Method and apparatus for microbial community data processing based on quantitative disc

Abstract

This invention provides a method and apparatus for processing microbial community data based on a quantitative plate, which can quickly and accurately acquire microbial community data on the quantitative plate. The method includes: capturing microbial community images on the quantitative plate using two cameras to obtain color image data; calculating the actual three-dimensional coordinates based on the two-dimensional coordinates of the calibration points in the two color image data, and obtaining the actual three-dimensional coordinates of all wells / slots based on the actual three-dimensional coordinates; segmenting and marking the well / slot area in the color image data according to the actual three-dimensional coordinates of the wells / slots; obtaining the color of each well / slot in the color image data, and performing grayscale processing on the color of each well / slot to obtain a grayscale image; performing binarization processing on the grayscale image according to a preset threshold to obtain a binary image; labeling the corresponding well / slot areas with different colors based on the binary image to obtain a color-processed image; and analyzing and identifying the number and position of corresponding color blocks in the color-processed image to obtain microbial community data.

Description

Technical Field

[0001] This invention relates to the field of environmental biological monitoring equipment, and more particularly to a method and apparatus for processing microbial community data based on a quantitative disc. Background Technology

[0002] In my country, the "total bacterial count" test is a common microbial safety inspection item in environmental sanitation, food, and pharmaceutical industries. Common methods for detecting microorganisms in water include plate counting, multiple-tube fermentation, membrane filtration, and enzyme substrate methods. The enzyme substrate method utilizes various forms, such as 51-well and 97-well quantitative discs (for detecting total coliforms, fecal coliforms, Escherichia coli, Pseudomonas aeruginosa, and Enterococci in water). However, current testing methods require observation and counting under a 365nm UV lamp, which can easily damage the eyes. With large sample volumes, the efficiency of laboratory personnel decreases over extended periods, and accuracy cannot be guaranteed. Data traceability is also lacking. Furthermore, some existing statistical instruments suffer from large counting errors due to uneven light distribution. Additionally, the fluted shape of the quantitative disc creates shadows at the edges under different light angles, potentially leading to significant errors in counting adjacent wells.

[0003] Therefore, there is an urgent need for an intelligent counting and reading system applicable to the detection of quantitative discs of various specifications, in order to solve the problems existing in the current technology. Summary of the Invention

[0004] The purpose of this invention is to provide a method and apparatus for processing microbial community data based on a quantitative plate, which can quickly and accurately acquire microbial community data on the quantitative plate.

[0005] To achieve the above objectives, this invention provides a method for processing microbial community data based on a quantitative plate. The quantitative plate has several wells and slots, comprising: Step 1, capturing microbial community images on the quantitative plate using two cameras spaced at the same horizontal level and with parallel optical axes to obtain two color image data sets; Step 2, identifying preset calibration points in the two color image data sets, calculating the actual three-dimensional coordinates of the calibration points based on their two-dimensional coordinates, and obtaining the actual three-dimensional coordinates of all wells and slots based on their actual three-dimensional coordinates; Step 3, cutting and marking the well and slot regions in the color image data based on the actual three-dimensional coordinates of all wells and slots; Step 4, obtaining the color of each well and slot in the color image data, and performing grayscale processing on the color of each well and slot to obtain a grayscale image; Step 5, performing binarization processing on the grayscale image based on a preset threshold to obtain a binarized image; Step 6, marking the corresponding well and slot regions with different colors based on the binarized image to obtain a color-processed image; Step 7, analyzing and identifying the number and position of corresponding color blocks in the color-processed image to obtain microbial community data.

[0006] Preferably, in step 2, according to the formula:

[0007] x=Dx / d, y=Dy / d, z=Df / d

[0008] The actual three-dimensional coordinates (x, y, z) are calculated; where D is the distance between the centers of the two cameras, d = x1 - x2; (x1, y1) are the two-dimensional coordinates of the color image data captured by the first camera, (x2, y2) are the two-dimensional coordinates of the color image data captured by the second camera, the two cameras are on the same straight line along the X-axis so that y1 = y2, and f is the focal length of the two cameras.

[0009] Preferably, in step 4, obtaining the color of each of the hole slots specifically includes: obtaining and calculating the average value of the colors of the pixels within a preset range around the center point of the hole slot as the color of the hole slot.

[0010] Preferably, in step 4, the weighted average of the colors of pixels within a preset range around the center point of the hole groove is obtained and calculated as the color of the hole groove, and the closer to the center point of the hole groove, the greater the weight.

[0011] Preferably, in step 4, pixels in the hole groove region that are outside the preset range of the average color value are removed, and the average value or weighted average value of other pixels in the hole groove region is calculated as the color of the hole groove, with the weight increasing as the distance from the center point of the hole groove increases.

[0012] Preferably, in step 4, when obtaining the color of each of the holes and slots, the positioning disk is divided into a first region and a second region. Of the two cameras, the first camera is located directly above the first region, and the second camera is located directly above the second region. The color of the holes and slots in the first region is obtained based on the color image data captured by the second camera, and the color of the holes and slots in the second region is obtained based on the color image data captured by the first camera or the color of the holes and slots in the second region.

[0013] In step 4, according to the formula Gray(x,y)=a*R(x,y)+b*G(x,y)+c*B(x,y) The color of the hole groove is grayscaled, where a, b, and c are preset constants, and R(x,y), G(x,y), and B(x,y) are the R, G, and B values ​​of the hole groove color in the color image data.

[0014] Specifically, the bacterial flora is Escherichia coli, according to the formula:

[0015] Gray(x,y)=0.299*R(x,y)+0.587*G(x,y)+0.114*B(x,y),

[0016] The color of the pores is grayscaled. The specific values ​​of a, b, and c are determined based on the current light intensity and the color characteristics of the current bacterial community.

[0017] Preferably, in step 5, binarizing the grayscale image to obtain a binarized image specifically includes: according to the formula:

[0018] ,

[0019] The pixel value of the hole in the grayscale image is denoted as , and T is the preset threshold of the corresponding bacterial community under the current light.

[0020] More preferably, turn on the white light and perform steps 1 to 7 under white light conditions. Then turn off the white light and turn on the ultraviolet light. Under ultraviolet light irradiation, perform steps 1 to 5 again to obtain a binarized image under ultraviolet light. Multiply the pixel data of the pore and groove regions in the binarized image under ultraviolet light with the pixel data of the pore and groove regions in the binarized image under white light to obtain a binarized image of the bacterial community under ultraviolet light. Mark the corresponding pore and groove regions with different colors according to the binarized image of the bacterial community under ultraviolet light to obtain a color-processed image of the bacterial community under ultraviolet light. Analyze and identify the number and position of the corresponding color blocks in the color-processed image of the bacterial community under ultraviolet light to obtain bacterial community data. This scheme can effectively obtain bacterial community data that has not changed effectively after ultraviolet light irradiation. The calculation is fast and accurate, making it easy for operators to quickly locate the corresponding bacterial community.

[0021] Specifically, in step 6, different colors are marked on the corresponding hole and slot regions according to the binarized image, including marking the hole and slot regions with pixel values ​​greater than T with a first color and marking the hole and slot regions with pixel values ​​less than or equal to T with a second color.

[0022] Preferably, in step 7, identifying the number and location of corresponding color blocks in the color-processed image to obtain microbial community data specifically includes: comparing the color-processed image with a preset standard comparison table to obtain the corresponding data of the microbial community.

[0023] Preferably, the calibration point is a preset mark on the metering disc, the edge of the metering disc, or a preset slot on the metering disc.

[0024] The present invention also provides a microbial community data processing device based on a quantitative plate, including a quantitative plate, a light source system, two cameras disposed on the quantitative plate, and a control processing mechanism. The two cameras are disposed at the same horizontal height and with parallel optical axes. The control processing mechanism is connected to the light source system and the two cameras, and includes at least one processor, a memory, and an operating program stored in the memory. The operating program is executed by the processor to perform the microbial community data processing method based on the quantitative plate as described above.

[0025] Compared with existing technologies, this invention, after acquiring corresponding color image data from two cameras, identifies preset calibration points on the quantitative plate. Based on the two-dimensional coordinates of these calibration points in the two color image data, it calculates the specific three-dimensional coordinates to determine the actual coordinates of the center points of all the orifices on the quantitative plate. This allows for precise division of the orifice / slot region on the color image data, ensuring accuracy, reliability, and simple calculation. Furthermore, after obtaining the orifice / slot region from the color image data, this invention also performs grayscale and binarization processing on the colors within the orifice / slot region to obtain a final dichromatic image. This enables rapid analysis and identification of bacterial community data, demonstrating high processing accuracy and simple calculation. Attached Figure Description

[0026] Figure 1 This is a flowchart of the microbial community data processing method based on quantitative discs according to the present invention.

[0027] Figure 2 This is a partial structural diagram of the microbial community data processing device based on a quantitative plate according to the present invention.

[0028] Figure 3 This is the color image data obtained under white light according to the present invention.

[0029] Figure 4 yes Figure 3 Color-processed images after processing medium-color image data.

[0030] Figure 5 These are color image data obtained under ultraviolet light according to the present invention.

[0031] Figure 6 yes Figure 5 Color-processed images after processing medium-color image data. Detailed Implementation

[0032] To illustrate the technical content, structural features, objectives, and effects of the present invention in detail, the following description is provided in conjunction with the embodiments and accompanying drawings.

[0033] refer to Figure 1 The present invention discloses a method for processing microbial community data based on a quantitative plate, wherein the quantitative plate has several holes and grooves, and includes steps S1 to S6.

[0034] Step S1: Two color image data are obtained by photographing the quantitative disk using two cameras positioned at the same horizontal height and with parallel optical axes. (Reference) Figure 3 This refers to color image data captured by a camera.

[0035] Step S2: Identify the preset calibration points in the two color image data, calculate the actual three-dimensional coordinates of the calibration points in the quantitative plate based on the two-dimensional coordinates (x, y) of the calibration points in the two color image data, and obtain the actual three-dimensional coordinates of all the holes and slots in the quantitative plate based on the actual three-dimensional coordinates of the calibration points.

[0036] The calibration points are preset marks on the metering disc, the edge of the metering disc, or preset slots on the metering disc. Multiple preset calibration points can be obtained in actual three-dimensional coordinates. Then, based on these coordinates, the actual position of the metering disc is obtained, thus acquiring the actual three-dimensional coordinates of the time slots. In this invention, the actual three-dimensional coordinates of the slot center point represent the actual three-dimensional coordinates of the slot.

[0037] Since the specifications of the metering disc are fixed (taking a 97-well metering disc as an example, all 97-well metering discs have the same dimensions, which also serves as a basis for distinguishing different metering disc styles), obtaining the actual three-dimensional coordinates of all the wells in the metering disc based on the actual three-dimensional coordinates of the calibration point specifically involves: obtaining the actual three-dimensional coordinates of all the wells in the metering disc based on the current metering disc specification data combined with the actual three-dimensional coordinates of the calibration point. The metering disc specification data includes the relative relationship between the positions of all the wells and the calibration point.

[0038] In step S2, the actual three-dimensional coordinates of the calibration point are calculated according to the formulas: x=Dx / d, y=Dy / d, z=Df / d, where D is the distance between the midpoints of the two cameras, d=x1-x2; (x1, y1) are the two-dimensional coordinates of the color image data captured by the first camera 11, and (x2, y2) are the two-dimensional coordinates of the color image data captured by the second camera 12. The two cameras are on the same straight line along the X-axis so that y1=y2, and f is the focal length of the camera.

[0039] Step S3: Based on the actual three-dimensional coordinates of all the holes and slots, cut out and mark the area of ​​each hole and slot in the color image data.

[0040] Specifically, the actual three-dimensional coordinates of the slots are converted into two-dimensional coordinates of the color image data. Then, based on the two-dimensional coordinates converted from the actual three-dimensional coordinates of the slot edges, the color image data is segmented to cut out the slot region where each slot is located in the color image data.

[0041] Step S4: Obtain the color of each hole in the color image data, and perform grayscale processing on the color of each hole to obtain a grayscale image.

[0042] Preferably, in step S4, according to the formula:

[0043] Gray(x,y)=a*R(x,y)+b*G(x,y)+c*B(x,y)

[0044] The color of the groove is grayscaled, where a, b, and c are preset constants. R ( x , y), G ( x , y )and B ( x , y ) represents the R, G, and B values ​​of the hole / groove in the color image data.

[0045] The bacterial flora is Escherichia coli, according to the formula:

[0046] Gray(x,y)=0.299*R(x,y)+0.587*G(x,y)+0.114*B(x,y)

[0047] The color of the aperture is grayscaled. The specific values ​​of a, b, and c are determined based on the current light intensity and the color characteristics of the current bacterial flora. In this embodiment, the bacterial flora is *Escherichia coli*.

[0048] Color image grayscale conversion involves making the R, G, and B component values ​​equal. Since the values ​​of R, G, and B range from 0 to 255, there are only 256 grayscale levels, meaning a grayscale image can only represent 256 colors. Further grayscale conversion using a weighted average method transforms the R, G, and B components of the color image into grayscale values ​​representing the image composed of each pixel.

[0049] Preferably, in this embodiment, in step S4, obtaining the color of each hole groove specifically includes: obtaining the average value of the colors of pixels within a preset range around the center point of the hole groove as the color of the hole groove.

[0050] In a preferred embodiment, in step S4, the average weight of the colors of pixels within a preset range around the center point of the hole groove is obtained as the color of the hole groove, and the closer the pixel is to the center point of the hole groove, the greater its weight.

[0051] In a preferred embodiment, in step S4, pixels in the hole region that exceed the preset range of the average color value are removed, and the average or weighted average color of the other pixels is taken as the color of the hole, with the weight increasing as the distance from the center point of the hole increases.

[0052] For the better option, refer to Figure 2 In step S4, to obtain the color of each of the aforementioned slots, the positioning disk 20 is divided into a first region and a second region. Of the two cameras, the first camera 11 is located directly above the first region 101, and the second camera 12 is located directly above the second region 102. The color of the slot 22 in the first region 101 is obtained based on the color image data captured by the second camera 11, or the color of the slot 22 in the second region 102 is obtained based on either the color image data captured by the first camera 11 or the color of the slot 22 in the second region 102. Alternatively, either color image data can be selected for obtaining the slot color. Alternatively, the two color image data can be merged, and obviously reflective or black spot areas can be deleted to filter out normal pixel data. The normal pixel data can then be either selected from two or the average of the merged data to obtain a processed color image data, and the slot color can then be obtained based on this processed color image data.

[0053] Step S5: Binarize the grayscale image according to a preset threshold to obtain a binarized image.

[0054] In step S5, binarizing the grayscale image to obtain a binarized image specifically includes: calculating the pixel data of the binarized image according to a formula. :

[0055] ,

[0056] in, The pixel value of the hole in the grayscale image is denoted as , and T is the preset threshold of the corresponding bacterial community under the current light.

[0057] Step S6: Mark the corresponding hole and groove areas with different colors according to the binarized image to obtain a color-processed image.

[0058] In step S6, different colors are marked on the corresponding hole and slot regions according to the binarized image. Specifically, this includes marking the hole and slot regions with pixel values ​​greater than T with a first color and marking the hole and slot regions with pixel values ​​less than or equal to T with a second color.

[0059] refer to Figure 4 , is the processed color image, with other values ​​indicating that the first color is orange and the second color is gray.

[0060] Step S7: Analyze and identify the number and location of corresponding color blocks in the color-processed image to obtain microbial community data.

[0061] In step S7, identifying the number and location of corresponding color blocks in the color-processed image to obtain microbial community data specifically includes: comparing the color-processed image with a preset standard comparison table to obtain the corresponding data of the microbial community.

[0062] Preferably, turn on the white light and perform steps S1 to S7 under white light conditions, wherein, Figure 3 This refers to color image data captured under white light conditions. Then, the white light is turned off, the ultraviolet light is turned on, and steps S1 to S5 are repeated under ultraviolet light irradiation. Figure 5 This refers to color image data captured under ultraviolet light. The goal is to obtain the pixel data (g) of the binarized image under ultraviolet light. uv (x, y) represents the pixel data g of the aperture region in the binarized image under ultraviolet light. uv (x, y) and pixel data of the slot region in the binarized image under white light Perform the product, and then obtain a binarized image of the bacterial community under ultraviolet light (e.g., Figure 5 As shown, based on the binarized image of the bacterial community under ultraviolet light, different colors are used to label the corresponding pore areas to obtain a color-processed image of the bacterial community under ultraviolet light. The number and position of the corresponding color blocks in the color-processed image of the bacterial community under ultraviolet light are analyzed and identified to obtain bacterial community data. In this embodiment, a 365nm ultraviolet lamp is used to emit ultraviolet light.

[0063] In other words, obtaining the pixel data g of the binarized image of ultraviolet light. uvAfter (x, y), the result is subjected to simple filtering, where g(x, y) is used as the filter coefficient. If g(x, y) = 0, it means that the data in that region is 0 after filtering; only when the filter coefficient g(x, y) = 1 and g uv Only after the (x, y) cut region (groove region) is defined are data statistics and labeling performed, and finally... Figure 6 The structure is shown. In this embodiment, taking Escherichia coli as an example, the corresponding data of Escherichia coli in water can be obtained by comparing the results of the processed images with the MPN table.

[0064] The relevant data for the microbial community includes the number of microorganisms.

[0065] refer to Figure 2 The present invention also provides a microbial community data processing device based on a quantitative plate, comprising: a quantitative plate, a light source system 10, two cameras disposed on the quantitative plate, and a control processing mechanism. The two cameras are disposed at the same horizontal height and with parallel optical axes. The control processing mechanism is connected to the light source system 10 and the two cameras 11 and 12, and includes at least one processor, a memory, and an operating program stored in the memory. The operating program is executed by the processor to perform the microbial community data processing method based on the quantitative plate as described above.

[0066] The light source system of the present invention includes a rectangular LED white light strip, two high-power 365nm ultraviolet lamps and two high-power 254nm ultraviolet lamps.

[0067] Two high-power 254nm UV lamps are used to sterilize the entire darkroom chamber after each use. Rectangular LED white light strips are distributed around the chamber walls, with a specific ratio to the edge of the metering tray (e.g., the length of the LED strip edge is 1.3:1, but this ratio is not limited to this). Two high-power 365nm UV lamps are symmetrically distributed on either side of the top of the chamber. Two high-power 254nm UV lamps are arranged side-by-side with the two 365nm lamps, with the 254nm lamps located inside. Two cameras are installed at the center of the top of the chamber, with the line connecting the cameras perpendicular to the distribution direction of the 365nm UV lamps. The two cameras are parallel, with parallel optical axes, overlapping imaging planes, and the same focal length, used to acquire images of bacterial colonies (well positions) in the metering tray. Preferably, the control processing mechanism also previews the image captured by the camera, compares the contrast between the quantitative plate and the background color in the captured image, and adjusts the power of the light source system when the contrast is less than a preset value or the clarity of the colonies is less than a preset value.

[0068] The above-disclosed embodiments are merely preferred embodiments of the present invention and should not be construed as limiting the scope of the present invention. Therefore, any equivalent variations made in accordance with the claims of the present invention are still within the scope of the present invention.

Claims

1. A method for processing microbial community data based on a quantitative plate, wherein the quantitative plate has several slots, characterized in that: include: Step 1: Take images of the bacterial community on the quantitative plate using two cameras that are set at the same horizontal height and have parallel optical axes to obtain two color image data; Step 2: Identify the preset calibration points in the two sets of color image data, calculate the actual three-dimensional coordinates of the calibration points based on the two-dimensional coordinates of the calibration points in the two sets of color image data, and obtain the actual three-dimensional coordinates of all holes and slots based on the actual three-dimensional coordinates of the calibration points. Step 3: Based on the actual three-dimensional coordinates of all the holes and slots, cut out and mark the area of ​​each hole and slot in the color image data; Step 4: Obtain the color of each hole in the color image data, and perform grayscale processing on the color of each hole to obtain a grayscale image; Step 5: Perform binarization processing on the grayscale image according to a preset threshold to obtain a binarized image; Step 6: Mark the corresponding hole and groove areas with different colors according to the binarized image to obtain a color-processed image; Step 7: Analyze and identify the number and location of corresponding color blocks in the color-processed image to obtain microbial community data.

2. The microbial community data processing method based on quantitative discs as described in claim 1, characterized in that: In step 2, according to the formula: x=Dx / d, y=Dy / d, z=Df / d The actual three-dimensional coordinates (x, y, z) are calculated. Where D is the distance between the centers of the two cameras, d = x1 - x2; (x1, y1) are the two-dimensional coordinates of the color image data captured by the first camera, (x2, y2) are the two-dimensional coordinates of the color image data captured by the second camera, the two cameras are on the same straight line along the X-axis so that y1 = y2, and f is the focal length of the two cameras.

3. The microbial community data processing method based on quantitative discs as described in claim 1, characterized in that: Step 4, obtaining the color of each hole groove, specifically includes: obtaining and calculating the average color of pixels within a preset range around the center point of the hole groove as the color of the hole groove; or obtaining and calculating the weighted average color of pixels within a preset range around the center point of the hole groove as the color of the hole groove, with the weight increasing as the distance from the center point of the hole groove increases; or removing pixels in the hole groove region that are outside the preset range of the average color value, and calculating the average value or weighted average value of other pixels in the hole groove region as the color of the hole groove, with the weight increasing as the distance from the center point of the hole groove increases.

4. The microbial community data processing method based on quantitative discs as described in claim 1, characterized in that: In step 4, to obtain the color of each of the holes, the quantitative disk is divided into a first region and a second region. Of the two cameras, the first camera is located directly above the first region and the second camera is located directly above the second region. The color of the holes in the first region is obtained based on the color image data captured by the second camera, and the color of the holes in the second region is obtained based on the color image data captured by the first camera or the color of the holes in the second region.

5. The method for processing microbial community data based on quantitative discs as described in claim 1, characterized in that: In step 4, the color of the hole groove is grayscaled according to the formula Gray(x,y)=a*R(x,y)+b*G(x,y)+c*B(x,y), where a, b, and c are preset constants. R ( x , y ) 、G ( x , y )and B ( x , y ) represents the R, G, and B values ​​of the color of the hole groove in the color image data.

6. The microbial community data processing method based on quantitative discs as described in claim 1, characterized in that: Step 5, which involves binarizing the grayscale image to obtain a binary image, specifically includes: Based on the formula: , The data represents the pixel data of the holes in the grayscale image, and T is the preset threshold of the corresponding bacterial community under the current light.

7. The microbial community data processing method based on quantitative discs as described in claim 6, characterized in that: Turn on the white light and perform steps 1 to 7 under white light conditions. Then turn off the white light and turn on the ultraviolet light. Under ultraviolet light irradiation, perform steps 1 to 5 again to obtain a binarized image under ultraviolet light. Multiply the pixel data of the pore and groove regions in the binarized image under ultraviolet light with the pixel data of the pore and groove regions in the binarized image under white light to obtain a binarized image of the bacterial community under ultraviolet light. Mark the corresponding pore and groove regions with different colors according to the binarized image of the bacterial community under ultraviolet light to obtain a color-processed image of the bacterial community under ultraviolet light. Analyze and identify the number and position of the corresponding color blocks in the color-processed image of the bacterial community under ultraviolet light to obtain the bacterial community data.

8. The microbial community data processing method based on quantitative discs as described in claim 6, characterized in that: In step 6, different colors are marked on the corresponding hole and slot regions according to the binarized image. Specifically, the first color is marked on the hole and slot regions with pixel values ​​greater than T, and the second color is marked on the hole and slot regions with pixel values ​​less than or equal to T.

9. The method for processing microbial community data based on quantitative discs as described in claim 1, characterized in that: Step 7, identifying the number and location of corresponding color blocks in the color-processed image to obtain microbial community data, specifically includes: comparing the color-processed image with a preset standard comparison table to obtain the corresponding data of the microbial community.

10. A microbial community data processing device based on a quantitative plate, characterized in that: include: The system comprises a quantitative plate, a light source system, two cameras mounted on the quantitative plate, and a control processing mechanism. The two cameras are arranged at the same horizontal height with parallel optical axes. The control processing mechanism is connected to the light source system and the two cameras, and includes at least one processor, a memory, and an operating program stored in the memory. The operating program is executed by the processor according to the method for processing microbial community data based on the quantitative plate as described in any one of claims 1-9.

Images