WO2018068511A1 - Image processing method and system for gene sequencing - Google Patents

Abstract

Disclosed are an image processing method and system for gene sequencing. The image processing method for gene sequencing comprises: an image preprocessing step, wherein in the step, an input image to be processed is analysed so as to remove noise of the image to be processed; and a bright point detection step, wherein the step comprises the steps of: analysing the image to be processed so as to calculate a bright point determination threshold value; and analysing the image to be processed, the noise of which is removed, so as to acquire a candidate pixel point, and determining whether the candidate pixel point is a bright point according to the bright point determination threshold value, if so, calculating a sub-pixel central coordinate of the bright point and an intensity value of the sub-pixel central coordinate, and if not, abandoning the candidate pixel point. In the image processing method for gene sequencing, de-noising processing is performed on an image via an image preprocessing step, which can reduce the calculation amount of a bright point detection step, and at the same time, a bright point determination threshold value is used to determine whether a candidate bright point is a bright point, which can improve the accuracy of determining an image bright point.

Description

Image processing method and system for gene sequencing

Priority information

The present application claims the priority and benefit of the prior application filed on October 10, 2016, to the Chinese National Intellectual Property Office, the entire disclosure of which is hereby incorporated by reference.

Technical field

The present invention relates to the field of gene sequencing technologies, and in particular, to an image processing method and system for gene sequencing and a computer readable storage medium.

Background technique

In the related art, image brightness localization has important applications in gene sequencers and LED light points.

Image analysis is an important part of systems that use optical imaging principles for sequence determination. The accuracy of image brightness positioning directly determines the accuracy of gene sequencing.

In the process of nucleic acid sequence determination, how to improve the accuracy of judging image bright spots has become one of the problems to be solved.

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. To this end, embodiments of the present invention need to provide an image processing method and system for gene sequencing and a computer readable storage medium.

An image processing method for gene sequencing according to an embodiment of the present invention includes: an image preprocessing step of analyzing an input image to be processed to remove noise of the image to be processed; a highlight detection step, the highlight detection step comprising Step: analyzing the to-be-processed image to calculate a bright spot determination threshold; analyzing the to-be-processed image of the noise to obtain a candidate pixel point, and determining whether the candidate pixel point is a bright point according to the bright spot determination threshold, and if so, calculating the bright spot The pixel center coordinate and the intensity value of the sub-pixel center coordinate, if not, discard the candidate pixel point.

The image processing method of the above gene sequencing, the image denoising process is performed by the image preprocessing step, which can reduce the calculation amount of the bright spot detecting step, and at the same time, determine whether the candidate bright spot is a bright spot by the bright spot judgment threshold, thereby improving the accuracy of determining the bright spot of the image. .

The image processing method of the present invention has no particular limitation on the image to be processed, that is, the original input data, and is applicable to processing analysis of images generated by any platform for performing nucleic acid sequence determination using optical detection principles, including but not limited to second generation and Three generations of sequencing, with high accuracy, high versatility and high precision, can get more effective information from the image.

In particular, currently, known sequencing image processing methods and/or systems are basically developed for image processing of a second-generation sequencing platform, since the sequencing chips used for second-generation sequencing are generally array-type, ie, sequencing cores. The on-chip probes are regularly arranged, and the images obtained by photographing are pattern images, which are easy to process and analyze. In addition, since the second-generation sequencing generally includes nucleic acid template amplification and amplification, high-intensity bright spots can be obtained during image acquisition, and it is easy to recognize. And positioning. The general second-generation sequencing image processing method does not require high positioning accuracy, and only needs to select and locate some brightly-bright spots (bright spots) to achieve sequence determination.

For the three-generation sequencing, single-molecule sequencing, limited by the current development of chip surface treatment related technology, the sequencing chip used is random, that is, the probes on the sequencing chip are randomly arranged, and the images obtained by photographing are random ( Random) image, which is difficult to process analysis; Moreover, image processing analysis of single-molecule sequencing is one of the most important factors determining the efficiency of the final sequence. It requires high image processing and bright spot positioning, and requires all images. Bright spots can be accurately located so that bases can be directly identified and data information is generated.

