An image processing-based bacterial image data analysis system and method

Abstract

The application discloses a kind of bacterial image data analysis system and method based on image processing, it is related to image data analysis field, system includes image acquisition module, image pre-processing module, feature extraction module, bacterial identification and counting module and data processing module;Through the image acquisition module intelligent adjustment magnification acquisition the image of bacteria in petri dish;Through the image pre-processing module, the image collected is pre-processed, improves the definition and resolution of image;Through the feature extraction module, specific bacterial characteristics are identified;Through the bacterial identification and counting module, the bacteria of the image collected are identified and the number thereof is counted;Through the data processing module, the number of bacteria is processed to generate the growth curve of bacteria;By intelligently segmenting regions and adjusting the magnification when collecting bacterial pictures, the completeness and clarity of the collected bacterial images are enhanced from the source of collection, thereby improving the efficiency and accuracy of bacterial counting.

Description

Technical Field

[0001] This invention relates to the field of image data analysis technology, specifically a bacterial image data analysis system and method based on image processing. Background Technology

[0002] In life science research and production practice, bacterial growth patterns and isolation and identification methods are two important research directions. Understanding bacterial growth patterns allows for better control and optimization of the bacterial growth environment, improving production efficiency. Correct bacterial isolation and identification methods can effectively distinguish different bacterial species, which is of great significance for disease diagnosis and treatment, and the development of new drugs. Currently, the main methods for studying bacterial growth patterns are: observing and recording the growth status of bacteria in different environments, and then drawing conclusions through statistical analysis; selecting a differential culture medium is a traditional method, which designs special culture media so that only specific bacteria can grow in them, thus achieving the purpose of identification; PCR technology is a modern molecular biology method that amplifies bacterial DNA and then detects the amplification results through electrophoresis and other methods to determine the bacterial species.

[0003] Existing technologies still have some problems in practical applications. Traditional turbidimetric methods can only measure the bacterial concentration at a certain moment, and cannot monitor the growth status of bacteria in real time. Moreover, the operation is cumbersome, the workload is large, and systematic errors are prone to occur. In addition, manual counting is prone to errors and is inefficient. Therefore, how to improve these technologies and improve the efficiency and accuracy of bacterial counting and growth status monitoring is an urgent problem to be solved in this field. Summary of the Invention

[0004] The purpose of this invention is to provide a bacterial image data analysis system and method based on image processing to solve the problems raised in the prior art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a bacterial image data analysis system based on image processing, the system comprising an image acquisition module, an image preprocessing module, a feature extraction module, a bacterial identification and counting module, and a data processing module;

[0006] The image acquisition module intelligently times and intelligently selects the magnification to acquire images of bacteria;

[0007] The acquired images are preprocessed using the image preprocessing module.

[0008] The feature extraction module extracts the external appearance and structural features of specific bacteria and stores these feature information.

[0009] The bacterial identification and counting module identifies specific bacteria in the acquired images and counts the number of those bacteria.

[0010] The data processing module analyzes and processes the data of the time of each image acquisition and the number of bacteria corresponding to the acquisition time, and generates a bacterial growth curve.

[0011] Furthermore, the image acquisition module uses a high-resolution microscope combined with a digital imaging system to intelligently acquire timed images of bacteria in the culture dish. It intelligently sets the image acquisition time interval according to the changes in the growth rate of the bacteria, records the image acquisition time, and uses different magnifications according to the distribution of the bacteria to acquire images of bacteria at different culture times.

[0012] Furthermore, the image preprocessing module includes a noise removal unit, an image enhancement unit, and a threshold segmentation unit. The noise removal unit removes random noise from the image using median filtering and Gaussian filtering. Different types of noise are generated during image acquisition due to external factors or circuit interference during transmission, affecting image clarity. The noise removal process removes these effects, thereby improving image clarity. The image enhancement unit enhances the contrast between the bacterial image and the background using image contrast adjustment and histogram equalization. The threshold segmentation unit separates the bacterial image from the background image using grayscale thresholding or morphological segmentation methods to generate a binarized image. The binarized image is clearer and sharper, which is beneficial for subsequent observation and counting of bacteria in the image.

[0013] Furthermore, the feature extraction module utilizes machine learning to identify the external structure and in vitro features of specific bacteria, including the bacterial outline, area, perimeter, and surface features, and stores the identified bacterial features in a memory. These extracted features serve as the basis for identifying specific bacteria.

