WO2020037573A1 - 检测图像上的亮斑的方法、装置和计算机程序产品 - Google Patents

检测图像上的亮斑的方法、装置和计算机程序产品 Download PDF

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WO2020037573A1
WO2020037573A1 PCT/CN2018/101818 CN2018101818W WO2020037573A1 WO 2020037573 A1 WO2020037573 A1 WO 2020037573A1 CN 2018101818 W CN2018101818 W CN 2018101818W WO 2020037573 A1 WO2020037573 A1 WO 2020037573A1
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image
pixel
bright spot
value
candidate
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PCT/CN2018/101818
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English (en)
French (fr)
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李林森
徐伟彬
金欢
姜泽飞
周志良
颜钦
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深圳市真迈生物科技有限公司
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Priority to EP18931064.2A priority Critical patent/EP3843034A4/en
Priority to US17/270,413 priority patent/US11847766B2/en
Priority to PCT/CN2018/101818 priority patent/WO2020037573A1/zh
Publication of WO2020037573A1 publication Critical patent/WO2020037573A1/zh

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    • 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
    • G06T5/00—Image enhancement or restoration
    • G06T5/20—Image enhancement or restoration using local operators
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/73—Deblurring; Sharpening
    • 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
    • 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
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/194—Segmentation; Edge detection involving foreground-background segmentation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/70—Determining position or orientation of objects or cameras
    • G06T7/73—Determining position or orientation of objects or cameras using feature-based methods
    • 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
    • G16B35/00—ICT specially adapted for in silico combinatorial libraries of nucleic acids, proteins or peptides
    • 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
    • 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
    • G06T2207/20021—Dividing image into blocks, subimages or windows
    • 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
    • G06T2207/20036—Morphological image processing
    • 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
    • G06T2207/30072—Microarray; Biochip, DNA array; Well plate

Definitions

  • the invention relates to the field of image processing, in particular to a method for detecting bright spots on an image, an apparatus for detecting bright spots on an image, and a computer program product.
  • image analysis is a very important part. It depends on the detection and recognition of bright spots on the image and the detection and recognition of bright spots into bases / nucleosides. Acid sequence to achieve nucleic acid sequence determination. The accuracy of detection and localization of bright spots on the image directly determines the accuracy of gene sequencing.
  • nucleic acid sequence determination In the application of nucleic acid sequence determination, how to simply, quickly and / or effectively detect bright spots on an image and use the bright spot information or accurately read the bright spot information needs to be developed and improved.
  • the embodiments of the present invention aim to solve at least one of the technical problems in the related art or provide at least one optional practical solution.
  • a method for detecting bright spots on an image includes: preprocessing the image to obtain a preprocessed image; determining a threshold value to simplify the preprocessed image, including The pixel value of a pixel on the preprocessed image is assigned a first preset value, and the pixel value of a pixel on the preprocessed image that is not less than a critical value is assigned a second preset value to obtain Simplify the image; determine the first bright spot detection threshold c1 based on the pre-processed image; identify candidate bright spots on the image based on the pre-processed image and the simplified image, including determining a pixel matrix that meets the following conditions as a candidate bright spot
  • a device for detecting bright spots on an image is provided.
  • the device is configured to implement the method for detecting bright spots on an image in the foregoing embodiment of the present invention.
  • a field of view of the base extension reaction There are multiple nucleic acid molecules with optically detectable labels on the field where the base extension reaction occurs. At least a portion of the nucleic acid molecules appear as bright spots on the image.
  • the device includes a preprocessing unit, It is used to preprocess the image to obtain the preprocessed image; the simplification unit is used to determine the critical value to simplify the preprocessed image from the preprocessing unit, including the pixel points on the preprocessed image that are less than the critical value.
  • a pixel value is assigned a first preset value, and a pixel value of a pixel point on a preprocessed image that is not less than a critical value is assigned a second preset value to obtain a simplified image; a first threshold determination unit is used to The pre-processed image from the pre-processing unit determines a first bright-spot detection threshold c1; a candidate bright-spot determining unit is used for pre-processing based on the pre-processing from the pre-processing unit.
  • the candidate image and the simplified image from the simplified unit identify the candidate bright spots on the image, including determining that a pixel matrix that meets the following conditions is a candidate bright spot, a) In the pre-processed image, the central pixel of the pixel matrix The pixel value of is the largest, the pixel matrix can be expressed as k1 * k2, k1 and k2 are both odd numbers greater than 1, k1 * k2 pixel matrix contains k1 * k2 pixels, b) In the simplified image, the pixel matrix The pixel value of the central pixel point of is the second preset value and the connected pixels of the pixel matrix are greater than 2/3 * k1 * k2, and c) the pixel value of the central pixel point of the pixel matrix in the preprocessed image Greater than the third preset value and satisfying g1 * g2> c1, g1 is the correlation coefficient of the two-dimensional Gaussian distribution in the range of m1 * m2 centered on the central pixel point of the
  • a computer-readable storage medium for storing a program for execution by a computer, and the execution of the program includes performing a bright spot detection method on an image in any of the foregoing embodiments.
  • the computer-readable storage medium may include: a read-only memory, a random access memory, a magnetic disk, or an optical disk.
  • a computer program product includes instructions for implementing detection of bright spots on an image.
  • the instructions When the computer executes the program, the instructions cause the computer to execute the bright spots in the embodiment of the present invention. Some or all steps of the detection method.
  • the so-called “bright spot” or “bright spot” refers to a light emitting point on an image, and one light emitting point occupies at least one pixel point.
