
Fig. 5 is that the present invention carries out the design sketch that Flame Image Process produces in the related computation process of diffractogram pixel gray scale.
Wherein:
1: particulate 2 flows: sample flow
3: coherent excitation light beam 4: the coherent scattering light beam
5: microcobjective 6: light splitting piece
8: the first polarizing filters of 7: the first narrow band pass filters
10: the first scattered lights of 9: the first condenser lenses
12: the first data I/Os of 11: the first imageing sensors mouth
14: the first image processing circuits of 13: the first imageing sensor power supplys and computing machine
16: the second polarizing filters of 15: the second narrow band pass filters
18: the second scattered lights of 17: the second condenser lenses
20: the second data I/Os of 19: the second imageing sensors mouth
22: the second image processing circuits of 21: the second imageing sensor power supplys and computing machine
23: the first center scattering angles 24: scattered beam
25: micro-eyepiece 26: light splitting piece
27: narrow band pass filter 28: polarizing filter
29: condenser lens 30: scattered light
31: imageing sensor 32: data I/O mouth
35: narrow band pass filter 36: polarizing filter
37: condenser lens 38: scattered light
39: imageing sensor 40: data I/O mouth
43: the second center scattering angle A: first scattered light is accepted the object lens part
B: first beam split and filtering portion C: first imaging measurement and data output section branch
D: image processing circuit and computing machine part A ': scattered light is accepted the object lens part
B ': beam split and filtering portion C ': imaging measurement and data output section branch
Embodiment
Below in conjunction with accompanying drawing and embodiment the diffraction image measuring and analysis system and the method for particulate distinguished automatically of the present invention made detailed description.
Automatically the diffraction image measuring and analysis system of particulate and a kind of implementation method of method distinguished of the present invention; Utilization may be adjusted to the diffraction image of picture systematic survey different wave length and polarization; Through the pixel grey scale relevant parameter of several diffraction image data of computer software express-analysis, extract image model statistical parameter vector and the group that comprises a large amount of particulates is planted heap sort fast then according to these parameter vectors and population resolution criterion.The present invention also can realize through the method that computer software is planted heap sort to the image model that is comprised in the diffraction image data to the group that comprises a large amount of particulates fast.Method described in the invention can also be through other optical system, and method and computer software are realized.
Automatically one of the diffraction image measuring and analysis system of particulate and scheme of method distinguished of the present invention; The scattered light that on different wave length, is concerned with separately that the particulate that is to use micro objective to accept to be excited by the multi-wavelength incident light produces; Combination through a plurality of imageing sensors and other optical elements; Be chosen in and measure the distribution of scattered light space angle under different wave length, polarization and the angular range condition respectively; Export the diffraction image data of several different wave lengths and polarization direction, utilize computer software analysis different wave length and polarizing diffraction view data separately with linked character, and then can quick and precisely analyze the method for distinguishing a large amount of particulates.Compare with incoherent image-type flow cytometer with traditional angle integral form, the diffraction image type method that the present invention narrated can be carried out rapid and accurate analysis to particulate through the diffraction image characteristic relevant with particulate interior three-dimensional structural form feature height and distinguished.
As shown in Figure 1; Automatically the diffraction image measuring and analysis system of distinguishing particulate of the present invention; Include the sample flow of being made up of the particulate 1 that flows 2, first scattered light of the coherent scattering light beam 4 with first center scattering angle 23 of the particulate ejaculation that also be provided with the coherent excitation light beam 3 that intersects with sample flow 2, is excited by coherent excitation light beam 3 is accepted the display part that object lens part A, first beam split and filtering part B, first imaging measurement and data output section are divided C, image processing circuit and computing machine part D and linked to each other with image processing circuit and computing machine part D;
Wherein,
Can be from the three-dimensional viewpoin scope of the tested coherent scattering light beam 4 that particulate penetrated that is excited at 0 to π sterad;
Described first scattered light is accepted the object lens part A and is arranged in order the microcobjective of forming with suitable numerical aperture and operating distance 5 by a plurality of lens and constitutes.It designs and produces need satisfy in the different three-dimensional viewpoins measures the scattered light distribution requirements through diffraction image.
The light intensity of described tested coherent scattering light beam 4 also is that scattering angle changes with the angle between the direction of beam propagation and the incident beam direction of propagation.By of the numerical aperture decision of the three-dimensional viewpoin scope of the tested coherent scattering light beam 4 of microcobjective 5 collections by microcobjective 5; Light intensity in this angular range promptly forms diffraction image with the variation of scattering angle, and the first center scattering angle 19 can be used to indicate the angle position, center of this angular range.The three-dimensional viewpoin scope of tested coherent scattering light beam 4 can be at 0 to π sterad, and the center scattering angle can be between 5 to 180 degree, and wherein, the center scattering angle is by 23 expressions of the first center scattering angle.According to the scope of its center scattering angle, tested coherent scattering light beam 4 can be described as forward direction, side direction or backscattering light beam.The forward scattering light beam is defined as its center scattering angle between 5 to 90 degree, and the lateral scattering light beam be its center scattering angle between 45 to 135 degree, the backscattering light beam be its center scattering angle 90 to 180 spend between.
