WO2018103345A1 - Single molecule recognizing and counting method and device - Google Patents

Abstract

A single molecule recognizing and counting method and a recognizing and counting device, the single molecule recognizing method comprising: inputting a time sequence of intensity of an image light spot (S01); forming a line chart of time and intensity of the image light spot according to the time sequence, the line chart being composed of a plurality of line segments (S02); performing grid division on the line chart so as to form a plurality of grids arranged in an array, and counting the number of times that the line segments and/or end points of the line segments fall on each of the grids (S03); grouping on the basis of magnitude of the intensity, and performing frequency calculation on the number of times so as to obtain a histogram (S04); searching for a maximum value point of the histogram and determining that the peak of a maximum value point meeting the following conditions corresponds to a single molecule: the value of the maximum value point is greater than a first set threshold and the width of the peak where the maximum value point is located is greater than a second set threshold (S05).

Description

Single molecule identification and counting method and device Technical field

The invention relates to the field of gene sequencing technology, in particular to a single molecule identification, counting method, identification, counting device and processing system.

Background technique

In the related art, the third generation sequencing technology is single molecule sequencing, and the single molecule sequencing technology based on imaging optical detection is a base recognition technology that relies on optical signals and electrical signals. Among them, the fluorescence group is determined by fluorescence, and the fluorescence emitted is the intensity of light emitted from the excited state to the ground state under laser irradiation of a specific power. However, due to the different lengths of luminescence of different fluorophores, the difference in emitted light intensity, and the presence of background noise, single-molecule recognition errors are caused. At the same time, the DNA strands are unevenly distributed, and the base clusters and the like also cause a decrease in effective single molecules.

The existing methods mainly rely on the human eye to perform single molecule recognition and counting on the collected fluorescent images, but such a method is labor-intensive and slow. With reference to speech recognition, the method based on HMM and machine learning not only requires a large number of samples to be trained, but also has low operational efficiency.

Summary of the invention

The embodiments of the present invention aim to at least solve one of the technical problems existing in the prior art. Therefore, the embodiments of the present invention need to provide a single molecule identification, counting method, and identification and counting device.

A method for identifying a single molecule according to an embodiment of the present invention includes the steps of: inputting a time series of intensity of an image bright spot; forming a line graph of time and intensity of the bright spot of the image according to the time series, wherein the line graph is composed of multiple a line segment composition; meshing the line graphs to form a plurality of grids arranged in an array, counting the number of times of the line segments and/or the end points of the line segments that fall on each of the grids; The intensity is grouped, frequency statistics are performed on the number of times to obtain a histogram; the maximum point of the histogram is searched, and a peak at which a maximum point satisfying the following condition is determined corresponds to a single molecule: The value of the maximum value point is greater than the first set threshold and the width of the peak at which the maximum value point is greater than the second set threshold. The above-mentioned method for identifying a single molecule can be quickly recognized for a single molecule by converting a time-series line graph of bright spot intensity into image processing to obtain a histogram, and the recognition accuracy is also high.

A single molecule counting method according to an embodiment of the present invention includes the steps of: inputting a time series of image bright point intensity; forming a line graph of time and intensity of the image bright point according to the time series, wherein the line graph is composed of multiple a line segment composition; meshing the line graphs to form a plurality of grids arranged in an array, counting the number of times of the line segments and/or the end points of the line segments that fall on each of the grids; The intensity is grouped, frequency statistics are performed on the number of times to obtain a histogram; the maximum point of the histogram is searched, and a peak at which a maximum point satisfying the following condition is determined corresponds to a single molecule: The value of the maximum value point is greater than the first set threshold value and the width of the peak at which the maximum value point is located is greater than the second set threshold value; the number S1 of single molecules is calculated. The counting method of the single molecule described above is converted into image processing by a line graph of a time series of bright spot intensity to obtain a histogram, and the single molecule can be quickly counted, and the counting accuracy is also high.

A single molecule counting method according to an embodiment of the present invention includes the steps of: inputting a time series of image bright point intensity; forming a line graph of time and intensity of the image bright point according to the time series, wherein the line graph is composed of multiple a line segment composition; meshing the line graphs to form a plurality of grids arranged in an array, counting the number of times of the line segments and/or the end points of the line segments that fall on each of the grids; The magnitude of the intensity is grouped, the frequency is counted to obtain a histogram; the maximum point of the histogram is searched, and when the following conditions are met, the count of the single molecule is added: the maximum point The value of the peak is greater than the first set threshold and the width of the peak at which the maximum point is located is greater than the second set threshold. The counting method of the single molecule described above is converted into image processing by a line graph of a time series of bright spot intensity to obtain a histogram, and the single molecule can be quickly counted, and the counting accuracy is also high.

A single molecule identification device for implementing one or all of the steps of the single molecule identification method of the above aspect of the present invention includes: an input unit for inputting a time series of image brightness intensity; and a conversion unit And a line graph for forming a time and an intensity of the image bright point according to the time series in the input unit, wherein the line graph is composed of a plurality of line segments; a grid statistical unit is configured to The line graph of the cells is meshed to form a plurality of grids arranged in an array, counting the number of times of the line segments and/or the end points of the line segments of each of the grids; a histogram unit, Grouping based on the magnitude of the intensity, performing frequency statistics on the number of times from the grid statistical unit to obtain a histogram; and determining a unit for finding the pole of the histogram from the histogram statistic unit a large value point, and determining that a peak at which a maximum value point satisfies the following condition corresponds to a single molecule: the value of the maximum value point is greater than a first set threshold value and the maximum The width of the peak at which the value point is located is greater than the second set threshold. The single-molecule identification device converts the time-series line graph of the intensity of the bright spot into image processing to obtain a histogram, and can quickly recognize the single molecule, and the recognition accuracy is also high.

A single molecule counting device according to an embodiment of the present invention for implementing the above-described single molecule counting method of one aspect of the present invention Or all the steps include: an input unit, configured to input a time series of image brightness intensity; and a conversion unit, configured to form a line graph of time and intensity of the image bright point according to the time sequence in the input unit, The line graph is composed of a plurality of line segments; a grid statistical unit is configured to mesh the line graphs from the conversion unit to form a plurality of grids arranged in an array, and statistics fall on each of the nets a number of times of the line segment and/or an end point of the line segment; a histogram statistic unit configured to perform grouping based on the magnitude of the intensity, and perform frequency statistics on the number of times from the grid statistical unit to obtain a histogram a determination unit, configured to find a maximum value point of the histogram from the histogram statistical unit, and determine that a peak of a maximum value point satisfying the following condition corresponds to a single molecule: the maximum value point The value of the peak is greater than the first set threshold and the width of the peak at which the maximum point is located is greater than the second set threshold; the calculation unit is configured to calculate the number S1 of single molecules. The single-molecule counting device converts the time-series line graph of the intensity of the bright spot into image processing to obtain a histogram, and can quickly count the single molecules, and the counting accuracy is also high.

A single molecule counting device according to an embodiment of the present invention, for performing some or all of the steps of the single molecule counting method of the above aspect of the present invention, comprising: an input unit for inputting a time series of image brightness intensity; and a conversion unit And a line graph for forming a time and an intensity of the image bright point according to the time series in the input unit, wherein the line graph is composed of a plurality of line segments; a grid statistical unit is configured to The line graph of the cells is meshed to form a plurality of grids arranged in an array, counting the number of times of the line segments and/or the end points of the line segments of each of the grids; a histogram unit, Grouping based on the magnitude of the intensity, performing frequency statistics on the number of times from the grid statistical unit to obtain a histogram; and determining a unit for finding the pole of the histogram from the histogram statistic unit a large value point, and determining that the following condition is satisfied, the count of the single molecule is increased by 1: the value of the maximum value point is greater than the first set threshold value and the peak of the maximum value point is Is greater than a second preset threshold. The single-molecule counting device converts the time-series line graph of the intensity of the bright spot into image processing to obtain a histogram, and can quickly count the single molecules, and the counting accuracy is also high.

A single molecule processing system according to an embodiment of the present invention includes: a data input device for inputting data; a data output device for outputting data; and a storage device for storing data, the data including a computer executable program; A processor for executing the computer executable program, the executing the computer executable program comprising performing the method of any of the above embodiments. The single molecule processing system enables single molecule recognition and/or single molecule counting.

A computer readable storage medium for storing a program for execution by a computer, the method comprising executing the method of any of the above embodiments. The computer readable storage medium may include read only memory, random access memory, magnetic or optical disks, and the like.

Additional aspects and advantages of the embodiments of the invention will be set forth in part in

DRAWINGS

The above and/or additional aspects and advantages of the embodiments of the present invention will become apparent and readily understood from

1 is a schematic flow chart of a method for identifying a single molecule according to an embodiment of the present invention.