  • the so-called “pixel” is the same as “pixel”.
  • the image comes from a sequencing platform that uses the optical imaging principle for sequence determination.
  • the so-called sequencing platform includes but is not limited to BGI-seq, Illumina / Solexa, Life Technologies, ABI, SOLiD, and Roche 454. Platform, the detection of the so-called "bright spots” is the detection of optical signals of extended bases or base clusters.
  • the method has no special restrictions on the detection image, ie, the original input data, and is applicable to the processing and analysis of images generated by any platform that uses the principle of optical detection for nucleic acid sequence determination, including image quality assessment for focus and focus, including alkali Based on the image processing and analysis of recognition, it has the characteristics of high accuracy and efficiency, and can obtain more information representing the sequence from the image.
  • second-generation sequencing generally contains nucleic acid templates.
  • Signal amplification (such as amplification).
  • a nucleic acid template exists in the form of a cluster containing at least hundreds or thousands of copies. That is, the signal of the nucleic acid template is a large number of signal sets of the nucleic acid template molecule.
  • the signal reflected on the image is strong and / or has specific morphological characteristics. It can also be said that the signal is significantly different from the non-target signal, and it is relatively easy to identify and locate. Therefore, the detection of bright spots on general second-generation sequencing images does not require special image processing and does not require a comprehensive and highly accurate identification of the bright spots of the corresponding sequence information. A large number of bright spots of the corresponding sequence can be obtained. Spot signals are then identified and converted into bright spot signals as sequence information.
  • the sequencing chip used is random, that is, the probes on the sequencing chip are randomly arranged, and the images obtained by taking pictures are random ( random) image, which is not easy to process and analyze; moreover, in general single-molecule sequencing, because the method does not include a nucleic acid template, the nucleic acid template exists as a single molecule or a few molecules, which is reflected in the image and is weak and easy to be disturbed / submerged
  • the accurate identification of the bright spots corresponding to the nucleic acid molecule and the amount of bright spots identified directly determine the throughput and the amount of effective data.
  • single-molecule sequencing has high requirements for image processing and bright spot positioning. It is hoped that all effective bright spots on the image can be identified and accurately located so that as much accurate data as possible can be obtained.
  • single molecule is meant one or a few molecules, such as no more than 10 molecules.
  • the method, device and / or corresponding computer product for detecting bright spots on an image according to the embodiments of the present invention are suitable for detecting bright spots on a sequenced image, especially for random images and signal recognition with high accuracy requirements, especially Advantages.
  • FIG. 1 is a schematic flowchart of a method for detecting bright spots on an image in a specific embodiment of the present invention.
  • FIG. 2 is a schematic diagram of a pixel matrix and connected pixels in a specific embodiment of the present invention.
  • FIG. 3 is a schematic diagram of pixel values in a range of m1 * m2 corresponding to a central pixel of a pixel matrix as a center on a simplified image in a specific embodiment of the present invention.
  • FIG. 4 is a schematic flowchart of a method for detecting bright spots on an image in a specific embodiment of the present invention.
  • FIG. 5 is a schematic diagram of bright spot detection results with and without S50 in a specific embodiment of the present invention.
  • FIG. 6 is a schematic diagram of a device for detecting bright spots on an image in a specific embodiment of the present invention.
  • FIG. 7 is a schematic diagram of an apparatus for detecting bright spots on an image in a specific embodiment of the present invention.
  • the sequencing in the embodiment of the present invention is also referred to as sequence determination, and refers to nucleic acid sequence determination, including DNA sequencing and / or RNA sequencing, including long-sequence sequencing and / or short-sequence sequencing.
  • Sequencing can be performed through a sequencing platform.
  • the sequencing platform can be selected but not limited to Hisq / Miseq / Nextseq sequencing platform from Illumina, IonTorrent platform from Thermo Fisher / Life Technologies, BGISEQ platform and single molecule sequencing platform from BGI; sequencing method You can choose single-end sequencing or double-end sequencing; the obtained sequencing results / data are the read out fragments, which are called reads. The length of the read segment is called the read length.
  • a method for detecting bright spots on an image includes: S10 preprocessing the image to obtain a preprocessed image; S20 determining a threshold value to simplify the preprocessed image, including The pixel value of the pixel point on the preprocessed image after the threshold value is assigned a first preset value, and the pixel value of the pixel point on the preprocessed image that is not less than the threshold value is assigned a second preset value.
  • S30 determines the first bright spot detection threshold c1 based on the pre-processed image; S40 identifies candidate bright spots on the simplified image based on the pre-processed image and the simplified image, including determining that at least two of the following a) -c) are satisfied
  • the conditional pixel matrix is a candidate bright spot.
  • the pixel value of the central pixel of the pixel matrix is the largest, and the pixel matrix can be expressed as k1 * k2, k1, and k2 are all odd numbers greater than 1.
  • the k1 * k2 pixel matrix contains k1 * k2 pixels.
  • the pixel value of the center pixel of the pixel matrix is the second preset value and The connected pixels of the pixel matrix are greater than 2/3 * k1 * k2, and c) the pixel value of the central pixel of the pixel matrix in the preprocessed image is greater than the third preset value, and satisfies g1 * g2> c1
  • G1 is the correlation coefficient of the two-dimensional Gaussian distribution in the range of m1 * m2 centered on the central pixel point of the pixel matrix
  • g2 is the pixel in the range of m1 * m2
  • m1 and m2 are both odd numbers greater than 1, m1 * m2
  • the range contains m1 * m2 pixels.