Described first beam split and filtering part B be used for to the scattered light of the particulate emission that excited by coherent excitation light beam 3 carry out beam split and filtering; Described beam split and filtering part B include light splitting piece 6; Receive light splitting piece 6 transmitted scattered light and first narrow band pass filter 7 that set gradually and first polarizing filter 8, and receive light splitting piece 6 specular scattering light and second narrow band pass filter 13 that set gradually and second polarizing filter 14.The back of described first polarizing filter 8 and second polarizing filter 14 can also be provided with the light intensity attenuation sheet, can adjust the light intensity that gets into sensor automatically according to the imageing sensor light intensity data, avoids the sensor overload saturated.
Beam split with and filtering part can obtain according to the different light paths design; Therefore; Beam split in described first minute and filtering part B can also be: include polarization spectro sheet and first narrow band filter slice 7 and second narrow band pass filter 15 that receive the different directions scattered light that is penetrated from the polarization spectro sheet, and be positioned at first polarizing filter 8 of first narrow band filter slice, 7 back and be positioned at second polarizing filter 16 of second narrow band pass filter, 15 back; Perhaps described beam split and filtering part B are made up of polarization arrowband light splitting piece, or are made up of light splitting piece and prism or diffraction grating.
The optical wavelength bandwidth of general narrow band pass filter be 0.5 nanometer between 50 nanometers, the bandwidth center wavelength-tunable, the light wave that has only wavelength to be positioned within the bandwidth could pass through under the condition of less decay.The light wave that polarizing filter only allows to be in certain polarization state passes through under the condition of less decay, for example horizontal linear polarization state or left-hand polarization state or the like.Owing to bandwidth center wavelength and polarization that the first optical filtering part and second filters part can be distinguished independent adjustment, can export image processing circuit respectively to by the first data I/O mouth 12 and the second data I/O mouth 20 behind the diffraction image according to different particulate analysis requirement acquisition different wave lengths and polarization like this.
Described first imaging measurement and data output section divide C to be used for beam split and filtered scattered light are carried out imaging measurement and data output, thereby obtain forward direction, side direction or wait the scattered light diffractogram of different angles scope dorsad.First condenser lens 9 and second condenser lens 17 that the scattered light that described first imaging measurement and data output section divide C to include respectively the different directions that beam split and filtering part B are exported focuses on; First imageing sensor 11 that is positioned at first condenser lens, 9 back be positioned at second imageing sensor 19 of second condenser lens, 17 back, and be connected the second data I/O mouth 20 after the first data I/O mouth 12 of data output input channel being provided and being connected second imageing sensor 19 behind first imageing sensor 11; Described first data I/O mouth 12 and the second data I/O mouth, 20 structures are identical; The supply voltage of required bias voltage of imageing sensor work and imageing sensor cooling power supply is provided as signalling channel; The clock signal that the imageing sensor control signal is provided and provides sensor to export the analog pulse data-signal, and the data-signal of exporting after the digitizing of Sensor Analog Relay System pulse data signal is provided.Wherein imageing sensor distributes scattered light and converts the diffraction image data into and export corresponding analog pulse data-signal according to control signal with space angle.Diffractogram pixel gray-scale value after the digitizing is chosen as 8 to 16; The grey scale pixel value figure place is high more generally speaking, and the dynamic range that picture signal is measured is just big more, but needs data quantity stored also big.
Described image processing circuit and computing machine part D are used to receive the output data that data output section is divided C, calculate and extract different wave length and polarizing diffraction image model characteristic and distinguish particulate.Include respectively the first imageing sensor power supply 13 and the second imageing sensor power supply 21 that bias voltage and supply voltage are provided to first imageing sensor 11 and second imageing sensor 19; Receive first image processing circuit and computing machine 14 and second image processing circuit and the computing machine 22 of picture signal respectively through the first data I/O mouth 12 and the second data I/O mouth 20
Wherein, described first image processing circuit and computing machine 14 and second image processing circuit and computing machine 22 structures are identical, and said computer department is divided into two different computing machines, also can be same computing machine; Image processing circuit and computing machine can carry out stores processor and calculating to the picture signal that is received, and extract different wave length and polarizing diffraction image model characteristic parameter and export display parameter data.