Therefore, the image processing method of the present invention can be adapted to use for second-generation sequencing and third-generation sequencing, especially for random images in three-generation sequencing and image processing with high precision requirements, and is particularly advantageous.

An image processing system for gene sequencing according to an embodiment of the present invention includes: an image preprocessing module, configured to analyze an input image to be processed to obtain a denoised image, the image to be processed including at least one bright spot The bright spot has at least one pixel; the bright spot detecting module is configured to: analyze the image to be processed to calculate a bright spot determination threshold, analyze the denoised image to obtain a candidate bright spot, and determine according to the bright spot The threshold determines whether the candidate bright spot is the bright spot.

The image processing system for sequencing the above-mentioned gene performs denoising processing on the image through the image preprocessing module, which can reduce the calculation amount of the bright spot detection module, and at the same time, determine whether the candidate bright spot is a bright spot through the bright spot judgment threshold, thereby improving the accuracy of determining the bright spot of the image. .

An image processing system for gene sequencing according to an embodiment of the present invention includes: a data input unit for inputting data; a data output unit for outputting data; and a storage unit 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 image processing system of the above gene sequencing can improve the accuracy of judging the bright spots of the image.

A computer readable storage medium for storing a program for execution by a computer, the executing the program comprising the method of any of the above embodiments. The computer readable storage medium storing the program can be used to detect bright spots and improve the accuracy of determining image highlights.

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 embodiments of the invention are from the following figures to embodiments The description will become obvious and easy to understand, where:

1 is a schematic flow chart of an image processing method for gene sequencing according to an embodiment of the present invention;

2 is another schematic flow chart of an image processing method for gene sequencing according to an embodiment of the present invention;

3 is a schematic flow chart of another embodiment of an image processing method for gene sequencing according to an embodiment of the present invention;

4 is a schematic diagram showing a Mexican hat filter of an image processing method for gene sequencing according to an embodiment of the present invention;

FIG. 5 is still another schematic flowchart of an image processing method for gene sequencing according to an embodiment of the present invention; FIG.

6 is still another flow chart of an image processing method for gene sequencing according to an embodiment of the present invention;

7 is a schematic diagram of 8-connected pixels in an image processing method for gene sequencing according to an embodiment of the present invention;

8 is still another flow chart of an image processing method for gene sequencing according to an embodiment of the present invention;

9 is a schematic diagram of an image to be processed of an image processing method for gene sequencing according to an embodiment of the present invention;

Figure 10 is a partial enlarged view of the image to be processed in Figure 9;

11 is a schematic diagram showing an image of a bright spot in an image processing method for gene sequencing according to an embodiment of the present invention;

Figure 12 is a partial enlarged view of the image identifying the bright spot in Figure 11;

13 is a block diagram showing an image processing system for gene sequencing according to an embodiment of the present invention;

14 is another block diagram of an image processing system for gene sequencing according to an embodiment of the present invention;

15 is another block diagram of an image processing system for gene sequencing according to an embodiment of the present invention;

16 is a block diagram showing still another module of an image processing system for gene sequencing according to an embodiment of the present invention;

17 is still another block diagram of an image processing system for gene sequencing according to 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 the terms "installation", "connected", and "connected" are to be understood broadly, and may be fixed or detachable, for example, unless otherwise explicitly defined and defined. Connected, or connected in one piece; may be mechanically connected, or may be electrically connected or may communicate with each other; They are directly connected, and may also be indirectly connected through an intermediate medium, which may be the internal communication of two elements or the interaction of two elements. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood on a case-by-case basis.

The present invention may be repeated with reference to the numerals and/or reference numerals in the various examples, which are for the purpose of simplification and clarity, and do not indicate the relationship between the various embodiments and/or settings discussed.

The "gene sequencing" and nucleic acid sequence determinations referred to in the embodiments of the present invention include DNA sequencing and/or RNA sequencing, including long fragment sequencing and/or short fragment sequencing.

