CN110689926A - An accurate detection method of high-throughput digital PCR image droplets - Google Patents

An accurate detection method of high-throughput digital PCR image droplets Download PDF

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CN110689926A
CN110689926A CN201810631641.5A CN201810631641A CN110689926A CN 110689926 A CN110689926 A CN 110689926A CN 201810631641 A CN201810631641 A CN 201810631641A CN 110689926 A CN110689926 A CN 110689926A
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刘满华
郝英杰
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Shanghai Jiao Tong University
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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

一种高通量数字PCR图像液滴的准确检测方法An accurate detection method of high-throughput digital PCR image droplets

技术领域technical field

本发明涉及基因测序技术领域,尤其涉及一种高通量数字PCR图像液滴的准确检测方法。The invention relates to the technical field of gene sequencing, in particular to an accurate detection method for high-throughput digital PCR image droplets.

背景技术Background technique

高通量基因测序技术也被称为第二代基因测序技术,是近几年应用非常广泛的基因测序法。其与第一代基因测序技术相比有高通量,低成本等特点。高温聚合酶链式反应技术(polymerase chain reaction)简称PCR技术,可以快速扩增DNA片段,在数个周期内实现DNA片段的指数增长,是目前最常用的DNA扩增技术。高通量PCR图像包含海量的待检测荧光液滴,具有数据量大,待测目标多,液滴间隔紧凑,时常发生重叠,图像质量不佳等不易于自动图像检测的特点。如何自动处理这些海量数据成为高通量测序技术的重要课题。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, it has the characteristics of high throughput and low cost. High-temperature polymerase chain reaction (polymerase chain reaction) is referred to as PCR technology, which can rapidly amplify DNA fragments and achieve exponential growth of DNA fragments in several cycles. It is currently the most commonly used DNA amplification technology. High-throughput PCR images contain a large number of fluorescent droplets to be detected, and have the characteristics of large amount of data, many targets to be detected, tight droplet intervals, frequent overlapping, and poor image quality, which are not easy for automatic image detection. How to automatically process these massive data has become an important topic of high-throughput sequencing technology.

在实际高通量海量液滴数字PCR成像过程中,大面积荧光成像系统的像场范围内有可能各个点的荧光收集效率不同,造成整个像场范围内亮度不均匀,一般同样亮度的微滴在像场中心亮度要远大于在像场边缘,这种亮度不均匀现象随着像场的增加而变得愈发突出,直接导致采集的图像对比度不高、图像不清晰,微滴提取不准确。如何更有效的海量液滴图像处理、准确检测液滴是高通量海量液滴数字PCR系统的重要组成部分。In the actual high-throughput mass droplet digital PCR imaging process, the fluorescence collection efficiency of each point in the image field of the large-area fluorescence imaging system may be different, resulting in uneven brightness in the entire image field. Generally, droplets with the same brightness The brightness at the center of the image field is much larger than that at the edge of the image field, and this uneven brightness becomes more prominent as the image field increases, which directly leads to low contrast, unclear images, and inaccurate droplet extraction. . How to more effectively process massive droplet images and accurately detect droplets is an important part of a high-throughput massive droplet digital PCR system.

目前数字PCR技术主要使用绝对定量的荧光监测法检测液滴数量,使用数字图像处理技术的检测法还比较少。在国内的几篇探讨数字图像处理的论文中,对微反应/孔板数字PCR和大规模集成芯片数字PCR的讨论比较多。这两类数字PCR技术,液滴排列较为简单规整,近似为矩阵,容易预测液滴的位置。或是使用相对较简单的方法对海量液滴进行检测,条件要求高。At present, digital PCR technology mainly uses the absolute quantitative fluorescence monitoring method to detect the number of droplets, and the detection method using digital image processing technology is still relatively small. Among several domestic papers discussing digital image processing, there are many discussions on micro-reaction/well plate digital PCR and large-scale integrated chip digital PCR. For these two types of digital PCR technologies, the droplet arrangement is relatively simple and regular, approximated as a matrix, and it is easy to predict the position of the droplet. Or use a relatively simple method to detect a large number of droplets, with high requirements.

发明内容SUMMARY OF THE INVENTION

鉴于目前存在的上述不足,本发明提供一种高通量数字PCR图像液滴的准确检测方法,能够准确检测液滴数量并定位液滴位置。In view of the above-mentioned shortcomings at present, the present invention provides an accurate detection method for high-throughput digital PCR image droplets, which can accurately detect the number of droplets and locate the position of the droplets.