  • the method for detecting bright spots on an image is determined by the inventor through training on a large amount of data.
  • This method can quickly and effectively detect the bright spots on an image, especially for a sequence collected from a nucleic acid.
  • the image of the reaction was measured.
  • the method has no special restrictions on the detection images, ie, the original input data, and is applicable to the processing and analysis of images generated by any platform that uses the principle of optical detection for nucleic acid sequence determination, including but not limited to second- and third-generation sequencing. Efficient feature, can get more representative sequence information from the image. It is especially advantageous for signal recognition with random images and high accuracy requirements.
  • the pixel values are the same as the grayscale values. If the image is a color image and one pixel of the color image has three pixel values, the color image can be converted into a grayscale image and then bright spot detection can be performed to reduce the calculation amount and complexity of the image detection process. You can choose, but are not limited to, converting non-grayscale images to grayscale images using floating-point algorithms, integer methods, shifting methods, or average methods.
  • S10 pre-processing the image includes: determining an image's background using an open operation; converting the image to a first image using a top-hat operation based on the background; performing Gaussian blur processing on the first image to obtain a second Image; sharpening the second image to obtain a so-called pre-processed image.
  • the image can be effectively reduced in noise or the signal-to-noise ratio of the image can be improved, which is beneficial to the accurate detection of bright spots.
  • the open operation is a morphological process, that is, the process of expanding and then corroding.
  • the etching operation will make the foreground (the part of interest) smaller, and the expansion will make the foreground larger.
  • the open operation can be used to eliminate small objects. Separates objects at points and does not significantly change their area while smoothing the boundaries of larger objects.
  • the size of the structural elements p1 * p2 (the basic template used to process the image) for the image open operation is not particularly limited, and p1 and p2 are odd numbers.
  • the structural elements p1 * p2 may be 15 * 15, 31 * 31, and the like, and finally, a pre-processed image that is favorable for subsequent processing and analysis can be obtained.
  • the top hat operation is often used to separate plaques that are brighter than neighboring points (bright spots / bright spots). In an image with a large background and small objects are more regular, the top hat operation can be used for background extraction.
  • performing a top hat transformation on an image includes first performing an open operation on the image, and then subtracting the result of the open operation from the original image to obtain a first image, which is the top hat transformed image.
  • the inventor believes that the result of the open operation enlarges the crack or local low-luminance area, so the image obtained after subtracting the open operation from the original image highlights a brighter area than the area around the outline of the original image.
  • the operation is related to the size of the selected kernel. It can be considered to be related to the expected size of the bright spots / bright spots. If the bright spots are not the expected size, the processed effect will cause a lot of small bumps in the whole picture. For details, refer to the virtual focus picture, that is, Bright spots / bright spots halo. In one example, the expected size of the bright spot, that is, the size of the selected kernel is 3 * 3, and the obtained top-hat transformed image is beneficial for subsequent further denoising processing.
  • Gaussian Blur also known as Gaussian filtering
  • Gaussian filtering is a linear smoothing filter that is suitable for eliminating Gaussian noise and is widely used in image reduction noise reduction processes.
  • Gaussian filtering is a process of weighted average of the entire image. The value of each pixel is obtained by weighted average of itself and other pixel values in the neighborhood.
  • the specific operation of Gaussian filtering is: use a template (or convolution, mask) to scan each pixel in the image, and use the weighted average gray value of the pixels in the neighborhood determined by the template to replace the value of the central pixel of the template.
  • Gaussian blur processing is performed on the first image, and the Gaussian Blur function is used in OpenCV.
  • the Gaussian distribution parameter Sigma is 0.9.
  • the two-dimensional filter matrix (convolution kernel) used is 3 * 3.
  • the Gaussian blur processing is performed on the image angle, the small protrusions on the first image are smoothed, and the edges of the image are smooth.
  • the second image that is, the Gaussian filtered image is sharpened, for example, two-dimensional Laplacian sharpening is performed. After processing from an image perspective, the edges are sharpened, and the Gaussian blurred image is restored.
  • S20 includes: determining a critical value based on the background and the pre-processed image; comparing the pixel value of a pixel on the pre-processed image with the critical value, and comparing the pre-processed
  • the pixel value of the pixel point on the image is assigned a first preset value
  • the pixel value of the pixel point on the preprocessed image not less than the critical value is assigned a second preset value to obtain a simplified image.
  • the pre-processed image is simplified, such as binarization, which is conducive to accurate detection of subsequent bright spots, accurate identification of subsequent bases, Get high-quality data and more.
  • S20 includes: dividing the sharpened result obtained in S10 by the result of the on operation to obtain a set of values corresponding to the image pixels; and using the set of values to determine the binarization prediction The critical value of the processed image.
  • the set of values can be sorted in ascending order, and the value corresponding to the 20th, 30th, or 40th percentile of the set of values is taken as the binarization threshold / threshold. In this way, the obtained binarized image facilitates accurate detection and recognition of subsequent bright spots.
  • the structure element of the open operation during the S10 image preprocessing is p1 * p2, which is called dividing the preprocessed image (the sharpened result) by the result of the operation to obtain a set of structure elements Size array / matrix p1 * p2, in each array, the p1 * p2 values contained in the array are sorted in ascending order, and the value corresponding to the thirtieth percentile in the array is taken as the area (value matrix ) Binarization threshold / threshold. In this way, the thresholds are determined to binarize each area on the image. The resulting binarization results highlight the real information while denoising, which is conducive to the accurate detection of subsequent bright spots. .
