CN110672530A - Hyperspectrum-combined non-visual biological tissue imaging detection device and method - Google Patents
Hyperspectrum-combined non-visual biological tissue imaging detection device and method Download PDFInfo
- Publication number
- CN110672530A CN110672530A CN201910869790.XA CN201910869790A CN110672530A CN 110672530 A CN110672530 A CN 110672530A CN 201910869790 A CN201910869790 A CN 201910869790A CN 110672530 A CN110672530 A CN 110672530A
- Authority
- CN
- China
- Prior art keywords
- biological tissue
- light
- detected
- tissue sample
- light intensity
- 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.)
- Pending
Links
Images
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
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/27—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands using photo-electric detection ; circuits for computing concentration
-
- 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
- G01N21/01—Arrangements or apparatus for facilitating the optical investigation
-
- 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
- G01N21/01—Arrangements or apparatus for facilitating the optical investigation
- G01N2021/0106—General arrangement of respective parts
- G01N2021/015—Apparatus with interchangeable optical heads or interchangeable block of optics and detector
-
- 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
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N2021/1765—Method using an image detector and processing of image signal
- G01N2021/177—Detector of the video camera type
- G01N2021/1772—Array detector
Landscapes
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- Theoretical Computer Science (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Investigating Or Analysing Materials By Optical Means (AREA)
Abstract
The invention discloses a hyperspectral non-visual biological tissue imaging detection device and a hyperspectral non-visual biological tissue imaging detection method. The method comprises the following steps: building the device and enabling the device to work; the motor controls the polarizer to rotate for a circle by a certain step length, and a corresponding light intensity image collected by the CCD camera is recorded when the polarizer rotates for one step length; continuously changing the light wavelength which is transmitted by the liquid crystal tunable filter, and acquiring a corresponding light intensity map for each light wavelength; performing inversion calculation on the light intensity graph to obtain a non-visual image of the biological tissue sample to be detected corresponding to each light wavelength; and acquiring a spectral curve graph according to the non-visual image or the light intensity graph, and then detecting the same or different tissues in the biological tissue sample to be detected by the spectral curve graph. Compared with the common spectrum analysis, the invention has higher contrast ratio, can effectively distinguish different tissues in the biological tissues and has higher detection precision.
Description
Technical Field
The invention belongs to the field of biological tissue characteristic analysis and processing, and particularly relates to a non-visual biological tissue imaging detection device and method combined with hyperspectrum.
Background
Light waves are electromagnetic radiation generated by electrons in the process of atom movement, and spectra refer to patterns in which light waves are orderly arranged according to the size of wavelengths after passing through a dispersion system. Because each atom has its own unique spectrum, the spectrum can be used to identify a substance and determine the chemical composition of the substance, a method known as spectroscopic analysis. Each atom has its own characteristic spectral line, and the chemical composition of the substance to be measured can be obtained by comparing the spectrum of the substance to be measured with the specific identification spectral line of each element, and the method is often applied to the detection and discovery of trace elements in various substances. The spectral analysis mainly depends on the absorption peaks of the comparison spectral lines for distinguishing the two substances, and for biological tissues, the carbon, hydrogen and other elements have large proportion, the absorption peaks are concentrated and similar, and the spectral analysis is difficult to distinguish different biological tissues.
In the existing microscopic imaging field, imaging technologies with nanoscale resolution, such as a scanning electron microscope technology, an atomic force microscope technology and the like, have damage to biological tissues and cannot completely detect the structure of the tissues, so that the efficiency is low in optical imaging measurement and the detection difficulty is high. Currently, many non-contact imaging techniques for imaging biological tissue structures are emerging, such as Laser Doppler Imaging (LDI), Optical Coherence Tomography (OCT), and Computed Tomography (CT). In LDI, biological tissues are irradiated with laser light having a wavelength of about 780nm, and frequency shift occurs in moving biological cells due to Doppler effect, so that the light intensity patterns obtained by us are all scattered. Spectral analysis of light intensity is performed from the resulting spectra, but this method yields too limited information from the spectra to meet the requirements for biological tissue imaging and analysis, and is only suitable for detection of viable tissue.
The non-visual imaging is completely different from the traditional microscopic imaging directly utilizing light intensity, and the non-visual imaging utilizes light vector parameters such as the polarization state of light in an imaging light path, the light wave phase and the like. The optical parameter changes directly represent the structure and multi-dimensional physical and chemical information of the measured substance, and the measured parameters are analyzed and fitted to obtain the related optical information of the measured substance according to the fitted curve.