Described first image processing circuit and computing machine 14 and second image processing circuit and computing machine 22; The image processing circuit that can carry out stores processor through image signal transmission line reception picture signal and to the picture signal that is received is arranged; Produce the circuit of imageing sensor control signal and clock signal, and link to each other with image processing circuit the image that is received is calculated extraction different wave length and polarizing diffraction image model characteristic parameter and exports the computing machine of display parameter data.Described computing machine display part is with analyzing, calculate and distinguishing that result's statistics shows.
Image processing circuit can be stored in the picture signal of input in the storer of inside circuit storer or robot calculator, and image processing circuit also can comprise and can carry out the specific mathematical computing to the image stored signal.
As shown in Figure 2; Automatically the diffraction image measuring and analysis system of distinguishing particulate of the present invention; On basis shown in Figure 1, can also be provided with second scattered light that is used to measure the coherent scattering light beam 24 that the particulate that excited by coherent excitation light beam 3 penetrates with second center scattering angle 43 accept the object lens part A '; The coherent scattering light beam 24 that is received is carried out second beam split and the filtering part B ' of beam split and filtering; Beam split and filtered scattered beam are carried out second imaging measurement and the data output section of imaging measurement and data output and divide C '; Described reception data output section divides C ' to link to each other with described image processing circuit and computing machine part D; Wherein, described second scattered light accept the object lens part A ', second beam split and filtering part B ' and second imaging measurement and data output section divide C ' correspondence accept object lens part A, first beam split and filtering part B and receive data output section branch C-structure with described first scattered light identical.Fig. 2 is a kind of embodiment of the present invention; The coherent scattering light beam that particulate produced that is wherein excited by coherent excitation light beam 3 divides different scattering angle scopes to be collected by two micro-eyepieces, generally can be any two between forward direction, side direction or the backscattering light beam.The first center scattering angle 19 of the coherent scattering light beam 4 shown in Fig. 2 is between 5 to 90 degree; Be the forward scattering light beam; The second center scattering angle 43 of coherent scattering light beam 24 is between 90 to 180 degree; Be the backscattering light beam, every each free light splitting piece of bundle coherent scattering light beam is divided into transmitted light and reflected light two parts, partly forms the output of four width of cloth diffraction image data through corresponding optical filtering part and imaging device again.
Computing machine comprises the computer software part of calculating extraction different wave length and polarizing diffraction image model characteristic, contains following steps: the reformation conversion, and coordinate transform, the image model characteristic parameter is extracted in the pattern feature analytical calculation.Greater than 8, pixel reformation conversion can be reduced to 8 with the grey scale pixel value figure place according to the distribution of diffractogram pixel gray-scale value like pixel gray-scale value figure place, can reduce the required memory space of picture signal file and accelerate image calculation analysis speed thereafter; Coordinate transform is for to do suitable pixel space evolution according to the light distribution pattern in the diffraction image; The pattern feature analytical calculation can be carried out computational analysis to the diffraction image after the coordinate transform according to different images analytical algorithm such as pixel gray scale association etc., uses statistics or other mathematical methods to obtain image model characteristic parameter data based on the pattern feature analysis result.
As shown in Figure 3, a kind of analytical approach of distinguishing the diffraction image measuring and analysis system of particulate automatically of the present invention, promptly the computer software part specifically includes following steps:
First step:, obtain corresponding wavelengthtunable and polarizing diffraction view data through imageing sensor and the particulate scattered light space distribution of corresponding light drive test amount under the multi-wavelength excitation light beam irradiates; Described wavelengthtunable and polarizing diffraction view data are: the particulate scattered light under the multi-wavelength excitation light beam irradiates is made up of corresponding multi-wavelength scattered light; The scattering polarization state of light of each wavelength is general different with incident light polarization state; It changes relevant with the inner three-dimensional structure form of particulate, wavelength and polarization through the selection of the light path element before the imageing sensor diffraction image of surveying when measuring the diffractogram data.
