CN106442463A - Method for counting algae cells and determining algae species on basis of line scanning Raman microscopy imaging - Google Patents
Method for counting algae cells and determining algae species on basis of line scanning Raman microscopy imaging Download PDFInfo
- Publication number
- CN106442463A CN106442463A CN201610845672.1A CN201610845672A CN106442463A CN 106442463 A CN106442463 A CN 106442463A CN 201610845672 A CN201610845672 A CN 201610845672A CN 106442463 A CN106442463 A CN 106442463A
- Authority
- CN
- China
- Prior art keywords
- frustule
- algae
- raman
- sample
- counting
- 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.)
- Granted
Links
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
- G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
- G01N21/65—Raman scattering
Abstract
The invention discloses a method for counting algae cells and determining algae species on basis of line scanning Raman microscopy imaging. The method comprises steps as follows: preparation of an algae cell detection counting board, pretreatment of algae cell samples, algae cell imaging and algae cell counting and algae specie determination based on algae cell Raman imaging. According to the method for counting the algae cells and determining the algae species on the basis of line scanning Raman microscopy imaging and multivariate data mining, algae cell counting and rapid determination and analysis of the algae species in an algae solution can be realized, and accurate counting of the algae cells and synchronous rapid recognition of the algae species are easy to realize.
Description
Technical field
The invention belongs to environmental monitoring and resist technology field are and in particular to a kind of scan Raman mapping based on line
Frustule counts and algae kind method of discrimination.
Background technology
Develop rapidly with economic, to the later stage nineties 20th century, in the lake of China investigation, have at 88.6%
In eutrophic state.Enter 21 century, lake eutrophication assumes the situation of high speed development.China has occurred and that eutrophy at present
The area of lake changed reaches 5000km2, possess and occur the area of lake of eutrophication condition to reach 14000km2.Due to eutrophication
Water body faces the problem frequently breaking out algae wawter bloom, therefore, using eutrophication water as drinking water source, can be to China resident
Safe drinking water cause seriously to threaten.
Cause in volume wawter bloom in freshwater lake eutrophication, most commonly seen with spring and summer microalgae wawter bloom, as the Taihu Lake of China
Basin, the Biomass of microalgae accounts for more than the 90% of algae total biomass；The microalgae wawter bloom of Dian Chi does not disappear throughout the year, and water quality is corrupt,
Algae toxins content remains high.With the aggravation of water pollution, increasing lake, reservoir or even river also break out water often
Magnificent phenomenon, accordingly, it would be desirable to distinguish that whether the wawter bloom of eutrophication water is caused by microalgae and determine the quantity of microalgae.
At present, the identification that algae researcher can be by microscope to frustule counting and algae kind, but for counting
Accuracy and algae kind identification, be limited to the accumulation of the experience at aspects such as frustule forms for the experimenter to a great extent.
With the development of computer image processing technology, develop a kind of cell counting based on frustule imaging and recognition methodss, should
Method needs to carry out optical imagery to frustule, and using complicated image processing techniquess, frustule is counted, and for
The identification of the type of frustule is based only upon the differentiation between cell size.Except tradition counts identification based on microscopical frustule
Outside method, the method that prior art can be used for frustule counting is also included at based on flow cytometer, micro-imaging digital picture
Reason, optical detecting method and its improved method be combineding with each other, these methods improve the counting of frustule to a certain extent
Precision and recognition accuracy.But, these methods have certain defective, if desired for carry out complexity pretreatment (ultrasonic,
Filter, dye etc.) it is impossible to directly counting or identifying, meanwhile, prior Criterion curve is needed using indirect method.
Therefore, a kind of quick, simple and effective frustule counting and algae kind method of discrimination are found for people in the art
It is necessary for member.
Content of the invention
In view of this, it is an object of the invention to provide a kind of excavated based on line scanning Raman mapping and multivariate data
Frustule count and algae kind discriminant analysis method, realize the simultaneously and rapidly discriminant analysiss of the counting of cell and algae kind in algae solution.