Disclosure of Invention
The invention aims to provide a device and a method for effectively distinguishing different biological tissues by combining non-visual images and hyperspectrum.
The technical solution for realizing the purpose of the invention is as follows: a non-visual biological tissue imaging detection device combined with hyperspectrum comprises a broad spectrum light source, a liquid crystal tunable filter, a polarizer, a semi-transparent semi-reflective mirror, an objective lens and a biological tissue sample to be detected, wherein the broad spectrum light source, the liquid crystal tunable filter, the polarizer and the semi-transparent semi-reflective mirror are coaxially and sequentially arranged, the objective lens and the biological tissue sample to be detected are sequentially arranged along the light direction of the semi-transparent semi-reflective mirror, and an 1/4 wave plate, an analyzer and a CCD camera are sequentially arranged along the; the polarizer and the analyzer are controlled by a motor to rotate, wherein the polarizer is used for changing the polarization state of incident light of the device, and the analyzer is used for detecting the polarization state of emergent light of the device.
A non-visual biological tissue imaging detection method combining hyperspectrum comprises the following steps:
step 1, a wide-spectrum light source emits light, the light beam is incident to a biological tissue sample to be detected through a liquid crystal tunable filter, a polarizer, a semi-transparent and semi-reflective mirror and an objective lens in sequence, and reflected light of the biological tissue sample to be detected is imaged to a CCD camera through the semi-transparent and semi-reflective mirror, an 1/4 wave plate and an analyzer in sequence;
step 4, performing inversion calculation on the light intensity maps obtained in the step 2 and the step 3 to obtain a non-visual image of the biological tissue sample to be detected corresponding to each light wavelength;
and 5, acquiring a spectral curve graph according to the non-visual image or the light intensity graph of the biological tissue sample to be detected corresponding to each light wavelength, and then detecting the same or different tissues in the biological tissue sample to be detected by the spectral curve graph.
Further, in step 4, performing inversion calculation on the light intensity maps obtained in step 2 and step 3 to obtain a non-visual image of the biological tissue sample to be detected, specifically:
step 4-1, under a certain incident light wavelength, aiming at each pixel point, obtaining the light intensity value of the pixel point under different polarization states;
step 4-2, regarding each pixel point as a linear birefringence model, and solving the phase difference delta between the incident light and the emergent light at the pixel point and the phase angle delta according to all light intensity values corresponding to each pixel point
The formula used is:
wherein I represents the light intensity of the pixel, I0Is the maximum of all light intensities corresponding to a pixel point,
normalizing the light intensity of the pixel point, wherein alpha is an included angle between the polarization direction of incident light and an X axis, and the X axis is a vector determined in any direction in a plane where a biological tissue sample to be detected is located;
step 4-3, setting a one-to-one corresponding relation between 0-360 degrees and gray values 0-255 in a user-defined mode, and enabling the phase angle of each pixel in the step 4-2 to be in accordance with the corresponding relationAnd mapping the light wavelength to a gray value, thereby obtaining a non-visual image of the biological tissue sample to be detected corresponding to the incident light wavelength.
Further, in step 5, a spectral curve graph is obtained according to the non-visual image or the light intensity map of the biological tissue sample to be detected corresponding to each light wavelength, and then the same or different tissues in the biological tissue sample to be detected are detected by the spectral curve graph, specifically:
step 5-1, selecting an interested region according to a non-visual image or a light intensity map of the biological tissue sample to be detected corresponding to each optical wavelength, calculating an average gray value of the interested region, and drawing a spectral curve graph by taking the optical wavelength as an independent variable and the average gray value as a dependent variable;
step 5-2, selecting a plurality of different interested areas, and repeating the step 5-1 to obtain a plurality of spectral curve graphs;
and 5-3, comparing all the spectral curve graphs so as to detect the same or different tissues in the biological tissue sample to be detected, and then judging whether each type of tissue is normal or abnormal through a subsequent process.
Compared with the prior art, the invention has the following remarkable advantages: 1) compared with the common spectrum analysis, the spectrum analysis method utilizes the non-visual quantity to carry out the spectrum analysis, and the non-visual quantity is not influenced by a diaphragm in a light path and the like and only reflects the self information of the sample, so that the difference between two different tissues can be obviously highlighted without being influenced by a test light path; 2) compared with the conventional spectral analysis, the non-visual spectral analysis method has higher contrast, can effectively distinguish different tissues in biological tissues, and has higher detection precision.