[0014] Furthermore, the bacterial identification and counting module identifies specific bacteria in the image by comparing and analyzing bacterial feature information in the memory, counts the number of such bacteria in the image, and stores the number of bacteria and the time when the image was acquired.

[0015] Furthermore, the data processing module analyzes the number of bacteria at different growth stages and uses the time of each image acquisition and the number of bacteria corresponding to the acquisition time in the memory to draw a bacterial growth curve with bacterial growth time as the horizontal axis and bacterial number as the vertical axis. The growth status of bacteria is analyzed based on the growth curve.

[0016] A bacterial image data analysis method based on image processing, characterized by the following steps:

[0017] S1: Intelligently sets the time interval and magnification for image acquisition to capture bacterial images in the petri dish;

[0018] S2: Preprocess the acquired bacterial images to remove interfering information;

[0019] S3: Extract and store the characteristics of specific bacteria, identify specific bacteria based on the extracted bacterial characteristics, and count the bacteria;

[0020] S4: Generate a bacterial growth curve based on the number of bacteria collected and their corresponding collection time.

[0021] Furthermore, in step S1: when acquiring images, the time interval for acquiring images is set according to the growth rate of bacteria, and the magnification of the high-power microscope is set according to the number of bacteria;

[0022] Four image acquisition time intervals were set: Δt1, Δt2, Δt3, and Δt4. The comparison of the magnitudes of the four image acquisition time intervals was Δt1 > Δt3 > Δt4 > Δt2. In the initial stage of bacterial culture, the image acquisition time interval Δt1 (the period of slow bacterial growth) was directly used. During bacterial culture, the image acquisition time interval was adjusted according to changes in bacterial growth rate. The bacterial growth rate continuously changed with culture time. The bacterial growth rate was calculated by analyzing the difference in bacterial count between two consecutive acquisitions and the time interval between the two acquisitions.

[0023]

[0024] Where, N m N represents the number of bacteria counted in the m-th bacterial image acquisition. m-1 This represents the number of bacteria counted in the (m-1)th bacterial image acquisition, where m represents the number of image acquisitions; v represents the bacterial growth rate at different culture times; Δt represents the time interval between two adjacent image acquisitions, and its value is assigned to one of the four values ​​Δt1, Δt2, Δt3, and Δt4 according to the actual situation. In the initial stage of bacterial culture, the image acquisition time interval is Δt1. The bacterial growth rate changes with the bacterial growth time. When the bacterial growth rate becomes v1, the image acquisition time interval is set from Δt1 to Δt2; when the bacterial growth rate becomes v2, the image acquisition time interval is set from Δt2 to Δt3; and when the bacterial growth rate becomes v3, the image acquisition time interval is set from Δt3 to Δt4.

[0025] The magnification is set according to the total number of bacteria at different times, and a scanning judgment program and a detection area segmentation program are added. During the scanning judgment, the percentage of the area with bacterial distribution in the scanned area relative to the total area determines whether further area segmentation is needed. A critical condition for determining whether to perform observation area segmentation is set. When the percentage of the area with bacterial distribution in the total area is greater than z, the first area segmentation is performed, dividing the detection area into x parts; where z represents the ratio of the area with bacterial distribution to the total observation area. After segmentation into x parts, the magnification of each segmented area is increased and scanned again. When the percentage of the area with bacterial distribution in the segmented area is greater than z′, the segmented area is segmented a second time, dividing it into y parts; where z′ represents the ratio of the area with bacterial distribution in each image area to the area of ​​the first segmented area. Then, the magnification is increased and images of bacterial distribution in each area are acquired separately.

[0026] Furthermore, in step S2: the acquired image is preprocessed to remove irrelevant information and improve the contrast and clarity of the acquired bacterial image; random noise in the image is removed by median filtering and Gaussian filtering to improve the image quality; the contrast between bacteria and background in the image is enhanced by image contrast adjustment and histogram equalization; and bacteria are separated from the background by grayscale thresholding or morphology-based segmentation methods to generate a binarized image.

