LU505223B1 - Method, system and electronic device for detecting microbial activity based on microscopic hyperspectral - Google Patents
Method, system and electronic device for detecting microbial activity based on microscopic hyperspectral Download PDFInfo
- Publication number
- LU505223B1 LU505223B1 LU505223A LU505223A LU505223B1 LU 505223 B1 LU505223 B1 LU 505223B1 LU 505223 A LU505223 A LU 505223A LU 505223 A LU505223 A LU 505223A LU 505223 B1 LU505223 B1 LU 505223B1
- Authority
- LU
- Luxembourg
- Prior art keywords
- detected
- hyperspectral
- cube data
- cell
- smear
- Prior art date
Links
- 238000000034 method Methods 0.000 title claims abstract description 47
- 230000000813 microbial effect Effects 0.000 title claims abstract description 26
- 238000000605 extraction Methods 0.000 claims abstract description 32
- 238000001228 spectrum Methods 0.000 claims abstract description 32
- 238000001514 detection method Methods 0.000 claims abstract description 27
- 239000007788 liquid Substances 0.000 claims abstract description 26
- 244000005700 microbiome Species 0.000 claims abstract description 24
- 230000001580 bacterial effect Effects 0.000 claims abstract description 23
- 230000000694 effects Effects 0.000 claims abstract description 23
- 210000004027 cell Anatomy 0.000 claims description 15
- 238000010606 normalization Methods 0.000 claims description 12
- 239000011521 glass Substances 0.000 claims description 9
- 238000007781 pre-processing Methods 0.000 claims description 8
- 230000011218 segmentation Effects 0.000 claims description 7
- 238000004590 computer program Methods 0.000 claims description 6
- 239000006059 cover glass Substances 0.000 claims description 5
- 239000008223 sterile water Substances 0.000 claims description 5
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Chemical compound O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 5
- 241000894006 Bacteria Species 0.000 claims description 4
- 238000004422 calculation algorithm Methods 0.000 claims description 4
- 210000002421 cell wall Anatomy 0.000 claims description 4
- 238000005119 centrifugation Methods 0.000 claims description 4
- 210000000805 cytoplasm Anatomy 0.000 claims description 4
- 238000001035 drying Methods 0.000 claims description 4
- 238000011049 filling Methods 0.000 claims description 4
- 238000012545 processing Methods 0.000 claims description 4
- 238000007637 random forest analysis Methods 0.000 claims description 4
- 238000012706 support-vector machine Methods 0.000 claims description 4
- 238000009423 ventilation Methods 0.000 claims description 4
- 238000003672 processing method Methods 0.000 claims 1
- 230000003595 spectral effect Effects 0.000 description 6
- 238000010186 staining Methods 0.000 description 5
- 238000005516 engineering process Methods 0.000 description 4
- 239000003921 oil Substances 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 239000010627 cedar oil Substances 0.000 description 2
- 238000012136 culture method Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000002360 preparation method Methods 0.000 description 2
- 238000010521 absorption reaction Methods 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 230000029918 bioluminescence Effects 0.000 description 1
- 238000005415 bioluminescence Methods 0.000 description 1
- 239000003795 chemical substances by application Substances 0.000 description 1
- 239000002131 composite material Substances 0.000 description 1
- 238000005260 corrosion Methods 0.000 description 1
- 230000007797 corrosion Effects 0.000 description 1
- 239000004148 curcumin Substances 0.000 description 1
- 230000014155 detection of activity Effects 0.000 description 1
- 239000003814 drug Substances 0.000 description 1
- 230000007717 exclusion Effects 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 238000007429 general method Methods 0.000 description 1
- 229910052736 halogen Inorganic materials 0.000 description 1
- 150000002367 halogens Chemical class 0.000 description 1
- 238000003709 image segmentation Methods 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 239000012528 membrane Substances 0.000 description 1
- 230000000877 morphologic effect Effects 0.000 description 1
- 150000007523 nucleic acids Chemical class 0.000 description 1
- 102000039446 nucleic acids Human genes 0.000 description 1
- 108020004707 nucleic acids Proteins 0.000 description 1
- 238000007747 plating Methods 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 238000007447 staining method Methods 0.000 description 1
- 239000006228 supernatant Substances 0.000 description 1
- 239000000725 suspension Substances 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
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
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
-
- 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
-
- C—CHEMISTRY; METALLURGY
- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
- C12M—APPARATUS FOR ENZYMOLOGY OR MICROBIOLOGY; APPARATUS FOR CULTURING MICROORGANISMS FOR PRODUCING BIOMASS, FOR GROWING CELLS OR FOR OBTAINING FERMENTATION OR METABOLIC PRODUCTS, i.e. BIOREACTORS OR FERMENTERS
- C12M1/00—Apparatus for enzymology or microbiology
- C12M1/34—Measuring or testing with condition measuring or sensing means, e.g. colony counters
-
- C—CHEMISTRY; METALLURGY
- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
- C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
- C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
- C12Q1/02—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving viable microorganisms
- C12Q1/04—Determining presence or kind of microorganism; Use of selective media for testing antibiotics or bacteriocides; Compositions containing a chemical indicator therefor
- C12Q1/06—Quantitative determination
-
- 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
- G01N21/84—Systems specially adapted for particular applications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
Landscapes
- Chemical & Material Sciences (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Organic Chemistry (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- Immunology (AREA)
- General Physics & Mathematics (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Zoology (AREA)
- Wood Science & Technology (AREA)
- Pathology (AREA)
- Biotechnology (AREA)
- Microbiology (AREA)
- Genetics & Genomics (AREA)
- Proteomics, Peptides & Aminoacids (AREA)
- Spectroscopy & Molecular Physics (AREA)
- General Engineering & Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Medicinal Chemistry (AREA)
- Sustainable Development (AREA)
- Theoretical Computer Science (AREA)
- Toxicology (AREA)
- Biophysics (AREA)
- Biomedical Technology (AREA)
- Molecular Biology (AREA)
- Investigating Or Analysing Materials By Optical Means (AREA)
- Investigating Or Analysing Biological Materials (AREA)
- Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)
Abstract
Provided are a method, a system and an electronic device for detecting microbial activity based on microscopic hyperspectral, including: acquiring hyperspectral cube data of a smear to be detected; the smear to be detected contains bacterial liquid to be detected; the bacterial liquid to be detected is bacterial liquid of microorganisms to be detected; the hyperspectral cube data include hyperspectral images in a plurality of channels; segmenting the hyperspectral cube data to extract single-cell cube data; performing spectrum extraction operation of regions of interest on the single-cell cube data to obtain characteristic spectra of a plurality of regions of interest; performing image feature extraction operation on the single-cell cube data to obtain single-cell image features; and taking the characteristic spectra and the single-cell image features as input data, and employing a trained classifier to detect the activity of the microorganisms to be detected to obtain an activity detection result.
Description
METHOD, SYSTEM AND ELECTRONIC DEVICE FOR DETECTING MICROBIAL
ACTIVITY BASED ON MICROSCOPIC HYPERSPECTRAL
The present invention relates to the technical field of microbial activity detection, in particular to a method, a system and an electronic device for detecting microbial activity based on microscopic hyperspectral.
It is often necessary to determine the activity of microorganisms in the process of inspecting and counting the microorganisms. The detection of activity of the microorganisms is of great significance in the fields of medicine and food. The existing microbial activity detection methods mainly include plate culture method, membrane dye absorption method, ATP bioluminescence method, dye exclusion experiment method, nucleic acid detection method, microfluidics technology, and the like, The most commonly used one is the plate culture method based on culturability of the microorganisms, through which the microbial activity is determined by observing the growth results after plating the microorganisms. In addition, a staining method based on various staining principles can determine the activity of the microorganisms more intuitively. However, the existing microbial activity detection methods have the shortcomings of long culture process, complex procedures, many required instruments and equipment, manual detection throughout the entire process and the like, which limits the speed and scale of detection.
In view of this, the present application provides a method, a system and an electronic device for detecting microbial activity based on microscopic hyperspectral to solve the technical problems of long culture process and complex procedures in the prior art.
In a first aspect, the present invention provides a method for detecting microbial activity based on microscopic hyperspectral, including: acquiring hyperspectral cube data of a smear to be detected; the smear to be detected contains bacterial liquid to be detected; the bacterial liquid to be detected is bacterial liquid of microorganisms to be detected; the hyperspectral cube data include hyperspectral images in a plurality of channels; segmenting the hyperspectral cube data to extract single-cell cube data; performing spectrum extraction operation of regions of interest on the single-cell cube data to obtain characteristic spectra of a plurality of regions of interest 909225 performing image feature extraction operation on the single-cell cube data to obtain single-cell image features; and taking the characteristic spectra and the single-cell image features as input data, and employing a trained classifier to detect the activity of the microorganisms to be detected to obtain an activity detection result.