The present invention is described in further detail below with reference to the attached drawing figures.
Drawings
FIG. 1 is a structural diagram of a non-visual biological tissue imaging detection device combining hyperspectrum according to the invention.
FIG. 2 is a flow chart of the non-intuitive biological tissue imaging detection method combining hyperspectrum of the invention.
FIG. 3 is a graph of 7 collected intensities of light at 620nm wavelength incident light with the polarizer rotated by 30 ° step size in an embodiment of the present invention; wherein, the graphs (a) to (g) are light intensity graphs corresponding to 0 degree, 30 degree, 60 degree, 90 degree, 120 degree, 150 degree and 180 degree of polarizer rotation under the incident light with the wavelength of 620 nm.
FIG. 4 is a non-visual image calculated from the intensity map collected in FIG. 3 according to an embodiment of the present inventionImage, in which the image (a) is a sample
The non-visual image of the parameters, and the image (b) is the non-visual image of the sample delta parameters.
FIG. 5 is a schematic diagram of an embodiment of the present invention, which selects 3 positions on a light intensity map of a given sample for analyzing non-visual spectrum, wherein the positions 1 and 2 belong to normal cell tissue, and the position 3 belongs to abnormal tissue of infection and lesion;
FIG. 6 is a graph comparing the results of the spectral analysis of the three positions of FIG. 5 in the example of the present invention, wherein (a) is a graph showing the results of the conventional spectral analysis, and the conventional light is unpolarized light; FIG. (b) is a non-intuitive spectral diagram of the present invention.
Detailed Description
With reference to fig. 1, the non-intuitive biological tissue imaging detection device with hyperspectral technology provided by the invention comprises a broad spectrum light source 1, a liquid crystal tunable filter 2, a polarizer 3, a half-mirror 4, an objective lens 5 and a biological tissue sample 6 to be detected, which are coaxially and sequentially arranged, wherein the objective lens 5 and the biological tissue sample 6 are sequentially arranged along the direction of light reflected by the half-mirror 4, and 1/4 wave plates 7, an analyzer 8 and a CCD camera 9 are sequentially arranged along the direction of the light after the reflected light is transmitted by the half-mirror 4; the polarizer 3 and the analyzer 8 are controlled by a motor to rotate, wherein the polarizer 3 is used for changing the polarization state of incident light of the device, and the analyzer 8 is used for detecting the polarization state of emergent light of the device.
Further exemplarily, the broad spectrum light source 1 is a halogen light source.
Further exemplarily, the CCD camera 9 specifically employs an area-array CCD camera.
With reference to fig. 2, the non-intuitive biological tissue imaging detection method with hyperspectrum provided by the invention comprises the following steps:
step 1, a wide-spectrum light source emits light, the light beam is incident to a biological tissue sample to be detected through a liquid crystal tunable filter, a polarizer, a semi-transparent and semi-reflective mirror and an objective lens in sequence, and reflected light of the biological tissue sample to be detected is imaged to a CCD camera through the semi-transparent and semi-reflective mirror, an 1/4 wave plate and an analyzer in sequence;
step 4, performing inversion calculation on the light intensity maps obtained in the step 2 and the step 3 to obtain a non-visual image of the biological tissue sample to be detected corresponding to each light wavelength;
and 5, acquiring a spectral curve graph according to the non-visual image or the light intensity graph of the biological tissue sample to be detected corresponding to each light wavelength, and then detecting the same or different tissues in the biological tissue sample to be detected by the spectral curve graph.
Further, in step 4, the light intensity maps obtained in step 2 and step 3 are subjected to inversion calculation to obtain a non-visual image of the biological tissue sample to be detected, specifically:
step 4-1, under a certain incident light wavelength, aiming at each pixel point, obtaining the light intensity value of the pixel point under different polarization states;
step 4-2, regarding each pixel point as a linear birefringence model, and solving the phase difference delta between the incident light and the emergent light at the pixel point and the phase angle delta according to all light intensity values corresponding to each pixel point
The formula used is:
wherein I represents the light intensity of the pixel, I0Is the maximum of all light intensities corresponding to a pixel point,normalizing the light intensity of the pixel point, wherein alpha is an included angle between the polarization direction of incident light and an X axis which is determined by any direction in a plane where the biological tissue sample to be detected is positionedA fixed vector;
step 4-3, setting a one-to-one corresponding relation between 0-360 degrees and gray values 0-255 in a user-defined mode, and enabling the phase angle of each pixel in the step 4-2 to be in accordance with the corresponding relation
And mapping the light wavelength to a gray value, thereby obtaining a non-visual image of the biological tissue sample to be detected corresponding to the incident light wavelength.