[0027] Furthermore, in step S3: based on the stored bacterial characteristics, identify the bacteria that need to be counted, then count the number of bacteria in all collected images and calculate the final bacterial count to obtain the total number N of bacteria in the images collected at this moment; the formula for summing and calculating the total number of bacteria is:

[0028]

[0029] Where i = 1, 2, 3, ..., x; j = 1, 2, 3, ..., y; where i is the number of the segmented observation region during the first region segmentation, and j is the number of the segmented observation region during the second region segmentation; n ij This refers to the number of bacteria in each segmented small region. Through the above calculation, the total number of bacteria N at this moment can be obtained. Then, based on the total number of bacteria corresponding to the image acquisition time, a bacterial growth curve can be plotted. Based on the bacterial growth curve, the growth status of bacteria can be analyzed, and whether there are any abnormalities, etc.

[0030] Compared with the prior art, the beneficial effects of the present invention are:

[0031] 1. By combining high-resolution microscopy and digital imaging systems to acquire bacterial images, manpower and resources are saved, and acquisition efficiency and accuracy are improved.

[0032] 2. By using intelligent analysis, different intervals for collecting bacterial images can be set based on the bacterial growth rate. Shorter intervals are used during periods of rapid bacterial growth to ensure more complete data collection, while longer intervals are used during periods of slow bacterial growth to improve collection efficiency.

[0033] 3. By intelligently selecting the number of region segments and the number of segmented regions based on the distribution ratio of bacteria, clearer bacterial images can be collected, making it easier to identify and count bacteria in the images later. Attached Figure Description

[0034] Figure 1 This is a schematic diagram of the structure of a bacterial image data analysis system based on image processing according to the present invention;

[0035] Figure 2 This is a flowchart illustrating a bacterial image data analysis method based on image processing according to the present invention. Detailed Implementation

[0036] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0037] like Figures 1-2 As shown, the present invention provides a technical solution: a bacterial image data analysis system based on image processing. The system includes an image acquisition module, an image preprocessing module, a feature extraction module, a bacterial identification and counting module, and a data processing module.

[0038] The image acquisition module intelligently times and intelligently selects the magnification to acquire images of bacteria;

[0039] The acquired images are preprocessed using the image preprocessing module.

[0040] The feature extraction module extracts the external appearance and structural features of specific bacteria and stores these feature information.

[0041] The bacterial identification and counting module identifies specific bacteria in the acquired images and counts the number of those bacteria.

[0042] The data processing module analyzes and processes the data of the time of each image acquisition and the number of bacteria corresponding to the acquisition time, and generates a bacterial growth curve.

[0043] The image acquisition module uses a high-resolution microscope combined with a digital imaging system to intelligently acquire timed images of bacteria in a culture dish. It intelligently sets the image acquisition time interval according to the changes in the growth rate of the bacteria, and records the image acquisition time. It uses different magnifications according to the distribution of the bacteria to acquire images of bacteria at different culture times.

[0044] The image preprocessing module includes a noise removal unit, an image enhancement unit, and a threshold segmentation unit. The noise removal unit removes random noise from the image using median filtering and Gaussian filtering. Different types of noise are generated during image acquisition due to external factors or circuit interference during transmission, affecting image clarity. The noise removal process removes these effects, thereby improving image clarity. The image enhancement unit enhances the contrast between the bacterial image and the background using image contrast adjustment and histogram equalization. The threshold segmentation unit separates the bacterial image from the background image using grayscale thresholding or morphological segmentation methods to generate a binarized image.

[0045] The feature extraction module uses machine learning to identify the external structure and in vitro features of specific bacteria, including the bacterial outline, area, perimeter, and surface features. The identified bacterial features are stored in a memory, and the stored bacterial feature information serves as the basis for identifying specific bacteria.

[0046] The bacterial identification and counting module identifies specific bacteria in an image by comparing and analyzing bacterial feature information stored in the memory, counts the number of bacteria in the image, and stores the number of bacteria and the time when the image was acquired.

[0047] The data processing module analyzes the number of bacteria at different growth stages and uses the time of each image acquisition and the number of bacteria corresponding to the acquisition time in the memory to draw a bacterial growth curve with bacterial growth time as the horizontal axis and bacterial number as the vertical axis. The growth status of bacteria is analyzed based on the growth curve.

[0048] A bacterial image data analysis method based on image processing, characterized by the following steps:

[0049] S1: Intelligently sets the time interval and magnification for image acquisition to capture bacterial images in the petri dish;

[0050] S2: Preprocess the acquired bacterial images to remove interfering information;

[0051] S3: Extract and store the characteristics of specific bacteria, identify specific bacteria based on the extracted bacterial characteristics, and count the bacteria;

[0052] S4: Generate a bacterial growth curve based on the number of bacteria collected and their corresponding collection time.