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 an embodiment of the invention, the image is from a sequencing platform that uses optical imaging principles for sequencing, and the so-called sequencing platforms include, but are not limited to, sequencing platforms such as CG (Complete Genomics), Illumina/Solexa, Life Technologies ABI SOLiD, and Roche 454. The detection of the so-called "bright spot" is the detection of an optical signal of an extended base or a base cluster.

In one embodiment of the invention, the image is from a single molecule sequencing platform, such as Helicos, the input raw data is a parameter of a pixel point of the image, and the detection of the so-called "bright spot" is the detection of a single molecule optical signal.

Referring to FIG. 1 , an image processing method for gene sequencing according to an embodiment of the present invention includes: an image preprocessing step S11. The image preprocessing step S11 analyzes an input image to be processed to obtain a denoised image, and the image to be processed includes at least one image. a bright spot, the bright spot has at least one pixel; the bright spot detecting step S12, the bright spot detecting step S12 includes the steps of: S21, analyzing the image to be processed to calculate a bright spot determination threshold, S22, analyzing the denoised image to obtain a candidate bright spot, S23, determining a threshold according to the bright spot Determine if the candidate highlight is a bright spot. The image processing method of the above gene sequencing, the image denoising process is performed by the image preprocessing step, which can reduce the calculation amount of the bright spot detecting step, and at the same time, determine whether the candidate bright spot is a bright spot by 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 image processing method of gene sequencing can be simplified.

In the image processing method of the gene sequencing of some embodiments, referring to FIG. 2, the bright spot detecting step further includes the step of: if the determination result is yes, S24, calculating the intensity of the sub-pixel center coordinates and/or the sub-pixel center coordinates of the bright spot. If the result of the determination is no, S25, the candidate highlights are discarded. In this way, the accuracy of the image processing method can be further improved by sub-pixels to characterize the intensity values of the center coordinates and/or the center coordinates of the bright spots.

In the image processing method of gene sequencing of some embodiments, please refer to FIG. 3, image preprocessing step. S11 includes a simplified step S01 and an image filtering step S02. The simplified step S01 simplifies the image processing to be processed into a simplified image, and the image filtering step S02 filters the simplified image to obtain a denoised image.

Thus, the simplification step S01 can reduce the subsequent calculation amount of the image processing method of the gene sequencing, and the image filtering step S02 can acquire the denoising image under the condition that the image detail features are retained as much as possible, thereby improving the accuracy of the image processing method.

In the image processing method of gene sequencing of some embodiments, the simplified image is a binarized image, and the image filtering step S02 performs Mexican hat filtering on the binarized image. As such, binarized images are easier to handle and have a wide range of applications. Mexican hat filtering for binarized images is also easy to implement, reducing the cost of image processing methods for gene sequencing. At the same time, Mexican hat filtering can improve the contrast between the foreground and the background, making the foreground brighter and making the background darker.

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 the image processing method of gene sequencing of some embodiments, when performing Mexican hat filtering, Gaussian filtering is performed on the binarized image using the m*m window, and two-dimensional Laplacing is performed on the Gaussian filtered binary image. Sharpening, m is a natural number and is an odd number greater than one. Thus, Mexican hat filtering is achieved in two steps.

公式1，其中，x和y表示像素点的坐标。首先使用m*m窗口对二值化图像进行高斯滤波，如下公式2所示：

公式2，其中，t1和t2表示滤波窗口的位置，w_t1,t2表示高斯滤波的权重。然后对二值化图像进行二维拉普拉斯锐化，如下公式3所示：

公式3，其中，K和k均表示拉普拉斯算子，与锐化目标有关，如果需要加强锐化和减弱锐化，就修改K和k。Specifically, please refer to Figure 4. The Mexican hat core can be expressed as:

Equation 1, where x and y represent the coordinates of the pixel points. First, Gaussian filtering is performed on the binarized image using the m*m window, as shown in Equation 2 below:

Equation 2, where t1 and t2 represent the positions of the filtering window, and w _{t1, t2} represent the weights of the Gaussian filtering. The binarized image is then subjected to two-dimensional Laplacian sharpening, as shown in Equation 3 below:

Equation 3, where 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 2 becomes:

In the image processing method of the gene sequencing of some embodiments, referring to FIG. 5, before the step S01 is simplified, the image pre-processing step S11 further includes a subtraction background step S00, and a subtraction background step S00 performs background subtraction processing on the image to be processed, Subtract the background image to replace the image to be processed with the background image. In this way, the noise of the image to be processed can be further reduced, and the accuracy of the image processing method for gene sequencing is higher.