为达到上述目的,本发明的实施例采用如下技术方案:To achieve the above object, the embodiments of the present invention adopt the following technical solutions:

一种高通量数字PCR图像液滴的准确检测方法,所述高通量数字PCR图像液滴的准确检测方法包括以下步骤:An accurate detection method for high-throughput digital PCR image droplets, the accurate detection method for high-throughput digital PCR image droplets includes the following steps:

获取高通量PCR数字图像;Obtain high-throughput PCR digital images;

进行图像预处理得到高质量图像;Perform image preprocessing to obtain high-quality images;

进行液滴定位;perform droplet positioning;

计算获得液滴面积。Calculate the droplet area.

依照本发明的一个方面,所述进行图像预处理得到高质量图像的具体步骤包括:According to an aspect of the present invention, the specific steps of performing image preprocessing to obtain high-quality images include:

利用文件读取方式,读入指定格式图像的灰度图像作为源图像;Using the file reading method, read the grayscale image of the specified format image as the source image;

对源图像进行自适应阈值化处理;Adaptive thresholding of the source image;

对源图像进行直接阈值化处理;Direct thresholding of the source image;

将自适应阈值化图像和直接阈值化图像做或操作,处理后的图像保存为合并图像;ORing the adaptive thresholding image and the direct thresholding image, and save the processed image as a merged image;

对得到的合并图像进行开运算操作,处理后的图像保存为开运算图像,完成图像的预处理。The open operation operation is performed on the obtained combined image, the processed image is saved as an open operation image, and the preprocessing of the image is completed.

依照本发明的一个方面,所述对源图像进行自适应阈值化处理包括:According to an aspect of the present invention, the adaptive thresholding of the source image includes:

对源图像按n*n(n>0)的滑动框对每一个像素点进行滑动操作;For the source image, press the n*n (n>0) sliding frame to slide each pixel point;

求滑动框内所有像素点灰度值的平均值;Find the average value of all pixel gray values in the sliding box;

取平均值减去一个差值delta作为阈值化所取阈值;Take the average value minus a difference delta as the threshold for thresholding;

对于滑动框中的每一个像素,若其灰度值高于阈值,将其灰度值置为最大灰度值,若小于等于阈值,将其灰度值置为0。For each pixel in the sliding box, if its gray value is higher than the threshold, set its gray value to the maximum gray value, and if it is less than or equal to the threshold, set its gray value to 0.

依照本发明的一个方面,所述对源图像进行直接阈值化处理包括:According to an aspect of the present invention, the direct thresholding of the source image comprises:

对源图像中所有像素点进行遍历,求得所有像素点灰度值的平均值;Traverse all the pixels in the source image to obtain the average value of the gray values of all the pixels;

取平均值乘以一个系数作为阈值化阈值;Take the average multiplied by a coefficient as the thresholding threshold;

对图像中所有像素点,若其灰度值高于阈值,将其灰度值置为最大灰度值,若小于等于阈值,将其灰度值置为0。For all pixels in the image, if the gray value is higher than the threshold, set the gray value to the maximum gray value; if it is less than or equal to the threshold, set the gray value to 0.

依照本发明的一个方面,所述将自适应阈值化图像和直接阈值化图像做或操作,处理后的图像保存为合并图像包括:According to an aspect of the present invention, performing OR operation on the adaptive thresholding image and the direct thresholding image, and saving the processed image as a merged image includes:

新建一个空白的图像,其大小与源图像一致;Create a new blank image with the same size as the source image;

比较自适应阈值化和直接阈值化图像,若对于两幅图像中同样位置为(x,y)的一个点,两幅图像在该点的灰度值都为最大灰度值,则在新建的空白图像中,将对应坐标为(x,y)的像素点的灰度值置为最大灰度值;否则,将空白图像中该点的灰度值置为0。Comparing the adaptive thresholding and direct thresholding images, if for a point at the same position (x, y) in the two images, the gray value of the two images at this point is the maximum gray value, then in the newly created image In the blank image, the gray value of the pixel corresponding to the coordinates (x, y) is set to the maximum gray value; otherwise, the gray value of the point in the blank image is set to 0.

依照本发明的一个方面,所述对得到的合并图像进行开运算操作,处理后的图像保存为开运算图像,完成图像的预处理包括:According to one aspect of the present invention, performing an open operation operation on the obtained combined image, the processed image is saved as an open operation image, and completing the preprocessing of the image includes:

对图像进行腐蚀操作;Corrode the image;

对进行腐蚀操作后的图像进行膨胀操作。Dilate the image after the erosion operation.

