WO2018068599A1 - 基因测序的图像处理方法及*** - Google Patents

基因测序的图像处理方法及*** Download PDF

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WO2018068599A1
WO2018068599A1 PCT/CN2017/101054 CN2017101054W WO2018068599A1 WO 2018068599 A1 WO2018068599 A1 WO 2018068599A1 CN 2017101054 W CN2017101054 W CN 2017101054W WO 2018068599 A1 WO2018068599 A1 WO 2018068599A1
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image
bright spot
processed
gene sequencing
image processing
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PCT/CN2017/101054
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English (en)
French (fr)
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徐伟彬
金欢
颜钦
姜泽飞
周志良
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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-mentioned gene sequencing, the image denoising process is performed by the image pre-processing step, the calculation amount of the bright spot detection step can be reduced, and at the same time, whether the candidate bright spot is a bright spot is determined by the bright spot judgment threshold, and the bright spot of the image can be improved. accuracy.
  • 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 in the second-generation sequencing are generally array-type, that is, the probes on the sequencing chip are Regularly arranged, the image obtained by photographing is a pattern image, which is 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 identify and locate.
  • 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 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;
  • the bright spot detection 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 determination threshold, whether the candidate bright spot is the Highlights.
  • the image processing system of the above-mentioned gene sequencing uses the image preprocessing module to denoise the image, 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 point judgment threshold, thereby improving the judgment of the image bright spot. Quasi Authenticity.
  • 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 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. Therefore, the above computer readable storage medium can improve the accuracy of judging 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 still another flow chart of an image processing method for gene sequencing according to an embodiment of the present invention.
  • FIG. 8 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 still another flow chart of an image processing method for gene sequencing according to an embodiment of the present invention.
  • FIG. 10 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 11 is a partial enlarged view of the image to be processed in Figure 10;
  • FIG. 12 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 13 is a partial enlarged view of the image identifying the bright spot in Figure 12;
  • FIG. 14 is a block diagram showing 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 another block diagram 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.
  • FIG. 18 is another schematic diagram of another module of the image processing system for gene sequencing according to an embodiment of the present invention.
  • FIG. 19 is another block diagram of an image processing system for gene sequencing according to an embodiment of the present invention.
  • FIG 20 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 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 integrally connected; may be mechanically connected, or may be electrically connected or may communicate with each other; may be directly connected or indirectly connected through an intermediate medium, may be internal communication of two elements or interaction of two elements relationship. 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 "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.
  • the image pre-processing step S11 includes an image filtering step S02, and the image filtering step S02 filters the image to be processed to obtain a denoised image.
  • the image filtering step S02 can obtain 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 image filtering step S02 performs Mexican hat filtering on the image to be processed.
  • Mexican hat filtering is 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.
  • Gaussian filtering is performed on the image to be processed using the m*m window, and two-dimensional Laplacian sharpening is performed on the image to be processed after Gaussian filtering.
  • m is a natural number and is an odd number greater than one.
  • the Mexican hat core can be expressed as:
  • Equation 2 Gaussian filtering is performed on the image to be processed using the m*m window, as shown in Equation 2 below:
  • t1 and t2 represent the positions of the filtering window
  • w t1, t2 represent the weights of the Gaussian filtering
  • Equation 3 The processed image is then subjected to two-dimensional Laplacian sharpening, as shown in Equation 3 below:
  • 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 to perform background subtraction processing on the image to be processed,
  • the background image is subtracted 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 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, and smooth the boundaries of larger objects without significantly changing the image area, so that the background image can be acquired more accurately.
  • the a*a window (for example, 15*15) is moved in the image f(x, y) to be processed (such as a grayscale image). Window), using the open operation (corrosion and re-expansion) to estimate the background of the image to be processed, as shown in Equation 6 and Equation 7 below:
  • g(x, y) is the expanded grayscale image
  • f(x, y) is the original grayscale image
  • B is the structural element
  • the image processing method includes a simplified step S01, which simplifies the denoised image into a simplified image, and the simplified image replacement Denoising the image. In this way, the amount of calculation of subsequent image processing can be reduced.
  • the simplified image is a binarized image. Binarized images are easy to handle and have a wide range of applications.
  • the simplification step acquires a signal to noise ratio matrix from the denoised image and simplifies the denoised image according to the signal to noise ratio matrix to obtain the reduced image.
  • the image to be processed may be subtracted from the background, and then the signal to noise ratio matrix may be obtained from the subtracted background image. In this way, it is advantageous for the subsequent obtaining information from the image with less noise, and the accuracy of the processing result obtained by the image processing method can be made higher.
  • the signal to noise ratio matrix can be expressed as: 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 simplified image is a binarized image
  • the binarized image can be obtained from the signal to noise ratio matrix.
  • the binarized image is as shown in Equation 5:
  • the image to be processed may be filtered and/or subtracted from the background.
  • the filtering step and the background subtraction process described in the above embodiment are obtained according to the subtractive background processing, and then the subtraction background is obtained.
  • the ratio matrix of the image to the background is obtained according to the subtractive background processing, and then the subtraction background is obtained.
  • 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 of the bright spot determination threshold is realized by a relatively mature and simple method, thereby improving the accuracy of the image processing method and reducing the cost of the image processing method.
  • 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 total average gray level of the image to be processed is recorded as ⁇
  • the variance between classes is recorded as var, which is:
  • Equation 12 Substituting Equation 12 into Equation 13 yields the equivalent formula 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.
  • the image processing method includes: processing the denoising map by the Otsu method Like to calculate the bright point decision threshold. Using the denoising image to search for the bright spot determination threshold can improve the efficiency and accuracy of the image processing method for gene sequencing.
  • the process of the Otsu method can be referred to the process of the Otsu method of the above embodiment.
  • the image processing method includes a simplified step S01, which includes simplifying the denoising image to obtain a simplified image to simplify the image instead of the denoising image. .