[0053] In step S1: When acquiring images, the time interval for acquiring images is set according to the growth rate of bacteria, and the magnification of the high-power microscope is set according to the number of bacteria;

[0054] Four image acquisition time intervals were set: Δt1, Δt2, Δt3, and Δt4. The comparison of the magnitudes of the four image acquisition time intervals was Δt1 > Δt3 > Δt4 > Δt2. In the initial stage of bacterial culture, the image acquisition time interval Δt1 (the period of slow bacterial growth) was directly used. During bacterial culture, the image acquisition time interval was adjusted according to changes in bacterial growth rate. The bacterial growth rate continuously changed with culture time. The bacterial growth rate was calculated by analyzing the difference in bacterial count between two consecutive acquisitions and the time interval between the two acquisitions.

[0055]

[0056] Where, N m N represents the number of bacteria counted in the m-th bacterial image acquisition. m-1 This represents the number of bacteria counted in the (m-1)th bacterial image acquisition, where m represents the number of image acquisitions; v represents the bacterial growth rate at different culture times; Δt represents the time interval between two adjacent image acquisitions, and its value is assigned to one of the four values ​​Δt1, Δt2, Δt3, and Δt4 according to the actual situation. In the initial stage of bacterial culture, the image acquisition time interval is Δt1. The bacterial growth rate changes with the bacterial growth time. When the bacterial growth rate becomes v1, the image acquisition time interval is set from Δt1 to Δt2; when the bacterial growth rate becomes v2, the image acquisition time interval is set from Δt2 to Δt3; and when the bacterial growth rate becomes v3, the image acquisition time interval is set from Δt3 to Δt4.

[0057] The magnification is set according to the total number of bacteria at different times, and a scanning judgment program and a detection area segmentation program are added. During the scanning judgment, the percentage of the area with bacterial distribution in the scanned area relative to the total area determines whether further area segmentation is needed. A critical condition for determining whether to perform observation area segmentation is set. When the percentage of the area with bacterial distribution in the total area is greater than z, the first area segmentation is performed, dividing the detection area into x parts; where z represents the ratio of the area with bacterial distribution to the total observation area. After segmentation into x parts, the magnification of each segmented area is increased and scanned again. When the percentage of the area with bacterial distribution in the segmented area is greater than z′, the segmented area is segmented a second time, dividing it into y parts; where z′ represents the ratio of the area with bacterial distribution in each image area to the area of ​​the first segmented area. Then, the magnification is increased and images of bacterial distribution in each area are acquired separately.

[0058] In step S2: the acquired image is preprocessed to remove irrelevant information and improve the contrast and clarity of the acquired bacterial image; random noise in the image is removed by median filtering and Gaussian filtering to improve the image quality; the contrast between bacteria and background in the image is enhanced by image contrast adjustment and histogram equalization; bacteria are separated from the background by grayscale thresholding or morphology-based segmentation methods to generate a binarized image.

[0059] In step S3: Based on the stored bacterial characteristics, identify the bacteria that need to be counted, then count the number of bacteria in all collected images and calculate the final bacterial count to obtain the total number N of bacteria in the images collected at this moment; the formula for summing and calculating the total number of bacteria is:

[0060]

[0061] Where i = 1, 2, 3, ..., x; j = 1, 2, 3, ..., y; where i is the number of the segmented observation region during the first region segmentation, and j is the number of the segmented observation region during the second region segmentation; n ij This refers to the number of bacteria in each segmented small region. Through the above calculation, the total number of bacteria N at this moment can be obtained. Then, based on the total number of bacteria corresponding to the image acquisition time, a bacterial growth curve can be plotted. Based on the bacterial growth curve, the growth status of bacteria can be analyzed, and whether there are any abnormalities, etc.

[0062] Example 1: A bacterial image data analysis method based on image processing, characterized by the following steps:

[0063] S1: Intelligently sets the time interval and magnification for image acquisition to capture images of E. coli in the culture dish;

[0064] S2: Preprocess the collected E. coli images to remove interfering information;

[0065] S3: Extract and store the characteristics of specific E. coli, identify specific bacteria based on the extracted E. coli characteristics, and count the bacteria;

[0066] S4: Generate a bacterial growth curve based on the number of E. coli collected and their corresponding collection time.