In the image processing method of gene sequencing of some embodiments, the simplification step acquires a signal to noise ratio matrix from the subtracted background image, and simplifies the subtraction of the background image according to the signal to noise ratio matrix to obtain a simplified image. In this way, a simplified image with less noise is realized, and the image processing method of gene sequencing is more accurate.

公式4，其中，x和y表示像素点的坐标，h表示图像的高度，w表示图像的宽度，i∈w,j∈h。Specifically, in one example, the signal to noise ratio matrix can be expressed as:

Equation 4, 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.

公式5。The binarized image can be obtained according to the signal to noise ratio matrix, and the binarized image is as shown in Equation 5:

Formula 5.

In the image processing method of the gene sequencing of some embodiments, the background processing of the image to be processed includes: determining an background of the image to be processed by using an opening operation, and performing background subtraction processing according to 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, smooth the boundaries of larger objects, and does not significantly change the image area, so that the background 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 6 and Equation 7 below: g(x,y)=erode[f(x,y),B]=min{f(x+x',y+y')-B( x', y')|(x', y') ∈ D _b } Equation 6, where g(x, y) is the etched grayscale image, f(x, y) is the original grayscale image, B Is a structural element; g(x,y)=dilate[f(x,y),B]=max{f(x-x',y-y')-B(x',y')|(x' , y') ∈ D _b } Equation 7, 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 = fg = {f (x, y) - g (x, y) | (x, y) ∈ D} Equation 9. Then find the ratio matrix of the background image and the background: R=f/g={f(x,y)/g(x,y)|(x,y)∈D}Formula 10, where D represents the image f Dimensions (height * width). From this we can find the SNR matrix:

公式11。

Formula 11.

In the image processing method of gene sequencing of some embodiments, the step of analyzing the image to be processed to calculate a bright spot determination threshold includes: processing the image to be processed by the Otsu method to calculate a bright spot determination threshold. In this way, the search for the bright spot determination threshold is realized by a more mature and simple method, thereby improving the accuracy of the image processing method for gene sequencing and reducing the cost of the image processing method for gene sequencing.

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 denoted as μ, and the variance between classes is denoted as var, then: μ = ω ₀ * μ ₀ + ω ₁ * μ ₁ Equation 12; var = ω ₀ (μ _{0 -} μ) ² + ω ₁ (μ ₁ -μ) ² Equation 13. Substituting Equation 12 into Equation 13 yields the equivalent equation 14: var = ω ₀ ω ₁ (μ _{1 -} μ ₀ ) ² Equation 14.

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 the image processing method of the gene sequencing of some embodiments, referring to FIG. 6, the step of determining whether the candidate bright spot is a bright spot according to the bright spot determination threshold includes: step S31, using image reconstruction based method, after performing Mexican hat filtering In the binarized image, find pixels that are larger than (m*m-1) connected and find the pixel points as the center of the candidate bright spots, m*m and the bright points are one-to-one correspondence, each in m*m The value corresponds to one pixel; in step S32, it is determined whether the center of the candidate bright spot satisfies the condition: I _max *A _BI *ceof _guass >T, where I _max is the strongest intensity of the center of the m*m window, and A _BI is m* The binarized image in the m window is the ratio of the set value, ceof _guass is the correlation coefficient of the pixel of the m*m window and the two-dimensional Gaussian distribution, and T is the bright spot determination threshold. If the above condition is met, S33 determines that the bright spot corresponding to the center of the candidate bright spot is a bright spot included in the image to be processed; if the above condition is not satisfied, S34, the bright spot corresponding to the center of the candidate bright spot is discarded. In this way, the detection of bright spots is achieved.