  • S30 includes determining the first bright spot detection threshold by using the Otsu method.
  • the Otsu method can also be called the maximum inter-class variance method.
  • the Otsu method uses the largest inter-class variance to segment the image, which means that the probability of misclassification is small and the accuracy is high.
  • T (c1) the foreground and background segmentation threshold of the preprocessed image
  • the proportion of pixels belonging to the foreground to the entire image is w0
  • the average gray scale is ⁇ 0
  • the ratio is w1, and its average gray scale is ⁇ 1.
  • the traversal method is used to obtain the segmentation threshold T that maximizes the variance between classes, that is, the first bright spot detection threshold c1 obtained.
  • S40 identifies candidate bright spots on the pre-processed image and the simplified image, and includes determining a pixel matrix that satisfies three conditions a) -c) as one candidate bright spot. In this way, the accuracy of subsequent determination of the nucleic acid sequence based on the bright spot information and the quality of the offline data can be effectively improved.
  • the conditions that need to be satisfied for determining the candidate bright spots include a), k1 and k2 may be equal or unequal.
  • the relevant parameters of the imaging system are: the objective lens is 60 times, the size of the electronic sensor is 6.5 ⁇ m, and the image formed by the microscope and then passed through the electronic sensor, the minimum size that can be seen is 0.1 ⁇ m. It can be a 16-bit grayscale or color image of 512 * 512, 1024 * 1024, or 2048 * 2048.
  • the values of k1 and k2 are both greater than 1 and less than 10.
  • the conditions that need to be met for determining the candidate bright spots include b).
  • the pixel value of the central pixel of the pixel matrix is a second preset value
  • the connected pixels of the pixel matrix are greater than 2 / 3 * k1 * k2, that is, the pixel value of the central pixel is larger than the critical value and the connected pixels are larger than two thirds of the matrix.
  • two or more pixels whose adjacent pixel values are the second preset value are called connected pixels / connectivity.
  • a simplified image is a binary image, and the first preset value is It is 0, and the second preset value is 1. As shown in FIG.
  • the bold indicates the center of the pixel matrix
  • the pixel point matrix does not satisfy the condition b), and is not a candidate bright spot.
  • the conditions that need to be met for determining the candidate bright spot include c).
  • g2 is a pixel in the range of m1 * m2 after correction, that is, the sum of pixels in the range of m1 * m2 after correction.
  • the method further includes S50 determining whether the candidate bright spot is a bright spot.
  • S50 includes: determining a second bright spot detection threshold based on the pre-processed image, and determining that the candidate bright spot whose pixel value is not less than the second bright spot detection threshold is a bright spot.
  • the pixel value of the pixel point at which the coordinates of the candidate bright spot are located is used as the pixel value of the candidate bright spot.
  • the second bright spot detection threshold determined based on the pre-processed image By further filtering the candidate bright spots by using the second bright spot detection threshold determined based on the pre-processed image, it is possible to exclude at least a part of the images that are more likely to be the background of the image but whose brightness (intensity) and / or shape appear as "bright spots". Bright spots are helpful for accurate recognition of subsequent bright spot-based sequences, and improve the quality of offline data.
  • the center of gravity method can be used to obtain the coordinates of candidate bright spots, including sub-pixel-level coordinates.
  • the gray value of the coordinate position of the candidate bright spot is calculated by a bilinear interpolation method.
  • S50 includes: dividing the pre-processed image into a set of blocks of a predetermined size, and sorting the pixel values of the pixels in the region to determine the second brightness corresponding to the region. Speckle detection threshold; for a candidate bright spot located in an area, it is determined that a candidate bright spot whose pixel value is not less than a second bright spot detection threshold corresponding to the area is a bright spot. In this way, distinguishing the differences in different areas of the image, such as the overall drop in light intensity, and further detecting and identifying bright spots, is conducive to accurately identifying bright spots and obtaining more bright spots.
  • the so-called pre-processed image is divided into a set of blocks of a predetermined size, and there may or may not be overlap between the blocks. In one example, there is no overlap between blocks.
  • the size of the pre-processed image is not less than 512 * 512, such as 512 * 512, 1024 * 1024, 1800 * 1800, or 2056 * 2056, etc., and the area of the predetermined size may be set to 200 * 200. In this way, it is beneficial to quickly calculate and identify bright spots.
  • the pixel values of the pixels in each block are arranged in ascending order by size, and p10 + (p10-p1) * 4.1 is taken as the corresponding value of the block.
  • the second bright spot detection threshold that is, the background of the block
  • p1 represents the pixel value of the hundredth percentile
  • p10 represents the pixel value of the tenth percentile.
  • the threshold is a relatively stable threshold obtained by the inventor through a large amount of data training tests, and can eliminate bright spots on a large number of backgrounds. Understandably, when the optical system is adjusted and the overall pixel distribution of the image is changed, this threshold may need to be adjusted appropriately.
  • Figure 5 is a schematic diagram of the comparison before and after the S50, that is, the bright spot detection results before and after the area background is excluded.
  • the upper half of Figure 5 is the bright spot detection result of the S50, and the lower half is the bright spot detection without the S50.
  • the cross marks are candidate bright spots or bright spots.
  • a "computer-readable storage medium” may be any device that can contain, store, communicate, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. .
  • computer-readable storage media include the following: electrical connections (electronic devices) with one or more wirings, portable computer disk cartridges (magnetic devices), random access memory (RAM) , Read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disk read-only memory (CDROM).
