LU505223B1 - Method, system and electronic device for detecting microbial activity based on microscopic hyperspectral - Google Patents
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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.
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