Specifically, I _max can be understood as the center strongest intensity of the candidate bright spot. In one example, m=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 of the center of the 3*3 window, A _BI is the ratio of the set value in the binarized image in the 3*3 window, ceof _guass is the pixel of the 3*3 window and the two-dimensional Gaussian distribution Correlation coefficient.

The set value in the binarized image may be a value corresponding to when the pixel point satisfies 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 m*m window. .

In the image processing method of gene sequencing of some embodiments, the step of calculating the intensity values of the sub-pixel center coordinates and/or the sub-pixel center coordinates of the bright points includes the steps of: calculating the sub-pixel center coordinates of the bright points by using quadratic function interpolation, And/or using quadratic spline interpolation to calculate the intensity values of the sub-pixel center coordinates. Thus, the method of quadratic function and/or quadratic spline can further improve the accuracy of judging the bright spot of the image.

In the image processing method of gene sequencing of some embodiments, referring to FIG. 8, the image processing method of gene sequencing further includes the step of: S13, using the identifier to mark the position of the image of the sub-pixel center coordinate of the bright spot. In this way, it is convenient for the user to observe whether the indication of the bright spot is correct, to determine whether it is necessary to re-light The positioning of the point.

Specifically, in one example, the position of the image at which the sub-pixel center coordinates of the bright spot are located is indicated by a cross. Referring to FIG. 9, FIG. 10, FIG. 11, and FIG. 12, FIG. 9 is an image to be positioned, and FIG. 10 is an enlarged schematic view of a range of 293*173 in the upper left corner of the image shown in FIG. Fig. 11 is an image showing a bright spot (after highlight positioning) with a cross, and Fig. 12 is an enlarged schematic view showing a range of 293*173 in the upper left corner of the image shown in Fig. 11.

Referring to FIG. 13, an image sequencing system 100 for gene sequencing according to an embodiment of the present invention includes: an image preprocessing module 102, which is configured to analyze an input image to be processed to obtain a denoised image, to be processed. The image includes at least one bright spot, and the bright spot has at least one pixel; the bright spot detecting module 104 is configured to: analyze the image to be processed to calculate a bright spot determination threshold, analyze the denoised image to obtain the candidate bright spot, and determine the threshold according to the bright spot determination threshold. Whether the candidate highlights are bright spots. The image processing system 100 of the gene sequencing performs denoising processing on the image by the image preprocessing module 102, which can reduce the calculation amount of the bright spot detecting module 104, and at the same time, determine whether the candidate bright spot is a bright spot by using the bright spot judgment threshold, thereby improving the judgment image bright spot. The accuracy.

It should be noted that the above explanation of the embodiment of the image processing method for gene sequencing is also applicable to the image processing system 100 for gene sequencing according to the embodiment of the present invention, and in order to avoid redundancy, it will not be developed in detail here.

In the image processing system 100 for gene sequencing of some embodiments, the bright spot detection module 104 is further configured to: if the determination result is yes, calculate the intensity value of the sub-pixel center coordinate and/or the sub-pixel center coordinate of the bright spot, if the determination result If no, discard the candidate highlights. As such, the accuracy of the image processing system 100 can be further improved by characterizing the intensity values of the center coordinates and/or the center coordinates of the bright dots by the sub-pixels.

In the image-sequencing system 100 of the genetic sequencing of certain embodiments, referring to FIG. 14, the image pre-processing module 102 includes a simplification module 106 and an image filtering module 108.

The simplification module 106 is for simplifying the image to be processed into a simplified image, and the image filtering module 108 is for filtering the simplified image to obtain a denoised image. As such, the simplification module 106 can reduce the amount of computation of the image processing system 100 for gene sequencing. The image filtering module 108 can acquire the denoised image under the condition that the image detail features are retained as much as possible, thereby improving the accuracy of the image processing system 100.