  • the computer-readable storage medium may even be paper or other suitable media on which the program can be printed, because, for example, by optically scanning the paper or other media and then editing, interpreting, or otherwise Processing is performed in a suitable manner to obtain the program electronically and then store it in a computer memory.
  • a device 1000 for detecting bright spots on an image according to an embodiment of the present invention.
  • the device is used to implement the method for detecting bright spots on an image in any of the foregoing embodiments of the present invention. From a field of view where a base extension reaction occurs, there are multiple nucleic acid molecules with an optically detectable label on the field where the base extension reaction occurs. At least a portion of the nucleic acid molecules appear as bright spots on the image.
  • the device includes a preprocessing unit.
  • simplification unit 200 which is used to determine a critical value to simplify the preprocessed image from the preprocessing unit, including The pixel value of a pixel is assigned a first preset value, and the pixel value of a pixel on a preprocessed image not less than a critical value is assigned a second preset value to obtain a simplified image; a first threshold determination unit 300 For determining the first bright spot detection threshold c1 based on the pre-processed image from the pre-processing unit; the candidate bright spot determining unit 400 is used for The processed image and the simplified image from the simplified unit identify candidate bright spots on the image, including determining a pixel matrix that meets the following conditions as a candidate bright spot, a) In the preprocessed image, the center pixel of the pixel matrix The pixel value of a point is the largest.
  • the pixel matrix can be expressed as k1 * k2, k1 and k2 are both odd numbers greater than 1.
  • the k1 * k2 pixel matrix contains k1 * k2 pixels.
  • B) In the simplified image, pixel points The pixel value of the central pixel of the matrix is the second preset value and the connected pixels of the pixel matrix are greater than 2/3 * k1 * k2, and c) the pixel of the central pixel of the pixel matrix in the preprocessed image The value is greater than the third preset value and satisfies g1 * g2> c1.
  • G1 is the correlation coefficient of the two-dimensional Gaussian distribution in the range of m1 * m2 centered on the central pixel point of the pixel matrix
  • g2 is the pixel in the range m1 * m2 Value
  • m1 and m2 are odd numbers greater than 1
  • the range of m1 * m2 includes m1 * m2 pixels.
  • the apparatus 1000 further includes a second threshold value determination unit 500 and a bright spot determination unit 600.
  • the second threshold value determination unit 500 is configured to be based on a preprocessed image from a preprocessing unit.
  • a second bright spot detection threshold is determined, and the bright spot determining unit 600 is configured to determine that a candidate bright spot whose pixel value is not less than the second bright spot detection threshold is a bright spot.
  • the pixel value of the candidate bright spot is referred to as the pixel value of the pixel where the coordinates of the candidate bright spot are located.
  • the second threshold determining unit 500 is configured to divide the preprocessed image into a set of regions of a predetermined size, and sort the pixel values of the pixels in the region to determine the second brightness corresponding to the region.
  • the speckle detection threshold; the speckle determination unit is configured to determine, for a candidate bright spot located in an area, that the candidate bright spot whose pixel value is not less than a second bright spot detection threshold corresponding to the area is a bright spot.
  • the preprocessing unit 100 is configured to perform the following: determine the background of the image using an open operation, convert the image to a first image based on the background using a top-hat operation, and perform Gaussian blur processing on the first image to obtain a second image , Sharpen the second image to obtain a pre-processed image.
  • the simplification unit 200 is configured to determine a critical value based on the background and the preprocessed image, and compare the pixel value of the pixel point on the preprocessed image with the critical value to obtain a simplified image.
  • g2 is a pixel in the range of m1 * m2 after correction, and the pixel in the range of m1 * m2 is corrected according to the proportion of pixels whose pixel value is the second preset value in the corresponding m1 * m2 range of the simplified image.
  • An embodiment of the present invention also provides a computer program product including instructions for implementing detection of bright spots on an image.
  • the instructions When the computer executes the program, the instructions cause the computer to execute the detection of bright spots on the image in any of the foregoing embodiments. All or part of the steps of a method.
  • controller in addition to implementing the controller / processor in a pure computer-readable program code manner, the controller can be controlled by logic gates, switches, ASICs, and editable logic by logically changing the method steps. Controller and embedded microcontroller to achieve the same function. Therefore, such a controller / processor can be considered as a hardware component, and a device included therein for implementing various functions can also be considered as a structure within the hardware component. Or even, the means for implementing various functions can be regarded as a structure that can be both a software module implementing the method and a hardware component.