2 is a schematic flow chart of another method for identifying a single molecule according to an embodiment of the present invention.

3 is a schematic flow chart showing another method of identifying a single molecule according to an embodiment of the present invention.

4 is a schematic flow chart showing another method of identifying a single molecule according to an embodiment of the present invention.

FIG. 5 is still another schematic flowchart of a method for identifying a single molecule according to an embodiment of the present invention.

6 is a schematic flow chart showing another method of identifying a single molecule according to an embodiment of the present invention.

7 is a schematic flow chart showing another method of identifying a single molecule according to an embodiment of the present invention.

FIG. 8 is still another schematic flow chart of a method for identifying a single molecule according to an embodiment of the present invention.

9 is a schematic diagram showing a Mexican hat filter of a single molecule identification method according to an embodiment of the present invention.

FIG. 10 is a schematic flow chart of still another method for identifying a single molecule according to an embodiment of the present invention.

11 is a schematic diagram of eight connected pixels in a single molecule identification method according to an embodiment of the present invention.

Fig. 12 is a schematic diagram showing a line graph of a single molecule identification method according to an embodiment of the present invention.

Fig. 13 is a schematic diagram showing the meshing of a line graph in the single molecule identification method according to the embodiment of the present invention.

Fig. 14 is a schematic diagram showing a line graph before filtering in the single molecule identification method according to the embodiment of the present invention.

Fig. 15 is a schematic diagram showing a filtered line graph in the single molecule identification method according to the embodiment of the present invention.

Fig. 16 is another schematic diagram of a line graph of a single molecule identification method according to an embodiment of the present invention.

Fig. 17 is a schematic diagram showing a histogram after equalization in the single molecule identification method according to the embodiment of the present invention.

Fig. 18 is a flow chart showing still another flow of the method for identifying a single molecule according to an embodiment of the present invention.

Fig. 19 is a schematic view showing the process of line corrosion in the single molecule identification method according to the embodiment of the present invention.

Fig. 20 is a schematic view showing another process of line corrosion in the single molecule identification method according to the embodiment of the present invention.

21 is a schematic diagram of an 8-connected window in a single molecule identification method according to an embodiment of the present invention.

Fig. 22 is a schematic diagram showing the identification of a connected region in the single molecule identification method according to the embodiment of the present invention.

23 is a schematic flow chart of a single molecule counting method according to an embodiment of the present invention.

Fig. 24 is a schematic flow chart showing another method of counting a single molecule according to an embodiment of the present invention.

Fig. 25 is a flow chart showing still another flow of the single molecule counting method according to the embodiment of the present invention.

Fig. 26 is a schematic flow chart showing still another method of counting a single molecule according to an embodiment of the present invention.

Figure 27 is a block diagram showing a single molecule identification device according to an embodiment of the present invention.

28 is a block diagram showing still another module of the single molecule identification device according to the embodiment of the present invention.

29 is a block diagram showing still another module of the single molecule identification device according to the embodiment of the present invention.

Figure 30 is a block diagram showing another module of the single molecule identification device of the embodiment of the present invention.

Figure 31 is a block diagram showing a single molecule counting device according to an embodiment of the present invention.

32 is a block diagram showing still another module of the single molecule counting device according to the embodiment of the present invention.

Figure 33 is a block diagram showing another module of the single molecule counting device of the embodiment of the present invention.

Figure 34 is still another block diagram of the single molecule counting device of the embodiment of the present invention.

Fig. 35 is a block diagram showing still another module of the single molecule counting device according to the embodiment of the present invention.

Fig. 36 is a schematic view showing still another module of the single molecule counting device according to the embodiment of the present invention.

37 is a block diagram of a single molecule processing system in accordance with an embodiment of the present invention.

detailed description

The embodiments of the present invention are described in detail below, and the examples of the embodiments are illustrated in the drawings, wherein the same or similar reference numerals indicate the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the drawings are intended to be illustrative of the invention and are not to be construed as limiting.

In the description of the present invention, it is to be understood that the terms "first" and "second" are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" or "second" may include one or more of the described features either explicitly or implicitly. In the description of the present invention, the meaning of "a plurality" is two or more unless specifically and specifically defined otherwise.

In the description of the present invention, it should be noted that, unless otherwise clearly defined and defined, "connected" should be understood broadly, for example, it may be a fixed connection, a detachable connection, or an integral connection; The mechanical connections may also be electrical connections or may communicate with each other; they may be directly connected or indirectly connected through an intermediate medium, and may be internal communication of two elements or an interaction relationship of two elements.

The single molecule identification method and counting method of the embodiments of the present invention can be applied to gene sequencing, and the "gene sequencing" and nucleic acid sequence determination, including DNA sequencing and/or RNA sequencing, including long fragment sequencing and / or short segment sequencing.

Referring to FIG. 1 , a single molecule identification method according to an embodiment of the present invention includes the steps of: S01, inputting a time series of image brightness intensity; and S02, forming a line graph of time and intensity of an image bright point according to a time series, a line chart Consists of a plurality of line segments; S03, meshing the line graphs to form a plurality of grids arranged in the array, and counting the number of times of the line segments and/or end points of each line segment; S04, based on the intensity Perform grouping, perform frequency statistics on the number of times to obtain a histogram; S05, find the maximum point of the histogram, and determine that the peak of a maximum point satisfying the following condition corresponds to a single molecule: the value of the maximum point is greater than The width of the first set threshold and the peak at which the maximum point is located is greater than the second set threshold. The above-mentioned method for identifying a single molecule can be quickly recognized for a single molecule by converting a time-series line graph of bright spot intensity into image processing to obtain a histogram, and the recognition accuracy is also high. The single-molecule identification method based on the histogram can accurately identify a single molecule according to the time series data of the intensity of the bright spot, and is particularly suitable for the case where the number of single molecules included in the bright spot is >3.

Specifically, in step S01, when the image bright point is formed, the test sample is irradiated with laser light of a specific wavelength, the test sample is excited to emit fluorescence, and then the image formed by the fluorescence is collected by the camera, and the image is emitted corresponding to the test sample. The image of the part (nucleic acid molecule) is bright. The so-called "bright spot" refers to the light-emitting point on the image, and one light-emitting point occupies at least one pixel. The so-called "pixel" is the same as "pixel."

In one embodiment of the present invention, the image is from a single molecule sequencing platform, such as a sequencing platform of Helicos, Pacific Biosciences (PacBio), and the input raw data is a parameter of a pixel point of the image, and the so-called "bright spot" is detected. For the detection of single-molecule optical signals.

In some embodiments, referring to FIG. 2, the single molecule identification method further includes: an image preprocessing step S31, the image preprocessing step analyzing the input image to be processed to obtain a first image, and the image to be processed includes at least one image. a bright spot, the image highlight has at least one pixel; the bright spot detecting step S32, the bright spot detecting step S32 includes the steps of: S321, analyzing the first image to calculate a bright spot determination threshold, S322, analyzing the first image to obtain a candidate bright spot, S323, determining according to the bright spot The threshold value determines whether the candidate bright spot is an image bright spot. If the determination result is yes, S324, the time series of the image bright spot intensity is acquired. If the determination result is no, S325, the candidate bright spot is discarded.

Therefore, the denoising process of the image to be processed by the image preprocessing step can reduce the calculation amount of the bright spot detecting step, and at the same time, determine whether the candidate bright spot is an image bright point by using the bright spot judgment threshold, thereby improving the accuracy of determining the bright spot of the image.

Specifically, in one example, the input image to be processed may be a 16-bit tiff format image of 512*512 or 2048*2048, and the image of the tiff format may be a grayscale image. In this way, the processing of the single molecule identification method can be simplified.

In some embodiments, referring to FIG. 3, the image pre-processing step S31 includes performing background subtraction processing on the image to be processed to obtain a first image. In this way, the noise of the image to be processed can be further reduced, so that the accuracy of the single molecule recognition and/or counting method is higher.

In some embodiments, referring to FIG. 4, the image pre-processing step S31 includes: performing a simplification process on the image to be processed after performing the background subtraction processing to obtain a first image. In this way, the amount of calculation of the subsequent single molecule recognition and/or counting method can be reduced.

In some embodiments, referring to FIG. 5, the image pre-processing step S31 includes performing filtering processing on the image to be processed to obtain a first image. In this way, filtering the image to be processed can acquire the first image under the condition that the image detail features are retained as much as possible, thereby improving the accuracy of the single molecule recognition and/or counting method.

In some embodiments, referring to FIG. 6, the image pre-processing step S31 includes: performing background subtraction processing on the image to be processed, and then performing filtering processing to obtain a first image. In this way, the image to be processed is filtered after subtracting the background, which can further reduce the noise of the image to be processed, so that the accuracy of the single molecule recognition and/or counting method is higher.