In the image-sequencing system 100 of the genetic sequencing of certain embodiments, the simplified image is a binarized image, and the image filtering module 108 performs Mexican hat filtering on the binarized image. As such, binarized images are easier to handle and have a wide range of applications. Mexican hat filtering of binarized images is also easy to implement, reducing the cost of image sequencing system 100 for gene sequencing. At the same time, Mexican hat filtering can enhance the contrast between foreground and background, making the foreground brighter and making the background darker.

In the image sequencing system 100 of the genetic sequencing of certain embodiments, the image filtering module 108 uses In the Mexican hat filtering, the m*m window is used to perform Gaussian filtering on the binarized image, and the Gaussian filtered binarized image 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.

In the image processing system 100 of the gene sequencing of some embodiments, referring to FIG. 15, the image pre-processing module 102 further includes a subtraction background module 110 for performing background subtraction processing on the image to be processed to obtain a subtractive background image. , replace the image to be processed with the subtraction background image. In this way, the noise of the image to be processed can be further reduced, and the accuracy of the image-sequencing system 100 for gene sequencing is higher.

In the image-sequencing system 100 of the genetic sequencing of certain embodiments, the simplification module 106 is configured to acquire a signal-to-noise ratio matrix from the subtracted background image and to simplify the subtracted background image from the signal-to-noise ratio matrix to obtain a simplified image. In this way, a simplified image with less noise is achieved, and the accuracy of the image sequencing system 100 for gene sequencing is higher.

In the image-sequencing system 100 of the genetic sequencing of some embodiments, the subtraction background module 110 is configured to: determine an background of the image to be processed by using an open operation, and perform 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, smooth the boundaries of larger objects, and does not significantly change the image area, so that the background image can be acquired more accurately.

In the image processing system of the genetic sequencing of certain embodiments, the bright spot detection module 104 is configured to process the image to be processed by the Otsu method to calculate a bright spot determination threshold. In this way, the search for the bright spot determination threshold is realized by a more mature and simple method, thereby improving the accuracy of the image sequencing system 100 for gene sequencing and reducing the cost of the image processing system 100 for gene sequencing.

In the image processing system of the gene sequencing of some embodiments, the bright spot detection module 104 is configured to: use the image reconstruction based method to find the greater than (m*m-1) in the binarized image after the Mexican hat filtering. Connected pixels and the found pixel as the center of the candidate bright spot, m*m and the bright point are in one-to-one correspondence, each value in m*m corresponds to one pixel; and it is determined whether the center of the candidate bright spot satisfies the condition: I _max *A _BI *ceof _guass >T, where I _max is the strongest intensity at the center of the m*m window, and A _BI is the ratio of the set value in the binarized image in the m*m window, ceof _guass For the pixel of the m*m window and the correlation coefficient of the two-dimensional Gaussian distribution, T is the bright point determination threshold. If the above condition is met, the bright spot corresponding to the center of the candidate bright spot is determined to be a bright spot. If the above condition is not met, the center of the candidate bright spot is discarded. Corresponding highlights. In this way, the detection of bright spots is achieved.

In the image sequencing system 100 of the genetic sequencing of some embodiments, the bright spot detection module 104 is configured to: calculate the sub-pixel center coordinates of the bright point using quadratic function interpolation, and/or calculate the sub-pixel center coordinate using the quadratic spline interpolation. Strength value. Thus, the method of quadratic function and/or quadratic spline can further improve the accuracy of judging the bright spot of the image.

In the image processing system 100 of the gene sequencing of some embodiments, referring to FIG. 16, the image processing system 100 for gene sequencing includes an identification module 112 for: using an identifier to indicate an image of a sub-pixel center coordinate of a bright spot. s position. In this way, it is convenient for the user to observe whether the indication of the bright spot is correct, to determine whether the positioning of the bright spot needs to be performed again.

Referring to FIG. 17, a gene sequencing image processing system 300 according to an embodiment of the present invention includes: a data input unit 302 for inputting data; a data output unit 304 for outputting data; and a storage unit 306 for storing data. The data includes a computer executable program; a processor 308 for executing the computer executable program, the executing the computer executable program comprising performing the method of any of the above embodiments. The image processing system 300 for sequencing the above genes can improve the accuracy of judging the bright spots of the image.