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Abstract

一种对图像上的亮斑进行检测的方法、装置，所称图像采集自发生碱基延伸反应的一个视野，发生碱基延伸反应的该视野上存在多个带有光学可检测标记的核酸分子，至少一部分核酸分子在图像上表现为亮斑，该方法包括：预处理所述图像，获得预处理后的图像（S10）；确定临界值以简化预处理后的图像，获得简化图像（S20）；基于预处理后的图像确定第一亮斑检测阈值c1（S30）；基于预处理后的图像和简化图像识别图像上的候选亮斑，包括判定满足a)-c)中的至少两个条件的像素点矩阵为一个候选亮斑（S40）。该方法能够快速有效地实现图像上的亮斑的准确检测，特别是对采集自核酸序列测定反应的图像。

Description

检测图像上的亮斑的方法、装置和计算机程序产品 技术领域
本发明涉及图像处理领域，特别是一种检测图像上的亮斑的方法、一种检测图像上的亮斑的装置和一种计算机程序产品。
背景技术
在相关技术中，图像上的亮斑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系统中，例如核酸序列测定，图像分析是很重要的一块，依靠对图像上的亮斑的检测识别、以及将检测识别的亮斑转化成碱基/核苷酸序列以实现核酸序列的测定。图像上亮斑的检测和定位的准确性直接决定了基因测序的准确性。
在核酸序列测定应用中，如何简单、快速和/或有效地检测出图像上的亮斑以及利用该些亮斑信息或者说准确读取这些亮斑信息，有待开发和改进。
发明内容
本发明实施方式旨在至少解决相关技术中存在的技术问题之一或者至少提供一种可选择的实用方案。
依据本发明的一个实施方式，提供一种对图像上的亮斑进行检测的方法，所称图像采集自发生碱基延伸反应的一个视野，发生碱基延伸反应的该视野上存在多个带有光学可检测标记的核酸分子，至少一部分核酸分子在图像上表现为亮斑，该方法包括：预处理图像，获得预处理后的图像；确定临界值以简化预处理后的图像，包括对小于临界值的预处理后的图像上的像素点的像素值赋值为第一预设值，对不小于临界值的预处理后的图像上的像素点的像素值赋值为第二预设值，以获得简化图像；基于预处理后的图像确定第一亮斑检测阈值c1；基于预处理后的图像和简化图像识别图像上的候选亮斑，包括判定满足以下条件的像素点矩阵为一个候选亮斑，a)在预处理后的图像中，像素点矩阵的中心像素点的像素值为最大，像素点矩阵可表示为k1*k2，k1和k2均为大于1的奇数，k1*k2像素点矩阵包含k1*k2个像素点，b)在简化图像中，像素点矩阵的中心像素点的像素值为第二预设值并且像素点矩阵的连通像素大于2/3*k1*k2，以及c)在预处理后的图像中的像素点矩阵的中心像素点的像素值大于第三预设值，并且满足g1*g2>c1，g1为以像素点矩阵的中心像素点为中心的m1*m2范围的二维高斯分布的相关系数，g2为该m1*m2范围的像素，m1和m2均为大于1的奇数，m1*m2范围包含m1*m2个像素点。
依据本发明的另一个实施方式，提供一种一种检测图像上的亮斑的装置，该装置用以实施上述本发明实施方式中的检测图像上的亮斑的方法，所称图像采集自发生碱基延伸反应的一个视野，发生碱基延伸反应的该视野上存在多个带有光学可检测标记的核酸分子，至少一部分核酸分子在图像上表现为亮斑，该装置包括：预处理单元，用于预处理图像，获得预处理后的图像；简化单元，用于确定临界值以简化来自预处理单元的预处理后的图像，包括对小于临界值的预处理后的图像上的像素点的像素值赋值为第一预设值，对不小于临界值的预处理后的图像上的像素点的像素值赋值为 第二预设值，以获得简化图像；第一阈值确定单元，用于基于来自预处理单元的预处理后的图像确定第一亮斑检测阈值c1；候选亮斑确定单元，用于基于来自预处理单元的预处理后的图像和来自简化单元的简化图像识别图像上的候选亮斑，包括判定满足以下条件的像素点矩阵为一个候选亮斑，a)在预处理后的图像中，像素点矩阵的中心像素点的像素值为最大，像素点矩阵可表示为k1*k2，k1和k2均为大于1的奇数，k1*k2像素点矩阵包含k1*k2个像素点，b)在简化图像中，像素点矩阵的中心像素点的像素值为第二预设值并且像素点矩阵的连通像素大于2/3*k1*k2，以及c)在预处理后的图像中的像素点矩阵的中心像素点的像素值大于第三预设值，并且满足g1*g2>c1，g1为以像素点矩阵的中心像素点为中心的m1*m2范围的二维高斯分布的相关系数，g2为m1*m2范围的像素值，m1和m2均为大于1的奇数，m1*m2范围包含m1*m2个像素点。
依据本发明的再一个实施方式，提供一种计算机可读存储介质，用于存储供计算机执行的程序，执行程序包括完成上述任一实施方式中的图像上的亮斑检测方法。计算机可读存储介质可以包括：只读存储器、随机存储器、磁盘或光盘等。
依据本发明的又一个实施方式，提供一种计算机程序产品，该产品包括实现图像上的亮斑的检测的指令，该指令在计算机执行程序时，使计算机执行上述本发明实施方式中的亮斑检测方法的部分或所有步骤。
所称的“亮点”或“亮斑”，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华大基因BGI-seq、Illumina/Solexa、Life Technologies ABI SOLiD和Roche 454等测序平台，对所称的“亮斑”的检测为对延伸碱基或碱基簇的光学信号的检测。
依据上述本发明任一实施方式提供的检测图像上的亮斑的方法、装置和系统/计算机程序产品，能够快速有效地实现图像上的亮斑(spots或者peaks)的检测，特别是对采集自核酸序列测定反应的图像。该方法对待检测图像即原始输入数据没有特别的限制，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，包括用以对焦追焦的图像质量评估，包括用以碱基识别的图像处理分析等，具有高准确性和高效的特点，能从图像中获取更多的代表序列的信息。