Further, before the step of "acquiring hyperspectral cube data of a smear to be detected", the method further includes: performing a plurality of centrifugation operations on bacteria liquid to be detected through a centrifuge to prepare a sample to be detected; placing the sample to be detected on a glass slide, and adding sterile water after ventilation and drying treatment; and placing a cover glass on the glass slide to prepare the smear to be detected.
Further, the step of "acquiring hyperspectral cube data of a smear to be detected" includes: placing the smear to be detected on a microscope carrier, collecting hyperspectral images of the smear to be detected through a microscopic hyperspectrometer, and acquiring the hyperspectral cube data of the smear to be detected.
Further, the step of "segmenting the hyperspectral cube data to extract single-cell cube data" includes: extracting a hyperspectral image under a single channel in the hyperspectral cube data, and performing binarization processing after image enhancement to obtain a binarized image containing organisms to be detected; extracting a center of mass of the organisms to be detected in a binary image, and extracting the single cell cube data by taking the center of mass as a center.
Further, the regions of interest include extracellular ring, cell wall, cytoplasm, and whole cell.
Further, the single cell image features include: area, perimeter, minimum bounding rectangle length, minimum bounding rectangle width, minimum bounding rectangle aspect ratio, eccentricity, area after filling, rectangularity, and circularity.
Further, after the step of "obtaining characteristic spectra of a plurality of regions of interest", the method further includes: performing normalization preprocessing on the characteristic spectra; where an operation formula of the normalization preprocessing is: +; = = where yi is the light intensity at the i* wave point after normalization, and xi is the intensity value at the i" wave point.
Further, the trained classifier includes: support vector machines, random forest classifier,
K-nearest neighbor algorithm classifier and discriminator. 0505223
In a second aspect, an embodiment of the present invention further provides a device for detecting microbial activity based on microscopic hyperspectral, including an acquisition module, a segmentation module, a first extraction module, a second extraction module and a detection module, where the acquisition module is configured to acquire hyperspectral cube data of a smear to be detected; the smear to be detected contains bacterial liquid to be detected; the bacterial liquid to be detected is bacterial liquid of microorganisms to be detected; the hyperspectral cube data include hyperspectral images in a plurality of channels; the segmentation module 1s configured to segment the hyperspectral cube data to extract single-cell cube data; the first extraction module is configured to perform spectrum extraction operation of regions of interest on the single-cell cube data to obtain characteristic spectra of a plurality of regions of interest, the second extraction module is configured to perform image feature extraction operation on the single-cell cube data to obtain single-cell image features; and the detection module 1s configured to take the characteristic spectra and the single-cell image features as input data, and employ a trained classifier to detect the activity of the microorganisms to be detected to obtain an activity detection result.
In a third aspect, the present invention further provides an electronic device, including a memory, a processor and a computer program stored on the memory and runnable on the processor, where the processor implements the steps of the method in the first aspect above when executing the computer program.
The present invention provides a method, a system and an electronic device for detecting microbial activity based on microscopic hyperspectral, which uses the microscopic hyperspectral technology to effectively integrate spectral information and image information, and achieve the purpose of activity detection without the need for such treatment as culture or staining, thereby solving the technical problems of long culture process and complex procedures in the prior art.
In order to more clearly illustrate technical solutions in the specific implementations of the present invention or in the prior art, a brief introduction to the accompanying drawings required for the description of the specific implementations or the prior art will be provided below.
Obviously, the accompanying drawings in the following description are some of the implementations of the present invention, and those of ordinary skill in the art may still derive 905223 other drawings from these accompanying drawings without any creative effort.
FIG. 1 is a flow chart of a method for detecting microbial activity based on microscopic hyperspectral according to an embodiment of the present invention;
FIG. 2 is a flow chart of a system for detecting microbial activity based on microscopic hyperspectral according to an embodiment of the present invention.
FIG. 3 is a flow chart of a device for detecting microbial activity based on microscopic hyperspectral according to an embodiment of the present invention.
Description of reference numerals: 1-heat-insulating light source; 2-microbial smear; 3-100X oil lens; 4-microscope barrel;
S-push-broom hyperspectrometer; 6-data cable; 7-computer; 10-acquisition module; 20-segmentation module; 30-first extraction module; 40-second extraction module; and 50-detection module.