  • the step of determining whether the candidate bright spot is a bright spot according to the bright spot determination threshold includes: step S31, searching for a pixel point larger than (p*p-1) connected in the simplified image and using the found pixel point as a center of the candidate bright spot, p* p is one-to-one corresponding to the bright point, each value in p*p corresponds to one pixel, p is a natural number and is an odd number greater than 1; 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 in the center of the p*p window, A BI is the ratio of the set values in the simplified image in the p*p window, and ceof guass is the pixel of the p*p window and The correlation coefficient of the two-dimensional Gaussian distribution, and T is the bright point 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.
  • p 3 looking for pixels that are greater than 8 connected, as shown in Figure 8. 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 simplified image in the 3*3 window
  • ceof guass is the correlation coefficient of the pixel of the 3*3 window and the two-dimensional Gaussian distribution. .
  • the simplified processing may be binarization processing, that is, the filtered image to be processed may be binarized to obtain a binarized image, and the set value in the binarized image may be that the pixel meets the set condition.
  • the value corresponding to the time 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 p*p 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 the positioning of the bright spot needs to be performed again.
  • FIG. 10 is an image to be positioned
  • FIG. 11 is an enlarged schematic view of a range of 293*173 in the upper left corner of the image shown in FIG. Fig. 12 is an image showing a bright spot (after highlight positioning) with a cross
  • Fig. 13 is an enlarged schematic view showing a range of 293*173 in the upper left corner of the image shown in Fig. 12.
  • an image sequencing system 100 for gene sequencing includes: an image preprocessing module 102 for analyzing 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 for sequencing the above-mentioned gene performs denoising processing on the image by the image preprocessing module 102, which can reduce the calculation amount of the bright spot detection module 104, and at the same time, determine whether the candidate bright spot is a bright spot through the bright spot determination threshold, thereby improving the judgment.
  • the accuracy of the image highlights are provided.
  • 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 an image filtering module 108.
  • the image filtering module 108 is configured to filter the image to be processed to obtain a denoised image.
  • 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 image filtering module 108 performs Mexican hat filtering on the image to be processed.
  • Mexican hat filtering is easy to implement, reducing the cost of the image sequencing system 100 for gene sequencing.
  • the Mexican hat filter can enhance the contrast between the foreground and the background, making the foreground brighter and the background darker.
  • the image filtering module 108 is used to perform a Mexican cap When filtering, Gaussian filtering is performed on the image to be processed using the m*m window, and the two-dimensional Laplacian sharpening is performed on the image to be processed after Gaussian filtering, where m is a natural number and is an odd number greater than 1.
  • Gaussian filtering is performed on the image to be processed using the m*m window
  • two-dimensional Laplacian sharpening is performed on the image to be processed after Gaussian filtering, where m is a natural number and is an odd number greater than 1.
  • the image preprocessing module 102 further includes a subtraction background module 110 for performing background subtraction processing on the image to be processed before filtering. , obtain a subtraction background image, and subtract the background image to replace the image to be processed. 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 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 image processing system 100 includes a simplification module 106 for simplifying the denoised image into a simplified image to simplify image replacement of the denoised image.
  • the simplification module 106 can reduce the amount of computation of the image processing system 100 for gene sequencing,
  • the simplified image is a binarized image.
  • binarized images are easier to handle and have a wide range of applications.
  • the simplification module 106 is operative to acquire a signal to noise ratio matrix from the denoised image and to simplify the denoised image from the signal to noise ratio matrix to obtain a simplified image.
  • 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 process the denoised image 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 image processing system 100 includes a simplification module 106 that simplifies the denoised image to obtain a simplified image to simplify image replacement for denoising. image.
  • the bright spot detection module 104 is configured to: find a pixel point larger than (p*p-1) connected in the simplified image and use the found pixel point as the center of the candidate bright spot, p is a natural number and is an odd number greater than 1; Whether the center satisfies the condition: I max *A BI *ceof guass >T, where I max is the strongest intensity in the center of the p*p window, and A BI is the set value in the simplified image in the p*p window The ratio, ceof guass is the correlation coefficient between the pixel of the p*p window and the two-dimensional Gaussian distribution, and 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 discard is omitted. The bright spot corresponding to the center of the candidate highlight. 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.
  • an image sequencing system 300 for gene sequencing 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; the processor 308 for executing a computer executable program, and the executing the computer executable program includes the method of performing any of the above embodiments. Therefore, the image processing system 300 of the above-described 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 program comprising the method of any of the above embodiments. Therefore, the above computer readable storage medium can improve the accuracy of judging image highlights.
  • 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.
  • the integrated modules, if implemented in the form of software functional modules and sold or used as stand-alone products, may also be stored in a computer readable storage medium.