需要说明的，目前已知的测序图像上的亮斑识别定位方法和/或系统基本是针对来自二代测序平台的图像开发的，由于二代测序使用的测序芯片多数是阵列型的，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，通常图像上的信号是有规则的，有效信号的准确识别相对容易；另外，由于二代测序一般包含核酸模板的信号放大(例如扩增)，一般一个核酸模板是以一个包含至少成百上千个拷贝的簇(cluster)的形式存在，即该核酸模板的信号是大量的该核酸模板分子的信号集，可以理解地，体现到图像上的信号较强和/或具有特定形态特征，也可以说与非目标信号差异较明显，相对易于识别定位。因此，一般的二代测序的图像上的亮斑检测不需要特别的图像处理以及不要求对对应序列信息的亮斑的识别进行全面和高准确的识别判断，就能获得大量的对应序列的亮斑信号，继而识别转化亮斑信号为序列信息。
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，一般的单分子测序由于不包含核酸模板的方法，核酸模板以单个分子或少数几个分子的形式存在，体现在图像上是微弱的、易被干扰/淹没的信号，对应核酸分子的亮斑的准确识别以及识别得的亮斑的量，直接决定下机通量和有效数据量的高低，一般地，单分子测序对图像处理、亮点定位的要求高，希望图像上的所有有效亮斑都能被识别出以及准确定位，以使能够获得尽可能多的准确数据。所称的“单分子”指一个或少数几个分子，例如不超过10个分子。
本发明的实施方式的图像上的亮斑的检测方法、装置和/或相应的计算机产品，适应用于测序图像上的亮斑检测，特别是对于随机图像及高准确度要求的信号识别，尤其具有优势。
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
图1是本发明具体实施方式中的检测图像上的亮斑的方法的流程示意图。
图2是本发明具体实施方式中的像素点矩阵和连通像素示意图。
图3是本发明具体实施方式中的简化图像上相应的以像素点矩阵的中心像素点为中心的m1*m2范围的像素值示意图。
图4是本发明具体实施方式中的检测图像上的亮斑的方法的流程示意图。
图5是本发明具体实施方式中的进行S50和不进行S50的亮斑检测结果示意图。
图6是本发明具体实施方式中的检测图像上的亮斑的装置的示意图。
图7是本发明具体实施方式中的检测图像上的亮斑的装置的示意图。
具体实施方式
下面详细描述本发明的实施方式，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，“第一”、“第二”、“第三”、“第四”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或者顺序。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。
本发明实施方式所称的测序，也称为序列测定，指核酸序列测定，包括DNA测序和/或RNA测序，包括长片段测序和/或短片段测序。
测序可以通过测序平台进行，测序平台可选择但不限于Illumina公司的Hisq/Miseq/Nextseq测序平台、Thermo Fisher/Life Technologies公司的Ion Torrent平台、华大基因的BGISEQ平台和单分子测序平台；测序方式可以选择单端测序，也可以选择双末端测序；获得的测序结果/数据即测读出来的片段，称为读段(reads)。读段的长度称为读长。
请参阅图1，本发明实施方式的一种对图像上的亮斑进行检测的方法，图像采集自发生碱基延伸反应的一个视野，发生碱基延伸反应的该视野上存在多个带有光学可检测标记的核酸分子，至少一部分核酸分子在图像上表现为亮斑，该方法包括：S10预处理图像，获得预处理后的图像；S20确定临界值以简化预处理后的图像，包括对小于临界值的预处理后的图像上的像素点的像素值赋值为第一预设值，对不小于临界值的预处理后的图像上的像素点的像素值赋值为第二预设值，以获得简化图像；S30基于预处理后的图像确定第一亮斑检测阈值c1；S40基于预处理后的图像和简化图像识别图像上的候选亮斑，包括判定满足以下a)-c)中至少两个条件的像素点矩阵为一个候选亮斑，a)在预处理后的图像中，像素点矩阵的中心像素点的像素值为最大，像素点矩阵可表示为k1*k2，k1和k2均为大于1的奇数，k1*k2像素点矩阵包含k1*k2个像素点，b)在简化图像中，像素点矩阵的中心像素点的像素值为第二预设值并且像素点矩阵的连通像素大于2/3*k1*k2，以及c)在预处理后的图像中的像素点矩阵的中心像素点的像素值大于第三预设值，并且满足g1*g2>c1，g1为以像素点矩阵的中心像素点为中心的m1*m2范围的二维高斯分布的相关系数，g2为该m1*m2范围的像素，m1和m2均为大于1的奇数，m1*m2范围包含m1*m2个像素点。
该检测图像上的亮斑的方法，包括判断条件或判断条件的组合是发明人通过大量数据训练确定的，该方法能够快速有效地实现图像上的亮斑的检测，特别是对采集自核酸序列测定反应的图像。该方法对待检测图像即原始输入数据没有特别的限制，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，具有高准确性和高效的特点，能从图像中获取更多的代表序列的信息。特别是对于随机图像及高准确度要求的信号识别，尤其具有优势。
对于灰度图像，像素值同灰度值。若图像是彩色图像，彩色图像的一个像素点具有三个像素值，可以将彩色图像转化为灰度图像，再进行亮斑检测，以降低图像检测过程的计算量和复杂度。可选择但不限于利用浮点算法、整数方法、移位方法或平均值法等将非灰度图像转换成灰度图像。