In order to further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined objects, the specific implementations, structures, features and effects of the present invention of the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments.
Example 1:
FIG. 1 is a flow chart of a method for detecting microbial activity based on microscopic hyperspectral according to an embodiment of the present invention. As shown in FIG. 1, the method specifically includes the following steps: step S102, hyperspectral cube data of a smear to be detected were acquired; the smear to be detected contained bacterial liquid to be detected; the bacterial liquid to be detected was bacterial liquid of microorganisms to be detected; and the hyperspectral cube data included hyperspectral images in a plurality of channels. step S104, the hyperspectral cube data were segmented, and single-cell cube data were extracted. step S106, spectrum extraction operation of regions of interest was performed on the single-cell cube data to obtain characteristic spectra of a plurality of regions of interest. step S108, image feature extraction operation was performed on the single-cell cube data to obtain single-cell image features. 0505223 step S110, the characteristic spectra and the single-cell image features were taken as input data, and a trained classifier was used to detect the activity of the microorganisms to be detected to obtain an activity detection result. 5 The present invention provides a method for detecting microbial activity based on microscopic hyperspectral, which uses the microscopic hyperspectral technology to effectively integrate spectral information and image information, and achieve the purpose of activity detection without the need for such treatment as culture or staining, thereby solving the technical problems of long culture process and complex procedures in the prior art.
Optionally, prior to the step S102, preparation of a smear to be detected is further included, specifically, including the following steps:
S1, a plurality of centrifugation operations were performed on bacteria liquid to be detected through a centrifuge to prepare a sample to be detected.
Specifically, 1 ml of bacterial liquid to be detected was taken and centrifuged in the centrifuge at 10000 r/min for 10 min at a temperature 4 °C, supernatant was discarded, sterile water was added for suspension, and the centrifugation was then performed again; and the steps above were repeated for three times, so that the sample to be detected was prepared. step S2, the sample to be detected was placed on a glass slide, and sterile water was added after ventilation and drying treatment. step S3, a cover glass was placed on the glass slide to prepare the smear to be detected.
Specifically, 2.5 ul of the sample to be detected was taken and placed on a clean glass slide, the clean glass slide was placed in a clean bench for ventilation and drying for 15 min, 1 ul of sterile water was placed on the cover glass after 15 min, the cover glass was covered on the glass slide, and the preparation of the smear to be detected was then completed.
In this embodiment of the present invention, the step S102 further includes: the smear to be detected was placed on a microscope carrier, hyperspectral images of the smear to be detected were collected through a microscopic hyperspectrometer, and the hyperspectral cube data of the smear to be detected were acquired.
Specifically, the smear to be detected was placed on the microscope carrier, 1 drop of cedar oil was dripped, a 100X objective lens was immersed in the cedar oil, and a focal length was adjusted to focus. After parameters were set, the microscopic hyperspectrometer and support 90953 software were used to collect hyperspectral images, and the spectrum collection range was 400 nm-1000 nm and has 128 channels.
Optionally, in this embodiment of the present invention, the step S104 includes the following steps: step S1041, a hyperspectral image under a single channel in the hyperspectral cube data was extracted, binarization processing after image enhancement was performed to obtain a binarized image containing organisms to be detected.
Preferably, the image enhancement methods include histogram equalization, linear transformation, median filtering, Otsu's binarization and area threshold screening. step S1042, a center of mass of the organisms to be detected in a binary image was extracted, and the single cell cube data were extracted by taking the center of mass as a center.
Specifically, the center of mass of the organisms to be detected was calculated for the binary image, and a mask image of 31 x 31 x 1 of a single bacterium was segmented with the center of mass as the center, so as to obtain single cell cube data.
Optionally, the step S106 further includes: spectral extraction on four regions of interest was performed on the extracted single cell cube data, where the regions of interest include extracellular ring, cell wall, cytoplasm, and whole cell.
Specifically, extraction steps of the four regions of interest are as follows: boundaries of the microorganisms to be detected were extracted, and characteristics of the images were extracted by further using multiple morphological corrosion or expansion method respectively.
An average treatment on the extracted spectra of the four regions of interest was performed, and four characteristic spectra were acquired for each microorganism to be detected.