In some embodiments, referring to FIG. 7, the image pre-processing step S31 includes: performing a simplified process on the image to be processed after performing the subtractive background processing to obtain the first image. In this way, the amount of calculation of the subsequent image processing method can be reduced.

In some embodiments, referring to FIG. 8, the image pre-processing step S31 includes performing a simplification process on the image to be processed to obtain a first image. In this way, the amount of calculation of the subsequent single molecule recognition and/or counting method can be reduced.

In some embodiments, performing background subtraction processing on the image to be processed includes: determining an background of the image to be processed by using an open operation, and performing background subtraction processing on the image to be processed according to the background. In this way, the open operation is used to eliminate small objects, separate objects at slender points, and smooth the boundaries of large objects without significantly changing the image area, so that the background-subtracted image can be acquired more accurately.

Specifically, in the embodiment of the present invention, the image to be processed f(x, y) (such as a grayscale image) is moved by an a*a window (for example, a 15*15 window), and an open operation (corrosion re-expansion) is used to estimate The background of the image is processed as shown in Equation 1 and Equation 2 below:

g(x,y)=erode[f(x,y),B]=min{f(x+x',y+y')-B(x',y')|(x',y') ∈D _b } Equation 1,

Where g(x, y) is the grayscale image after etching, f(x, y) is the original grayscale image, and B is the structural element.

g(x,y)=dilate[f(x,y),B]=max{f(x-x',y-y')-B(x',y')|(x',y') ∈D _b } Equation 2.

Where g(x, y) is the expanded grayscale image, f(x, y) is the original grayscale image, and B is the structural element.

Therefore, the background noise g=imopen(f(x,y),B)=dilate[erode(f(x,y),B)] can be obtained.

Decrease the background of the original image:

f=f-g={f(x,y)-g(x,y)|(x,y)∈D} Equation 4.

It can be understood that the specific method of performing background subtraction processing on the image to be processed in the embodiment may be applied to the step of performing background subtraction processing on the image to be processed mentioned in any of the above embodiments.

In some embodiments, the filtering process is a mexican hat filtering process. Mexican cap filtering is easy to implement, reducing the cost of single-molecule identification and/or counting methods. At the same time, Mexican cap filtering improves the contrast between the foreground and the background, making the foreground brighter and making the background darker.

In the Mexican hat filtering, the m*m window is used to perform Gaussian filtering on the image to be processed before the filtering process, and the Gaussian filtered image to be processed is subjected to two-dimensional Laplacian sharpening, where m is a natural number and is greater than 1. odd number. Thus, Mexican hat filtering is achieved in two steps.

Specifically, please refer to Figure 9, the Mexican hat core can be expressed as:

Where x and y represent the coordinates of the pixel points.

First, Gaussian filtering is performed on the image to be processed using the m*m window, as shown in Equation 6 below:

Where t1 and t2 represent the position of the filter window, and wt1 and t2 represent the weight of the Gaussian filter.

The image to be processed is then subjected to two-dimensional Laplacian sharpening, as shown in Equation 7 below:

Among them, K and k both represent Laplacian operators, which are related to sharpening targets. If it is necessary to strengthen sharpening and weaken sharpening, modify K and k.

In one example, m=3, so m*m=3*3, when performing Gaussian filtering, Equation 6 becomes:

It can be understood that the specific method of the mecha mask filtering of the present embodiment can be applied to the step of performing filtering processing on the image to be processed mentioned in any of the above embodiments.

In some embodiments, the simplified image is a binarized image. Such binarized images are easy to handle and have a wide range of applications.

Specifically, in one example, the binarized image may include two values of 0 and 1 characterizing different attributes of the pixel, and the binarized image may be expressed as:

In some embodiments, when the simplification process is performed, the signal to noise ratio matrix is obtained according to the image to be processed before the simplification processing, and the image to be processed before the processing is simplified according to the signal to noise ratio matrix to obtain the first image.

In a specific example, the image to be processed may be subjected to subtractive background processing, and then the signal to noise ratio matrix is obtained according to the image to be processed after subtracting the background processing. In this way, it is advantageous for the subsequent acquisition of information from images with less noise, so that the accuracy of obtaining the processing result by the single molecule recognition and/or counting method is higher.

Specifically, in one example, the signal to noise ratio matrix can be expressed as:

Equation 8, where x and y represent the coordinates of the pixel, h represents the height of the image, and w represents the width of the image, i∈w, j∈h.

In one example, the simplified image is a binarized image, and the binarized image can be obtained from the signal to noise ratio matrix. The binarized image is as shown in Equation 9:

When calculating the signal-to-noise ratio matrix, the background image to be processed may be subjected to subtractive background processing and/or filtering processing. The background subtraction background processing step and the filtering processing step of the above embodiment may be followed by subtracting the background processing to obtain the formula 4, and then subtracting the background. The ratio matrix of the processed image to the background after processing:

R=f/g={f(x,y)/g(x,y)|(x,y)∈D} Equation 10, where D represents the dimension (height*width) of the image f.

From this we can find the SNR matrix:

In some embodiments, the step of analyzing the first image to calculate a bright spot determination threshold comprises: processing the first image by the Otsu method to calculate a bright spot determination threshold. In this way, the search of the bright spot determination threshold is realized by a more mature and simple method, thereby improving the accuracy of the single molecule recognition and/or counting method and reducing the cost of the single molecule identification and/or counting method. At the same time, using the first image to perform the search of the bright spot determination threshold can improve the efficiency and accuracy of the single molecule recognition and/or counting method.

Specifically, the Otsu method (OTSU algorithm) can also be called the maximum inter-class variance method. The Otsu method uses the largest variance between classes to segment the image, which means that the probability of misclassification is the smallest and the accuracy is high. Suppose that the segmentation threshold of the foreground and background of the image to be processed is T, the ratio of the number of pixels belonging to the foreground to the entire image is ω ₀ , and the average gradation is μ ₀ ; the ratio of the number of pixels belonging to the background to the entire image is ω ₁ , the average gray level is μ ₁ . The total average gray level of the image to be processed is recorded as μ, and the variance between classes is recorded as var, which is:

μ=ω ₀ *μ ₀ +ω ₁ *μ ₁ Equation 11;

Var=ω ₀ (μ ₀ -μ) ² +ω ₁ (μ ₁ -μ) ² Equation 12.

Substituting the formula 11 into the formula 12 yields the equivalent formula 13:

Var=ω ₀ ω ₁ (μ ₁ -μ ₀ ) ² Equation 13.

The traversal method is used to obtain a segmentation threshold T that maximizes the variance between classes, that is, the desired spot determination threshold T.

In some embodiments, referring to FIG. 10, the step of determining whether the candidate bright spot is an image bright spot according to the bright spot determination threshold includes:

Step S41, searching for a pixel point larger than (h*h-1) in the first image and using the found pixel point as the center of the candidate bright point, h*h and the bright point are in one-to-one correspondence, in h*h Each value corresponds to one pixel, and h is a natural number and is an odd number greater than one;

Step S42, determining whether the center of the candidate bright spot satisfies the condition: I _max *A _BI *ceof _guass >T, where I _max is the center strongest intensity of the h*h window, and A _BI is the first image in the h*h window For the ratio of the set values, ceof _guass is the correlation coefficient between the pixels of the h*h window and the two-dimensional Gaussian distribution, and T is the bright point determination threshold.

If the above condition is met, S43, determining that the bright spot corresponding to the center of the candidate bright spot is an image bright point included in the image to be processed;

If the above condition is not satisfied, S44, the bright spot corresponding to the center of the candidate bright spot is discarded. In this way, the detection of image highlights is achieved.

Specifically, I _max can be understood as the center strongest intensity of the candidate bright spot. In one example, h=3, looking for pixels that are greater than 8 connected, as shown in FIG. The found pixel point is used as the pixel point of the candidate bright spot. I _max is the strongest intensity in the center of the 3*3 window, A _BI is the ratio of the set value in the first image in the 3*3 window, and ceof _guass is the correlation between the pixel of the 3*3 window and the two-dimensional Gaussian distribution. coefficient.

The first image is a simplified image, for example, the first image may be a binarized image, that is, the set value in the binarized image may be a value corresponding to when the pixel meets the set condition. In another example, the binarized image may contain two values of 0 and 1 characterizing different attributes of the pixel, the set value is 1, and A _BI is the ratio of 1 in the binarized image in the h*h window. . For example, please refer to Equation 9. When SNR<=mean(SNR), BI=1.

Additionally, in some embodiments, the value of h may be equal to the value of m selected when performing Mexican hat filtering, ie, h = m.

In some embodiments, when acquiring the above image, the camera sequentially performs fluorescence acquisition of a plurality of fields of view (FOV) in time series. Therefore, when image data is obtained, the intensity of the image highlights contained in the image data corresponds to the time series acquired by the camera.

