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Microbe quantity estimation method based on image processing

A technology of microbial count and image processing, applied in computing, computer parts, instruments, etc., can solve the problems of complicated operation procedures, danger to experimenters, and cumbersome operation.

Active Publication Date: 2018-01-30
HARBIN INST OF TECH AT WEIHAI
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

This method is suitable for samples with high cell concentration under solid or liquid conditions, but the operation procedure is more complicated
[0008] It can be seen that the existing methods for measuring the number of microorganisms are relatively cumbersome to operate and require the use of biological experiments for measurement, and because there are a considerable number of steps that require manual operations, if the number of harmful bacteria is measured, there is a certain potential danger for the experimenter.
In addition, each method has special requirements for the microorganisms to be measured, and it is difficult to quickly implement it with a computer.

Method used

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  • Microbe quantity estimation method based on image processing
  • Microbe quantity estimation method based on image processing
  • Microbe quantity estimation method based on image processing

Examples

Experimental program
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Effect test

example 1

[0068] Example 1: In this example, two photos of the same petri dish were taken at different times ( Figure 3-1 , Figure 3-2 ) for bacterial statistics, the specific process is as follows.

[0069] 1: Read the original image I, and perform grayscale processing to obtain the grayscale image I 0 ;

[0070] 2: Remove the part outside the edge of the petri dish in the image: find the radius and center of the contour circle of the petri dish, set the gray level of the edge contour to 0, and set the processed image to I 1 ;

[0071] 3: Use non-local mean filtering algorithm to I 1 Carry out denoising processing, set the image after denoising as I 2 . The specific steps are:

[0072] (1) Suppose the currently processed pixel is i, set Ω i is a search window centered on pixel i with a size of 5×5. Let a certain pixel in the search area be j, N i , N j Represent the neighborhood windows centered on i and j respectively, with a size of 2×2. N i The gray value vector forme...

example 2

[0113] Example 2: In this example, two photos of the same petri dish were taken at different times ( Figure 3-3 , Figure 3-4 ) for bacterial statistics, the specific process is as follows.

[0114] 1: Read the original image I, and perform grayscale processing to obtain the grayscale image I 0 ;

[0115] 2: Remove the part outside the edge of the petri dish in the image: find the radius and center of the contour circle of the petri dish, set the gray level of the edge contour to 0, and set the processed image to I 1 ;

[0116] 3: Use non-local mean filtering algorithm to I 1 Carry out denoising processing, set the image after denoising as I 2 . The specific steps are:

[0117] (1) Suppose the currently processed pixel is i, set Ω i is a search window centered on pixel i with a size of 5×5. Let a certain pixel in the search area be j, N i , N j Represent the neighborhood windows centered on i and j respectively, with a size of 2×2. N i The gray value vector forme...

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Abstract

The invention belongs to the application of the field of digital image processing, and particularly provides a microbe quantity estimation method based on image processing. According to the method, certain processing steps are performed on a bacteriological culture dish image photographed under the microscope so that the number of pixels in the processed binary image is enabled to be used for estimating the number of bacteria in the culture dish. The brand-new microbe quantity measurement method is provided based on image binarization processing. According to the method, the culture dish imagephotographed under the microscope is processed without performing experiment on the bacteria, and the method belongs to a microbe quantity indirect measurement method. The non-local means (NL-means)denoising algorithm is applied so as to be stable for the statistical result of the bacteria images of the same culture dish photographed at different times without generating high error due to the change of the bacteria position, and thus the adaptability is high.

Description

Technical field: [0001] The present invention belongs to the application in the field of digital image processing. Specifically, certain processing steps are adopted for the image of the bacterial petri dish taken under the microscope, so that the number of pixels in the processed binary image can be used to estimate the number of bacteria in the petri dish. Estimation method of microbial population based on image processing. Background technique: [0002] At present, the commonly used methods for determining the number of microorganisms include microscope direct counting method, plate colony counting method, photoelectric turbidimetric method, cell weight measurement method, and cell total nitrogen or total carbon content determination. [0003] The microscope direct counting method is to take a certain volume of sample cell suspension and put it in the counting chamber of the counter, and observe and count it with a microscope. It is a fast and intuitive method. The common...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/34G06K9/40
Inventor 王好贤周志权李可喻
Owner HARBIN INST OF TECH AT WEIHAI
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