Preferably, in this embodiment of the present invention, the single cell image features include a total of nine geometric features: area, perimeter, minimum bounding rectangle length, minimum bounding rectangle width, minimum bounding rectangle aspect ratio, eccentricity, area after filling, rectangularity, and circularity.
Preferably, in this embodiment of the present invention, after the characteristic spectra of the plurality of regions of interest are acquired, the method further includes: performing normalization preprocessing on the characteristic spectra, where an operation formula of the normalization preprocessing is: 10905223 x, — miinfx,) in the formula, yi is the light intensity at the i wave point after normalization, and xi is the intensity value at the i" wave point.
Preferably, the trained classifier in this embodiment of the present invention is a coincidence classifier, specifically including four types, that is, support vector machines, random forest classifier, K-nearest neighbor algorithm classifier and discriminator.
As can be seen from the above description, this embodiment of the present invention provides a method for detecting microbial activity based on microscopic hyperspectral, specific steps include: taking a bacterial liquid to be detected for centrifuging, collecting hyperspectral cube data by using a microscopic imaging system; performing the single cell segmentation on the collected hyperspectral cube data, and extracting image information and spectral information; and finally, inputting the two types of information into a composite model to obtain detection results. Compared with the prior art, the method provided by this embodiment of the present invention has the following technical effects: 1. compared with the prior art, the method provided by this embodiment of the invention features rapidness, high efficiency, simple and convenient operation, with no need of culture or staining agent and the like, and can save manual operation and detection cost; and 2. the method provided by this embodiment of the present invention can effectively integrate spectral information and image information, and can directly obtain more information different from a general method, it can also respectively integrate specific spectra on the morphology of microorganisms to obtain more scientific and high-explanatory classification results.
Example 2:
FIG. 2 is a flow chart of a system for detecting microbial activity based on microscopic hyperspectral according to an embodiment of the present invention. As shown in FIG. 2, the system includes: an acquisition module 10, a segmentation module 20, a first extraction module 30, a second extraction module 40 and a detection module 50.
Specifically, the acquisition module 10 is configured to acquire hyperspectral cube data of a smear to be detected; the smear to be detected contains bacterial liquid to be detected; the bacterial liquid to be detected is bacterial liquid of microorganisms to be detected; and the 905223 hyperspectral cube data include hyperspectral images in a plurality of channels.
The segmentation module 20 is configured to segment the hyperspectral cube data, so as to extract single-cell cube data.
The first extraction module 30 is configured to perform spectrum extraction operation of regions of interest on the single-cell cube data to obtain characteristic spectra of a plurality of regions of interest. Optionally, the regions of interest include extracellular ring, cell wall, cytoplasm, and whole cell.
The second extraction module 40 is configured to perform image feature extraction operation on the single-cell cube data to obtain single-cell image features. Optionally, the single cell image features include: area, perimeter, minimum bounding rectangle length, minimum bounding rectangle width, minimum bounding rectangle aspect ratio, eccentricity, area after filling, rectangularity, and circularity.
The detection module 50 is configured to take the characteristic spectra and the single-cell image features as input data, and employ a trained classifier to detect the activity of the microorganisms to be detected to obtain an activity detection result.
Optionally, the trained classifier includes: support vector machines, random forest classifier,
K-nearest neighbor algorithm classifier and discriminator.
The present invention provides a system for detecting microbial activity based on microscopic hyperspectral, which uses the microscopic hyperspectral technology to effectively integrate spectral information and image information, and achieve the purpose of activity detection without the need for such treatment as culture or staining, thereby solving the technical problems of long culture process and complex procedures in the prior art.
Optionally, the acquisition module 10 is further used to place the smear to be detected on a microscope carrier, collect hyperspectral images of the smear to be detected through a microscopic hyperspectrometer, and acquire the hyperspectral cube data of the smear to be detected.
Optionally, the detection module 50 is further used to extract a hyperspectral image under a single channel in the hyperspectral cube data, perform binarization processing after image enhancement to obtain a binarized image containing organisms to be detected; extract a center of mass of the organisms to be detected in a binary image, and extract the single cell cube data py 905223 taking the center of mass as a center.
Optionally, the detection module 50 is further used to perform normalization preprocessing on the characteristic spectra; where an operation formula of the normalization preprocessing is: x; — mindy) ¥ = max(x;) — min{x; in the formula, yi is the light intensity at the ith wave point after normalization, and xi is the intensity value at the ith wave point.