For achieving the above object, the invention provides following technical scheme：
Counted and algae kind method of discrimination based on the frustule that line scans Raman mapping, comprise the steps：
1) prepare frustule detection counting chamber：Etch sensing chamber on a silicon substrate, described sensing chamber is 1mm*1mm*
Described sensing chamber is divided into 3*3 little sensing chamber in sensing chamber's bottom lines, in sensing chamber by the cuboid groove of 0.1mm
Two opposite side etch sample inlet raceway groove and sample export raceway groove more respectively；
2) pretreatment of frustule sample：Algae solution sample is fully diluted to frustule not phase mutual respect with ultra-pure water
Folded, covered on frustule detection counting chamber, the sample after dilution is added to the inspection of counting chamber from sample inlet raceway groove
Survey in room, unnecessary sample then flows out from sample export passage, frustule detection counting chamber is placed on the operating board of level, quiet
Put and adsorb at sensing chamber bottom as much as possible to frustule, nonvoluntary travelling；
3) frustule imaging：Select 532nm laser instrument, 50 times/100 times telephoto lens, the large area based on line scanning is altogether
Burnt Raman image splicing, carries out quick Overlap-scanning, sample room large area imaging is spliced to the sample in frustule counting chamber, with
The feature peak height of frustule, peak area, peak height or peak area ratio and characteristic peak frequency are moved automated imaging and are obtained based on line scanning
Large area splices the Raman image figure of frustule；
4) frustule based on frustule Raman image counts：Become based on the Raman that the large area of line scanning splices frustule
As figure, respectively cluster analyses are carried out to the frustule Raman signatures information being in four angle imaging regions of sensing chamber, realize algae thin
Born of the same parents count respectively, obtain four region frustule number averages, 9 times of this average are the quantity of frustule in this sensing chamber, enter
And realize frustule and count, described frustule Raman signatures information includes characteristic peak height, peak area or ratio, peak frequency displacement；
5) identification of algae kind：
First, set up the confocal Raman spectra standard database of different algae cells, draw including different the confocal of algae kind
Graceful spectrum, the Raman signatures information of algae kind；By the confocal Raman image chart database of known algae kind cell, minimum based on discriminant
Two multiplication algorithms, support vector classification or artificial neural network, set up algae kind discriminant analysiss model；
Based on step 4) frustule count result, by a certain cluster sample characteristic information carry out averagely, as this
The center of class sample, scans for comparing with algae kind cell database and then algae kind is identified；Or, with a certain cluster
All spectroscopic datas be forecast set, based on the algae kind discriminant analysiss model of aforementioned foundation, this cluster frustule is identified.
Preferably, step 3) in order that frustule imaging is apparent, frustule image is filtered and baseline correction
Pretreatment, final obtain the Raman image figure splicing frustule based on the large area of line scanning.
Preferably, step 3) described filtering and the preprocessing process of baseline correction can carry out according to two methods, Yi Zhongfang
Method is based on Raman image figure, is processed using two-dimensional filtering and baseline correction algorithm；Another kind of method is to be imaged
In single Raman spectrum based on, processed using one-dimensional filtering and baseline correction algorithm, filtering can be wavelet filtering, flat
Sliding filtering；Baseline correction is chosen as wavelet transformation, asymmetric least square, smoothing spline matching or is based on genetic groups baseline school
Normal operation method.
The beneficial effects of the present invention is：One kind that the present invention provides is based on line scanning Raman mapping and multivariate data
The frustule excavating counts and algae kind discriminant analysis method, and in achievable algae solution, the Quick of the counting of cell and algae kind divides
Analysis, it is easy to accomplish the simultaneously and rapidly identification of the accurate counting of frustule and algae kind.
Brief description
In order that the purpose of the present invention, technical scheme and beneficial effect are clearer, the present invention provides drawings described below：
Fig. 1 represents that the frustule being excavated based on line scanning Raman mapping and multivariate data is counted and algae kind discriminant analysiss
Scheme.
Fig. 2 represents frustule detection counting chamber；Wherein 1 quartz substrate；2 sample raceway grooves；3 quartz cover glass
Piece；4 algae solution deposition regions；5 sample raceway grooves；6 scanning lasers and scattered signal are collected；7 telephoto lens；
8 incident lasers and scattered signal collect passage.
Fig. 3 represents the foundation of algae kind discrimination model and the identification of algae kind to be measured.
Fig. 4 represents the blue-green algae cell 1522cm based on line scanning-1Characteristic peak area Raman image schematic diagram nearby.
Fig. 5 represents the blue-green algae cell 1522cm based on line scanning-1Feature frequency displacement Raman image schematic diagram nearby.