That is, use imageing sensor and the particulate scattered light space distribution of corresponding light drive test amount under the multi-wavelength excitation light beam irradiates, obtain wavelengthtunable and polarizing diffraction view data; The multi-wavelength excitation light beam can be by two or multi beam different wavelength of laser Shu Zucheng more; Every Shu Jiguang height phase dry doubling on wavelength separately is in certain polarization state; And excite particulate to produce the scattered light of corresponding wavelength; Highly relevant on wavelength separately; Its polarization state is general different with the polarization state of corresponding excitation beam, and its variation is relevant with particulate interior three-dimensional structure, can after selecting measured scattering light wavelength and polarization state, obtain corresponding diffraction image through narrow band pass filter and polarizing filter.；
Second step: all particulates in the measured tested particulate group are differentiated criterion different wave length and polarizing diffraction view data and population be transferred to and supply the next step graphical analysis to use in the image processing circuit storer in the computing machine;
The described population of second step is differentiated criterion and is obtained by particulate group training data.A kind of implementation method is: wavelengthtunable and the polarizing diffraction view data of at first passing through the particulate group sample of measurement known internal three-dimensional structure morphological feature; Obtain each particulate diffraction image feature parameter vector wherein and, confirm the population resolution criterion relevant at last with particulate interior three-dimensional structural form characteristic in the distribution of feature parameter vector sample space.Another kind of implementation method is: the at first wavelengthtunable and the polarizing diffraction view data of the different particulate population samples through measurement known features (as being in the cell cycle different phase); Obtain each particulate diffraction image feature parameter vector wherein and, confirm the population resolution criterion relevant at last with particulate interior three-dimensional structural form characteristic in the distribution of feature parameter vector sample space.
Described diffraction image data can be from first imageing sensor 11 and second imageing sensor, the 19 direct alternate transmission image processing circuit storer in the computing machine; Also can temporarily be stored in the storer that is provided with in the imageing sensor, alternate transmission arrives the image processing circuit storer in the computing machine in batches then.
Third step: according to the distribution of the grey scale pixel value of diffraction image with the grey scale pixel value figure place by being 8 greater than 8 potential drop rank and carrying out the image space coordinate transform according to its image distribution pattern; Described diffractogram pixel gray-scale value figure place can have not isotopic number according to the imageing sensor of different manufacturers production, and like 12 or 16 etc., the grey scale pixel value figure place is big more; Computational accuracy is high more generally speaking; But needed storer of image operation and calculated amount are also big more, with the grey scale pixel value figure place when becoming 8 through normalization data greater than 8, distribute according to grey scale pixel value; Its minimum value is made as 20-1 (=0), and maximal value is made as 2
8-1 (=255); Other grey scale pixel values then become the round values between 0 to 255 by corresponding proportion; The figure place conversion of accomplishing through such normalization data can not influence under the condition of image calculation precision basically, reduces needed storer of image operation and calculated amount; Described image space coordinate transform refers to according to the diffraction image distribution pattern that measures, like divergence form hot spot distribution radially etc., carries out rectangular coordinate and polar coordinate transform, makes hot spot distribute to become main edge laterally or vertically to distribute.
The 4th step: different wave length after the conversion and polarizing diffraction image are carried out characteristic examination and selected characteristic zone through utilization characteristic discriminating method (like wavelet transformation, bent wave conversion, Fourier transform).Simultaneously image is removed noise and figure image intensifying.The processing of this step has reduced the computational complexity of next step when having strengthened the characteristic contrast.
The 5th step: in characteristic area, different wave length and polarizing diffraction image after the characteristic examination are chosen the diffraction image pattern feature.Described diffraction image pattern feature is based on the related diffraction image pattern feature that calculates of pixel grey scale.Described being based on based on the related computing method of pixel grey scale is divided into the parallel algorithm of calculating respectively after a plurality of data stream with input image data.
The statistical nature choosing method can be a LAWS texture energy wave filter, domain model at random, a kind of or various combination between the methods such as gray level co-occurrence matrixes.How this joint has detailed through based on the related statistical nature choosing method that calculates and obtain the image model characteristic parameter of the pixel grey scale of gray level co-occurrence matrixes, and the calculating of gray level co-occurrence matrixes can be through example explanation down.
If (m n) is a width of cloth diffraction image to I, and m and n are respectively the positive integer of the horizontal and vertical coordinate in represent pixel position: 1≤m≤Ly, and 1≤n≤Lx, sum of all pixels order are Ly * Lx, and the rank of grey scale pixel value I is N
g, also be 1≤I≤N
g, then the matrix element of its gray level co-occurrence matrixes P is defined as
Here # represent to satisfy collection ... | ... In the pixel of condition (by symbol | the system of equations representative of back) to the number of (its position by | the location of pixels coordinate representative of front), d is the positive integer of represent pixel alternate position spike distance, (=0 ° of angle value δ thereafter; 45 °; 90 °, 135 °) be the right direction relations of pixel, i; J is the internal grey scale pixel value of remarked pixel and represent the horizontal and vertical coordinate of gray level co-occurrence matrixes unit position respectively, between 1 and N
gBetween integer.