  • 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

一种基因测序的图像处理方法及***及计算机可读存储介质，基因测序的图像处理方法包括：图像预处理步骤，该图像预处理步骤分析输入的待处理图像以去掉该待处理图像的噪声；亮点检测步骤，该亮点检测步骤包括步骤：分析该待处理图像以计算亮点判定阈值；分析去掉该噪声的该待处理图像以获取候选像素点，并根据该亮点判定阈值判断该候选像素点是否为亮点，若是，计算该亮点的亚像素中心坐标及该亚像素中心坐标的强度值，若否，丢弃该候选像素点。因此，上述基因测序的图像处理方法，通过图像预处理步骤对图像进行去噪处理，可减少亮点检测步骤的计算量，同时，通过亮点判断阈值判断候选亮点是否为亮点，可提高判断图像亮点的准确性。

Description

基因测序的图像处理方法及***
优先权信息
本申请请求2016年10月10日递交至中国国家知识产权局、专利申请号为201610882547.8的在先申请的优先权和权益，并且通过参照将其全文并入此处。
技术领域
本发明涉及基因测序技术领域，尤其涉及一种基因测序的图像处理方法及***及计算机可读存储介质。
背景技术
在相关技术中，图像亮度定位在基因测序仪和LED灯光点中都有重要应用。
在利用光学成像原理进行序列测定的***中，图像分析是很重要的一块。图像亮度定位的准确性直接决定了基因测序的准确性。
在核酸序列测定的过程中，如何提高判断图像亮点的准确性，成为待解决的问题之一。
发明内容
本发明实施方式旨在至少解决现有技术中存在的技术问题之一。为此，本发明实施方式需要提供一种基因测序的图像处理方法及***及计算机可读存储介质。
本发明实施方式的一种基因测序的图像处理方法，包括：图像预处理步骤，该图像预处理步骤分析输入的待处理图像以去掉该待处理图像的噪声；亮点检测步骤，该亮点检测步骤包括步骤：分析该待处理图像以计算亮点判定阈值；分析去掉该噪声的该待处理图像以获取候选像素点，并根据该亮点判定阈值判断该候选像素点是否为亮点，若是，计算该亮点的亚像素中心坐标及该亚像素中心坐标的强度值，若否，丢弃该候选像素点。
因此，上述基因测序的图像处理方法，通过图像预处理步骤对图像进行去噪处理，可减少亮点检测步骤的计算量，同时，通过亮点判断阈值判断候选亮点是否为亮点，可提高判断图像亮点的准确性。
本发明的这一图像处理方法，对待处理图像即原始输入数据的没有特别的限制，适用于任何利用光学检测原理进行核酸序列测定的平台所产生的图像的处理分析，包括但不限于二代和三代测序，具有高准确性、高通用性和高精度的特点，能从图像中获取更多的有效信息。
特别地，目前，已知的测序图像处理方法和/***基本是针对二代测序平台的图像处理开发的，由于二代测序使用的测序芯片一般是阵列型的，即测序芯片上的探针是规则排列的，拍照获得的图像是模式(pattern)图像，易于处理分析；另外，由于二代测序一般包含核酸模板扩增放大，图像采集时能够获得高强度的亮点，易于识别和定位。一般的二代测序的图像处理方法不要求高的定位精度，只需要挑选定位一些发光较强较好的点(亮点)，就能实现序列测定。
而对于三代测序即单分子测序，受限于目前芯片表面处理相关技术的发展，其使用的测序芯片是随机型的，即测序芯片上的探针是无规则排列，拍照获得的图像是随机(random)图像，不易处理分析；而且，单分子测序的图像处理分析是决定最终序列(reads)的有效率的最重要的因素之一，对图像处理、亮点定位的要求高，要求图像上的所有亮点都能准确定位，以使能够直接识别出碱基，产生数据信息。
因此，本发明的图像处理方法可适应用于二代测序和三代测序，特别是对于三代测序中的随机图像及高精度要求的图像处理，尤其具有优势。
本发明实施方式的一种基因测序的图像处理***，包括：
图像预处理模块，所述图像预处理模块用于分析输入的待处理图像以获得去噪图像，所述待处理图像包含至少一个亮点，所述亮点具有至少一个像素点；
亮点检测模块，所述亮点检测模块用于：分析所述待处理图像以计算亮点判定阈值，分析所述去噪图像以获取候选亮点，根据所述亮点判定阈值判断所述候选亮点是否为所述亮点。
因此，上述基因测序的图像处理***，通过图像预处理模块对图像进行去噪处理，可减少亮点检测模块的计算量，同时，通过亮点判断阈值判断候选亮点是否为亮点，可提高判断图像亮点的准 确性。
本发明实施方式的一种基因测序的图像处理***，包括：
数据输入单元，用于输入数据；
数据输出单元，用于输出数据；
存储单元，用于存储数据，所述数据包括计算机可执行程序；
处理器，用于执行所述计算机可执行程序，执行所述计算机可执行程序包括完成如上任一实施方式所述的方法。
因此，上述基因测序的图像处理***可提高判断图像亮点的准确性。
本发明实施方式的一种计算机可读存储介质，用于存储供计算机执行的程序，执行所述程序包括完成如上任一实施方式所述的方法。因此，上述计算机可读存储介质可提高判断图像亮点的准确性。
本发明实施方式的附加方面和优点将在下面的描述中部分给出，部分将从下面的描述中变得明显，或通过本发明实施方式的实践了解到。
附图说明
本发明实施方式的上述和/或附加的方面和优点从结合下面附图对实施方式的描述中将变得明显和容易理解，其中：
图1是本发明实施方式的基因测序的图像处理方法的流程示意图；
图2是本发明实施方式的基因测序的图像处理方法的另一流程示意图；
图3是本发明实施方式的基因测序的图像处理方法的再一流程示意图；
图4是本发明实施方式的基因测序的图像处理方法的墨西哥帽滤波的曲线示意图；
图5是本发明实施方式的基因测序的图像处理方法的又一流程示意图；
图6是本发明实施方式的基因测序的图像处理方法的又另一流程示意图；
图7是本发明实施方式的基因测序的图像处理方法的又再一流程示意图；
图8是本发明实施方式的基因测序的图像处理方法中8连通像素的示意图；
图9是本发明实施方式的基因测序的图像处理方法的又另一流程示意图；