Fig. 6 represents blue-green algae cell 1522cm-1Feature frequency displacement Raman original image effect nearby.
Blue-green algae cell 1522cm after Fig. 7 expression process-1Feature frequency displacement Raman image effect nearby.
Specific embodiment
In order that the purpose of the embodiment of the present invention, technical scheme and advantage are clearer, with reference to the embodiment of the present invention
In accompanying drawing, to the present invention be embodied as carry out clear, complete description.Obvious, described embodiment is the present invention
In a part of embodiment, rather than whole embodiment.Based on the embodiment in the present invention, those of ordinary skill in the art exist
The every other embodiment being obtained on the premise of not making creative work, broadly falls into protection scope of the present invention.
Embodiment 1
The first step, prepared using micro-processing method frustule detection counting chamber
On a silicon substrate, using lithographic methods, frustule sensing chamber, detection are processed according to counting chamber structure shown in Fig. 2
The room length of side is 1mm*1mm, and including 3*3 little sensing chamber, the depth of sensing chamber is 0.1mm.I.e. when algae solution fills with sensing chamber,
Algae solution volume is 0.1mm3.Then in sensing chamber's overlying lid quartz cover slip, in case the detection of frustule and counting.
Second step, sample preprocessing and Preparatory work of experiment
Algae solution sample is carried out the dilution of certain multiple, shake up, a certain amount of algae solution is taken using pipettor, drops in frustule
The injection port 2 of counting chamber 1, algae solution, under siphonage, is distributed to each sensing chamber 4, unnecessary algae solution absorbent paper is from sample
Raceway groove 5 suctions out.Frustule counting chamber is placed on the operating board of level, standing a period of time, so that frustule is adsorbed as much as possible
At sensing chamber bottom, nonvoluntary travelling, facilitate confocal Raman to carry out large-area Overlap-scanning, and then frustule can be counted and algae
The identification planted.
3rd step, frustule imaging
Select 532nm laser instrument, 50 times of telephoto lens, 1% laser power, the time of integration is 2 seconds.Laser light quartz
Coverslip focuses on the algae solution sample of sensing chamber, based on the large area confocal Raman image splicing of line scanning, frustule is counted
The sample of plate is quickly scanned.Sample room large area imaging splicing, with frustule characteristic peak 1522cm-1(belong to carotenoids
Element) based on automated imaging.With blue-green algae cell 1522cm-1Nearby as a example characteristic peak area Raman image, as shown in figure 4, algae
Cell characteristic significantly presents.In order to clearly present frustule feature, need to frustule image further
Pretreatment, including filtering and baseline correction, obtains the large area imaging figure of frustule in counting chamber.With the single Raman in being imaged
Based on spectrum, processed using one-dimensional filtering and baseline correction algorithm, finally realized the pretreatment of frustule image.Its
Middle filtering adopts wavelet filtering；Baseline correction asymmetric least square baseline correction algorithm.
4th step, the frustule based on frustule Raman image count
Based on Fig. 4, with 1522cm-1Feature frequency displacement nearby is imaged to blue-green alge Characteristic Raman, as shown in figure 5, due to
There is certain error of fitting in imaging process, frequency displacement Raman image has larger error, that is, do not have the place of frustule to go out
Show feature frequency shift information.There is no the region of frustule in theory, frustule Raman signatures peak area is zero.Therefore, based on algae
Cell imaging (Fig. 4) and frequency displacement imaging (Fig. 5) it is considered to peak error of fitting, produce error frequency displacement characteristic peak area less or
Very big, based on Matlab platform, pretreatment is carried out to primitive character frequency displacement imaging (Fig. 6), obtain more clearly frustule frequency
Move into as (Fig. 7).
Based on the Raman image figure that large area splices frustule, respectively to being in four angle imaging regions of sensing chamber
Frustule raman characteristic peak frequency displacement carries out cluster analyses, realizes frustule and counts respectively, obtains four region frustule number averages,
9 times of this average are the quantity of frustule in this sensing chamber, and then realize frustule counting.
5th step, the identification of algae kind
First, set up the confocal Raman spectra data base of different algae cells, including the confocal Raman light of different algae kinds
Spectrum, Raman signatures information of algae kind etc., in case the quick identification of algae kind；
By the confocal Raman image chart database of known algae kind cell, based on discriminant least-squares algorithm, set up algae kind
Discrimination model；
The result being counted based on aforementioned frustule and algae kind discriminant Least square analysis model, with the institute in a certain cluster
There is spectroscopic data to be forecast set, this cluster frustule is identified, realize counting and the automatic identification of frustule.