Pixel grey scale distribution pattern in the gray level co-occurrence matrixes reflected diffraction view data of calculating according to above-mentioned definition, the quantitative test extraction of factors such as direction that can be through every pair of grey scale pixel value is distributed, adjacent spaces, amplitude of variation and the characteristics of image of particulate three-dimensional structure form height correlation.It provides a kind of approach of basic model information such as the texture pattern that can be used for analyzing diffraction image and queueing discipline; And available several two dimensional gray symbiosis graphical representations: each gray level co-occurrence matrixes unit is represented by a gray scale symbiosis image pixel; The horizontal and vertical coordinate of location of pixels is respectively i and j, and grey scale pixel value is P; Every width of cloth gray scale symbiosis image is represented the combination of certain d and δ; Like diffraction image grey scale pixel value figure place is 8 o'clock, and then the sum of all pixels of corresponding every width of cloth gray scale symbiosis image is 256x256.This shows; The grey scale pixel value figure place exponent function relation of the pixel count of the matrix element number of gray level co-occurrence matrixes or gray scale symbiosis image and diffraction image increases diffraction image grey scale pixel value figure place and can cause the memory space and the calculated amount of gray scale symbiosis image to increase as quick as thought.Through statistical study for gray level co-occurrence matrixes or gray scale symbiosis image, can extract a plurality of diffraction image pattern feature parameters (comprise second order apart from/with energy, contrast, correlativity, auto-correlation; Inertia, quadratic sum, population variance, overall average, total entropy; Otherness, entropy, homogeney, intermediate value, covariance; Variance is poor, and entropy is poor, maximal value, parameters such as maximum correlation coefficient), be used for the analysis of particulate is distinguished.
The 6th step: form the diffractogram feature parameter vector and confirm the position of measured particulate at the feature parameter vector sample space according to this vector according to different wave length and polarizing diffraction image model characteristic parameter;
Described diffraction image feature parameter vector can comprise a plurality of components; Like 3 to 20 components; Each component value is a certain characteristics of image parameter value that the self-diffraction image calculation obtains, will from same measuring fine particles to the vector that combines as component value of all characteristic ginseng values of different wave length and polarizing diffraction image be the diffractogram feature parameter vector of representing this particulate; Described feature parameter vector sample space is a vector space; Its dimension equals the component number of diffraction image feature parameter vector; Each institute's micrometer grain is by the position representative of its diffraction image feature parameter vector sample space; The position is then by the important decision of the institute of the diffractogram feature parameter vector of this particulate, and tested particulate group is in the location point set representative of feature parameter vector sample space by all particulates.
The 7th step: confirm all particulates in the tested particulate group behind the position of diffraction image feature parameter vector sample space, differentiate criterion according to particulate the population of the position distribution of diffraction image feature parameter vector sample space and input tested particulate group is categorized into different populations automatically and exports corresponding data.
It is to obtain through the particulate group training data that method as previously mentioned obtains that population is differentiated criterion.Data analysis in diffraction image feature parameter vector sample space and the method that the particulate classification generally combines based on linear and nonlinear analysis can realize through the hybrid plan that for example neural network combines with support vector machine.Support vector machine belongs to pervasive linear classifier, and its advantage is to minimize simultaneously how much marginariums of experience error and maximization, therefore also often is called as the maximal margin sorter.In the method, we have adopted the extending method of support vector machine.In the kernel of support vector machine, introduced nonlinear algorithm (comprise polynomial expression, tanh, based on RBF, Gauss is based on RBF etc.), this method has not only kept the accuracy of nonlinear algorithm but also has improved classification effectiveness.
Fig. 4 is be used for distinguishing the automatically related calculating of diffractogram pixel gray scale of particulate and the schematic flow sheet of particulate being classified based on the diffraction image feature parameter vector of the present invention.At first according to the distribution of diffractogram pixel gray-scale value with the grey scale pixel value figure place by being 8 greater than 8 potential drops; Carry out coordinate transform then by rectangular coordinate to polar coordinates; Diffractogram after the conversion carried out characteristic area is screened and the normalization of pixel value, last calculating pixel gray level co-occurrence matrixes also extracts diffraction image pattern feature parameter according to corresponding gray scale symbiosis image.The diffractogram pixel gray scale of accomplishing all tested particulates is related calculate after, its image model characteristic parameter is classified to all particulates in the tested particulate group in diffraction image feature parameter vector sample space as diffraction image feature parameter vector input support vector machine automatically.Fig. 4 also shows and can the diffraction image data of input be divided into N data stream, utilizes parallel algorithm to improve the speed of image analysis calculation.
Fig. 5 is for carrying out the design sketch that Flame Image Process produces in the related computation process of diffractogram pixel gray scale according to the present invention.Among the figure: a diffraction image; Diffraction image behind the b coordinate transforming; Diffraction image characteristic area after the c characteristic is screened; D gray scale symbiosis image.