[0067] In step S1: When acquiring images, the time interval for image acquisition is set according to the growth rate of *E. coli*, and the magnification of the high-power microscope is set according to the number of *E. coli*. Four image acquisition time intervals are set: Δt1 = 120 min, Δt2 = 40 min, Δt3 = 90 min, and Δt4 = 60 min. The comparison of the four image acquisition time intervals is Δt1 > Δt3 > Δt4 > Δt2. In the initial stage of *E. coli* culture, the image acquisition time interval Δt1 = 120 min during the slow growth phase of *E. coli* is directly used. During the *E. coli* culture process, the image acquisition time interval is changed according to the changes in the growth rate of *E. coli*. The growth rate of *E. coli* changes continuously with the culture time. The growth rate of *E. coli* is calculated by analyzing the difference in the number of *E. coli* between two consecutive acquisitions and the time interval between the two acquisitions.

[0068]

[0069] Where, N m N represents the number of E. coli samples collected during the m-th E. coli image acquisition. m-1 This represents the number of E. coli samples collected during the (m-1)th image acquisition, where m represents the number of image acquisitions; v represents the growth rate of E. coli at different culture times; Δt is the time interval between two consecutive image acquisitions, and its value is assigned to one of the four values ​​Δt1, Δt2, Δt3, and Δt4 according to the actual situation. In the initial stage of E. coli culture, the image acquisition time interval is Δt1. The growth rate of E. coli changes with the growth time. When the growth rate of E. coli becomes v1, the image acquisition time interval is changed from Δt1 to Δt2; when the growth rate of E. coli becomes v2, the image acquisition time interval is changed from Δt2 to Δt3; and when the growth rate of E. coli becomes v3, the image acquisition time interval is changed from Δt3 to Δt4.

[0070] The magnification is set according to the total number of E. coli at different times, and a scanning judgment program and a detection area segmentation program are added. During the scanning judgment, the percentage of the area with colony distribution in the scanned area is used to determine whether to perform further area segmentation. A critical condition for determining whether to perform observation area segmentation is set. When the percentage of the area with E. coli distribution in the total area is greater than z = 30%, the first area segmentation is performed, dividing the detection area into 100 parts. Here, z represents the ratio of the area with E. coli distribution to the total observation area. After segmentation into 100 parts, the magnification of each segmented area is increased and scanned again. When the percentage of the area with E. coli distribution in the segmented area is greater than z′ = 20% of the area after the first segmentation, the segmented area is segmented into 50 parts. Here, z′ represents the ratio of the area with E. coli distribution in each image area after the first segmentation to the area of ​​the segmented area. Then, the magnification is increased and images of the E. coli distribution in each area are collected separately.

[0071] In step S2: the acquired image is preprocessed to remove irrelevant information and improve the contrast and clarity of the acquired E. coli image; random noise in the image is removed by median filtering and Gaussian filtering to improve image quality; the contrast between E. coli and background in the image is enhanced by image contrast adjustment and histogram equalization; E. coli is separated from the background by grayscale thresholding or morphology-based segmentation methods to generate a binarized image.

[0072] In step S3: Based on the stored characteristics of E. coli, identify the E. coli that need to be counted, then count the number of E. coli in all collected images and calculate the final number of E. coli to obtain the total number N of E. coli in the images collected at this moment; the formula for summing and calculating the total number of E. coli is:

[0073]

[0074] Where i = 1, 2, 3, ..., 100; j = 1, 2, 3, ..., 50; where i is the number of the segmented observation region during the first region segmentation, and j is the number of the segmented observation region during the second region segmentation; n ij This is a count of E. coli in each segmented region, where the number of E. coli, n, was collected experimentally from each segmented region. ij Then, the total number of E. coli N at this moment can be obtained through the above calculation. Then, based on the total number of E. coli corresponding to the image acquisition time, its growth curve is plotted, and the growth status of E. coli is analyzed based on the growth curve.