依照本发明的一个方面,所述对图像进行腐蚀操作包括:According to an aspect of the present invention, the etching operation on the image includes:

对图像按m*m(m>0)的滑动框对每一个像素点进行滑动操作;Perform sliding operation on each pixel point according to the sliding frame of m*m (m>0) on the image;

比较滑动框中所有像素点的灰度值,求得最小值;Compare the gray value of all pixels in the sliding box to find the minimum value;

将滑动框中所有像素点的灰度值置为所得最小值。Set the gray value of all pixels in the sliding box to the minimum value obtained.

依照本发明的一个方面,所述对进行腐蚀操作后的图像进行膨胀操作包括:According to an aspect of the present invention, the dilation operation on the image after the erosion operation includes:

对图像按k*k(k>0)的滑动框对每一个像素点进行滑动操作;Perform sliding operation on each pixel point according to the sliding frame of k*k (k>0) on the image;

比较滑动框中所有像素点的灰度值,求得最大值;Compare the grayscale values of all pixels in the sliding box to obtain the maximum value;

将滑动框中所有像素点的灰度值置为所得最大值。Set the gray value of all pixels in the sliding box to the maximum value obtained.

依照本发明的一个方面,所述进行液滴定位的具体步骤包括:According to an aspect of the present invention, the specific steps of performing droplet positioning include:

对开运算图像进行canny边缘检测得到边缘检测图像;Perform canny edge detection on the open operation image to obtain an edge detection image;

对边缘检测图像进行轮廓检测,得到所有液滴的轮廓信息,确定液滴的位置,并绘制在源图像上。Contour detection is performed on the edge detection image, the contour information of all droplets is obtained, the position of the droplet is determined, and it is drawn on the source image.

依照本发明的一个方面,所述计算获得液滴面积包括:利用轮廓面积计算函数对所得的液滴轮廓信息中的每个液滴轮廓进行计算,得到所有液滴面积。According to an aspect of the present invention, the calculating to obtain the droplet area includes: using a contour area calculation function to calculate each droplet contour in the obtained droplet contour information to obtain all droplet areas.

本发明实施的优点:Advantages of the implementation of the present invention:

1.这是一种无损的、非接触式的检测。1. This is a non-destructive, non-contact inspection.

2.测量、反应速度快;所见即所得的测量方式。2. Fast measurement and response; WYSIWYG measurement.

3.测量范围广,测量对象多;可以对整体的复杂状态直接测量和计算,一张照片可以保存非常大的数据量。3. The measurement range is wide and there are many measurement objects; the complex state of the whole can be directly measured and calculated, and a photo can save a very large amount of data.

4.可以随时摄取图像,在线采集、查询、处理。4. Images can be captured at any time, online collection, query and processing.

5.可以调焦,随时在整体和局部间调节。5. The focus can be adjusted, and it can be adjusted between the whole and the part at any time.

6.设备简单,需要的仅是一套照相设备,模数转化模块,以及数据处理模块,程序可以复制到这些设备中。6. The equipment is simple. All that is needed is a set of photographic equipment, an analog-to-digital conversion module, and a data processing module, and the program can be copied to these devices.

附图说明Description of drawings

为了更清楚地说明本发明实施例中的技术方案,下面将对实施例中所需要使用的附图作简单地介绍,显而易见地,下面描述中的附图仅仅是本发明的一些实施例,对于本领域普通技术人员来讲,在不付出创造性劳动的前提下,还可以根据这些附图获得其他的附图。In order to illustrate the technical solutions in the embodiments of the present invention more clearly, the following briefly introduces the drawings required in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can also be obtained from these drawings without any creative effort.

图1为步骤S21源图像待处理灰度图像;Fig. 1 is the grayscale image to be processed of the source image in step S21;

图2为步骤S22自适应阈值化图像;Fig. 2 is step S22 adaptive thresholding image;

图3为步骤S23直接阈值化图像;3 is a direct thresholding image in step S23;

图4为步骤S24合并图像;Fig. 4 is step S24 merging images;

图5为步骤S25开运算图像;Fig. 5 is step S25 open operation image;

图6为步骤S31边缘检测图像;Fig. 6 is the edge detection image of step S31;

图7为步骤S32绘制图像;Fig. 7 is that step S32 draws an image;

图8本本发明所述的高通量数字PCR图像液滴准确检测方法示意图。FIG. 8 is a schematic diagram of the accurate detection method of high-throughput digital PCR image droplets according to the present invention.