图10是本发明实施方式的基因测序的图像处理方法的待处理图像的示意图；
图11是图10中的待处理图像的局部放大图；
图12是本发明实施方式的基因测序的图像处理方法的标识出亮点的图像示意图；
图13是图12中的标识出亮点的图像的局部放大图；
图14是本发明实施方式的基因测序的图像处理***的模块示意图；
图15是本发明实施方式的基因测序的图像处理***的另一模块示意图；
图16是本发明实施方式的基因测序的图像处理***的又一模块示意图；
图17是本发明实施方式的基因测序的图像处理***的又另一模块示意图；
图18是本发明实施方式的基因测序的图像处理***的另又一模块示意图；
图19是本发明实施方式的基因测序的图像处理***的再一模块示意图；
图20是本发明实施方式的基因测序的图像处理***的又再一模块示意图。
具体实施方式
下面详细描述本发明的实施方式，所述实施方式的示例在附图中示出，其中自始至终相同或类似的标号表示相同或类似的元件或具有相同或类似功能的元件。下面通过参考附图描述的实施方式是示例性的，仅用于解释本发明，而不能理解为对本发明的限制。
在本发明的描述中，需要理解的是，术语“第一”、“第二”仅用于描述目的，而不能理解为指示或暗示相对重要性或者隐含指明所指示的技术特征的数量。由此，限定有“第一”、“第二”的特征可以明示或者隐含地包括一个或者更多个所述特征。在本发明的描述中，“多个”的含义是两个或两个以上，除非另有明确具体的限定。
在本发明的描述中，需要说明的是，除非另有明确的规定和限定，术语“安装”、“相连”、“连接”应做广义理解，例如，可以是固定连接，也可以是可拆卸连接，或一体地连接；可以是机械连接，也可以是电连接或可以相互通信；可以是直接相连，也可以通过中间媒介间接相连，可以是两个元件内部的连通或两个元件的相互作用关系。对于本领域的普通技术人员而言，可以根据具体情况理解上述术语在本发明中的具体含义。
本发明实施方式所称的“基因测序”同核酸序列测定，包括DNA测序和/或RNA测序，包括长片段测序和/或短片段测序。
所称的“亮点”，指图像上的发光点，一个发光点占有至少一个像素点。所称“像素点”同“像素”。
在本发明的实施方式中，图像来自利用光学成像原理进行序列测定的测序平台，所称的测序平台包括但不限于CG(Complete Genomics)、Illumina/Solexa、Life Technologies ABI SOLiD和Roche 454等测序平台，对所称的“亮点”的检测为对延伸碱基或碱基簇的光学信号的检测。
在本发明的一个实施例中，图像来自单分子测序平台，例如Helicos，输入的原始数据为图像的像素点的参数，对所称的“亮点”的检测为对单分子光学信号的检测。
请参阅图1，本发明实施方式的一种基因测序的图像处理方法，包括：图像预处理步骤S11，图像预处理步骤S11分析输入的待处理图像以获得去噪图像，待处理图像包含至少一个亮点，亮点具有至少一个像素点；亮点检测步骤S12，亮点检测步骤S12包括步骤：S21，分析待处理图像以计算亮点判定阈值，S22，分析去噪图像以获取候选亮点，S23，根据亮点判定阈值判断候选亮点是否为亮点。
上述基因测序的图像处理方法，通过图像预处理步骤对图像进行去噪处理，可减少亮点检测步骤的计算量，同时，通过亮点判断阈值判断候选亮点是否为亮点，可提高判断图像亮点的准确性。
具体地，在一个例子中，输入的待处理图像可为512*512或2048*2048的16位tiff格式的图像，tiff格式的图像可为灰度图像。如此，可简化基因测序的图像处理方法的处理过程。
在某些实施方式的基因测序的图像处理方法中，请参图2，亮点检测步骤还包括步骤：若判断结果为是，S24，计算亮点的亚像素中心坐标和/或亚像素中心坐标的强度值，若判断结果为否，S25，丢弃候选亮点。如此，通过亚像素来表征亮点的中心坐标和/或中心坐标的强度值，可进一步提高图像处理方法的准确性。
在某些实施方式的基因测序的图像处理方法中，请参图3，图像预处理步骤S11包括图像滤波步骤S02，图像滤波步骤S02对待处理图像进行滤波以获取去噪图像。该图像滤波步骤S02可在尽量保留图像细节特征的条件下获取去噪图像，进而可提高图像处理方法的准确性。
在某些实施方式的基因测序的图像处理方法中，图像滤波步骤S02对待处理图像进行墨西哥帽滤波。墨西哥帽滤波易于实现，降低了基因测序的图像处理方法的成本，同时，墨西哥帽滤波能提升前景与背景的对比度，使前景更亮，使背景更暗。
在某些实施方式的基因测序的图像处理方法中，在进行墨西哥帽滤波时，使用m*m窗口对待处理图像进行高斯滤波，对高斯滤波后的待处理图像进行二维拉普拉斯锐化，m为自然数且为大于1的奇数。如此，通过两步骤实现了墨西哥帽滤波。
具体地，请参图4，墨西哥帽核可表示为：
Figure PCTCN2017101054-appb-000001
其中，x和y表示像素点的坐标。
首先使用m*m窗口对待处理图像进行高斯滤波，如下公式2所示：
Figure PCTCN2017101054-appb-000002
其中，t1和t2表示滤波窗口的位置，wt1,t2表示高斯滤波的权重。
然后对待处理图像进行二维拉普拉斯锐化，如下公式3所示：
Figure PCTCN2017101054-appb-000003
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。
在一个例子中，m＝3，因此m*m＝3*3，进行高斯滤波时，公式2变为：
Figure PCTCN2017101054-appb-000004
在某些实施方式的基因测序的图像处理方法中，请参图5，在图像滤波步骤S02之前，图像预处理步骤S11还包括减背景步骤S00，减背景步骤S00对待处理图像进行减背景处理，获得减背景图像，以减背景图像代替待处理图像。如此，能够进一步减少待处理图像的噪声，使基因测序的图像处理方法的准确性更高。