The specific frustule counting based on line scanning Raman mapping and multivariate data excavation and algae kind discriminant analysiss
Scheme is as shown in figure 1, Fig. 3 represents the foundation of algae kind discrimination model and the identification process figure of algae kind to be measured.
In sum, a kind of frustule being excavated based on line scanning Raman mapping and multivariate data that the present invention provides
Count and algae kind discriminant analysis method, the Quick analysis of the counting of cell and algae kind in achievable algae solution, it is easy to accomplish algae
The accurate counting of cell and the simultaneously and rapidly identification of algae kind.
Finally illustrate, preferred embodiment above only in order to technical scheme to be described and unrestricted, although logical
Cross above preferred embodiment the present invention to be described in detail, it is to be understood by those skilled in the art that can be
In form and various changes are made to it, without departing from claims of the present invention limited range in details.
Claims (3)
1. counted based on the frustule that line scans Raman mapping and algae kind method of discrimination is it is characterised in that include following walking
Suddenly：1) prepare frustule detection counting chamber：Etch sensing chamber on a silicon substrate, described sensing chamber is 1mm*1mm*0.1mm's
Described sensing chamber is divided into 3*3 little sensing chamber in sensing chamber's bottom lines, two in sensing chamber right by cuboid groove
Side etches sample inlet raceway groove and sample export raceway groove more respectively；
2) pretreatment of frustule sample：Algae solution sample is fully diluted to frustule with ultra-pure water do not overlap,
Covered on frustule detection counting chamber, the sample after dilution is added to the sensing chamber of counting chamber from sample inlet raceway groove
In, unnecessary sample then flows out from sample export passage, frustule detection counting chamber is placed on the operating board of level, stand to
Frustule adsorbs at sensing chamber bottom as much as possible, nonvoluntary travelling；
3) frustule imaging：Select 532nm laser instrument, 50 times/100 times telephoto lens, the large area based on line scanning is confocal to draw
Graceful imaging joint, carries out quick Overlap-scanning to the sample in frustule counting chamber, and wherein, sample room large area imaging is spliced,
Move automated imaging with the feature peak height of frustule, peak area, peak height or peak area ratio and characteristic peak frequency to obtain scanning based on line
Large area splice frustule Raman image figure；
4) frustule based on frustule Raman image counts：Splice the Raman image of frustule based on the large area of line scanning
Figure, carries out cluster analyses to the frustule Raman signatures information being in four angle imaging regions of sensing chamber respectively, realizes frustule
Count respectively, obtain four region frustule number averages, 9 times of this average are the quantity of frustule in this sensing chamber, and then
Realize frustule to count, described frustule Raman signatures information includes characteristic peak height, peak area or ratio, peak frequency displacement；
5) identification of algae kind：
First, set up the confocal Raman spectra standard database of different algae cells, including the confocal Raman light of different algae kinds
Spectrum, the Raman signatures information of algae kind；By the confocal Raman image chart database of known algae kind cell, based on discriminant least square
Algorithm, support vector classification or artificial neural network, set up algae kind discriminant analysiss model；
Based on step 4) frustule count result, by a certain cluster sample characteristic information carry out averagely, as this class sample
This center, scans for comparing with algae kind cell database and then algae kind is identified；Or, with the institute in a certain cluster
There is spectroscopic data to be forecast set, based on the algae kind discriminant analysiss model of aforementioned foundation, this cluster frustule is identified.
2. counted and algae kind method of discrimination based on the frustule that line scans Raman mapping according to claim 1, it is special
Levy and be, step 3) in order that frustule imaging is apparent, frustule image is filtered and baseline correction pretreatment,
The final Raman image figure obtaining the large area splicing frustule based on line scanning.