具体实施方式Detailed ways

下面将结合本发明实施例中的附图,对本发明实施例中的技术方案进行清楚、完整地描述,显然,所描述的实施例仅仅是本发明一部分实施例,而不是全部的实施例。基于本发明中的实施例,本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例,都属于本发明保护的范围。The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

如图1、图2、图3、图4、图5、图6、图7和图8所示,一种高通量数字PCR图像液滴准确检测方法,包括:As shown in Figure 1, Figure 2, Figure 3, Figure 4, Figure 5, Figure 6, Figure 7 and Figure 8, a high-throughput digital PCR image droplet accurate detection method, including:

S1、数字图像的获取:利用CCD等感光元件获得目标区域的数字图像;S1. Acquisition of digital images: use CCD and other photosensitive elements to obtain digital images of the target area;

S2、图像预处理:利用直方图均衡、阈值化等图像处理方法提高图像质量;S2. Image preprocessing: use image processing methods such as histogram equalization and thresholding to improve image quality;

S3、液滴定位:确定各液滴在图像中的位置,剔除误检测液滴;S3. Droplet positioning: determine the position of each droplet in the image, and remove the falsely detected droplets;

S4、液滴面积计算:利用数字图像处理方法计算得到各个液滴的面积。S4. Calculation of droplet area: the area of each droplet is calculated by using a digital image processing method.

所述步骤S2包括:The step S2 includes:

S21、利用文件读取方式,读入指定格式图像的灰度图像,此图像称为源图像,源图像如图1所示;S21, using the file reading method, read in the grayscale image of the image in the specified format, this image is called the source image, and the source image is shown in Figure 1;

S22、对S21所述源图像进行自适应阈值化处理,所述步骤S22包括:S22. Perform adaptive thresholding processing on the source image in S21. The step S22 includes:

S221、对图像按n*n的滑动框对每一个像素点进行滑动操作,n的取值比如为9;S221, perform a sliding operation on each pixel point according to the sliding frame of n*n on the image, and the value of n is, for example, 9;

S222、求滑动框内所有像素点灰度值的平均值;S222. Find the average value of the grayscale values of all the pixels in the sliding frame;

S223、取平均值减去一个差值delta作为阈值化所取阈值;S223, take the average value minus a difference delta as the threshold value taken by thresholding;

S224、对于滑动框中的每一个像素,若其灰度值高于阈值,将其灰度值置为最大灰度值,若小于等于阈值,将其灰度值置为0;S224. For each pixel in the sliding frame, if its gray value is higher than the threshold, set its gray value to the maximum gray value, and if it is less than or equal to the threshold, set its gray value to 0;

自适应阈值化处理后的图像如图2所示;The image after adaptive thresholding is shown in Figure 2;

S23、对S21所述源图像进行直接阈值化处理,所述步骤S23包括:S23. Perform direct thresholding processing on the source image in S21, and the step S23 includes:

S231、对图像中所有像素点进行遍历,求得所有像素点灰度值的平均值;S231, traversing all the pixels in the image to obtain the average value of the gray values of all the pixels;

S232、取平均值乘以一个系数作为阈值化阈值,系数的取值比如为0.75;S232, taking the average value and multiplying it by a coefficient as the thresholding threshold, and the value of the coefficient is, for example, 0.75;

S233、对图像中所有像素点,若其灰度值高于阈值,将其灰度值置为最大灰度值,若小于等于阈值,将其灰度值置为0;S233. For all pixels in the image, if the gray value is higher than the threshold, set the gray value to the maximum gray value, and if it is less than or equal to the threshold, set the gray value to 0;

直接阈值化处理后的图像如图3所示;The image after direct thresholding is shown in Figure 3;

S24、将S22所述自适应阈值化图像和S23所述直接阈值化图像做或操作,所属步骤S24包括:S24, performing an OR operation on the adaptive thresholding image described in S22 and the direct thresholding image described in S23, and step S24 includes:

S241、新建一个空白的图像,其大小与源图像一致;S241. Create a new blank image whose size is the same as that of the source image;

