WO2018068511A1 - Procédé et système de traitement d'image pour séquençage de gènes - Google Patents

Procédé et système de traitement d'image pour séquençage de gènes Download PDF

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WO2018068511A1
WO2018068511A1 PCT/CN2017/085439 CN2017085439W WO2018068511A1 WO 2018068511 A1 WO2018068511 A1 WO 2018068511A1 CN 2017085439 W CN2017085439 W CN 2017085439W WO 2018068511 A1 WO2018068511 A1 WO 2018068511A1
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image
bright spot
gene sequencing
processed
image processing
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PCT/CN2017/085439
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Chinese (zh)
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徐伟彬
金欢
颜钦
姜泽飞
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深圳市瀚海基因生物科技有限公司
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • G—PHYSICS
    • G02—OPTICS
    • G02B—OPTICAL ELEMENTS, SYSTEMS OR APPARATUS
    • G02B21/00—Microscopes
    • G02B21/36—Microscopes arranged for photographic purposes or projection purposes or digital imaging or video purposes including associated control and data processing arrangements
    • G02B21/365—Control or image processing arrangements for digital or video microscopes
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/70—Denoising; Smoothing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/11—Region-based segmentation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/136—Segmentation; Edge detection involving thresholding
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B25/00—ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10056—Microscopic image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20024—Filtering details
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G16B40/10—Signal processing, e.g. from mass spectrometry [MS] or from PCR

Definitions

  • 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.
  • 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.
  • 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 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.
  • 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.
  • 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.
  • 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;
  • 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.
  • 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 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 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.
  • the computer readable storage medium storing the program can be used to detect bright spots and improve the accuracy of determining image highlights.
  • FIG. 1 is a schematic flow chart of an image processing method for gene sequencing according to an embodiment of the present invention
  • FIG. 2 is another schematic flow chart of an image processing method for gene sequencing according to an embodiment of the present invention.
  • FIG. 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.
  • FIG. 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.
  • FIG. 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.
  • FIG. 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;
  • FIG. 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;
  • FIG. 13 is a block diagram showing an image processing system for gene sequencing according to an embodiment of the present invention.
  • FIG. 14 is another block diagram of an image processing system for gene sequencing according to an embodiment of the present invention.
  • FIG. 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.
  • FIG 17 is still another block diagram of an image processing system for gene sequencing according to an embodiment of the present invention.
  • 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.
  • features defining “first” or “second” may include one or more of the described features either explicitly or implicitly.
  • the meaning of "a plurality" is two or more unless specifically and specifically defined otherwise.
  • connection is 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.
  • intermediate medium which may be the internal communication of two elements or the interaction of two elements.
  • 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.”
  • the image is from a sequencing platform that uses optical imaging principles for sequencing
  • 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.
  • 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
  • the detection of the so-called "bright spot” is the detection of a single molecule optical signal.
  • an image processing method for gene sequencing 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. .
  • 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.
  • 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.
  • 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
  • the image filtering step S02 filters the simplified image to obtain a denoised image.
  • 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.
  • the simplified image is a binarized image
  • the image filtering step S02 performs Mexican hat filtering on the binarized image.
  • 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.
  • Mexican hat filtering can improve the contrast between the foreground and the background, making the foreground brighter and making the background darker.
  • 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:
  • 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.
  • Mexican hat filtering is achieved in two steps.
  • the Mexican hat core can be expressed as: Equation 1, where x and y represent the coordinates of the pixel points.
  • 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.
  • Equation 2 when performing Gaussian filtering, Equation 2 becomes:
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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) is the etched grayscale image
  • f(x, y) is the original grayscale image
  • B Is a structural element;
  • 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.
  • 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.
  • the Otsu method 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.
  • 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 ⁇ 0
  • the average gradation is ⁇ 0
  • the ratio of the number of pixels belonging to the background to the entire image is ⁇ 1
  • the average gray level is ⁇ 1 .
  • 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.
  • 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
  • T is the bright spot determination threshold.
  • 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.
  • I max can be understood as the center strongest intensity of the candidate bright spot.
  • 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.
  • 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. .
  • 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.
  • the method of quadratic function and/or quadratic spline can further improve the accuracy of judging the bright spot of the image.
  • 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.
  • FIG. 9 is an image to be positioned
  • 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
  • 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.
  • an image sequencing system 100 for gene sequencing 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 is improved.
  • 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.
  • 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.
  • 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.
  • the simplified image is a binarized image
  • the image filtering module 108 performs Mexican hat filtering on the binarized image.
  • 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.
  • Mexican hat filtering can enhance the contrast between foreground and background, making the foreground brighter and making the background darker.
  • 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.
  • Mexican hat filtering is achieved in two steps.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • the method of quadratic function and/or quadratic spline can further improve the accuracy of judging the bright spot of the image.
  • 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.
  • a gene sequencing image processing system 300 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.
  • 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).
  • 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.

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Abstract

L'invention concerne un procédé et un système de traitement d'image pour séquençage de gènes. Le procédé de traitement d'image pour séquençage de gènes comprend : une étape de prétraitement d'image dans laquelle une image d'entrée à traiter est analysée de façon à éliminer le bruit de l'image à traiter ; et une étape de détection de point brillant, l'étape comprenant les étapes consistant à : analyser l'image à traiter de manière à calculer une valeur seuil de détermination de point lumineux ; et analyser l'image à traiter, dont le bruit est éliminé, de manière à acquérir un point de pixel candidat, et à déterminer si le point de pixel candidat est un point lumineux selon la valeur seuil de détermination de point lumineux, si tel est le cas, calculer une coordonnée centrale de sous-pixel du point lumineux et une valeur d'intensité de la coordonnée centrale de sous-pixel, et si tel n'est pas le cas, abandonner le point de pixel candidat. Dans le procédé de traitement d'image pour séquençage de gène, un traitement de débruitage est effectué sur une image par l'intermédiaire d'une étape de prétraitement d'image, qui peut réduire la quantité de calcul d'une étape de détection de point lumineux, et en même temps, une valeur de seuil de détermination de point lumineux est utilisée pour déterminer si un point lumineux candidat est un point lumineux, ce qui peut améliorer la précision de détermination d'un point lumineux d'image.
PCT/CN2017/085439 2016-10-10 2017-05-23 Procédé et système de traitement d'image pour séquençage de gènes WO2018068511A1 (fr)

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PCT/CN2017/101054 WO2018068599A1 (fr) 2016-10-10 2017-09-08 Procédé et système de traitement d'image pour le séquençage des gènes
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