Claims (14)
1. diffraction image measuring and analysis system of distinguishing particulate automatically; Include the sample flow of forming by the particulate (1) that flows (2); It is characterized in that; Also be provided with the coherent excitation light beam (3) that intersects with sample flow (2), measure first scattered light of the coherent scattering light beam (4) with first center scattering angle (23) of the particulate ejaculation that is excited by coherent excitation light beam (3) and accept object lens parts (A), first beam split and filtering part (B); First imaging measurement and data output section branch (C), image processing circuit and computing machine part (D) and the display part that links to each other with image processing circuit and computing machine part (D); Wherein,
Described first beam split and filtering part (B) are used for the scattered light of the particulate emission that is received is carried out beam split and filtering; Described first imaging measurement and data output section branch (C) are used for imaging measurement and data output are carried out in beam split and filtered scattered light; Thereby the diffraction image that acquisition is formed by coherent scattering light beam (4); Include respectively first condenser lens (9) and second condenser lens (17) that the scattered light of the different directions that beam split and filtering part (B) are penetrated focuses on; Be positioned at first imageing sensor (11) of first condenser lens (9) back and be positioned at second imageing sensor (19) of second condenser lens (17) back, and the back that is connected first imageing sensor (11) is carried out the first data I/O mouth (12) of data output input and is connected the second data I/O mouth (20) of second imageing sensor (19) back;
Described image processing circuit and computing machine part (D) are used to receive the output information of first imaging measurement and data output section branch (C), and extraction different wave length and polarizing diffraction view data characteristic also calculated, analyzed and distinguish;
Described display part is with calculating, analyze and distinguishing that result's statistics shows.
2. the diffraction image measuring and analysis system of distinguishing particulate automatically according to claim 1; It is characterized in that second scattered light that also is provided with the coherent scattering light beam (24) with second center scattering angle (43) that is used for measuring the particulate ejaculation that is excited by coherent excitation light beam (3) is accepted object lens parts (A '); The scattered light that is received is carried out second beam split and the filtering part (B ') of beam split and filtering; Beam split and filtered scattered light are carried out second imaging measurement and the data output section branch (C ') of imaging measurement and output scattered light diffractogram; Described second imaging measurement and data output section branch (C ') link to each other with described image processing circuit and computing machine part (D); Wherein, described second scattered light is accepted object lens part (A '), second beam split and filtering part (B ') and second imaging measurement and data output section branch (C ') and described first scattered light and is accepted object lens parts (A), first beam split and filtering partly (B) and first imaging measurement and data output section branch (C) structure are identical.
3. the diffraction image measuring and analysis system of distinguishing particulate automatically according to claim 1 is characterized in that, the three-dimensional viewpoin scope of the coherent scattering light beam (4) that the measured particulate that is excited by coherent excitation light beam (3) penetrates is at 0 to π sterad.
4. the diffraction image measuring and analysis system of distinguishing particulate automatically according to claim 1 is characterized in that, described first scattered light is accepted object lens parts (A) and is arranged in order the microcobjective of forming (5) by a plurality of lens.
5. the diffraction image measuring and analysis system of distinguishing particulate automatically according to claim 1; It is characterized in that; Described first beam split and filtering part (B) include light splitting piece (6); Scattered light and first narrow band pass filter that set gradually (7) and first polarizing filter (8) that reception transmits from light splitting piece (6), and receive scattered light and second narrow band pass filter that set gradually (15) and second polarizing filter (16) that reflects from light splitting piece (6).
6. the diffraction image measuring and analysis system of distinguishing particulate automatically according to claim 1; It is characterized in that; Described first beam split and filtering part (B) includes light splitting piece and first narrow band filter slice (7) and second narrow band pass filter (15) that receive from the different directions scattered light that light splitting piece penetrated, and is positioned at first polarizing filter (8) of first narrow band filter slice (7) back and is positioned at second polarizing filter (16) of second narrow band pass filter (15) back; Perhaps described beam split and filtering part (B) are made up of polarization arrowband light splitting piece, or are made up of light splitting piece and prism or diffraction grating.
7. the diffraction image measuring and analysis system of distinguishing particulate automatically according to claim 6 is characterized in that, the back of described first polarizing filter (8) and second polarizing filter (16) is provided with the light intensity attenuation sheet.
8. the diffraction image measuring and analysis system of distinguishing particulate automatically according to claim 1; It is characterized in that; The described first data I/O mouth (12) and second data I/O mouth (20) structure are identical; The supply voltage of required bias voltage of imageing sensor work and imageing sensor cooling power supply is provided; The clock signal of imageing sensor control signal and sensor output analog pulse data-signal is provided, and the data-signal of exporting after the digitizing of Sensor Analog Relay System pulse data signal is provided.