In step S02, after the desired image highlights are obtained, the intensity of the image highlights corresponding to the adjacent acquisition times are point-connected, and a line graph of the time and intensity of the image highlights is formed, as shown in FIG. In Fig. 12, the horizontal axis represents the time at which fluorescence is collected, in milliseconds (ms), and the vertical axis represents the intensity of the image bright spot. In one example, the time interval between two adjacent acquisitions of fluorescence is 20 ms.

The vertical axis is the corresponding bright point intensity value. In the embodiment of the present invention, the bright spot intensity value is a bright pixel value, and for a 16-bit tiff image, the bright pixel value is in the range of 0-65535, and for the 8-bit grayscale image, The bright pixel values are in the range 0-255. A 16-bit tiff image is used in the embodiment of the present invention.

In step S03, the waveform of the line graph is converted into image processing for subsequent histogram statistics. Image processing of the line graph includes meshing the line graph.

In some embodiments, meshing the line graph is divided by the number of time frames and the intensity of the acquisition intensity. In this way, the line graph can be relatively simplely processed to obtain mesh division, which reduces the cost of the single molecule identification method. Specifically, it can be divided into M according to the number of time frames and N according to the size of the intensity, that is, M*N grids are formed. The number of time frames of the acquired intensity is the time interval between two adjacent acquisitions of fluorescence. In one embodiment, a mesh may be referred to as a longitudinal direction along a horizontal axis and a height direction along a longitudinal axis. The length of a grid can be set to several times the number of time frames, such as 1x, 2x, 2.5x, and so on. The height of a grid can be flexibly set. For example, for a 16-bit tiff image, the value of the vertical axis is 0-65535. When meshing, the value of the vertical axis can be normalized and divided into 50 parts, then one network. The height of the grid is set to 0.02, which is N=50.

In one example, the time interval between two adjacent acquisitions of fluorescence is 20 ms, the length of one grid is equal to one time interval, and the height = 0.02. Referring to Figure 16, in such an example, the number of segments falling on a grid can be 0, 1, or 2 times. The black dots in Figure 16 represent the time series of the intensity of the image highlights.

In one example, referring to Figure 13, the line graph is divided into 8*6 grids and the number of times that fall on the endpoints of each grid's line segments and/or line segments is counted. In Fig. 13, the number of times of the line segment falling on each grid (i.e., the number of times each grid is passed by the line segment) is counted, and the number in the grid represents the number of lines falling on each grid. The black dots in Figure 13 represent the time series of the intensity of the image highlights.

In some embodiments, step S04 includes the steps of dividing into N groups according to the strength, and counting the frequency of the statistics in the N groups:

Where n _i represents the sum of the frequencies of the number of times falling on the i-th row of the grid, j represents the number of time frames, g _ij represents the frequency of the number of times falling on the grid (i, j), and M represents the number of time frames . In this way, the number of times can be converted into a histogram of the intensity and the number of times, so that the subsequent single molecule recognition method is simpler.

Specifically, in one example, the horizontal axis of the histogram represents the number of groups, and the vertical axis represents the frequency at which the number of times falls within the corresponding number of groups. It should be noted that the value of N is equal to the value of N formed in the M*N grids described above. The value of M is equal to the value of M described above which forms M*N grids.

In some embodiments, grouping based on the magnitude of the intensity, frequency statistics on the number of times to obtain a histogram includes the steps of performing histogram equalization by L window:

Wherein, n _p n _i expressed equalization, n _i 'denotes the size L of the equalization result and n _i, p is an integer associated with the window, and where the i-th row. In this way, the distribution of the histogram can be made more uniform and easy to recognize. The L window is used for histogram equalization. The value of L is related to the decay rate of single molecule fluorescence. Generally, if the single molecule fluorescence is quenched quickly, the value of L should not be too large. The accuracy of the histogram is affected by the size of the L window, and the value of L can be flexibly set to select the accuracy of the appropriate histogram. In one example, L has a value range of [5, 15].

Referring to FIG. 17, FIG. 17 is an equalized histogram. The horizontal axis of the histogram indicates the number of groups, and the vertical axis indicates the frequency at which the number of times falls within the corresponding number of groups.

In step S05, all the maximum points of the histogram can be found by the derivative. The first set threshold Q and the second set threshold H are related to the shape of the peak of the line graph. The sharper the peak, the larger the first set threshold Q is, and the second set threshold H is smaller; the peak is fatter, the first The smaller the set threshold Q is, the larger the second set threshold H is. In one example, the first set threshold Q has a value range of [2, 6], and the second set threshold H has a value range of [4, 10].

In some embodiments, prior to meshing the line graph, the single molecule identification method further includes the step of filtering the line graph. In this way, the sudden change caused by the light intensity flicker and the camera sampling can be eliminated, and the waveform of the line graph is smoothed. Specifically, the modification of the waveform may employ a median filtering based on an L2 size window: R = medium(Z _i ). In an example, please refer to FIG. 14 and FIG. 15, FIG. 14 is a line diagram before filtering, and FIG. 15 is a line diagram after filtering. It can be seen from the figure that the waveform of the filtered line chart is smoother, which is beneficial to improve. The accuracy and efficiency of single molecule recognition.

In the embodiment of the present invention, the maximum point is the peak point, and the maximum point is the vertex (inflection point) of the peak, that is, the peak at which a maximum point satisfying the condition is judged corresponds to a single molecule.

In some embodiments, referring to FIG. 18, the single molecule identification method further includes the step of: S51, performing line etch on the meshed line graph according to the number of times corresponding to each grid to perform network aging. The line graph after division is converted into a simplified map; S52, run-length coding is performed on the simplified map to identify the connected region; S53, the area of each connected region is calculated, and a connected region satisfying the following condition is determined to correspond to a single molecule: the connected region The area is greater than the third set threshold.

In this way, the single molecule recognition and/or counting method can be applied in a wider range. The single-molecule recognition method based on run-length coding can accurately identify a single molecule according to time series data of the intensity of a bright spot, and is particularly suitable for a case where the number of single molecules included in one bright spot is not more than 3. In this embodiment, in combination with histogram-based and run-length coding-based methods to identify single molecules, it is possible to accurately identify single molecules in a line graph of various waveforms (time series of bright spot intensity).

For online corrosion, the following equation can be used for morphological corrosion operations: g(x,y)=erode[f(x,y),B]=min{f(x+x',y+y')-B (x', y')|(x', y') ∈ D _b }. Preferably, the structural element of the line, such as the window size of W*1, may be selected. If the number of times of the grid in the window exceeds the threshold T, the grid is marked as the first value, otherwise it is marked as the second value. In this way, the meshed line graph can be converted into a simplified map including the first value and the second value. In some embodiments, the simplified map is a binarized map. If the first value is 1 and the second value is 0.

In an example, please refer to Figure 19. The length of a grid is L1, W=2*L1, T=2. Figure 19 shows five grids arranged along the length. The numbers in the grid represent the number of times, then When performing line etching, the window is aligned with the grid. After the warp is etched, the five grids are labeled 0, 1, 0, 0, and 0, respectively.

In another example, please refer to Figure 20. The length of a grid is L1, W=2*L1, T=2. Figure 20 shows five grids arranged along the length. The numbers in the grid represent the number of times. Then, when performing line etching, the window is staggered from the grid. After the warp is etched, the five grids are marked as 0, 1, 0, 0, and 0, respectively.

It should be noted that the value of W is greater than or equal to the length of a grid. Preferably, W is an integer multiple of the length of a grid. In the example, W>=L1, preferably, W is an integer multiple of L1.

In other examples, the threshold T has a value range of [6, 8], and its selection is related to the fluctuation of the waveform of the line graph. The smaller the fluctuation, the larger the value of the threshold T is.

For ease of understanding, in describing the run length coding, the following description will be made by taking 1 and 0 in the binarization diagram as an example. It will be understood that other types of the figures and other values of the first and second values may be modified by those skilled in the art in light of the following description.

In the run-length encoding, an 8-connected approach can be used. According to the grid, the respective connected areas are recursively connected according to the principle of 8 connections, and then the connected areas are identified by the run length coding. Specifically, by 8 connectivity (such as using the 3*3 window shown in FIG. 21), starting from a non-zero grid Q, if the grids of the 8 directions of the grid Q are all non-zero, the grid will be The grid in the 8 directions of Q is identified as the same value as the grid Q, and so on. After completing the entire simplified diagram, an identification map as shown in FIG. 18 can be obtained.

In Fig. 22, different connected areas are identified by different numerical values. When calculating the area of each connected area, the number of occurrences of the same number is recorded as the area of the connected area, as shown in Fig. 22, the number of occurrences of the number 9 If it is 9, the area of the connected area corresponding to the number 9 is 9, and the number of occurrences of the number 7 is 20, and the area of the connected area corresponding to the number 7 is 20.