Optionally, this embodiment of the present invention further provides a device for detecting microbial activity based on microscopic hyperspectral. As shown in FIG. 3, the device includes a heat-insulating light source 1, a microbial smear 2, a 100X oil lens 3, a microscope barrel 4, push-broom hyperspectrometer 5, a data cable 6 and a computer 7.
The light from a halogen lamp in the heat-insulating light source 1 passes through a condensing lens, and penetrates through the microbial smear 2 carrying microorganisms, the light carrying microbial information passes through the 100X oil lens 3 and the microscope barrel 4, and is collected by the push-broom hyperspectrometer 5, converted into an electric signal and transmitted into the computer 7 through the data cable 6 for analysis and identification.
The analysis and identification includes image enhancement, image segmentation, extraction of interest, information extraction and classifier classification. Optionally, the microscope model used in the present invention is Nikon E100, and the hyperspectrometer model is SOC710 (SURFACCE OPTICS).
The present invention further provides an electronic device, including a memory, a processor and a computer program stored on the memory and runnable on the processor, where the processor implements the steps of the method in Example 1 when executing the computer program.
The above description is merely preferred embodiments of the present invention, and is not intended to limit the present invention in any form. Although the present invention has been disclosed in preferred embodiments, but they are not intended to limit the present invention.
Without departing from the scope of the technical solution of the present invention, those skilled in the art may make many possible changes and modifications to the technical solution of the present invention by using the above disclosed technical contents. Any indirect alterations,
equivalent changes and modifications which are made to the above embodiments in accordance 905223 with the technical essence of the present invention shall fall within the scope of protection scope of the technical solution of the present invention.
Claims (10)
1. A method for detecting microbial activity based on microscopic hyperspectral, comprising: acquiring hyperspectral cube data of a smear to be detected; the smear to be detected contains bacterial liquid to be detected; the bacterial liquid to be detected is bacterial liquid of microorganisms to be detected; and the hyperspectral cube data comprise hyperspectral images in a plurality of channels; segmenting the hyperspectral cube data to extract single-cell cube data; performing spectrum extraction operation of regions of interest on the single-cell cube data to obtain characteristic spectra of a plurality of regions of interest; performing image feature extraction operation on the single-cell cube data to obtain single-cell image features; and taking the characteristic spectra and the single-cell image features as input data, and employing a trained classifier to detect the activity of the microorganisms to be detected to obtain an activity detection result.
2. The method according to claim 1, wherein before the step of "acquiring hyperspectral cube data of a smear to be detected", the method further comprises: performing a plurality of centrifugation operations on bacteria liquid to be detected through a centrifuge to prepare a sample to be detected, placing the sample to be detected on a glass slide, and adding sterile water after ventilation and drying treatment; and placing a cover glass on the glass slide to prepare the smear to be detected.
3. The method according to claim 1, wherein the step of "acquiring hyperspectral cube data of a smear to be detected" comprises: placing the smear to be detected on a microscope carrier, collecting hyperspectral images of the smear to be detected through a microscopic hyperspectrometer, and acquiring the hyperspectral cube data of the smear to be detected.
4. The method according to claim 1, wherein the step of "segmenting the hyperspectral cube data to extract single-cell cube data" comprises: LUS05228 extracting a hyperspectral image under a single channel in the hyperspectral cube data, and performing binarization processing after image enhancement to obtain a binarized image containing organisms to be detected; and extracting a center of mass of the organisms to be detected in a binary image, and extracting the single cell cube data by taking the center of mass as a center.
5. The method according to claim 1, wherein the regions of interest comprise extracellular ring, cell wall, cytoplasm, and whole cell.
6. The method according to claim 1, wherein the single cell image features comprise: area, perimeter, minimum bounding rectangle length, minimum bounding rectangle width, minimum bounding rectangle aspect ratio, eccentricity, area after filling, rectangularity, and circularity.
7. The method according to claim 1, wherein after the characteristic spectra of the plurality of regions of interest are acquired, the method further comprises: performing normalization preprocessing on the characteristic spectra; wherein an operation formula of the normalization preprocessing 1s: / x, — min{x} = max{x,} — minx} in the formula, yi is the light intensity at the i wave point after normalization, and xi is the intensity value at the i" wave point.
8. The method according to claim 1, the trained classifier comprises: support vector machines, random forest classifier, K-nearest neighbor algorithm classifier and discriminator.