Further, in step 5, a spectral curve graph is obtained according to the non-visual image or the light intensity map of the biological tissue sample to be detected corresponding to each light wavelength, and then the same or different tissues in the biological tissue sample to be detected are detected by the spectral curve graph, specifically:
step 5-1, selecting an interested region according to a non-visual image or a light intensity map of the biological tissue sample to be detected corresponding to each optical wavelength, calculating an average gray value of the interested region, and drawing a spectral curve graph by taking the optical wavelength as an independent variable and the average gray value as a dependent variable;
step 5-2, selecting a plurality of different interested areas, and repeating the step 5-1 to obtain a plurality of spectral curve graphs;
and 5-3, comparing all the spectral curve graphs so as to detect the same or different tissues in the biological tissue sample to be detected, and then judging whether each type of tissue is normal or abnormal through a subsequent process.
Examples
The light source adopted by the embodiment of the invention has the wavelength of 620nm, the polarizer is rotated by the step length of 30 degrees, 7 light intensity graphs of a biological tissue sample of a certain known tissue are collected together and are shown in figure 3, the light intensity graphs are subjected to inversion calculation, and a non-visual image of the biological tissue sample is obtained and is shown in figure 4.
For a certain light intensity map of the biological tissue sample, three positions of the known tissues as normal or abnormal are marked on the map, and the conventional spectral analysis method and the method of the present invention are respectively used for spectral analysis, and the result is shown in fig. 6, comparing fig. 6(a) and fig. 6(b), it can be seen that: in conventional spectral analysis, spectral curves of positions 1,2 and 3 are relatively close, and the contrast is relatively low; in the non-visual spectrum of the present invention, the spectral curve at position 3 is far from positions 1 and 2, the contrast is high, and the difference in tissue characteristics between positions 1,2 and 3 can be easily distinguished. The result of the conventional spectral analysis is possibly influenced by vignetting of a microscope objective, nonuniform filter effect space and the like, so that the whole spectral curve at the corresponding position moves up and down, and when different positions are compared, the judgment of the result is possibly influenced; but the non-intuitive parameters represent the parameter characteristics of the sample, are irrelevant to the light source and the imaging light path, and cannot cause spectral line shift similar to that in conventional spectrum comparison, so that the analysis result is more accurate.
In conclusion, compared with the common spectral analysis, the method has higher contrast ratio, can effectively distinguish different tissues in biological tissues, and has higher detection precision.
Claims (7)
1. A non-visual biological tissue imaging detection device combined with hyperspectrum is characterized by comprising a broad spectrum light source (1), a liquid crystal tunable filter (2), a polarizer (3), a half-transmitting and half-reflecting mirror (4), an objective lens (5) and a biological tissue sample (6) to be detected, wherein the broad spectrum light source, the liquid crystal tunable filter (2), the polarizer (3) and the half-transmitting and half-reflecting mirror (4) are coaxially and sequentially arranged, the objective lens (5) and the biological tissue sample (6) are sequentially arranged along the direction of reflected light of the half-transmitting and half-reflecting mirror (4), and an 1/4 wave plate (7), an analyzer (; polarizer (3), analyzer (8) rotate through motor control, wherein polarizer (3) are used for changing the polarization state of device incident light, and analyzer (8) are used for detecting the polarization state of device emergent light.
2. The non-intuitive biological tissue imaging detection apparatus combining hyperspectral according to claim 1, wherein the broad spectrum light source (1) specifically employs a halogen light source.
3. The non-intuitive biological tissue imaging detection apparatus in combination with hyperspectral according to claim 1, characterized in that the CCD camera (9) is specifically an area array CCD camera.