The above example uses a recursive algorithm, and in other examples, a traversal algorithm can also be employed to find connected regions.

If the area of the connected area is greater than the third set threshold P, one such connected area corresponds to a single molecule. The magnitude of P is related to the decay time of single molecule fluorescence. In one example, the third set threshold P has a value range of [5, 10].

Referring to FIG. 23, a single molecule counting method according to an embodiment of the present invention includes the steps of: S81, inputting a time series of image brightness intensity; S82, forming a line graph of time and intensity of an image bright point according to a time series, a line chart Consists of multiple line segments; S83, on the line graph The rows are meshed to form a plurality of grids arranged in the array, and the number of times of the line segments and/or the end points of the line segments of each grid are counted; S84, grouping based on the intensity, and frequency statistics are performed to obtain the frequency statistics. Histogram; S85, find the maximum point of the histogram, and determine that the peak of a maximum point satisfying the following condition corresponds to a single molecule: the value of the maximum point is greater than the first set threshold and the maximum point is The width of the peak is greater than the second set threshold; S86, the number of single molecules S1 is calculated. The counting method of the single molecule described above is converted into image processing by a line graph of a time series of bright spot intensity to obtain a histogram, and the single molecule can be quickly counted, and the counting accuracy is also high. It should be noted that the description of the technical features and advantages of the single molecule identification and/or counting method in any of the above embodiments and examples includes explanations and explanations of steps, parameter settings, and image preprocessing bright spot detection, and the like. Also applicable to the single molecule counting method of the present embodiment, in order to avoid redundancy, it will not be developed in detail here.

For example, in some embodiments, prior to meshing the line graph in step S83, the single molecule counting method further includes the step of filtering the line graph. For example, in some embodiments, referring to FIG. 24, the single molecule counting method further includes the step of: S91, performing line etching on the meshed line graph according to the number of times corresponding to each grid. Converting the line graph after meshing into a simplified graph; S92, performing run-length encoding on the simplified graph to identify the connected region; S93, calculating the area of each connected region, and determining that one connected region satisfying the following condition corresponds to a single molecule: The area of the connected region is greater than a third set threshold; S94, the number of single molecules S2 is calculated; S95, and the smaller of S1 and S2 is taken as the final single molecule number. The single-molecule counting method based on histogram is particularly suitable for accurately finding the single molecule number >3 of the bright spot, and the single-molecule counting method based on the run-length encoding is particularly suitable for accurately finding the single molecule number contained in the bright spot <=3 Case. In this embodiment, combining the two methods, it is possible to accurately find and count single molecules in a line graph of various waveforms. In some embodiments, the simplified map is a binarized map.

Referring to FIG. 25, a single molecule counting method according to an embodiment of the present invention includes the steps of: S61, inputting a time series of image brightness intensity; S62, forming a line graph of time and intensity of an image bright point according to a time series, a line chart Consisting of a plurality of line segments; S63, meshing the line graphs to form a plurality of grids arranged in the array, counting the number of times of the line segments and/or end points of each line segment; S64, based on the intensity Perform grouping, perform frequency statistics on the number of times to obtain a histogram; S65, find the maximum point of the histogram, and determine that the count of the single molecule is increased by 1 when the following conditions are satisfied: the value of the maximum point is greater than the first setting The width of the peak at which the threshold and the maximum point are located is greater than the second set threshold. The counting method of the single molecule described above is converted into image processing by a line graph of a time series of bright spot intensity to obtain a histogram, and the single molecule can be quickly counted, and the counting accuracy is also high.

It should be noted that the description of the technical features and advantages of the single molecule identification and/or counting method in any of the above embodiments and examples includes explanations and explanations of steps, parameter settings, and image preprocessing bright spot detection, etc. The same applies to the single molecule counting method of the present embodiment, and in order to avoid redundancy, it will not be developed in detail here.

For example, in some embodiments, prior to meshing the line graph in step S63, the single molecule counting method further includes the step of filtering the line graph. For example, in some embodiments, referring to FIG. 26, the single molecule counting method further includes the step of: S71, performing line etching on the meshed line graph according to the number of times corresponding to each grid. Converting the line graph after the meshing into a simplified graph; S72, performing run-length encoding on the simplified graph to identify the connected region; S73, calculating the area of each connected region, and determining the count of the single molecule when the following conditions are satisfied Plus 1: the area of the connected region is greater than a third set threshold; S74, the smaller of the single molecule count based on the histogram and the single molecule count obtained based on the run length encoding is taken as the final single molecule number. In this way, the single molecule counting method can be applied in a wider range, and a more accurate single molecule number can be obtained.

The single-molecule counting method based on the histogram is especially suitable for accurately finding the single molecule number >3 of the bright spot, and the single-molecule counting method based on the run-length encoding is particularly suitable for accurately finding the single molecule number <=3 included in the bright spot. Happening. In this embodiment, combining the two methods, it is possible to accurately find and count single molecules in a line graph of various waveforms. For example, the number of single molecules acquired based on the histogram is S1, the number of single molecules acquired based on the run length encoding is S2, and the sizes of S1 and S2 are compared, and the smaller ones of S1 and S2 are taken as the final single molecule number.

Referring to FIG. 27, a single molecule identification device 200 according to an embodiment of the present invention is used to implement all or part of the steps of the single molecule identification method in any of the above embodiments or examples. The molecular recognition device 200 includes: a first input unit 202 for inputting a time series of image brightness intensity; and a first conversion unit 204 configured to form a time and intensity of the image bright point according to the time sequence in the first input unit 202. a line graph, the line graph is composed of a plurality of line segments; a first grid statistic unit 206 is configured to mesh the line graphs from the first transform unit 204 to form a plurality of grids arranged in the array, and the statistics fall on each The number of times of the line segments and/or the end points of the line segments; the first histogram statistics unit 208 is configured to perform grouping based on the magnitude of the intensity, and perform frequency statistics on the number of times from the first mesh statistical unit 206 to obtain a histogram a first determining unit 210, configured to find a maximum value point of the histogram from the first histogram statistic unit 208, and determine a peak pair of a maximum value point that satisfies the following condition A single molecule: the value is greater than a first maximum set point value and the threshold value width of the peak point where the maximum is greater than a second predetermined threshold value. The single-molecule identification device 200 converts a time-series line graph of bright spot intensity into image processing to obtain a histogram, and can quickly recognize a single molecule, and the recognition accuracy is also high.

It should be noted that the explanation and description of the technical features and beneficial effects of the single molecule identification method in any of the above embodiments and embodiments are also applicable to the single molecule identification device 200 of the present embodiment, in order to avoid redundancy. It will not be expanded in detail here.

For example, in some embodiments, referring to FIG. 28, the single molecule identification device 200 further includes a first filtering unit 212, and the first network. The grid statistics unit 206 is coupled for filtering the line graph from the first conversion unit 204 prior to meshing the line graph.

In some embodiments, in the first mesh statistic unit 206, meshing the line graph is divided according to the number of time frames of the acquisition intensity and the magnitude of the intensity.

In some embodiments, in the first histogram statistic unit 208, grouping is performed based on the magnitude of the intensity, and frequency statistics are performed on the number of times to obtain a histogram, including: dividing into N groups according to the magnitude of the intensity, and the number of statistics falls on Frequency in N groups:

Where n _i represents the sum of the frequencies of the number of times falling on the i-th row of the grid, j represents the number of time frames, g _ij represents the frequency of the number of times falling on the grid (i, j), and M represents the number of time frames Quantity.

In some embodiments, in the first histogram statistic unit 208, grouping is performed based on the magnitude of the intensity, and frequency statistics are performed on the number of times to obtain a histogram including: performing histogram equalization by the L window:

Wherein, n _p n _i expressed equalization, n _i 'denotes the size L of the equalization result and n _i, p is an integer associated with the window, and where the i-th row.

In some embodiments, referring to FIG. 29, the single-molecule identification device 200 further includes: a first simplification unit 214, configured to perform a meshed line graph according to the number of times corresponding to each grid Line etching to convert the line graph after meshing into a simplified map; a first identifying unit 216 for run-length encoding the simplified graph to identify a connected region; and in the first determining unit 210, calculating each connected region The area determines that one connected region satisfying the following condition corresponds to one single molecule: the area of the connected region is larger than the third set threshold. In some embodiments, the simplified map is a binarized map.

In some embodiments, referring to FIG. 30, the single-molecule identification device 200 further includes: a first image pre-processing unit 218 for analyzing the input image to be processed to obtain a first image. The image to be processed includes at least one image bright point, the image bright spot has at least one pixel point; the first bright spot detecting unit 220 is configured to: analyze the first image to calculate a bright spot determination threshold, analyze the first image to obtain The candidate bright spot determines whether the candidate bright spot is an image bright spot according to the bright spot determination threshold. If the determination result is yes, the time series of the image bright spot intensity is acquired, and if the determination result is negative, the candidate bright spot is discarded.