在某些实施方式的基因测序的图像处理方法中，对待处理图像进行减背景处理，包括：利用开运算确定待处理图像的背景，根据背景对待处理图像进行减背景处理。利用开运算用来消除小物体、在纤细点处分离物体、平滑较大物体的边界的同时并不明显改变图像面积，可更准确地获取减背景图像。
具体地，在本发明实施方式中，在待处理图像f(x,y)(如灰度图像)移动a*a窗口(例如15*15 窗口)，利用开运算(先腐蚀再膨胀)估计待处理图像的背景，如下公式6及公式7所示：
g(x,y)＝erode[f(x,y),B]＝min{f(x+x',y+y')-B(x',y')|(x',y')∈Db}  公式6，其中，g(x,y)为腐蚀后的灰度图像，f(x,y)为原灰度图像，B为结构元素。
g(x,y)＝dilate[f(x,y),B]＝max{f(x-x',y-y')-B(x',y')|(x',y')∈Db}  公式7。
其中，g(x,y)为膨胀后的灰度图像，f(x,y)为原灰度图像，B为结构元素。
故可得背景噪声g＝imopen(f(x,y),B)＝dilate[erode(f(x,y),B)]  公式8。
对原图进行减背景：f＝f-g＝{f(x,y)-g(x,y)|(x,y)∈D}  公式9。
在某些实施方式的基因测序的图像处理方法中，请参图6，图像处理方法包括简化步骤S01，所述简化步骤S01将所述去噪图像简化为简化图像，以所述简化图像替换所述去噪图像。如此，可减少后续图像处理的计算量。
在某些实施方式的基因测序的图像处理方法中，简化图像为二值化图像。二值化图像易于处理，且应用范围广。
在某些实施方式的基因测序的图像处理方法中，简化步骤根据所述去噪图像获取信噪比矩阵，并根据所述信噪比矩阵简化所述去噪图像以得到所述简化图像。在一个具体例子中，可先对待处理图像进行减背景，之后再根据减背景图像获取信噪比矩阵。如此，利于后续从噪声更少的图像获得信息，能够使该图像处理方法获得处理结果的准确性更高。
具体地，在一个例子中，信噪比矩阵可表示为：
Figure PCTCN2017101054-appb-000005
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。
在一个例子中，简化图像为二值化图像，可根据信噪比矩阵得到二值化图像，二值化图像如公式5所示：
Figure PCTCN2017101054-appb-000006
在计算信噪比矩阵时，可先对待处理图像进行滤波和/或减背景处理，如上实施方式所述的滤波步骤和减背景处理过程，根据减背景处理后得到公式9，再求得减背景图像与背景的比值矩阵：
R＝f/g＝{f(x,y)/g(x,y)|(x,y)∈D}  公式10，其中，D表示图像f的维度(高*宽)。
由此可以求得SNR矩阵：
Figure PCTCN2017101054-appb-000007
在某些实施方式的基因测序的图像处理方法中，分析待处理图像以计算亮点判定阈值的步骤，包括：通过大津法处理待处理图像以计算亮点判定阈值。如此，通过较成熟及简单的方法实现了亮点判定阈值的查找，进而提高了该图像处理方法的准确性及降低了图像处理方法的成本。
具体地，大津法(OTSU算法)也可称为最大类间方差法，大津法利用类间方差最大来分割图像，意味着错分概率最小，准确性高。假设待处理图像的前景和背景的分割阈值为T，属于前景的像素点数占整幅图像的比例为ω0，其平均灰度为μ0；属于背景的像素点数占整幅图像的比例为ω1，其平均灰度为μ1。待处理图像的总平均灰度记为μ，类间方差记为var，则有：
μ＝ω0*μ0+ω1*μ1   公式12；
var＝ω0(μ0-μ)2+ω1(μ1-μ)2    公式13。
将公式12代入公式13，得到等价公式14：
var＝ω0ω1(μ1-μ0)2   公式14。
采用遍历的方法得到使类间方差最大的分割阈值T，即为所求的亮点判定阈值T。
在某些实施方式的基因测序的图像处理方法中，图像处理方法包括：通过大津法处理所述去噪图 像以计算所述亮点判定阈值。用去噪图像进行亮点判定阈值的查找，可提高了基因测序的图像处理方法的效率和准确性。具体地，大津法处理的过程可参以上实施方式的大津法处理的过程。
在某些实施方式的基因测序的图像处理方法中，请参图7，图像处理方法包括简化步骤S01，简化步骤S01包括对去噪图像进行简化处理以得到简化图像，以简化图像代替去噪图像。
根据亮点判定阈值判断候选亮点是否为亮点的步骤，包括：步骤S31，在简化图像中查找大于(p*p-1)连通的像素点并将查找到的像素点作为候选亮点的中心，p*p与亮点是一一对应的，p*p中的每个值对应一个像素点，p为自然数且为大于1的奇数；步骤S32，判断候选亮点的中心是否满足条件：Imax*ABI*ceofguass＞T，其中，Imax为p*p窗口的中心最强强度，ABI为p*p窗口中简化图像中为设定值所占的比率，ceofguass为p*p窗口的像素和二维高斯分布的相关系数，T为亮点判定阈值。若满足上述条件，S33，判断候选亮点的中心对应的亮点为待处理图像所包含的亮点；若不满足上述条件，S34，弃去候选亮点的中心对应的亮点。如此，实现了亮点的检测。
具体地，Imax可理解为候选亮点的中心最强强度。在一个例子中，p＝3，查找大于8连通的像素点，如图8所示。将查找到的像素点作为候选亮点的像素点。Imax为3*3窗口的中心最强强度，ABI为3*3窗口中简化图像中为设定值所占的比率，ceofguass为3*3窗口的像素和二维高斯分布的相关系数。
简化处理可为二值化处理，也就是说，可对滤波后的待处理图像进行二值化处理以得到二值化图像，二值化图像中的设定值可为像素点满足设定条件时所对应的值。在另一个例子中，二值化图像可包含表征像素点不同属性的0和1二个数值，设定值为1，ABI为p*p窗口中二值化图像中为1所占的比率。
另外，在某些实施方式中，p的数值可与在进行墨西哥帽滤波时所选取的m的数值相等，即p＝m。