[0075] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A bacterial image data analysis system based on image processing, characterized in that: The system includes an image acquisition module, an image preprocessing module, a feature extraction module, a bacterial identification and counting module, and a data processing module; The image acquisition module intelligently acquires bacterial images at timed intervals and intelligently selects magnification levels. The acquired images are preprocessed using the image preprocessing module. The feature extraction module extracts the external appearance and structural features of specific bacteria and stores this feature information. The bacterial identification and counting module identifies specific bacteria in the acquired images and counts the number of those bacteria. The data processing module analyzes and processes the data of the time of each image acquisition and the number of bacteria corresponding to the acquisition time, and generates a bacterial growth curve. When acquiring images, the time interval for acquiring images is set according to the growth rate of bacteria, and the magnification of the high-power microscope is set according to the number of bacteria; Four image acquisition time intervals were set: Δt1, Δt2, Δt3, and Δt4. The comparison of the magnitudes of the four image acquisition time intervals showed that Δt1 > Δt3 > Δt4 > Δt2. In the initial stage of bacterial culture, the image acquisition time interval Δt1 (the period of slow bacterial growth) was directly used. During bacterial culture, the image acquisition time interval was adjusted according to changes in the bacterial growth rate. The bacterial growth rate was calculated by analyzing the difference in bacterial count between two consecutive acquisitions and the time interval between the two acquisitions. ； Where, N m N represents the number of bacteria counted in the m-th bacterial image acquisition. m-1 This represents the number of bacteria counted in the (m-1)th bacterial image acquisition, where m represents the number of image acquisitions; v represents the bacterial growth rate at different culture times; Δt represents the time interval between two adjacent image acquisitions, and its value is assigned to one of the four values ​​Δt1, Δt2, Δt3, and Δt4 according to the actual situation. In the initial stage of bacterial culture, the image acquisition time interval is Δt1. The bacterial growth rate changes with the bacterial growth time. When the bacterial growth rate becomes v1, the image acquisition time interval is changed from Δt1 to Δt2; when the bacterial growth rate becomes v2, the image acquisition time interval is changed from Δt2 to Δt3; and when the bacterial growth rate becomes v3, the image acquisition time interval is changed from Δt3 to Δt4. The magnification is set based on the total number of bacteria at different times, and a scanning judgment program and a detection area segmentation program are added. During the scanning judgment, the percentage of the area with bacterial distribution in the scanned area relative to the total area determines whether further area segmentation is needed. A critical condition for determining whether to perform observation area segmentation is set. When the percentage of the area with bacterial distribution in the total area is greater than z, the first area segmentation is performed, dividing the detection area into x parts; where z represents the ratio of the area with bacterial distribution to the total observation area. After segmentation into x parts, the magnification of each segmented area is increased and scanned again. When the percentage of the area with bacterial distribution in the segmented area is greater than z′, the segmented area is segmented a second time, dividing it into y parts; where z′ represents the ratio of the area with bacterial distribution in each image area to the area of ​​the first segmented area. Then, the magnification is increased to acquire bacterial distribution images in each area separately.

2. The bacterial image data analysis system based on image processing according to claim 1, characterized in that: The image acquisition module uses a high-resolution microscope combined with a digital imaging system to intelligently acquire timed images of bacteria in a culture dish. It intelligently sets the image acquisition time interval according to the changes in the growth rate of the bacteria, and records the image acquisition time. It uses different magnifications according to the distribution of the bacteria to acquire images of bacteria at different culture times.

3. The bacterial image data analysis system based on image processing according to claim 1, characterized in that: The image preprocessing module includes a noise removal unit, an image enhancement unit, and a threshold segmentation unit; The noise removal unit removes random noise from the image using median filtering and Gaussian filtering; the image enhancement unit enhances the contrast between the bacterial image and the background using image contrast adjustment and histogram equalization; and the threshold segmentation unit separates the bacterial image from the background image using grayscale thresholding or morphology-based segmentation methods to generate a binarized image.

4. The bacterial image data analysis system based on image processing according to claim 1, characterized in that: The feature extraction module uses machine learning to identify the external structure and in vitro features of specific bacteria, including the bacterial outline, area, perimeter, and surface features. The identified bacterial features are stored in a memory, and the stored bacterial feature information serves as the basis for identifying specific bacteria.

5. The bacterial image data analysis system based on image processing according to claim 1, characterized in that: The bacterial identification and counting module compares and analyzes the bacterial features in the acquired image with the bacterial feature information in the memory to identify specific bacteria in the image, counts the number of the bacteria in the image, and stores the number of bacteria and the time when the image was acquired.