In some embodiments, the first image pre-processing unit 218 includes a first subtraction background unit 226 for performing background subtraction processing on the image to be processed to obtain a first image.

In some embodiments, the first image pre-processing unit 218 includes a first image reduction unit 222 for performing a simplification process on the image to be processed after the background subtraction process to obtain a first image.

In some embodiments, the first image pre-processing unit 218 includes a first image filtering unit 224 for performing a filtering process on the image to be processed to obtain a first image.

In some embodiments, the first image pre-processing unit 218 includes a first subtraction background unit 226 and a first image filtering unit 224 for performing background subtraction processing on the image to be processed, the first image filtering unit 224 is configured to perform filtering processing on the image to be processed after performing background subtraction processing to obtain a first image.

In some embodiments, the first image pre-processing unit 218 includes a first image simplification unit 222, and the first image simplification unit 222 is configured to simplify the image to be processed after the background processing is performed, Obtain the first image.

In some embodiments, the first image pre-processing unit 218 includes a first image reduction unit 222 for performing a simplification process on the image to be processed to obtain a first image.

In some embodiments, in the first subtraction background unit 226, performing background subtraction processing on the image to be processed includes: determining an background of the image to be processed by using an open operation, and performing background subtraction processing on the image to be processed according to the background. In some embodiments, the filtering process is a mexican hat filtering process. In some embodiments, the simplification process is a binarization process.

In some embodiments, the first image reduction unit 222 is configured to acquire a signal to noise ratio matrix according to the image to be processed before the simplified processing, and simplify the simplified image before processing according to the signal to noise ratio matrix to obtain the first image.

In some embodiments, in the first bright spot detecting unit 220, analyzing the first image to calculate the bright spot determination threshold comprises: processing the first image by the Otsu method to calculate a bright spot determination threshold.

In some embodiments, determining, in the first bright spot detecting unit 220, whether the candidate bright spot is an image bright point according to the bright spot determination threshold includes: searching for a pixel point larger than (h*h-1) in the first image and The found pixel is the center of the candidate bright spot, h is a natural number and is an odd number greater than 1; determining whether the center of the candidate bright spot satisfies the condition: I _max *A _BI *ceof _guass >T, where I _max is h*h window The strongest intensity of the center, A _BI is the ratio of the set value in the first image in the h*h window, ceof _guass is the correlation coefficient of the pixel of the h*h window and the two-dimensional Gaussian distribution, and T is the bright point decision threshold If the above conditions are met, it is determined that the bright spot corresponding to the center of the candidate bright spot is an image bright spot, and if the above condition is not satisfied, the bright spot corresponding to the center of the candidate bright spot is discarded.

Referring to FIG. 31, a single molecule counting device 400 according to an embodiment of the present invention is used to implement all or part of the single molecule counting method in any of the above embodiments and examples of the present invention. In the step, the single molecule counting device 400 includes: a second input unit 402 is configured to input a time series of image brightness intensity; a second conversion unit 404 is configured to form a line graph of time and intensity of the image bright point according to the time sequence in the second input unit 402, and the line graph is composed of The second line statistic unit 406 is configured to mesh the line graphs from the second conversion unit 404 to form a plurality of grids arranged in the array, and the statistics fall on the line segments of each grid and/or Or the number of times of the end points of the line segment; the second histogram statistic unit 408 is configured to perform grouping based on the magnitude of the intensity, perform frequency statistics on the number of times from the second grid statistical unit 406 to obtain a histogram; and the second determining unit 410 uses Finding a maximum value point of the histogram from the second histogram statistic unit 408, and determining that a peak of a maximum value point satisfying the following condition corresponds to a single molecule: the value of the maximum value point is greater than the first set threshold value and The width of the peak where the maximum point is located is greater than the second set threshold; and the calculation unit 412 is configured to calculate the number S1 of single molecules. The above-described single-molecule counting device 400 converts a time-series line graph of bright spot intensity into image processing to obtain a histogram, and can quickly count a single molecule, and the counting accuracy is also high.

It should be noted that the explanation and description of the technical features and beneficial effects of the single molecule counting method in any of the above embodiments and examples are also applicable to the single molecule counting device 400 of the present embodiment, in order to avoid redundancy, It will not be expanded in detail here.

For example, in some embodiments, referring to FIG. 32, the single molecule counting device 400 further includes a second filtering unit 414 coupled to the second mesh statistical unit 406 for meshing the line graph before it is meshed. The line graph from the second conversion unit 404 is filtered.

In some embodiments, referring to FIG. 33, the single-molecule counting device 400 further includes: a second simplifying unit 416, configured to perform the meshed line graph according to the number of times corresponding to each grid Line etching to convert the meshed line graph into a simplified map; a second identifying unit 418 for run-length encoding the simplified map to identify the connected region; and in the second determining unit 410, calculating each connected region The area determines that one connected region satisfying the following condition corresponds to one single molecule: the area of the connected region is larger than the third set threshold; in the calculating unit 412, the number S2 of single molecules is calculated, and the smaller one of S1 and S2 is taken as The final single molecule number.

Referring to FIG. 34, a single-molecule counting device 600 according to an embodiment of the present invention includes: a third input unit 602 for inputting a time series of image brightness intensity; and a third converting unit 604 for using a third input unit. a time series in 602, a line graph of time and intensity of the image highlights, the line graph consisting of a plurality of line segments; and a third grid statistics unit 606 for meshing the line graphs from the third transforming unit 604 Forming a plurality of grids arranged in an array, counting the number of times falling on the end points of the line segments and/or line segments of each grid; a third histogram statistics unit 608 for grouping based on the magnitude of the intensity, from the third grid The number of statistics unit 606 is frequency counted to obtain a histogram; the third determining unit 610 is configured to find a maximum value point of the histogram from the third histogram statistic unit 608, and determine that the single condition is satisfied when the following conditions are met. Count plus 1: The value of the maximum point is greater than the first set threshold and the width of the peak at which the maximum point is located is greater than the second set threshold. The above-described single-molecule counting device 600 converts a time-series line graph of bright spot intensity into image processing to obtain a histogram, and can quickly count a single molecule, and the counting accuracy is also high.

It should be noted that the explanation and description of the technical features and beneficial effects of the counting method for single molecules in any of the above embodiments and embodiments are also applicable to the single molecule counting device 600 of the present embodiment, in order to avoid redundancy, It will not be expanded in detail here.

For example, in some embodiments, referring to FIG. 35, the single molecule counting device 600 further includes a third filtering unit 612 coupled to the third mesh statistical unit 606 for use in meshing the line graph. The line graph from the third conversion unit 604 is filtered.

In some embodiments, referring to FIG. 36, the single-molecule counting device 600 further includes: a third simplifying unit 614, configured to perform the meshed line graph according to the number of times corresponding to each grid. Line etching to convert the meshed line graph into a simplified map; a third identifying unit 616 for run-length encoding the simplified map to identify the connected region; and in the third determining unit 610, calculating each connected region Area, and determine that the following conditions are met, the count of the single molecule is increased by 1: the area of the connected region is larger than the third set threshold; and the single molecule count obtained based on the histogram and the single molecule obtained based on the run length coding The smaller of the counts is the final single molecule number.

Referring to FIG. 37, a single molecule processing system 300 according to an embodiment of the present invention includes: a data input device 302 for inputting data; a data output device 304 for outputting data; and a storage device 306 for storing data. The data includes a computer executable program; a processor 308 for executing a computer executable program, and executing the computer executable program includes the method of performing any of the above embodiments.

A computer readable storage medium for storing a program for execution by a computer, the program comprising the method of any of the above embodiments. The computer readable storage medium may include read only memory, random access memory, magnetic or optical disks, and the like.

In the description of the present specification, the description with reference to the terms "one embodiment", "some embodiments", "illustrative embodiment", "example", "specific example", or "some examples", etc. The specific features, structures, materials or characteristics described in the embodiments or examples are included in at least one embodiment or example of the invention. In the present specification, the schematic representation of the above terms does not necessarily mean the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in a suitable manner in any one or more embodiments or examples.

In addition, each functional unit in each embodiment of the present invention may be integrated into one processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above integrated modules can be implemented in the form of hardware or in the form of software functional modules.

Although the embodiments of the present invention have been shown and described, it is understood that the above-described embodiments are illustrative and are not to be construed as limiting the scope of the invention. The embodiments are subject to changes, modifications, substitutions and variations.