在某些实施方式的基因测序的图像处理方法中，计算亮点的亚像素中心坐标和/或亚像素中心坐标的强度值的步骤，包括步骤：采用二次函数插值计算亮点的亚像素中心坐标，和/或采用二次样条插值计算亚像素中心坐标的强度值。如此，采用二次函数和/或二次样条的方法能够进一步提高判断图像亮点的准确性。
在某些实施方式的基因测序的图像处理方法中，请参图9，基因测序的图像处理方法还包括步骤：S13，利用标识标示出亮点的亚像素中心坐标所在图像的位置。如此，可方便用户观察亮点的标示是否正确，以决定是否需重新进行亮点的定位。
具体地，在一个例子中，利用十字叉标示出亮点的亚像素中心坐标所在图像的位置。请参图10、图11、图12及图13，图10为待定位的图像，图11是图10所示的图像左上角293*173范围的放大示意图。图12为用十字叉标出亮点(亮点定位后)的图像，图13是图12所示的图像左上角293*173范围的放大示意图。
请参图14，本发明实施方式的一种基因测序的图像处理***100，包括：图像预处理模块102，图像预处理模102块用于分析输入的待处理图像以获得去噪图像，待处理图像包含至少一个亮点，亮点具有至少一个像素点；亮点检测模块104，该亮点检测模块104用于：分析待处理图像以计算亮点判定阈值，分析去噪图像以获取候选亮点，根据亮点判定阈值判断候选亮点是否为亮点。因此，上述基因测序的图像处理***100，通过图像预处理模块102对图像进行去噪处理，可减少亮点检测模块104的计算量，同时，通过亮点判断阈值判断候选亮点是否为亮点，可提高判断图像亮点的准确性。
需要说明的是，上述对基因测序的图像处理方法的实施方式的解释说明也适用于本发明实施方式的基因测序的图像处理***100，为避免冗余，在此不再详细展开。
在某些实施方式的基因测序的图像处理***100中，亮点检测模块104还用于：若判断结果为是，计算亮点的亚像素中心坐标和/或亚像素中心坐标的强度值，若判断结果为否，丢弃候选亮点。如此，通过亚像素来表征亮点的中心坐标和/或中心坐标的强度值，可进一步提高图像处理***100的准确性。
在某些实施方式的基因测序的图像处理***100中，请参图15，图像预处理模块102包括图像滤波模块108。图像滤波模块108用于对待处理图像进行滤波以获取去噪图像。如此，图像滤波模块108可在尽量保留图像细节特征的条件下获取去噪图像，进而可提高图像处理***100的准确性。
在某些实施方式的基因测序的图像处理***100中，图像滤波模块108对待处理图像进行墨西哥帽滤波。如此，墨西哥帽滤波易于实现，降低了基因测序的图像处理***100的成本，同时，墨西哥帽滤波能提升前景与背景的对比度，使前景更亮，使背景更暗。
在某些实施方式的基因测序的图像处理***100中，图像滤波模块108用于，在进行墨西哥帽 滤波时，使用m*m窗口对待处理图像进行高斯滤波，对高斯滤波后的待处理图像进行二维拉普拉斯锐化，m为自然数且为大于1的奇数。如此，通过两步骤实现了墨西哥帽滤波。
在某些实施方式的基因测序的图像处理***100中，请参图16，图像预处理模块102还包括减背景模块110，减背景模块110用于，在滤波之前，对待处理图像进行减背景处理，获得减背景图像，以减背景图像代替待处理图像。如此，能够进一步减少待处理图像的噪声，使基因测序的图像处理***100的准确性更高。
在某些实施方式的基因测序的图像处理***100中，减背景模块110用于：利用开运算确定待处理图像的背景，根据背景对待处理图像进行减背景处理。如此，开运算用来消除小物体、在纤细点处分离物体、平滑较大物体的边界的同时并不明显改变图像面积，可更准确地获取减背景图像。
在某些实施方式的基因测序的图像处理***100中，请参图17，图像处理***100包括简化模块106，简化模块106用于将去噪图像简化为简化图像，以简化图像替换去噪图像。如此，简化模块106可减少基因测序的图像处理***100后续的计算量，
在某些实施方式的基因测序的图像处理***100中，简化图像为二值化图像。如此，二值化图像更易于处理，且应用范围广。
在某些实施方式的基因测序的图像处理***100中，简化模块106用于根据去噪图像获取信噪比矩阵，并根据信噪比矩阵简化去噪图像以得到简化图像。
如此，实现了噪声较少的简化图像，使基因测序的图像处理***100的准确性更高。
在某些实施方式的基因测序的图像处理***100中，亮点检测模块104用于通过大津法处理待处理图像以计算亮点判定阈值。如此，通过较成熟及简单的方法实现了亮点判定阈值的查找，进而提高了基因测序的图像处理***100的准确性及降低了基因测序的图像处理***100的成本。
在某些实施方式的基因测序的图像处理***中，亮点检测模块104用于通过大津法处理去噪图像以计算亮点判定阈值。如此，通过较成熟及简单的方法实现了亮点判定阈值的查找，进而提高了基因测序的图像处理***100的准确性及降低了基因测序的图像处理***100的成本。
在某些实施方式的基因测序的图像处理***100中，请参图18，图像处理***100包括简化模块106，简化模块106对去噪图像进行简化处理以得到简化图像，以简化图像代替去噪图像。
亮点检测模块104用于：在简化图像中查找大于(p*p-1)连通的像素点并将查找到的像素点作为候选亮点的中心，p为自然数且为大于1的奇数；判断候选亮点的中心是否满足条件：Imax*ABI*ceofguass＞T，其中，Imax为p*p窗口的中心最强强度，ABI为p*p窗口中简化图像中为设定值所占的比率，ceofguass为p*p窗口的像素和二维高斯分布的相关系数，T为亮点判定阈值，若满足上述条件，判断候选亮点的中心对应的亮点为亮点，若不满足上述条件，弃去候选亮点的中心对应的亮点。如此，实现了亮点的检测。
在某些实施方式的基因测序的图像处理***100中，亮点检测模块104用于：采用二次函数插值计算亮点的亚像素中心坐标，和/或采用二次样条插值计算亚像素中心坐标的强度值。如此，采用二次函数和/或二次样条的方法能够进一步提高判断图像亮点的准确性。