A computer readable storage medium for storing a program for execution by a computer, the executing the program comprising the method of any of the above embodiments. The above computer readable storage medium can improve the accuracy of judging image highlights. A computer readable storage medium may be any apparatus that can contain, store, communicate, propagate, or transport the program for use by the instruction execution system, apparatus, or device, or in conjunction with the instruction execution system, apparatus, or device. More specific examples (non-exhaustive list) of computer readable media include the following: electrical connections (electronic devices) having one or more wires, portable computer disk cartridges (magnetic devices), random access memory (RAM), Read only memory (ROM), erasable editable read only memory (EPROM or flash memory), fiber optic devices, and portable compact disk read only memory (CDROM). In addition, the computer readable medium may even be a paper or other suitable medium on which the program can be printed, as it may be optically scanned, for example by paper or other medium, followed by editing, interpretation or, if appropriate, other suitable The method is processed to obtain the program electronically and then stored in computer memory. The above mentioned storage medium may be a read only memory, a magnetic disk or an optical disk or 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. Particular features, structures, materials or features 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.

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 (26)

An image processing method for gene sequencing, comprising: An image preprocessing step, the image preprocessing step analyzing the input image to be processed to obtain a denoised image, the image to be processed comprising at least one bright point, the bright point having at least one pixel; a bright spot detecting step, the bright spot detecting step comprising the steps of: Analyzing the image to be processed to calculate a bright spot determination threshold, Analyzing the denoised image to obtain candidate highlights, Determining, according to the bright spot determination threshold, whether the candidate bright spot is the bright spot. The image processing method for gene sequencing according to claim 1, wherein the bright spot detecting step further comprises the steps of: If the determination result is yes, calculate a sub-pixel center coordinate of the bright point and/or an intensity value of the sub-pixel center coordinate, If the result of the determination is no, the candidate highlights are discarded. The image processing method for gene sequencing according to claim 1, wherein the image preprocessing step comprises a simplification step and an image filtering step, The simplification step simplifies the image to be processed into a simplified image, The image filtering step filters the simplified image to obtain the denoised image. The image processing method for gene sequencing according to claim 3, wherein the simplified image is a binarized image, and the image filtering step performs Mexican hat filtering on the binarized image. The image processing method for gene sequencing according to claim 4, wherein when performing mexican cap filtering, Gaussian filtering is performed on the binarized image using a m*m window, and the binarized image after Gaussian filtering is performed. Perform two-dimensional Laplacian sharpening, where m is a natural number and is an odd number greater than one. The image processing method for gene sequencing according to claim 3, wherein before the simplifying step, the image preprocessing step further comprises a subtraction background step, wherein the subtracting background step subtracts the image to be processed The background processing obtains a subtractive background image, and replaces the image to be processed with the reduced background image. The image processing method for gene sequencing according to claim 6, wherein the simplification step acquires a signal to noise ratio matrix according to the subtraction background image, and simplifies the subtraction background image according to the signal to noise ratio matrix to obtain The simplified image. The image processing method for gene sequencing according to claim 6, 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. The image processing method for gene sequencing according to any one of claims 1 to 8, wherein the step of analyzing the image to be processed to calculate a bright spot determination threshold comprises: The image to be processed is processed by the Otsu method to calculate the highlight determination threshold. The image processing method for gene sequencing according to claim 5, wherein the step of determining whether the candidate highlight is the bright spot according to the brightness determination threshold comprises: Using the image reconstruction-based method, in the binarized image after the Mexican hat filtering, searching for pixels that are greater than (m*m-1) connected and using the found pixel points as the candidate bright spots center; 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 m*m window, and A _BI is the binarization in the m*m window The ratio of the set value in the image, ceof _guass is the correlation coefficient of the pixel of the m*m window and the two-dimensional Gaussian distribution, and T is the threshold of the bright spot 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 bright spot. If the above conditions are not met, the corresponding bright spot of the center of the candidate highlight is discarded. The image processing method for gene sequencing according to claim 2, wherein the step