Claims (47)

1. A single molecule identification method, comprising the steps of:

   Enter a time series of image highlight strengths;

   Forming, according to the time series, a line graph of time and intensity of the bright spot of the image, the line graph being composed of a plurality of line segments;

   Dividing the line graph to form a plurality of grids arranged in an array, and counting the number of times of falling on the line segment of each of the grids and/or the end points of the line segments;

   Grouping based on the magnitude of the intensity, performing frequency statistics on the number of times to obtain a histogram;

   Finding a maximum value point of the histogram, and determining that a peak of a maximum value point satisfying the following condition corresponds to a single molecule: the value of the maximum value point is greater than a first set threshold value and the maximum value point The width of the peak is greater than the second set threshold.
2. A single molecule counting method, comprising the steps of:

   Enter a time series of image highlight strengths;

   Forming, according to the time series, a line graph of time and intensity of the bright spot of the image, the line graph being composed of a plurality of line segments;

   Dividing the line graph to form a plurality of grids arranged in an array, and counting the number of times of falling on the line segment of each of the grids and/or the end points of the line segments;

   Grouping based on the magnitude of the intensity, performing frequency statistics on the number of times to obtain a histogram;

   Finding a maximum value point of the histogram, and determining that a peak of a maximum value point satisfying the following condition corresponds to a single molecule: the value of the maximum value point is greater than a first set threshold value and the maximum value point The width of the peak where it is located is greater than the second set threshold;

   The number of single molecules S1 is calculated.
3. A single molecule counting method, comprising the steps of:

   Enter a time series of image highlight strengths;

   Forming, according to the time series, a line graph of time and intensity of the bright spot of the image, the line graph being composed of a plurality of line segments;

   Dividing the line graph to form a plurality of grids arranged in an array, and counting the number of times of falling on the line segment of each of the grids and/or the end points of the line segments;

   Grouping based on the magnitude of the intensity, performing frequency statistics on the number of times to obtain a histogram;

   Finding a maximum value point of the histogram, and determining that the count of the single molecule is increased by 1 when the following condition is satisfied: the value of the maximum value point is greater than the first set threshold value and the peak of the maximum value point is The width is greater than the second set threshold.
4. The method according to any one of claims 1-3, further comprising the steps of: before meshing the line graph:

   The line graph is filtered.
5. The method according to any one of claims 1 to 4, wherein the meshing of the line graph is divided according to the number of time frames in which the intensity is collected and the magnitude of the intensity.
6. The method according to any one of claims 1-5, wherein the grouping is performed based on the magnitude of the intensity, and the frequency statistics are performed on the number of times to obtain a histogram. The steps include:

   Dividing into N groups according to the magnitude of the strength, and counting the frequency of the number of times falling in the N groups:

   

   Where n _i represents the sum of the frequencies of the number of times falling on the i-th row of the grid, j represents the number of time frames, and g _ij represents the frequency of the number of times falling on the grid (i, j), M Indicates the number of time frames.
7. The method according to claim 6, wherein said grouping is performed based on said magnitude of said intensity, and said step of performing frequency statistics on said number of times to obtain a histogram comprises the steps of: performing histogram equalization by L window :

   

   Wherein, n _p n _i expressed equalization, n _i 'denotes the size L of the equalization result and n _i, p is an integer associated with the window, and where the i-th row.
8. The method of claim 1 further comprising the step of:

   Performing line etch on the line graph after meshing according to the number of times corresponding to each of the grids to convert the line graph after meshing into a simplified map, optionally, The simplified diagram is a binarization map;

   Performing run-length encoding on the simplified map to identify a connected area;

   Calculating an area of each of the connected regions, and determining that one of the connected regions satisfying the following condition corresponds to one single molecule: an area of the connected region is greater than a third set threshold.
9. The method of claim 2 further comprising the step of:

   Performing line etch on the line graph after meshing according to the number of times corresponding to each of the grids to convert the line graph after meshing into a simplified map, optionally, The simplified diagram is a binarization map;

   Performing run-length encoding on the simplified map to identify a connected area;

   Calculating an area of each of the connected areas, and determining that one of the connected areas corresponding to the following condition corresponds to a single molecule: an area of the connected area is greater than a third set threshold;

   Calculating the number of single molecules S2;

   The smaller of S1 and S2 is taken as the final single molecule number.
10. The method of claim 3 further comprising the step of:

    Performing line etch on the line graph after meshing according to the number of times corresponding to each of the grids to convert the line graph after meshing into a simplified map, optionally, The simplified diagram is a binarization map;

    Performing run-length encoding on the simplified map to identify a connected area;

    Calculating an area of each of the connected regions, and determining that the following condition is satisfied, adding a count to the single molecule: the area of the connected region is greater than a third set threshold;

    The smaller of the count of the single molecule acquired based on the histogram and the count of the single molecule acquired based on the run length encoding is taken as the final single molecule number.
11. The method according to any one of claims 1 to 10, further comprising:

    An image preprocessing step, the image preprocessing step analyzing the input image to be processed to obtain a first image, the image to be processed comprising at least one of the image highlights, the image highlight having at least one pixel;

    a bright spot detecting step, the bright spot detecting step comprising the steps of:

    Analyzing the first image to calculate a bright spot determination threshold,

    Analyzing the first image to obtain candidate highlights,

    Determining, according to the bright spot determination threshold, whether the candidate bright spot is the image bright spot,

    If the result of the determination is yes, the time series of the intensity of the bright spot of the image is obtained,

    If the result of the determination is no, the candidate highlights are discarded.
12. The method according to claim 11, wherein said image preprocessing step comprises: performing background subtraction processing on said image to be processed to obtain said first image.
13. The method according to claim 12, wherein the image pre-processing step comprises: performing a simplification process on the image to be processed after performing the subtractive background processing to obtain the first image.
14. The method according to claim 11, wherein said image preprocessing step comprises: performing filtering processing on said image to be processed to obtain said first image.
15. The method according to claim 11, wherein the image pre-processing step comprises: performing background subtraction processing on the image to be processed, and performing filtering processing to obtain the first image.
16. The method according to claim 15, wherein the image pre-processing step comprises: performing a simplification process on the image to be processed after performing the subtractive background processing and then performing the filtering process to obtain the first image.
17. The method according to claim 11, wherein said image preprocessing step comprises: performing a simplification process on said image to be processed to obtain said first image.
18. The method according to claim 12, 13, 15, or 16, wherein the background processing of the image to be processed comprises:

    Determining the background of the image to be processed by using an open operation,

    The background image to be processed is subjected to background subtraction processing according to the background.
19. The method according to any one of claims 14-16, wherein the filtering process is a mexican hat filtering process.
20. The method according to claim 13, 16 or 17, wherein the simplification processing is a binarization process.
21. The method according to claim 13, 16 or 17, wherein, in performing the simplification processing, a signal to noise ratio matrix is obtained according to an image to be processed before the simplification processing, and the SNR is simplified according to the SNR matrix The image to be processed before processing is simplified to obtain the first image.
22. The method according to claim 11, wherein the step of analyzing the first image to calculate a bright spot determination threshold comprises:

    The first image is processed by the Otsu method to calculate the bright spot determination threshold.
23. The method according to claim 13, 16 or 17, wherein the determining, according to the bright point determination threshold, whether the candidate bright spot is the image bright point comprises: searching for greater than (h) in the first image *h-1) connected pixel points and the found pixel points as the center of the candidate bright point, h being a natural number and an odd number greater than one;

    Determining whether the center of the candidate bright spot satisfies a condition: I _max *A _BI *ceof _guass >T, where I _max is the center strongest intensity of the h*h window, and A _BI is the first image in the h*h window Where is the ratio of the set value, ceof _guass is the correlation coefficient of the pixel of the h*h window and the two-dimensional Gaussian distribution, and T is the threshold of the bright point determination.

    If the above condition is met, it is determined that the bright spot corresponding to the center of the candidate bright spot is the image bright spot.