9. the diffraction image measuring and analysis system of distinguishing particulate automatically according to claim 1; It is characterized in that; Described image processing circuit and computing machine part; (D) include respectively to first imageing sensor; (11) and second imageing sensor; (19) the first imageing sensor power supply of bias voltage and supply voltage is provided; (13) and the second imageing sensor power supply; (21); Respectively through the first data I/O mouth; (12) and the second data I/O mouth; (20) first image processing circuit and the computing machine of reception picture signal; (14) and second image processing circuit and computing machine; (22)
Wherein, described first image processing circuit and computing machine (14) and second image processing circuit and computing machine (22) are two identical in structure computing machines, or are same computing machine; Image processing circuit and computing machine can carry out stores processor and calculating to the picture signal that is received, and extract different wave length and polarizing diffraction image model characteristic parameter and export display parameter data.
10. the described analytical approach of distinguishing the diffraction image measuring and analysis system of particulate automatically of claim 1 is characterized in that, includes following steps:
First step:, obtain corresponding wavelengthtunable and polarizing diffraction view data through imageing sensor and the particulate scattered light space distribution of corresponding light drive test amount under the multi-wavelength excitation light beam irradiates;
Second step: all particulates in the tested particulate group are differentiated criterion different wave length and polarizing diffraction view data and population be transferred to and supply the next step graphical analysis to use in the image processing circuit storer in the computing machine;
Third step: distribute the grey scale pixel value figure place by being 8 greater than 8 potential drop rank and carrying out the image space coordinate transform according to its image distribution pattern according to the grey scale pixel value of diffraction image;
The 4th step: carry out characteristic through different wave length after using the characteristic discriminating method to conversion and polarizing diffraction image and screen also selected characteristic zone;
The 5th step: in characteristic area, different wave length and polarizing diffraction image after the characteristic examination are chosen the diffraction image pattern feature;
The 6th step: form the diffraction image feature parameter vector and confirm the position of measured particulate at diffraction image feature parameter vector sample space according to this vector according to the pattern feature parameter of different wave length and polarizing diffraction image;
The 7th step: confirm all particulates in the tested particulate group behind the position of diffraction image feature parameter vector sample space, differentiate criterion according to particulate the population of the position distribution of feature parameter vector sample space and input tested particulate group is categorized into different populations automatically and exports corresponding data.
11. the analytical approach of distinguishing the diffraction image measuring and analysis system of particulate automatically according to claim 10; It is characterized in that described wavelengthtunable of first step and polarizing diffraction view data are: scattering light wavelength and the polarization of when measuring the diffractogram data, selecting the particulate under the multi-wavelength excitation light beam irradiates to penetrate through the light path element before the imageing sensor.
12. the analytical approach of distinguishing the diffraction image measuring and analysis system of particulate automatically according to claim 10 is characterized in that, the described population of second step is differentiated criterion and is obtained by particulate group training data.
13. the analytical approach of distinguishing the diffraction image measuring and analysis system of particulate automatically according to claim 10 is characterized in that, the described diffraction image pattern feature of the 5th step is based on the related diffraction image pattern feature that calculates of pixel grey scale.
14. the analytical approach of distinguishing the diffraction image measuring and analysis system of particulate automatically according to claim 13; It is characterized in that, the 5th step described based on the pixel grey scale coulometer at last based on input image data being divided into the parallel algorithm of calculating respectively after a plurality of data stream.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2010102217147A CN101943663B (en) | 2010-07-09 | 2010-07-09 | Measuring analytical system and measuring analytical method for distinguishing diffraction image of particles automatically |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2010102217147A CN101943663B (en) | 2010-07-09 | 2010-07-09 | Measuring analytical system and measuring analytical method for distinguishing diffraction image of particles automatically |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN101943663A CN101943663A (en) | 2011-01-12 |
| CN101943663B true CN101943663B (en) | 2012-07-25 |
Family
ID=43435767