3. counted and algae kind method of discrimination based on the frustule that line scans Raman mapping according to claim 2, it is special
Levy and be, step 3) preprocessing process of described filtering and baseline correction can carry out according to two methods, and a kind of method is to draw
Based on graceful image, processed using two-dimensional filtering and baseline correction algorithm；Another kind of method is with single in being imaged
Based on Raman spectrum, processed using one-dimensional filtering and baseline correction algorithm, filtering can be wavelet filtering, smothing filtering；
Baseline correction is chosen as wavelet transformation, asymmetric least square, smoothing spline matching or is based on genetic groups baseline correction algorithm.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610845672.1A CN106442463B (en) | 2016-09-23 | 2016-09-23 | Frustule based on line scanning Raman mapping counts and algae method of discrimination |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610845672.1A CN106442463B (en) | 2016-09-23 | 2016-09-23 | Frustule based on line scanning Raman mapping counts and algae method of discrimination |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106442463A true CN106442463A (en) | 2017-02-22 |
| CN106442463B CN106442463B (en) | 2019-03-08 |
Family
ID=58167148
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610845672.1A Active CN106442463B (en) | 2016-09-23 | 2016-09-23 | Frustule based on line scanning Raman mapping counts and algae method of discrimination |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106442463B (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107941783A (en) * | 2017-12-14 | 2018-04-20 | 中国科学院重庆绿色智能技术研究院 | A kind of water environment based on the scattering of frustule Characteristic Raman disturbs appraisal procedure |
| CN108627494A (en) * | 2018-05-09 | 2018-10-09 | 吉林大学 | A kind of system for the imaging of fast two-dimensional Raman spectroscopy scans |
| CN108802065A (en) * | 2018-05-30 | 2018-11-13 | 大连理工大学 | The test device and method of algae and bacterial content in a kind of fast slowdown monitoring water |
| CN109949284A (en) * | 2019-03-12 | 2019-06-28 | 天津瑟威兰斯科技有限公司 | Deep learning convolution neural network-based algae cell analysis method and system |
| CN109991208A (en) * | 2019-05-05 | 2019-07-09 | 中国科学院重庆绿色智能技术研究院 | Blue-green alge Species Competition allelopathy mechanism research method based on Surface Enhanced Raman Scattering Spectrum |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1404875A1 (en) * | 2001-06-08 | 2004-04-07 | ChemoMetec A/S | A method and a system for counting cells from a plurality of species |
| CN101103263A (en) * | 2004-11-17 | 2008-01-09 | 霍尼韦尔国际公司 | Raman detection based flow cytometer |
| CN103411952A (en) * | 2013-08-08 | 2013-11-27 | 浙江大学 | Alga sort classification identification method based on Raman spectroscopy technique |
| CN103499560A (en) * | 2013-09-29 | 2014-01-08 | 浙江大学 | Method for identifying alga species by combining Raman spectroscopic technology and spectral peak ratio method |
| EP2803014A1 (en) * | 2012-01-12 | 2014-11-19 | Université De Nice Sophia Antipolis | Method for the supervised classification of cells included in microscopy images |
| CN105223124A (en) * | 2015-09-30 | 2016-01-06 | 朱耀辉 | Disposable band groove cytometer several pieces |
-
2016
- 2016-09-23 CN CN201610845672.1A patent/CN106442463B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1404875A1 (en) * | 2001-06-08 | 2004-04-07 | ChemoMetec A/S | A method and a system for counting cells from a plurality of species |
| CN101103263A (en) * | 2004-11-17 | 2008-01-09 | 霍尼韦尔国际公司 | Raman detection based flow cytometer |
| EP2803014A1 (en) * | 2012-01-12 | 2014-11-19 | Université De Nice Sophia Antipolis | Method for the supervised classification of cells included in microscopy images |
| CN103411952A (en) * | 2013-08-08 | 2013-11-27 | 浙江大学 | Alga sort classification identification method based on Raman spectroscopy technique |
| CN103499560A (en) * | 2013-09-29 | 2014-01-08 | 浙江大学 | Method for identifying alga species by combining Raman spectroscopic technology and spectral peak ratio method |
| CN105223124A (en) * | 2015-09-30 | 2016-01-06 | 朱耀辉 | Disposable band groove cytometer several pieces |
Non-Patent Citations (6)
| Title |
|---|