of calculating a sub-pixel center coordinate of the bright point and/or an intensity value of the sub-pixel center coordinate comprises: The sub-pixel center coordinates of the bright point are calculated using quadratic function interpolation, and/or the intensity values of the sub-pixel center coordinates are calculated using quadratic spline interpolation. The image processing method for gene sequencing according to claim 2, further comprising the steps of: The position of the image in which the sub-pixel center coordinates of the bright spot are located is indicated by the marker. An image processing system for gene sequencing, comprising: An image preprocessing module, configured to analyze an input image to be processed to obtain a denoised image, the image to be processed includes at least one bright point, the bright point having at least one pixel; a bright spot detection module, the bright spot detection module is configured to: Analyzing the image to be processed to calculate a bright spot determination threshold, Analyzing the denoised image to obtain candidate highlights, Determining, according to the bright spot determination threshold, whether the candidate bright spot is the bright spot. The image processing system for gene sequencing according to claim 13, wherein the bright spot detection module is further configured to: If the determination result is yes, calculate a sub-pixel center coordinate of the bright point and/or an intensity value of the sub-pixel center coordinate, If the result of the determination is no, the candidate highlights are discarded. The image processing system for gene sequencing according to claim 13, wherein the image preprocessing module comprises a simplification module and an image filtering module. The simplification module is for simplifying the image to be processed into a simplified image, The image filtering module is configured to filter the simplified image to obtain the denoised image. The image processing system for gene sequencing according to claim 15, wherein the simplified image is a binarized image, and the image filtering module performs Mexican hat filtering on the binarized image. The image processing system for gene sequencing according to claim 16, wherein the image filtering module is configured to perform Gaussian filtering on the binarized image using an m*m window when performing mexican cap filtering, The Gaussian filtered binarized image is subjected to two-dimensional Laplacian sharpening, where m is a natural number and is an odd number greater than one. The image processing system for gene sequencing according to claim 15, wherein the image preprocessing module further comprises a subtraction background module, wherein the subtraction background module is configured to perform background subtraction processing on the image to be processed, and obtain a subtraction. a background image in which the image to be processed is replaced with the reduced background image. The image processing system for gene sequencing according to claim 18, wherein the simplification module is configured to acquire a signal to noise ratio matrix according to the subtraction background image, and simplify the subtraction background image according to the signal to noise ratio matrix To obtain the simplified image. The image processing system for gene sequencing according to claim 18, wherein the subtraction background module is configured to: 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. The image processing system for gene sequencing according to any one of claims 13 to 20, wherein the bright spot detection module is configured to process the image to be processed by the Otsu method to calculate the bright spot determination threshold. The image processing system for gene sequencing according to claim 17, wherein the bright spot detection module is configured to: Using the image reconstruction-based method, in the binarized image after the Mexican hat filtering, searching for pixels that are greater than (m*m-1) connected and using the found pixel points as the candidate bright spots Center, m*m and the bright point are one-to-one correspondence, and each value in m*m corresponds to one pixel point; 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 m*m window, and A _BI is the binarization in the m*m window In the image, the ratio of the set value, ceof _guass is the correlation coefficient of the pixel of the m*m 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 bright spot. If the above conditions are not met, the corresponding bright spot of the center of the candidate highlight is discarded. The image processing system for gene sequencing according to claim 14, wherein the bright spot detection module is configured to: The sub-pixel center coordinates of the bright point are calculated using quadratic function interpolation, and/or the intensity values of the sub-pixel center coordinates are calculated using quadratic spline interpolation. The image processing system for gene sequencing according to claim 14, wherein the image processing system for gene sequencing comprises an identification module, and the identification module is configured to: The position of the image in which the sub-pixel center coordinates of the bright spot are located is indicated by the marker. An image processing system for gene sequencing, comprising: a data input unit for inputting data; a data output unit for outputting data; a storage unit 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 1-12. A computer readable storage medium for storing a program for execution by a computer, the executing the program comprising performing the method of any of claims 1-12.

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