    If the above conditions are not met, the corresponding bright spot of the center of the candidate highlight is discarded.
24. A single molecule identification device, comprising:

    An input unit for inputting a time series of image highlight intensity;

    a conversion unit, configured to form a line graph of time and intensity of the bright spot of the image according to the time sequence in the input unit, where the line graph is composed of a plurality of line segments;

    a grid statistics unit for meshing the line graphs from the conversion unit to form a plurality of grids arranged in an array, and counting the line segments and/or locations falling on each of the grids The number of times the endpoint of the line segment is described;

    a histogram statistic unit, configured to perform grouping based on the magnitude of the intensity, and perform frequency statistics on the number of times from the grid statistical unit to obtain a histogram;

    a determining unit, configured to find a maximum value point of the histogram from the histogram statistical unit, and determine that a peak of a maximum value point satisfying the following condition corresponds to a single molecule: a value of the maximum value point The width of the peak greater than the first set threshold and the maximum point is greater than the second set threshold.
25. A single molecule counting device, comprising:

    An input unit for inputting a time series of image highlight intensity;

    a conversion unit, configured to form a line graph of time and intensity of the bright spot of the image according to the time sequence in the input unit, where the line graph is composed of a plurality of line segments;

    a grid statistics unit for meshing the line graphs from the conversion unit to form a plurality of grids arranged in an array, and counting the line segments and/or locations falling on each of the grids The number of times the endpoint of the line segment is described;

    a histogram statistic unit, configured to perform grouping based on the magnitude of the intensity, and perform frequency statistics on the number of times from the grid statistical unit to obtain a histogram;

    a determining unit, configured to find a maximum value point of the histogram from the histogram statistical unit, and determine that a peak of a maximum value point satisfying the following condition corresponds to a single molecule: a value of the maximum value point a width greater than a first set threshold and a peak at which the maximum point is greater than a second set threshold;

    A calculation unit for calculating the number S1 of single molecules obtained.
26. A single molecule counting device, comprising:

    An input unit for inputting a time series of image highlight intensity;

    a conversion unit, configured to form a line graph of time and intensity of the bright spot of the image according to the time sequence in the input unit, where the line graph is composed of a plurality of line segments;

    a grid statistics unit for meshing the line graphs from the conversion unit to form a plurality of grids arranged in an array, and counting the line segments and/or locations falling on each of the grids The number of times the endpoint of the line segment is described;

    a histogram statistic unit, configured to perform grouping based on the magnitude of the intensity, and perform frequency statistics on the number of times from the grid statistical unit to obtain a histogram;

    a determining unit, configured to find a maximum value point of the histogram from the histogram statistical unit, and determine that the count of the single molecule is increased by 1 when the following condition is satisfied: the value of the maximum value point is greater than the first setting The threshold is determined and the width of the peak at which the maximum point is located is greater than a second set threshold.
27. The device according to any one of claims 24 to 26, further comprising a filtering unit connected to the grid statistical unit for meshing the line graph before The line graph from the conversion unit is filtered.
28. The apparatus according to any one of claims 24 to 27, wherein in the grid statistical unit, meshing the line graph is according to the number of time frames in which the intensity is collected and the intensity The size is divided.
29. The apparatus according to any one of claims 24 to 28, wherein, in the histogram statistic unit, grouping is performed based on the magnitude of the intensity, and frequency statistics are performed on the number of times to obtain a histogram, including:

    Dividing into N groups according to the magnitude of the strength, and counting the frequency of the number of times falling in the N groups:

    

    Where n _i represents the sum of the frequencies of the number of times falling on the i-th row of the grid, j represents the number of time frames, and g _ij represents the frequency of the number of times falling on the grid (i, j), M Indicates the number of time frames.
30. The apparatus according to any one of claims 24 to 26, wherein, in the histogram statistic unit, grouping is performed based on the magnitude of the intensity, and frequency statistics are performed on the number of times to obtain a histogram. Press the histogram equalization of the L window:

    

    Wherein, n _p n _i expressed equalization, n _i 'denotes the size L of the equalization result and n _i, p is an integer associated with the window, and where the i-th row.
31. The device according to claim 24, further comprising:

    a simplification unit, configured to perform line eroding on the line graph after meshing according to the number of times corresponding to each of the grids to convert the line graph after meshing into a simplified graph, Optionally, the simplified graph is a binarized graph;

    An identifier unit, configured to perform run length encoding on the simplified map to identify a connected area;

    In the determining unit, an area of each of the connected regions is calculated, and it is determined that one of the connected regions satisfying the following condition corresponds to one single molecule: an area of the connected region is greater than a third set threshold.
32. The device according to claim 25, further comprising:

    a simplification unit, configured to perform line eroding on the line graph after meshing according to the number of times corresponding to each of the grids to convert the line graph after meshing into a simplified graph, Optionally, the simplified graph is a binarized graph;

    An identifier unit, configured to perform run length encoding on the simplified map to identify a connected area;

    In the determining unit, calculating an area of each of the connected regions, and determining that one of the connected regions corresponding to the following condition corresponds to a single molecule: an area of the connected region is greater than a third set threshold;

    In the calculation unit, the number S2 of single molecules is calculated, and the smaller of S1 and S2 is taken as the final single molecule number.
33. The device of claim 26, further comprising:

    a simplification unit, configured to perform line eroding on the line graph after meshing according to the number of times corresponding to each of the grids to convert the line graph after meshing into a simplified graph, Optionally, the simplified graph is a binarized graph;

    An identifier unit, configured to perform run length encoding on the simplified map to identify a connected area;

    In the determining unit, calculating an area of each of the connected regions, and determining that the following condition is satisfied, adding a count to the single molecule: the area of the connected region is greater than a third set threshold;

    The smaller one of the count of the single molecule acquired based on the histogram and the count of the single molecule acquired based on the run length encoding is used as the final single molecule number.
34. The device according to any one of claims 24 to 33, further comprising:

    An image pre-processing unit, configured to analyze the input image to be processed to obtain a first image, the image to be processed includes at least one of the image bright points, the image bright point having at least one pixel point;

    a bright spot detecting unit, wherein the bright spot detecting unit is configured to:

    Analyzing the first image to calculate a bright spot determination threshold,

    Analyzing the first image to obtain candidate highlights,

    Determining, according to the bright spot determination threshold, whether the candidate bright spot is the image bright spot,

    If the result of the determination is yes, the time series of the intensity of the bright spot of the image is obtained,

    If the result of the determination is no, the candidate highlights are discarded.
35. The apparatus according to claim 34, wherein said image pre-processing unit comprises a subtraction background unit, and said subtraction background unit is configured to perform background subtraction processing on said image to be processed to obtain said first image.
36. The apparatus according to claim 35, wherein said image pre-processing unit comprises an image reduction unit, and said image reduction unit is configured to perform a simplified process on the image to be processed after performing background subtraction processing to obtain said An image.
37. The apparatus according to claim 34, wherein the image pre-processing unit comprises an image filtering unit, and the image filtering unit is configured to perform filtering processing on the image to be processed to obtain the first image.
38. The apparatus according to claim 34, wherein said image pre-processing unit comprises a subtraction background unit and an image filtering unit, said subtraction background unit configured to perform background subtraction processing on said image to be processed, said image filtering The unit is configured to perform a filtering process on the image to be processed after performing the subtractive background processing to obtain the first image.
39. The apparatus according to claim 38, wherein the image pre-processing unit comprises a simplification unit, and the simplification unit is configured to perform a simplified process on the image to be processed after performing the subtractive background processing and then performing the filtering process to obtain The first image.
40. The apparatus according to claim 34, wherein said image pre-processing unit comprises an image reduction unit for performing a simplification process on said image to be processed to obtain said first image.
41. The device according to claim 34, 36, 38 or 39, wherein in the subtracting background unit, performing background subtraction processing on the image to be processed comprises:

    Determining the background of the image to be processed by using an open operation,

    The background image to be processed is subjected to background subtraction processing according to the background.
42. Apparatus according to any of claims 37-39, wherein said filtering process is a mexican hat filtering process.
43. The apparatus according to claim 36, 39 or 40, wherein said simplification processing is binarization processing.
44. The apparatus according to claim 36, 39 or 40, wherein said image simplification unit is configured to acquire a signal to noise ratio matrix according to the image to be processed before the simplification processing, and to simplify said according to said signal to noise ratio matrix The image to be processed before processing is simplified to obtain the first image.
45. The device according to claim 34, wherein in the bright spot detecting unit, analyzing the first image to calculate a bright spot determination threshold comprises:

    The first image is processed by the Otsu method to calculate the bright spot determination threshold.
46. The device according to claim 36, 39 or 40, wherein in the bright spot detecting unit, the determining, according to the bright spot determination threshold, whether the candidate bright spot is the image bright spot comprises: Finding a pixel point larger than (h*h-1) in the first image and using the found pixel point as the center of the candidate bright point, h being a natural number and an odd number greater than 1;

    Determining whether the center of the candidate bright spot satisfies a condition: I _max *A _BI *ceof _guass >T, where I _max is the center strongest intensity of the h*h window, and A _BI is the first image in the h*h window Where is the ratio of the set value, ceof _guass is the correlation coefficient of the pixel of the h*h window and the two-dimensional Gaussian distribution, and T is the threshold of the bright point determination.

    If the above condition is met, it is determined that the bright spot corresponding to the center of the candidate bright spot is the image bright spot.

    If the above conditions are not met, the corresponding bright spot of the center of the candidate highlight is discarded.
47. A single molecule processing system, comprising:

    a data input device for inputting data;

    a data output device for outputting data;

    a storage device for storing data, the data comprising a computer executable program;

    A processor for executing the computer executable program, the executing the computer executable program comprising performing the method of any of claims 1-23.

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