6. The bacterial image data analysis system based on image processing according to claim 1, characterized in that: The data processing module analyzes the number of bacteria at different growth stages and uses the time of each image acquisition and the number of bacteria corresponding to the acquisition time in the memory to draw a bacterial growth curve with bacterial growth time as the horizontal axis and bacterial number as the vertical axis. The growth status of bacteria is analyzed based on the growth curve.

7. A bacterial image data analysis method based on image processing, characterized in that: Includes the following steps: S1: Intelligently sets the time interval and magnification for image acquisition to capture bacterial images in the petri dish; S2: Preprocess the acquired bacterial images to remove interfering information; S3: Extract and store the characteristics of specific bacteria, identify specific bacteria based on the extracted bacterial characteristics, and count the bacteria; S4: Generate a bacterial growth curve based on the number of bacteria collected and their corresponding collection time; In step S1: When acquiring images, the time interval for acquiring images is set according to the growth rate of bacteria, and the magnification of the high-power microscope is set according to the number of bacteria; Four image acquisition time intervals were set: Δt1, Δt2, Δt3, and Δt4. The comparison of the magnitudes of the four image acquisition time intervals showed that Δt1 > Δt3 > Δt4 > Δt2. In the initial stage of bacterial culture, the image acquisition time interval Δt1 (the period of slow bacterial growth) was directly used. During bacterial culture, the image acquisition time interval was adjusted according to changes in the bacterial growth rate. The bacterial growth rate was calculated by analyzing the difference in bacterial count between two consecutive acquisitions and the time interval between the two acquisitions. ； Where, N m N represents the number of bacteria counted in the m-th bacterial image acquisition. m-1 This represents the number of bacteria counted in the (m-1)th bacterial image acquisition, where m represents the number of image acquisitions; v represents the bacterial growth rate at different culture times; Δt represents the time interval between two adjacent image acquisitions, and its value is assigned to one of the four values ​​Δt1, Δt2, Δt3, and Δt4 according to the actual situation. In the initial stage of bacterial culture, the image acquisition time interval is Δt1. The bacterial growth rate changes with the bacterial growth time. When the bacterial growth rate becomes v1, the image acquisition time interval is changed from Δt1 to Δt2; when the bacterial growth rate becomes v2, the image acquisition time interval is changed from Δt2 to Δt3; and when the bacterial growth rate becomes v3, the image acquisition time interval is changed from Δt3 to Δt4. The magnification is set according to the total number of bacteria at different times, and a scanning judgment program and a detection area segmentation program are added. During the scanning judgment, the percentage of the area with bacterial distribution in the scanned area relative to the total area determines whether further area segmentation is needed. A critical condition for determining whether to perform observation area segmentation is set. When the percentage of the area with bacterial distribution in the total area is greater than z, the first area segmentation is performed, dividing the detection area into x parts; where z represents the ratio of the area with bacterial distribution to the total observation area. After segmentation into x parts, the magnification of each segmented area is increased and scanned again. When the percentage of the area with bacterial distribution in the segmented area is greater than z′, the segmented area is segmented a second time, dividing it into y parts; where z′ represents the ratio of the area with bacterial distribution in each image area to the area of ​​the first segmented area. Then, the magnification is increased and images of bacterial distribution in each area are acquired separately.

8. The bacterial image data analysis method based on image processing according to claim 7, characterized in that: In step S2: the acquired image is preprocessed to remove irrelevant information and improve the contrast and clarity of the acquired bacterial image; random noise in the image is removed by median filtering and Gaussian filtering to improve the image quality; the contrast between bacteria and background in the image is enhanced by image contrast adjustment and histogram equalization; bacteria are separated from the background by grayscale thresholding or morphology-based segmentation methods to generate a binarized image.

9. The bacterial image data analysis method based on image processing according to claim 7, characterized in that: In step S3: Based on the stored bacterial characteristics, identify the bacteria that need to be counted, then count the number of bacteria in all collected images and calculate the final bacterial count to obtain the total number N of bacteria in the images collected at this moment; the formula for summing and calculating the total number of bacteria is: ； Where i = 1, 2, 3, ..., x; j = 1, 2, 3, ..., y; Where i is the number of the segmented observation region during the first region segmentation, and j is the number of the segmented observation region during the second region segmentation; n ij It is the number of bacteria in each segmented small region.

Images