4. A non-visual biological tissue imaging detection method combined with hyperspectrum is characterized by comprising the following steps:
step 1, a wide-spectrum light source emits light, the light beam is incident to a biological tissue sample to be detected through a liquid crystal tunable filter, a polarizer, a semi-transparent and semi-reflective mirror and an objective lens in sequence, and reflected light of the biological tissue sample to be detected is imaged to a CCD camera through the semi-transparent and semi-reflective mirror, an 1/4 wave plate and an analyzer in sequence;
step 2, controlling the polarizer to rotate for a circle by the step length delta theta by the motor, and recording a light intensity image acquired by the corresponding CCD camera every time the polarizer rotates for the circle by the step length delta theta;
step 3, continuously changing the optical wavelength which is transmitted by the liquid crystal tunable filter, and repeating the process of the step 2 for each optical wavelength, thereby obtaining a plurality of light intensity maps;
step 4, performing inversion calculation on the light intensity maps obtained in the step 2 and the step 3 to obtain a non-visual image of the biological tissue sample to be detected corresponding to each light wavelength;
and 5, acquiring a spectral curve graph according to the non-visual image or the light intensity graph of the biological tissue sample to be detected corresponding to each light wavelength, and then detecting the same or different tissues in the biological tissue sample to be detected by the spectral curve graph.
5. The method for detecting non-intuitive biological tissue imaging combining hyperspectral imaging according to claim 4, wherein Δ θ in step 2 is 30 °.
6. The hyperspectral non-visual biological tissue imaging detection method according to claim 4 is characterized in that step 4 is performed by performing inversion calculation on the light intensity maps obtained in step 2 and step 3 to obtain a non-visual image of a biological tissue sample to be detected, and specifically comprises the following steps:
step 4-1, under a certain incident light wavelength, aiming at each pixel point, obtaining the light intensity value of the pixel point under different polarization states;
step 4-2, regarding each pixel point as a linear birefringence model, and solving the point entrance of the pixel point according to all light intensity values corresponding to each pixel pointPhase difference delta and phase angle between incident light and emergent light
The formula used is:
wherein I represents the light intensity of the pixel, I0Is the maximum of all light intensities corresponding to a pixel point,
normalizing the light intensity of the pixel point, wherein alpha is an included angle between the polarization direction of incident light and an X axis, and the X axis is a vector determined in any direction in a plane where a biological tissue sample to be detected is located;
step 4-3, setting a one-to-one corresponding relation between 0-360 degrees and gray values 0-255 in a user-defined mode, and enabling the phase angle of each pixel in the step 4-2 to be in accordance with the corresponding relation
And mapping the light wavelength to a gray value, thereby obtaining a non-visual image of the biological tissue sample to be detected corresponding to the incident light wavelength.
7. The hyperspectral non-visual biological tissue imaging detection method according to claim 4 is characterized in that in the step 5, a spectral curve graph is obtained according to the non-visual image or the light intensity map of the biological tissue sample to be detected corresponding to each light wavelength, and then the same or different tissues in the biological tissue sample to be detected are detected by the spectral curve graph, specifically:
step 5-1, selecting an interested region according to a non-visual image or a light intensity map of the biological tissue sample to be detected corresponding to each optical wavelength, calculating an average gray value of the interested region, and drawing a spectral curve graph by taking the optical wavelength as an independent variable and the average gray value as a dependent variable;
step 5-2, selecting a plurality of different interested areas, and repeating the step 5-1 to obtain a plurality of spectral curve graphs;
and 5-3, comparing all the spectral curve graphs so as to detect the same or different tissues in the biological tissue sample to be detected, and then judging whether each type of tissue is normal or abnormal through a subsequent process.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910869790.XA CN110672530A (en) | 2019-09-16 | 2019-09-16 | Hyperspectrum-combined non-visual biological tissue imaging detection device and method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910869790.XA CN110672530A (en) | 2019-09-16 | 2019-09-16 | Hyperspectrum-combined non-visual biological tissue imaging detection device and method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110672530A true CN110672530A (en) | 2020-01-10 |