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN2010102217147A Expired - Fee Related CN101943663B (en) | 2010-07-09 | 2010-07-09 | Measuring analytical system and measuring analytical method for distinguishing diffraction image of particles automatically |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN101943663B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107305177A (en) * | 2016-04-21 | 2017-10-31 | 易幼文 | A kind of particulate matter visualization device and portable particulate matter detecting system |
Families Citing this family (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102507508B (en) * | 2011-09-28 | 2014-03-12 | 天津大学 | Flow measurement system for detecting tumor cells and analysis and monitoring method |
| WO2013101675A2 (en) * | 2011-12-29 | 2013-07-04 | Abbott Laboratories | Flow cytometry systems and methods for blocking diffraction patterns |
| CN103728236B (en) * | 2012-10-12 | 2016-01-20 | 厦门福流生物科技有限公司 | A kind of method detecting nano particle |
| CN105651656B (en) * | 2016-04-01 | 2018-09-11 | 黄恺 | The device and its working method of grain shape are analyzed based on laser hologram imaging method |
| CN105717035B (en) * | 2016-04-08 | 2019-04-23 | 清华大学 | Flow cytometry device and method |
| CN108270953A (en) * | 2017-12-26 | 2018-07-10 | 吉林大学 | A kind of biometric device and biological sample imaging method |
| JP2020094925A (en) * | 2018-12-13 | 2020-06-18 | 住友電気工業株式会社 | Method for evaluating quality |
| CN113495043B (en) * | 2020-03-18 | 2023-11-10 | 江苏长电科技股份有限公司 | Micro-scale diffraction image detection device |
| CN111751266A (en) * | 2020-07-10 | 2020-10-09 | 西安交通大学 | Mark-free cell nucleus scattering inversion method based on multi-scale gray level co-occurrence matrix |
| CN112297422B (en) * | 2020-10-09 | 2021-08-31 | 南开大学 | One shot forming's 3D printing device |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1158997A (en) * | 1995-11-17 | 1997-09-10 | 东亚医用电子株式会社 | Standard liquid for flow-type cytometer |
| JP2005055204A (en) * | 2003-08-06 | 2005-03-03 | Rion Co Ltd | Particle detector |
| US20060068371A1 (en) * | 1999-01-25 | 2006-03-30 | Amnis Corporation | Methods for analyzing inter-cellular phenomena |
| CN101629903A (en) * | 2009-08-19 | 2010-01-20 | 南京信息工程大学 | Optical system of scattering-type cloud particle detector |
-
2010
- 2010-07-09 CN CN2010102217147A patent/CN101943663B/en not_active Expired - Fee Related
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1158997A (en) * | 1995-11-17 | 1997-09-10 | 东亚医用电子株式会社 | Standard liquid for flow-type cytometer |
| US20060068371A1 (en) * | 1999-01-25 | 2006-03-30 | Amnis Corporation | Methods for analyzing inter-cellular phenomena |
| JP2005055204A (en) * | 2003-08-06 | 2005-03-03 | Rion Co Ltd | Particle detector |
| CN101629903A (en) * | 2009-08-19 | 2010-01-20 | 南京信息工程大学 | Optical system of scattering-type cloud particle detector |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107305177A (en) * | 2016-04-21 | 2017-10-31 | 易幼文 | A kind of particulate matter visualization device and portable particulate matter detecting system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN101943663A (en) | 2011-01-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN101943663B (en) | Measuring analytical system and measuring analytical method for distinguishing diffraction image of particles automatically | |
| US11542461B2 (en) | Analysis device | |
| US9013692B2 (en) | Flow cytometer apparatus for three dimensional difraction imaging and related methods | |
| JP7075126B2 (en) | Image-based cell sorting system and method | |
| CN103890561B (en) | The optical detection of particle and analysis | |
| EP1474033B1 (en) | Optical projection imaging system and method for automatically detecting cells | |
| EP1886139B1 (en) | Methods for analyzing inter-cellular phenomena | |
| US10552956B2 (en) | Reconstruction method of biological tissue image, apparatus therefor, and image display apparatus using the biological tissue image | |
| US20210321963A1 (en) | Systems and methods for enhanced imaging and analysis | |
| CN102023148A (en) | Fluorescent nano-microscopic method | |
| Wang et al. | Pattern recognition and classification of two cancer cell lines by diffraction imaging at multiple pixel distances | |
| CN103154708A (en) | Method for measuring diffusion characteristic value of particle by detecting single light-emitting particle | |
| CN102507508B (en) | Flow measurement system for detecting tumor cells and analysis and monitoring method | |
| CN102288530B (en) | Time delay integral imaging system for measuring diffraction image of moving particle | |
| CN112041660A (en) | System, apparatus and method for three-dimensional imaging of moving particles | |
| EP2332073B1 (en) | Shape parameter for hematology instruments | |
| CN109478230A (en) | The method for checking distributed objects by segmentation general view image | |
| CN110537089B (en) | Method and device for analysing cells | |
| DE112021001620T5 (en) | PARTICLE ANALYSIS SYSTEM AND PARTICLE ANALYSIS METHOD | |
| WO2023223851A1 (en) | Biological sample analysis system, information processing device, information processing method, and biological sample analysis method | |
| Dong et al. | Study of cell classification with a diffraction imaging flow cytometer method | |
| Hu et al. | Rapid analysis of white blood cells with diffraction imaging flow cytometry | |
| Jiang | Study of Morphology Based Cell Assay by Diffraction Imaging Flow Cytometry |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| 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: 20120725 Termination date: 20210709 |