在某些实施方式的基因测序的图像处理***100中，请参图19，基因测序的图像处理***100包括标识模块112，标识模块112用于：利用标识标示出亮点的亚像素中心坐标所在图像的位置。如此，可方便用户观察亮点的标示是否正确，以决定是否需重新进行亮点的定位。
请参图20，本发明实施方式的一种基因测序的图像处理***300，包括：数据输入单元302，用于输入数据；数据输出单元304，用于输出数据；存储单元306，用于存储数据，数据包括计算机可执行程序；处理器308，用于执行计算机可执行程序，执行计算机可执行程序包括完成如上任一实施方式的方法。因此，上述基因测序的图像处理***300可提高判断图像亮点的准确性。
本发明实施方式的一种计算机可读存储介质，用于存储供计算机执行的程序，执行程序包括完成如上任一实施方式的方法。因此，上述计算机可读存储介质可提高判断图像亮点的准确性。
在本说明书的描述中，参考术语“一个实施方式”、“一些实施方式”、“示意性实施方式”、“示例”、“具体示例”、或“一些示例”等的描述意指结合所述实施方式或示例描述的具体特征、结构、材料或者特点包含于本发明的至少一个实施方式或示例中。在本说明书中，对上述术语的示意性表述不一定指的是相同的实施方式或示例。而且，描述的具体特征、结构、材料或者特点可以在任何的一个或多个实施方式或示例中以合适的方式结合。
流程图中或在此以其他方式描述的任何过程或方法描述可以被理解为，表示包括一个或更多个用于实现特定逻辑功能或过程的步骤的可执行指令的代码的模块、片段或部分，并且本发明的优选实施方式的范围包括另外的实现，其中可以不按所示出或讨论的顺序，包括根据所涉及的功能按基本同时的方式或按相反的顺序，来执行功能，这应被本发明的实施方式所属技术领域的技术人员所 理解。
本技术领域的普通技术人员可以理解实现上述实施方式方法携带的全部或部分步骤是可以通过程序来指令相关的硬件完成，所述的程序可以存储于一种计算机可读存储介质中，该程序在执行时，包括方法实施方式的步骤之一或其组合。
此外，在本发明各个实施方式中的各功能单元可以集成在一个处理模块中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个模块中。上述集成的模块既可以采用硬件的形式实现，也可以采用软件功能模块的形式实现。所述集成的模块如果以软件功能模块的形式实现并作为独立的产品销售或使用时，也可以存储在一个计算机可读取存储介质中。上述提到的存储介质可以是只读存储器，磁盘或光盘等。
尽管上面已经示出和描述了本发明的实施方式，可以理解的是，上述实施方式是示例性的，不能理解为对本发明的限制，本领域的普通技术人员在本发明的范围内可以对上述实施方式进行变化、修改、替换和变型。

Claims (32)

  1. 一种基因测序的图像处理方法，其特征在于，包括：
    图像预处理步骤，所述图像预处理步骤分析输入的待处理图像以获得去噪图像，所述待处理图像包含至少一个亮点，所述亮点具有至少一个像素点；
    亮点检测步骤，所述亮点检测步骤包括步骤：
    分析所述待处理图像以计算亮点判定阈值，
    分析所述去噪图像以获取候选亮点，
    根据所述亮点判定阈值判断所述候选亮点是否为所述亮点。
  2. 根据权利要求1所述的基因测序的图像处理方法，其特征在于，所述亮点检测步骤还包括步骤：
    若判断结果为是，计算所述亮点的亚像素中心坐标和/或所述亚像素中心坐标的强度值，
    若判断结果为否，丢弃所述候选亮点。
  3. 根据权利要求1或2所述的基因测序的图像处理方法，其特征在于，所述图像预处理步骤包括图像滤波步骤，所述图像滤波步骤对所述待处理图像进行滤波以获取所述去噪图像。
  4. 根据权利要求3所述的基因测序的图像处理方法，其特征在于，所述图像滤波步骤对所述待处理图像进行墨西哥帽滤波。
  5. 根据权利要求4所述的基因测序的图像处理方法，其特征在于，在进行墨西哥帽滤波时，使用m*m窗口对所述待处理图像进行高斯滤波，对高斯滤波后的待处理图像进行二维拉普拉斯锐化，m为自然数且为大于1的奇数。
  6. 根据权利要求3所述的基因测序的图像处理方法，其特征在于，在所述图像滤波步骤之前，所述图像预处理步骤还包括减背景步骤，所述减背景步骤对所述待处理图像进行减背景处理，获得减背景图像。
  7. 根据权利要求6所述的基因测序的图像处理方法，其特征在于，对所述待处理图像进行减背景处理，包括：
    利用开运算确定所述待处理图像的背景，
    根据所述背景对所述待处理图像进行减背景处理。
  8. 根据权利要求1所述的基因测序的图像处理方法，其特征在于，所述图像处理方法包括简化步骤，所述简化步骤将所述去噪图像简化为简化图像。
  9. 根据权利要求8所述的基因测序的图像处理方法，其特征在于，所述简化图像为二值化图像。
  10. 根据权利要求8所述的基因测序的图像处理方法，其特征在于，所述简化步骤根据所述去噪图像获取信噪比矩阵，并根据所述信噪比矩阵简化所述去噪图像以得到所述简化图像。
  11. 根据权利要求1-10任一项所述的基因测序的图像处理方法，其特征在于，所述分析所述待处理图像以计算亮点判定阈值的步骤，包括：
    通过大津法处理所述待处理图像以计算所述亮点判定阈值。
  12. 根据权利要求1-10任一项所述的基因测序的图像处理方法，其特征在于，所述分析所述待处理图像以计算亮点判定阈值的步骤，包括：
    通过大津法处理所述去噪图像以计算所述亮点判定阈值。
  13. 根据权利要求3所述的基因测序的图像处理方法，其特征在于，所述图像处理方法包括简化步骤，所述简化步骤包括对所述去噪图像进行简化处理以得到简化图像；
    在所述简化图像中查找大于(p*p-1)连通的像素点并将查找到的所述像素点作为所述候选亮点的中心，p为自然数且为大于1的奇数；