9. A system for detecting microbial activity based on microscopic hyperspectral, comprising an acquisition module, a segmentation module, a first extraction module, a second extraction module and a detection module, wherein: the acquisition module is configured to acquire hyperspectral cube data of a smear to be detected; the smear to be detected contains bacterial liquid to be detected; the bacterial liquid to be detected is bacterial liquid of microorganisms to be detected; and the hyperspectral cube data comprise hyperspectral images in a plurality of channels; the segmentation module is configured to segment the hyperspectral cube data, so as to extract single-cell cube data;
the first extraction module is configured to perform spectrum extraction operation bf505223 regions of interest on the single-cell cube data to obtain characteristic spectra of a plurality of regions of interest; the second extraction module is configured to perform image feature extraction operation on the single-cell cube data to obtain single-cell image features; and the detection module is configured to take the characteristic spectra and the single-cell image features as input data, and employ a trained classifier to detect the activity of the microorganisms to be detected to obtain an activity detection result.
10. An electronic device, comprising a memory, a processor and a computer program stored on the memory and runnable on the processor, wherein the processor implements the processing method according to any one of claims 1-10 when executing the computer program.
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211312861.4A CN115629042A (en) | 2022-10-25 | 2022-10-25 | Method and system for detecting microbial activity based on microscopic hyperspectrum |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| LU505223B1 true LU505223B1 (en) | 2024-03-28 |
Family
ID=84907361
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| LU505223A LU505223B1 (en) | 2022-10-25 | 2023-09-28 | Method, system and electronic device for detecting microbial activity based on microscopic hyperspectral |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN115629042A (en) |
| LU (1) | LU505223B1 (en) |
-
2022
- 2022-10-25 CN CN202211312861.4A patent/CN115629042A/en active Pending
-
2023
- 2023-09-28 LU LU505223A patent/LU505223B1/en active IP Right Grant
Also Published As
| Publication number | Publication date |
|---|---|
| CN115629042A (en) | 2023-01-20 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108021903B (en) | Error calibration method and device for artificially labeling leucocytes based on neural network | |
| CN112070772B (en) | Blood leukocyte image segmentation method based on UNet++ and ResNet | |
| EP2927311B1 (en) | Cell observation device, cell observation method and program thereof | |
| CN107977682B (en) | Lymphocyte classification method and device based on polar coordinate transformation data enhancement | |
| Jayakody et al. | Microscope image based fully automated stomata detection and pore measurement method for grapevines | |
| Madhloom et al. | An image processing application for the localization and segmentation of lymphoblast cell using peripheral blood images | |
| CN103604737A (en) | Automatic blood cell recognition device and operating method thereof | |
| Liu et al. | Laser tweezers Raman spectroscopy combined with deep learning to classify marine bacteria | |
| CN112784767A (en) | Cell example segmentation algorithm based on leukocyte microscopic image | |
| CN113450317A (en) | Immunofluorescence image detection method for gynecological clinical microbial infection | |
| CN111492065A (en) | System and method for identifying gram type of bacteria | |
| CN112001315A (en) | Bone marrow cell classification and identification method based on transfer learning and image texture features | |
| Wei et al. | Identification of microalgae by hyperspectral microscopic imaging system | |
| CN117576103B (en) | Urinary sediment microscopic examination analysis system integrating electric control microscope and deep learning algorithm | |
| CN114387596A (en) | Automatic interpretation system for cytopathology smear | |
| LU505223B1 (en) | Method, system and electronic device for detecting microbial activity based on microscopic hyperspectral | |
| CN109948544A (en) | A kind of automatic positioning of target bacterium colony and recognition methods | |
| US20190362491A1 (en) | Computer-implemented apparatus and method for performing a genetic toxicity assay | |
| Priyankara et al. | An extensible computer vision application for blood cell recognition and analysis | |
| CN116596925A (en) | Gynecological vaginal flora estimation system based on fluorescence scanning image technology | |
| CN116757998A (en) | Screening method and device for CTC cells and CTC-like cells based on AI | |
| US20220383629A1 (en) | Label-free cell classification and screening system based on hybrid transfer learning | |
| CN114152557B (en) | Image analysis-based blood cell counting method and system | |
| Sten et al. | Sporify: An Automated Tool to Quantify Spores in Z-Stacked 3D Samples | |
| CN112507821A (en) | Cell drug resistance detection method based on high content imaging, medium and electronic equipment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| FG | Patent granted |
Effective date: 20240328 |