S242、比较自适应阈值化和直接阈值化图像,若对于两幅图像中同样位置为(x,y)的一个点,两幅图像在该点的灰度值都为最大灰度值,则在新建的空白图像中,将对应坐标为(x,y)的像素点的灰度值置为最大灰度值。否则,将空白图像中该点的灰度值置为0;S242. Comparing the adaptive thresholding and direct thresholding images, if for a point at the same position (x, y) in the two images, the gray value of the two images at this point is the maximum gray value, then In the newly created blank image, the gray value of the pixel corresponding to the coordinates (x, y) is set as the maximum gray value. Otherwise, set the gray value of the point in the blank image to 0;

合并后图像如图4所示;The combined image is shown in Figure 4;

S25、对S24中得到的合并图像进行开运算操作,所述步骤S25包括:S25, perform an opening operation on the merged image obtained in S24, and the step S25 includes:

S251、对图像进行腐蚀操作,所述步骤S251包括:S251, perform an erosion operation on the image, and the step S251 includes:

S2511、对图像按n*n的滑动框对每一个像素点进行滑动操作,n的取值比如为15;S2511, perform a sliding operation on each pixel point according to the sliding frame of n*n on the image, and the value of n is, for example, 15;

S2512、比较滑动框中所有像素点的灰度值,求得最小值;S2512, compare the gray values of all the pixels in the sliding frame, and obtain the minimum value;

S2513、将滑动框中所有像素点的灰度值置为所得最小值;S2513, set the gray value of all pixels in the sliding frame as the obtained minimum value;

S252、对进行腐蚀操作后的图像进行膨胀操作,所属步骤S252包括:S252, performing an expansion operation on the image after the corrosion operation, and the step S252 includes:

S2511、对图像按n*n的滑动框对每一个像素点进行滑动操作,n的取值比如为15;S2511, perform a sliding operation on each pixel point according to the sliding frame of n*n on the image, and the value of n is, for example, 15;

S2512、比较滑动框中所有像素点的灰度值,求得最大值;S2512, compare the gray values of all pixels in the sliding frame, and obtain the maximum value;

S2513、将滑动框中所有像素点的灰度值置为所得最大值;S2513, setting the gray value of all pixels in the sliding frame as the obtained maximum value;

开运算操作得到的图像如图5所示;The image obtained by the open operation operation is shown in Figure 5;

S31、对S25所述开运算图像进行canny边缘检测;S31, performing canny edge detection on the open operation image described in S25;

所得图像如图6所示;The resulting image is shown in Figure 6;

S32、对S31所述边缘检测图像进行轮廓检测,得到所有液滴的轮廓信息,确定液滴的位置,并绘制在源图像上;S32, perform contour detection on the edge detection image described in S31, obtain contour information of all droplets, determine the positions of the droplets, and draw them on the source image;

所得的图像如图7所示;The resulting image is shown in Figure 7;

利用轮廓面积计算函数对S32所得的液滴轮廓信息中的每个液滴轮廓进行计算,得到所有液滴面积,将结果存储在本地文件中。Use the contour area calculation function to calculate each droplet contour in the droplet contour information obtained in S32 to obtain all droplet areas, and store the results in a local file.

本发明实施的优点:Advantages of the implementation of the present invention:

1.这是一种无损的、非接触式的检测。1. This is a non-destructive, non-contact inspection.

2.测量、反应速度快;所见即所得的测量方式。2. Fast measurement and response; WYSIWYG measurement.

3.测量范围广,测量对象多;可以对整体的复杂状态直接测量和计算,一张照片可以保存非常大的数据量。3. The measurement range is wide and there are many measurement objects; the complex state of the whole can be directly measured and calculated, and a photo can save a very large amount of data.

4.可以随时摄取图像,在线采集、查询、处理。4. Images can be captured at any time, online collection, query and processing.

5.可以调焦,随时在整体和局部间调节。5. The focus can be adjusted, and it can be adjusted between the whole and the part at any time.

6.设备简单,需要的仅是一套照相设备,模数转化模块,以及数据处理模块,程序可以复制到这些设备中。6. The equipment is simple. All that is needed is a set of photographic equipment, an analog-to-digital conversion module, and a data processing module, and the program can be copied to these devices.

以上所述,仅为本发明的具体实施方式,但本发明的保护范围并不局限于此,任何熟悉本领域技术的技术人员在本发明公开的技术范围内,可轻易想到的变化或替换,都应涵盖在本发明的保护范围之内。因此,本发明的保护范围应以所述权利要求的保护范围为准。The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited to this. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, All should be included within the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the 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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