Family
ID=69078295
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910869790.XA Pending CN110672530A (en) | 2019-09-16 | 2019-09-16 | Hyperspectrum-combined non-visual biological tissue imaging detection device and method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110672530A (en) |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101021478A (en) * | 2007-02-13 | 2007-08-22 | 江西农业大学 | Laser inducing fluorescent high spectral image detecting method and device for fruit quality |
| CN103323410A (en) * | 2013-05-24 | 2013-09-25 | 暨南大学 | A device and a method based on a liquid-crystal filtering device for microscopic spectral imaging |
| CN103471989A (en) * | 2013-01-08 | 2013-12-25 | 刘学峰 | Optical vector modeling based non-visual imaging method |
| CN106073701A (en) * | 2016-05-26 | 2016-11-09 | 南京理工大学 | A kind of tissue infrared non-intuitive microscopic imaging device and method |
| CN107121414A (en) * | 2017-06-11 | 2017-09-01 | 湖北器长光电股份有限公司 | A kind of non-intuitive dim light super-resolution imaging measuring system and method |
| CN107290279A (en) * | 2017-06-30 | 2017-10-24 | 湖北器长光电股份有限公司 | A kind of gene molecule fluorescence non-intuitive microscopic imaging device and method |
| CN108414088A (en) * | 2017-02-10 | 2018-08-17 | 南京理工大学 | A kind of micro- polarization imaging device of EO-1 hyperion and method |
-
2019
- 2019-09-16 CN CN201910869790.XA patent/CN110672530A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101021478A (en) * | 2007-02-13 | 2007-08-22 | 江西农业大学 | Laser inducing fluorescent high spectral image detecting method and device for fruit quality |
| CN103471989A (en) * | 2013-01-08 | 2013-12-25 | 刘学峰 | Optical vector modeling based non-visual imaging method |
| CN103323410A (en) * | 2013-05-24 | 2013-09-25 | 暨南大学 | A device and a method based on a liquid-crystal filtering device for microscopic spectral imaging |
| CN106073701A (en) * | 2016-05-26 | 2016-11-09 | 南京理工大学 | A kind of tissue infrared non-intuitive microscopic imaging device and method |
| CN108414088A (en) * | 2017-02-10 | 2018-08-17 | 南京理工大学 | A kind of micro- polarization imaging device of EO-1 hyperion and method |
| CN107121414A (en) * | 2017-06-11 | 2017-09-01 | 湖北器长光电股份有限公司 | A kind of non-intuitive dim light super-resolution imaging measuring system and method |
| CN107290279A (en) * | 2017-06-30 | 2017-10-24 | 湖北器长光电股份有限公司 | A kind of gene molecule fluorescence non-intuitive microscopic imaging device and method |
Non-Patent Citations (1)
| Title |
|---|
| 刘国彦 等: "纳米颗粒近场空间光谱散射建模与显微成像分析", 《光谱学与光谱分析》 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10229310B2 (en) | High throughput partial wave spectroscopic microscopy and associated systems and methods | |
| JP5083315B2 (en) | Inspection device, inspection method, and program | |
| JP6594294B2 (en) | Image quality evaluation of microscopic images | |
| AU2002227343B2 (en) | System for normalizing spectra | |
| CN105403514B (en) | A kind of multi-wavelength incidence single-shot ellipsometry method | |
| WO2014031411A1 (en) | Determining material properties using speckle statistics | |
| AU2002227343A1 (en) | System for normalizing spectra | |
| US11499870B2 (en) | Imaging spectropolarimeter | |
| CN107167456A (en) | Transmission-type differential confocal CARS micro-spectrometer method and devices | |
| CN111369484B (en) | Rail profile detection method and device | |
| CN112129734A (en) | Depth-distinguishable fluorescence imaging system | |
| Ning et al. | Single-shot measurement of polarization state at low light field using Mueller-mapping star test polarimetry | |
| JP2001141602A (en) | System and method for evaluating double refraction | |
| KR102229881B1 (en) | Apparatus of measuring photothermal reflectance spectrum and operating method thereof | |
| US11927481B2 (en) | Optical technique for material characterization | |
| CN110672530A (en) | Hyperspectrum-combined non-visual biological tissue imaging detection device and method | |
| CN104990908B (en) | The confocal induced breakdown Raman spectrum imaging detection method of laser twin shaft and device | |
| CN110763341B (en) | Stokes-Mueller spectral imaging system and detection method | |
| CN112129715A (en) | Vane nondestructive testing device based on hyperspectrum | |
| CN107421639B (en) | Plasma three-dimensional information diagnosis system in material surface treatment process | |
| CN113075159A (en) | Terahertz spectrum-based cell imaging system for slice inspection | |
| CN113758939A (en) | Method for representing metal surface cleanliness by using metal surface reflection and scattering spectrum | |
| Adams et al. | Simultaneous spectral temporal modelling for a time-resolved fluorescence emission spectrum | |
| De Kerf et al. | Quantitative detection of corrosion minerals in carbon steel using shortwave infrared hyperspectral imaging | |
| US20230057928A1 (en) | Imaging Spectropolarimeter and Sample Characterization Methodology Utilizing the Same |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20200110 |
|
| RJ01 | Rejection of invention patent application after publication |