    判断所述候选亮点的中心是否满足条件：Imax*ABI*ceofguass＞T，其中，Imax为p*p窗口的中心最强强度，ABI为p*p窗口中所述简化图像中为设定值所占的比率，ceofguass为p*p窗口的像素和二维高斯分布的相关系数，T为所述亮点判定阈值，
    若满足上述条件，判断所述候选亮点的中心对应的亮点为所述亮点，
    若不满足上述条件，弃去所述候选亮点的中心对应的亮点。
  14. 根据权利要求2所述的基因测序的图像处理方法，其特征在于，计算所述亮点的亚像素中心坐标和/或所述亚像素中心坐标的强度值的步骤，包括：
    采用二次函数插值计算所述亮点的亚像素中心坐标，和/或采用二次样条插值计算所述亚像素中心坐标的强度值。
  15. 根据权利要求2所述的基因测序的图像处理方法，其特征在于，还包括步骤：
    利用标识标示出所述亮点的亚像素中心坐标所在图像的位置。
  16. 一种基因测序的图像处理***，其特征在于，包括：
    图像预处理模块，所述图像预处理模块用于分析输入的待处理图像以获得去噪图像，所述待处理图像包含至少一个亮点，所述亮点具有至少一个像素点；
    亮点检测模块，所述亮点检测模块用于：
    分析所述待处理图像以计算亮点判定阈值，
    分析所述去噪图像以获取候选亮点，
    根据所述亮点判定阈值判断所述候选亮点是否为所述亮点。
  17. 根据权利要求16所述的基因测序的图像处理***，其特征在于，所述亮点检测模块还用于：
    若判断结果为是，计算所述亮点的亚像素中心坐标和/或所述亚像素中心坐标的强度值，
    若判断结果为否，丢弃所述候选亮点。
  18. 根据权利要求16或17所述的基因测序的图像处理***，其特征在于，所述图像预处理模块包括图像滤波模块，
    所述图像滤波模块用于对所述待处理图像进行滤波以获取所述去噪图像。
  19. 根据权利要求18所述的基因测序的图像处理***，其特征在于，所述图像滤波模块对所述待处理图像进行墨西哥帽滤波。
  20. 根据权利要求19所述的基因测序的图像处理***，其特征在于，所述图像滤波模块用于，在进行墨西哥帽滤波时，使用m*m窗口对所述待处理图像进行高斯滤波，对高斯滤波后的待处理图像进行二维拉普拉斯锐化，m为自然数且为大于1的奇数。
  21. 根据权利要求18所述的基因测序的图像处理***，其特征在于，所述图像预处理模块还包括减背景模块，所述减背景模块用于，在滤波之前，对所述待处理图像进行减背景处理，获得减背景图像。
  22. 根据权利要求21所述的基因测序的图像处理***，其特征在于，所述减背景模块用于：
    利用开运算确定所述待处理图像的背景，
    根据所述背景对所述待处理图像进行减背景处理。
  23. 根据权利要求16所述的基因测序的图像处理***，其特征在于，所述图像处理***包括简化模块，所述简化模块用于将所述去噪图像简化为简化图像。
  24. 根据权利要求23所述的基因测序的图像处理***，其特征在于，所述简化图像为二值化图像。
  25. 根据权利要求23所述的基因测序的图像处理***，其特征在于，所述简化模块用于根据所述去噪图像获取信噪比矩阵，并根据所述信噪比矩阵简化所述去噪图像以得到所述简化图像。
  26. 根据权利要求16-25任一项所述的基因测序的图像处理***，其特征在于，所述亮点检测模块用于：
    通过大津法处理所述待处理图像以计算所述亮点判定阈值。
  27. 根据权利要求16-25任一项所述的基因测序的图像处理***，其特征在于，所述亮点检测模块用于：
    通过大津法处理所述去噪图像以计算所述亮点判定阈值。
  28. 根据权利要求18所述的基因测序的图像处理***，其特征在于，所述图像处理***包括简化模块，所述简化模块对所述去噪图像进行简化处理以得到简化图像；
    所述亮点检测模块用于：
    在所述简化图像中查找大于(p*p-1)连通的像素点并将查找到的所述像素点作为所述候选亮点的中心，p为自然数且为大于1的奇数；
    判断所述候选亮点的中心是否满足条件：Imax*ABI*ceofguass＞T，其中，Imax为p*p窗口的中心最强强度，ABI为p*p窗口中所述简化图像中为设定值所占的比率，ceofguass为p*p窗口的像素和二维高斯分布的相关系数，T为所述亮点判定阈值，
    若满足上述条件，判断所述候选亮点的中心对应的亮点为所述亮点，
    若不满足上述条件，弃去所述候选亮点的中心对应的亮点。
  29. 根据权利要求17所述的基因测序的图像处理***，其特征在于，所述亮点检测 模块用于：
    采用二次函数插值计算所述亮点的亚像素中心坐标，和/或采用二次样条插值计算所述亚像素中心坐标的强度值。
  30. 根据权利要求17所述的基因测序的图像处理***，其特征在于，所述基因测序的图像处理***包括标识模块，所述标识模块用于：
    利用标识标示出所述亮点的亚像素中心坐标所在图像的位置。
  31. 一种基因测序的图像处理***，其特征在于，包括：
    数据输入单元，用于输入数据；
    数据输出单元，用于输出数据；
    存储单元，用于存储数据，所述数据包括计算机可执行程序；
    处理器，用于执行所述计算机可执行程序，执行所述计算机可执行程序包括完成根据权利要求1-15任一项所述的方法。
  32. 一种计算机可读存储介质，其特征在于，用于存储供计算机执行的程序，执行所述程序包括完成根据权利要求1-15任一项所述的方法。
PCT/CN2017/101054 2016-10-10 2017-09-08 基因测序的图像处理方法及*** WO2018068599A1 (zh)

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