| ANDREJ DEMENTJEV等: "Applying the method of Coherent Anti-stokes Raman microscopy for imaging of carotenoids in microalgae and cyanobacteria", 《J. RAMAN SPECTROSC.》 * |
| JI QI等: "Performance of line-scan Raman microscopy for high throughput chemical imaging of cell population", 《APPLIED OPTICS》 * |
| 农业部人事劳动司等组织编写: "《海水水生动物苗种繁育技术 下册》", 30 September 2004, 中国农业出版社 * |
| 刘志媛等: "《海藻生物技术》", 31 July 2015, 海洋出版社 * |
| 卢俊等: "活性盐藻细胞计数法的研究", 《盐业与化工》 * |
| 潘健: "基于光谱的微藻藻种鉴别及内部信息（色素、油脂）检测的研究", 《中国优秀硕士学位论文全文数据库 基础科学辑》 * |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107941783A (en) * | 2017-12-14 | 2018-04-20 | 中国科学院重庆绿色智能技术研究院 | A kind of water environment based on the scattering of frustule Characteristic Raman disturbs appraisal procedure |
| CN108627494A (en) * | 2018-05-09 | 2018-10-09 | 吉林大学 | A kind of system for the imaging of fast two-dimensional Raman spectroscopy scans |
| CN108627494B (en) * | 2018-05-09 | 2020-11-10 | 吉林大学 | System for rapid two-dimensional Raman spectrum scanning imaging |
| CN108802065A (en) * | 2018-05-30 | 2018-11-13 | 大连理工大学 | The test device and method of algae and bacterial content in a kind of fast slowdown monitoring water |
| CN109949284A (en) * | 2019-03-12 | 2019-06-28 | 天津瑟威兰斯科技有限公司 | Deep learning convolution neural network-based algae cell analysis method and system |
| CN109991208A (en) * | 2019-05-05 | 2019-07-09 | 中国科学院重庆绿色智能技术研究院 | Blue-green alge Species Competition allelopathy mechanism research method based on Surface Enhanced Raman Scattering Spectrum |
| CN109991208B (en) * | 2019-05-05 | 2021-06-15 | 中国科学院重庆绿色智能技术研究院 | Blue-green algae population competition allelopathy mechanism research method based on surface enhanced Raman scattering spectrum |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106442463B (en) | 2019-03-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106442463A (en) | Method for counting algae cells and determining algae species on basis of line scanning Raman microscopy imaging | |
| US10944896B2 (en) | Single-frame autofocusing using multi-LED illumination | |
| US9297995B2 (en) | Automatic stereological analysis of biological tissue including section thickness determination | |
| CN103984979B (en) | The algae automatic detection counting device and method being imaged without Lenses Diffractive | |
| CN109145830A (en) | A kind of intelligence water gauge recognition methods | |
| CN105651656B (en) | The device and its working method of grain shape are analyzed based on laser hologram imaging method | |
| Yourassowsky et al. | High throughput holographic imaging-in-flow for the analysis of a wide plankton size range | |
| Li et al. | Automated discrimination between digs and dust particles on optical surfaces with dark-field scattering microscopy | |
| CN103345617A (en) | Method and system for recognizing traditional Chinese medicine | |
| CN101806583A (en) | Microscopic image-based fiber fineness measurement method | |
| CN111220588A (en) | Flow field radiation convergence and dispersion measurement method based on oil film fluorescence brightness | |
| CN113794839A (en) | Focal plane automatic focusing method of imaging ellipsometer | |
| WO2019161406A1 (en) | Systems, devices and methods for three-dimensional imaging of moving particles | |
| Boening et al. | Applicability of an EM-CCD for spatially resolved TIR-ICS | |
| Deng et al. | Use of SeaWiFS imagery to detect three-dimensional distribution of suspended sediment | |
| CN112513707B (en) | Depiction of objects | |
| Johnsrud et al. | Semi-automated segmentation of pollen grains in microscopic images: a tool for three imaging modes | |
| CN105987866A (en) | Heterogeneous liquid settlement automatic-monitoring method and device | |
| Agarwal et al. | Three-dimensional DNA image cytometry by optical projection tomographic microscopy for early cancer diagnosis | |
| JP3679680B2 (en) | Cell lineage extraction method | |
| Vojnovic et al. | A High Sensitivity, High Throughput, Automated Single-Cell Gel Electrophoresis (‘Comet’) DNA Damage Assay | |
| Culley et al. | Made to measure: An introduction to quantifying microscopy data in the life sciences | |
| Azam et al. | Algae–water–silica interactions in low and high ionic strength environments | |
| Sten et al. | Sporify: An Automated Tool to Quantify Spores in Z-Stacked 3D Samples | |
| CN111412987B (en) | Dynamic scanning detection device and method based on spectral characteristics |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |