CN110689926A - Accurate detection method for high-throughput digital PCR image droplets - Google Patents
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- 238000007847 digital PCR Methods 0.000 title claims abstract description 24
- 238000001514 detection method Methods 0.000 title claims description 11
- 239000007788 liquid Substances 0.000 claims abstract description 32
- 238000000034 method Methods 0.000 claims abstract description 19
- 238000007781 pre-processing Methods 0.000 claims abstract description 10
- 238000003708 edge detection Methods 0.000 claims description 9
- 230000003044 adaptive effect Effects 0.000 claims description 6
- 238000005260 corrosion Methods 0.000 claims description 5
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- 230000003628 erosive effect Effects 0.000 claims description 5
- 238000005516 engineering process Methods 0.000 description 10
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- 238000003752 polymerase chain reaction Methods 0.000 description 5
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- 238000003672 processing method Methods 0.000 description 2
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- 230000004544 DNA amplification Effects 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 238000000799 fluorescence microscopy Methods 0.000 description 1
- 238000012165 high-throughput sequencing Methods 0.000 description 1
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Abstract
The invention discloses a method for accurately detecting droplets of a high-throughput digital PCR image, which comprises the following steps of: acquiring a high-throughput PCR digital image; carrying out image preprocessing to obtain a high-quality image; positioning liquid drops; calculating to obtain the area of the liquid drop; the measuring range is wide, and the number of measuring objects is large; the complex state of the whole body can be directly measured and calculated, and a picture can store a very large amount of data; the number of the liquid drops can be accurately detected and the positions of the liquid drops can be positioned.
Description
Technical Field
The invention relates to the technical field of gene sequencing, in particular to an accurate detection method of high-throughput digital PCR image droplets.
Background
High-throughput gene sequencing technology, also known as second generation gene sequencing technology, is a very widely used gene sequencing method in recent years. Compared with the first generation gene sequencing technology, the method has the characteristics of high throughput, low cost and the like. The high temperature Polymerase Chain Reaction (PCR) technology is called PCR technology for short, can quickly amplify DNA fragments and realize exponential growth of the DNA fragments in a plurality of cycles, and is the most commonly used DNA amplification technology at present. The high-flux PCR image contains a large amount of fluorescent liquid drops to be detected, and has the characteristics of large data volume, more targets to be detected, compact liquid drop intervals, frequent overlapping, poor image quality and the like, and is not easy to automatically detect images. How to automatically process these massive data becomes an important issue of high-throughput sequencing technology.
In the actual high-flux massive liquid drop digital PCR imaging process, the fluorescence collection efficiency of each point in the image field range of a large-area fluorescence imaging system is different, so that the brightness in the whole image field range is uneven, the brightness of a droplet with the same brightness in the center of the image field is generally far higher than that of the droplet at the edge of the image field, the uneven brightness phenomenon becomes more prominent along with the increase of the image field, and the acquired image is low in contrast, the image is not clear, and the droplet extraction is not accurate. How to more effectively process the massive liquid drop image and accurately detect the liquid drop is an important component of a high-throughput massive liquid drop digital PCR system.
At present, the digital PCR technology mainly uses an absolute quantitative fluorescence monitoring method to detect the quantity of liquid drops, and the detection method using the digital image processing technology is less. In several papers discussing digital image processing in China, the discussion of the digital PCR of micro reaction/well plate and the digital PCR of large scale integrated chip is more. The two types of digital PCR technologies have the advantages that the arrangement of liquid drops is simple and regular, the liquid drops are approximate to a matrix, and the positions of the liquid drops are easy to predict. Or a relatively simple method is used for detecting the mass liquid drops, and the requirement on conditions is high.
Disclosure of Invention
In view of the above-mentioned shortcomings, the present invention provides a method for accurately detecting droplets in high-throughput digital PCR images, which can accurately detect the number of droplets and locate the positions of the droplets.
In order to achieve the above purpose, the embodiment of the invention adopts the following technical scheme:
a method for accurately detecting high-throughput digital PCR image droplets comprises the following steps:
acquiring a high-throughput PCR digital image;
carrying out image preprocessing to obtain a high-quality image;
positioning liquid drops;
and calculating to obtain the area of the liquid drop.
According to one aspect of the present invention, the specific steps of performing image preprocessing to obtain a high quality image include:
reading in a gray image of an image in a specified format as a source image by using a file reading mode;
carrying out self-adaptive thresholding treatment on a source image;
performing direct thresholding on the source image;
performing OR operation on the self-adaptive thresholding image and the direct thresholding image, and storing the processed images as a combined image;
and performing opening operation on the obtained combined image, and storing the processed image as an opening operation image to finish the image preprocessing.
According to one aspect of the invention, the adaptively thresholding the source image comprises:
carrying out sliding operation on each pixel point on a source image according to a sliding frame of n x n (n is more than 0);
calculating the average value of the gray values of all pixel points in the sliding frame;
subtracting a difference value delta from the average value to be used as a threshold value for thresholding;
for each pixel in the sliding frame, if the gray value is higher than the threshold value, the gray value is set as the maximum gray value, and if the gray value is smaller than or equal to the threshold value, the gray value is set as 0.
In accordance with one aspect of the invention, the directly thresholding the source image comprises:
traversing all pixel points in the source image to obtain an average value of gray values of all the pixel points;
taking the average value multiplied by a coefficient as a threshold value;
and setting the gray value of all pixel points in the image as the maximum gray value if the gray value is higher than the threshold value, and setting the gray value as 0 if the gray value is less than or equal to the threshold value.
According to one aspect of the invention, said performing or operation on the adaptive thresholded image and the direct thresholded image, the saving of the processed image as a merged image comprises:
newly building a blank image, wherein the size of the blank image is consistent with that of the source image;
comparing the self-adaptive thresholding image with the direct thresholding image, and setting the gray value of a pixel point with the corresponding coordinate (x, y) as the maximum gray value in a newly-built blank image if the gray values of the two images at the point with the same position (x, y) in the two images are the maximum gray value; otherwise, setting the gray value of the point in the blank image as 0.
According to an aspect of the present invention, the performing an on operation on the obtained merged image, and the processed image being saved as an on operation image, the performing the pre-processing of the image includes:
carrying out corrosion operation on the image;
and performing expansion operation on the image subjected to the corrosion operation.
According to one aspect of the invention, the performing an erosion operation on the image comprises:
performing sliding operation on each pixel point of the image according to a sliding frame of m by m (m is more than 0);
comparing the gray values of all pixel points in the sliding frame to obtain the minimum value;
and setting the gray values of all pixel points in the sliding frame as the obtained minimum value.
According to one aspect of the invention, the expanding the image after the erosion operation comprises:
performing sliding operation on each pixel point of the image according to a sliding frame of k x k (k is more than 0);
comparing the gray values of all pixel points in the sliding frame to obtain the maximum value;
and setting the gray values of all pixel points in the sliding frame as the maximum value.
According to one aspect of the invention, the specific step of performing droplet positioning comprises:
carrying out canny edge detection on the opening operation image to obtain an edge detection image;
and carrying out contour detection on the edge detection image to obtain contour information of all liquid drops, determining the positions of the liquid drops and drawing the liquid drops on the source image.
According to one aspect of the invention, the calculating to obtain the droplet area comprises: and calculating the outline of each droplet in the obtained droplet outline information by utilizing an outline area calculation function to obtain the area of all droplets.
The implementation of the invention has the advantages that:
1. this is a non-destructive, non-contact test.
2. The measuring and reaction speed is high; what you see is the resulting measurement.
3. The measuring range is wide, and the number of measuring objects is large; the complex state of the whole body can be directly measured and calculated, and a picture can store a very large data volume.
4. The image can be shot at any time, and online acquisition, query and processing can be realized.
5. The focusing can be adjusted at any time between the whole and the local.
6. The equipment is simple, only a set of camera equipment, an analog-digital conversion module and a data processing module are needed, and programs can be copied into the equipment.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a gray scale image to be processed from the source image of step S21;
FIG. 2 is the adaptive thresholded image of step S22;
FIG. 3 is a diagram of the step S23 of directly thresholding the image;
fig. 4 is the merged image of step S24;
FIG. 5 is a block diagram of the operation of step S25;
fig. 6 is an edge detection image of step S31;
fig. 7 is a drawing image of step S32;
FIG. 8 is a schematic diagram of a high throughput digital PCR image droplet accurate detection method according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, fig. 2, fig. 3, fig. 4, fig. 5, fig. 6, fig. 7, and fig. 8, a high-throughput digital PCR image droplet accurate detection method includes:
s1, acquisition of digital images: acquiring a digital image of a target area by using a photosensitive element such as a CCD (charge coupled device);
s2, image preprocessing: image quality is improved by using image processing methods such as histogram equalization, thresholding and the like;
s3, droplet positioning: determining the position of each liquid drop in the image, and eliminating the liquid drops which are detected by mistake;
s4, calculating the area of the liquid drop: and calculating the area of each liquid drop by using a digital image processing method.
The step S2 includes:
s21, reading in a gray level image of an image with a specified format in a file reading mode, wherein the image is called a source image, and the source image is shown in figure 1;
s22, carrying out adaptive thresholding on the source image S21, wherein the step S22 comprises the following steps:
s221, performing sliding operation on each pixel point of the image according to a n-x-n sliding frame, wherein the value of n is 9 for example;
s222, solving the average value of the gray values of all pixel points in the sliding frame;
s223, subtracting a difference value delta from the average value to serve as a threshold value for thresholding;
s224, setting the gray value of each pixel in the sliding frame to be the maximum gray value if the gray value is higher than the threshold value, and setting the gray value to be 0 if the gray value is less than or equal to the threshold value;
the image after the adaptive thresholding is shown in FIG. 2;
s23, directly thresholding the source image S21, wherein the step S23 comprises the following steps:
s231, traversing all pixel points in the image to obtain an average value of gray values of all the pixel points;
s232, multiplying the average value by a coefficient to serve as a threshold value, wherein the value of the coefficient is 0.75 for example;
s233, setting the gray value of all pixel points in the image to be the maximum gray value if the gray value is higher than the threshold value, and setting the gray value to be 0 if the gray value is less than or equal to the threshold value;
the image after direct thresholding is shown in FIG. 3;
s24, OR-operating the adaptive thresholded image S22 and the direct thresholded image S23, wherein the step S24 comprises:
s241, creating a blank image, wherein the size of the blank image is consistent with that of the source image;
and S242, comparing the self-adaptive thresholding image with the direct thresholding image, and setting the gray value of the pixel point with the corresponding coordinate (x, y) as the maximum gray value in the newly-built blank image if the gray values of the two images at the point with the same position (x, y) in the two images are the maximum gray value. Otherwise, setting the gray value of the point in the blank image as 0;
the merged image is shown in FIG. 4;
s25, performing an on operation on the merged image obtained in S24, wherein the step S25 includes:
s251, performing a corrosion operation on the image, where the step S251 includes:
s2511, performing a sliding operation on each pixel point according to a n × n sliding frame of the image, where a value of n is, for example, 15;
s2512, comparing the gray values of all pixel points in the sliding frame to obtain the minimum value;
s2513, setting the gray values of all pixel points in the sliding frame as the minimum value;
s252, performing expansion operation on the image subjected to the erosion operation, wherein the step S252 comprises the following steps:
s2511, performing a sliding operation on each pixel point according to a n × n sliding frame of the image, where a value of n is, for example, 15;
s2512, comparing the gray values of all pixel points in the sliding frame to obtain the maximum value;
s2513, setting the gray values of all pixel points in the sliding frame as the maximum values;
the image resulting from the on operation is shown in fig. 5;
s31, canny edge detection is carried out on the on operation image of S25;
the resulting image is shown in FIG. 6;
s32, carrying out contour detection on the edge detection image of S31 to obtain contour information of all liquid drops, determining the positions of the liquid drops, and drawing the liquid drops on a source image;
the resulting image is shown in FIG. 7;
and calculating each droplet contour in the droplet contour information obtained in the step S32 by using a contour area calculation function to obtain all droplet areas, and storing the result in a local file.
The implementation of the invention has the advantages that:
1. this is a non-destructive, non-contact test.
2. The measuring and reaction speed is high; what you see is the resulting measurement.
3. The measuring range is wide, and the number of measuring objects is large; the complex state of the whole body can be directly measured and calculated, and a picture can store a very large data volume.
4. The image can be shot at any time, and online acquisition, query and processing can be realized.
5. The focusing can be adjusted at any time between the whole and the local.
6. The equipment is simple, only a set of camera equipment, an analog-digital conversion module and a data processing module are needed, and programs can be copied into the equipment.
The above description is only an embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention disclosed herein are intended to be covered by the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (10)
1. The accurate detection method of the high-throughput digital PCR image droplets is characterized by comprising the following steps of:
acquiring a high-throughput PCR digital image;
carrying out image preprocessing to obtain a high-quality image;
positioning liquid drops;
and calculating to obtain the area of the liquid drop.
2. The method for accurately detecting the droplets in the high-throughput digital PCR image according to claim 1, wherein the specific step of performing image preprocessing to obtain a high-quality image comprises:
reading in a gray image of an image in a specified format as a source image by using a file reading mode;
carrying out self-adaptive thresholding treatment on a source image;
performing direct thresholding on the source image;
performing OR operation on the self-adaptive thresholding image and the direct thresholding image, and storing the processed images as a combined image;
and performing opening operation on the obtained combined image, and storing the processed image as an opening operation image to finish the image preprocessing.
3. The method for accurately detecting the high-throughput digital PCR image droplets as set forth in claim 2, wherein the adaptively thresholding the source image comprises:
carrying out sliding operation on each pixel point on a source image according to a sliding frame of n x n (n is more than 0);
calculating the average value of the gray values of all pixel points in the sliding frame;
subtracting a difference value delta from the average value to be used as a threshold value for thresholding;
for each pixel in the sliding frame, if the gray value is higher than the threshold value, the gray value is set as the maximum gray value, and if the gray value is smaller than or equal to the threshold value, the gray value is set as 0.
4. The method for accurately detecting droplets in high-throughput digital PCR images according to claim 2, wherein the directly thresholding the source image comprises:
traversing all pixel points in the source image to obtain an average value of gray values of all the pixel points;
taking the average value multiplied by a coefficient as a threshold value;
and setting the gray value of all pixel points in the image as the maximum gray value if the gray value is higher than the threshold value, and setting the gray value as 0 if the gray value is less than or equal to the threshold value.
5. The method for accurately detecting droplets in a high-throughput digital PCR image according to claim 2, wherein the performing or operation on the adaptive thresholded image and the direct thresholded image, and the saving of the processed images as a combined image comprises:
newly building a blank image, wherein the size of the blank image is consistent with that of the source image;
comparing the self-adaptive thresholding image with the direct thresholding image, and setting the gray value of a pixel point with the corresponding coordinate (x, y) as the maximum gray value in a newly-built blank image if the gray values of the two images at the point with the same position (x, y) in the two images are the maximum gray value; otherwise, setting the gray value of the point in the blank image as 0.
6. The method for accurately detecting the droplets in the high-throughput digital PCR image according to claim 2, wherein the merging images obtained by the method are subjected to an on operation, the processed images are saved as on operation images, and the preprocessing of the images comprises:
carrying out corrosion operation on the image;
and performing expansion operation on the image subjected to the corrosion operation.
7. The method for accurate detection of droplets in high-throughput digital PCR images according to claim 6, wherein said performing erosion operations on the images comprises:
performing sliding operation on each pixel point of the image according to a sliding frame of m by m (m is more than 0);
comparing the gray values of all pixel points in the sliding frame to obtain the minimum value;
and setting the gray values of all pixel points in the sliding frame as the obtained minimum value.
8. The method for accurately detecting the high-throughput digital PCR image droplets as claimed in claim 7, wherein the expanding the image after the erosion operation comprises:
performing sliding operation on each pixel point of the image according to a sliding frame of k x k (k is more than 0);
comparing the gray values of all pixel points in the sliding frame to obtain the maximum value;
and setting the gray values of all pixel points in the sliding frame as the maximum value.
9. The method for accurately detecting high-throughput digital PCR image droplets according to any one of claims 2 to 8, wherein the specific step of performing droplet positioning comprises:
carrying out canny edge detection on the opening operation image to obtain an edge detection image;
and carrying out contour detection on the edge detection image to obtain contour information of all liquid drops, determining the positions of the liquid drops and drawing the liquid drops on the source image.
10. The method for accurately detecting droplets in high-throughput digital PCR images according to claim 9, wherein the calculating the droplet area comprises: and calculating the outline of each droplet in the obtained droplet outline information by utilizing an outline area calculation function to obtain the area of all droplets.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113028993A (en) * | 2021-03-29 | 2021-06-25 | 深圳市博瑞生物科技有限公司 | Method for describing relative positions of micro-drop type digital PCR (polymerase chain reaction) liquid drops |
| CN113283697A (en) * | 2021-04-21 | 2021-08-20 | 耐优生物技术(上海)有限公司 | Intelligent generation method of experiment system |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104732536A (en) * | 2015-03-18 | 2015-06-24 | 广东顺德西安交通大学研究院 | Sub-pixel edge detection method based on improved morphology |
| WO2015157369A1 (en) * | 2014-04-08 | 2015-10-15 | University Of Washington Through Its Center For Commercialization | Methods and apparatus for performing digital assays using polydisperse droplets |
| CN106067174A (en) * | 2016-05-30 | 2016-11-02 | 西安邮电大学 | It is applicable to thresholding method and the device of low contrast Noise image |
| CN106520963A (en) * | 2016-11-18 | 2017-03-22 | 埃提斯生物技术（上海）有限公司 | Capturing probe and kit for high-throughput sequencing detection of human circulating tumor DNA KRAS gene |
| WO2017084391A1 (en) * | 2015-11-19 | 2017-05-26 | 广景视睿科技（深圳）有限公司 | Method and system for performing fluorescence processing on an image of a projector |
| CN106770333A (en) * | 2017-02-20 | 2017-05-31 | 重庆邮电大学 | A kind of BGA defects automatic testing method |
| CN107844683A (en) * | 2017-10-17 | 2018-03-27 | 华东医药（杭州）基因科技有限公司 | A kind of computational methods of digital pcr concentration of liquid drops |
-
2018
- 2018-06-19 CN CN201810631641.5A patent/CN110689926A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2015157369A1 (en) * | 2014-04-08 | 2015-10-15 | University Of Washington Through Its Center For Commercialization | Methods and apparatus for performing digital assays using polydisperse droplets |
| CN106414772A (en) * | 2014-04-08 | 2017-02-15 | 华盛顿大学商业中心 | Methods and apparatus for performing digital assays using polydisperse droplets |
| CN104732536A (en) * | 2015-03-18 | 2015-06-24 | 广东顺德西安交通大学研究院 | Sub-pixel edge detection method based on improved morphology |
| WO2017084391A1 (en) * | 2015-11-19 | 2017-05-26 | 广景视睿科技（深圳）有限公司 | Method and system for performing fluorescence processing on an image of a projector |
| CN106067174A (en) * | 2016-05-30 | 2016-11-02 | 西安邮电大学 | It is applicable to thresholding method and the device of low contrast Noise image |
| CN106520963A (en) * | 2016-11-18 | 2017-03-22 | 埃提斯生物技术（上海）有限公司 | Capturing probe and kit for high-throughput sequencing detection of human circulating tumor DNA KRAS gene |
| CN106770333A (en) * | 2017-02-20 | 2017-05-31 | 重庆邮电大学 | A kind of BGA defects automatic testing method |
| CN107844683A (en) * | 2017-10-17 | 2018-03-27 | 华东医药（杭州）基因科技有限公司 | A kind of computational methods of digital pcr concentration of liquid drops |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113028993A (en) * | 2021-03-29 | 2021-06-25 | 深圳市博瑞生物科技有限公司 | Method for describing relative positions of micro-drop type digital PCR (polymerase chain reaction) liquid drops |
| CN113283697A (en) * | 2021-04-21 | 2021-08-20 | 耐优生物技术(上海)有限公司 | Intelligent generation method of experiment system |
| CN113283697B (en) * | 2021-04-21 | 2023-11-24 | 耐优生物技术(上海)有限公司 | Intelligent generation method of experiment system |
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