在某些具体实施方式中，S10预处理图像，包括：利用开运算确定图像的背景；基于背景，利用顶帽运算将图像转化为第一图像；对第一图像进行高斯模糊处理，获得第二图像；对第二图像进行锐化，以获得所称的预处理后的图像。如此，能对图像进行有效的降噪或者说提高图像的信噪比，利于亮斑的准确检测。
开运算是一种形态学处理，即先膨胀后腐蚀的过程，腐蚀操作会使得前景(感兴趣的部分)变小，而膨胀会使得前景变大；开运算可以用来消除小物体，在纤细点处分离物体，并且在平滑较大物体的边界的同时不明显改变其面积。该实施方式对图像做开运算的结构元p1*p2(用来处理图像的基本模板)的大小不作特别限制，p1和p2为奇数。在一个示例中，结构元p1*p2可以为15*15、31*31等，最终都能够获得利于后续处理分析的预处理后的图像。
顶帽运算往往用来分离比临近点(亮点/亮斑)亮一些的斑块，在一幅图像具有大幅的背景，而微小物品比较有规律的情况下，可以使用顶帽运算进行背景提取。在一个示例中，对图像进行顶帽变换包括先对图像做开运算，进而利用原图像减去开运算结果，获得第一图像即顶帽变换后的图像。顶帽变换的数学表达式为dst＝tophat(src,element)＝src-open(src,element)。发明人认为，开运算的结 果放大了裂缝或者局部低亮度的区域，因此从原图中减去开运算后的图，得到的图像突出了比原图轮廓周围的区域更明亮的区域，这一操作与选择的核的大小相关，可以认为与亮点/亮斑的预期大小相关，若亮点不是预期大小，处理后的效果会使得整张图产生许多小凸起，具体可以参考虚焦图片，即亮点/亮斑晕染成一团。在一个示例中，亮点的预期大小即选择的核的大小为3*3，得到的顶帽变换后的图像利于后续进一步去噪处理。
高斯模糊(Gaussian Blur)也称为高斯滤波，是一种线性平滑滤波，适用于消除高斯噪声，广泛应用于图像处理的减噪过程。通俗的讲，高斯滤波就是对整幅图像进行加权平均的过程，每一个像素点的值，都由其本身和邻域内的其他像素值经过加权平均后得到。高斯滤波的具体操作是：用一个模板(或称卷积、掩模)扫描图像中的每一个像素，用模板确定的邻域内像素的加权平均灰度值去替代模板中心像素点的值。在一个示例中，对第一图像进行高斯模糊处理，在OpenCV中使用高斯滤波GaussianBlur函数进行，高斯分布参数Sigma取0.9，所使用的二维滤波器矩阵(卷积核)是3*3，从图像角度看经过该高斯模糊处理后，第一图像上的小突起被抹平，图像边缘光滑。进一步地，对第二图像即高斯过滤后的图像进行锐化，例如进行二维拉普拉斯锐化，从图像角度看经过处理后，边缘被锐化，高斯模糊后的图像得以恢复。
在某些具体实施方式中，S20包括：基于背景和预处理后的图像，确定临界值；比较预处理后的图像上的像素点的像素值与临界值，对小于临界值的预处理后的图像上的像素点的像素值赋值为第一预设值，对不小于临界值的预处理后的图像上的像素点的像素值赋值为第二预设值，获得简化图像。如此，根据发明人大量测试数据总结的确定临界值的方式以及确定的临界值，据此将预处理后的图像简化，例如二值化，利于后续亮斑准确检测，利于后续碱基准确识别、获得高质量数据等。
具体地，在某些具体实施方式中，S20包括，将S10获得的锐化后的结果除以开运算结果，获得和图像像素点对应的一组数值；通过该组数值，确定二值化预处理后的图像的临界值。例如，可将该组数值按大小升序排列，取该组数值中第20、30或40百分位数对应的数值作为二值化临界值/阈值。如此，获得的二值化图像利于后续亮斑的准确检测识别。
在一个示例中，S10图像预处理时的开运算的结构元为p1*p2，所称的将预处理后的图像(锐化后的结果)除以开运算结果，获得一组和结构元一样大小的数组/矩阵p1*p2，在每个数组中，将该数组包含的p1*p2个数值按大小升序排列，取该数组中第三十百分位数对应的数值作为该区域(数值矩阵)的二值化临界值/阈值，如此，分别确定阈值对图像上的各个区域进行二值化，最终获得的二值化结果在去噪的同时更加突出真实信息，利于后续亮斑的准确检测。
在某些具体实施方式中，S30包括利用大津法进行第一亮斑检测阈值的确定。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，意味着错分概率小，准确性高。假设预处理后的图像的前景和背景的分割阈值为T(c1)，属于前景的像素点数占整幅图像的比例为w0，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w1，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＝ω 0*μ 0+ω 1*μ 1；var＝ω 0(μ 0-μ) 2+ω 1(μ 1-μ) 2，将后者代入前者，得到等价公式： var＝ω 0ω 1(μ 1-μ 0) 2。采用遍历的方法得到使类间方差最大的分割阈值T，即为所求的第一亮斑检测阈值c1。
在某些具体实施方式中，S40基于预处理后的图像和简化图像识别图像上的候选亮斑，包括判断同时满足a)-c)三个条件的像素点矩阵为一个候选亮斑。如此，能有效地提高后续基于亮斑信息确定核酸序列的准确性和下机数据的质量。
具体地，在一个示例中，候选亮斑的判定需要满足的条件包括a)，k1、k2可以相等也可以不相等。在一个示例中，成像系统相关参数为：物镜60倍，电子传感器的尺寸为6.5μm，经过显微镜成的像再经过电子传感器，能看到的最小尺寸为0.1μm，获得的图像或者输入的图像可为512*512、1024*1024或2048*2048的16位的灰度或彩色图像，k1和k2的取值范围均为大于1且小于10。在一个示例中，在一个预处理后的图像中，依据亮斑的预期大小设置k1＝k2＝3；在另一个示例中，设置k1＝k2＝5。
在一个示例中，候选亮斑的判定需要满足的条件包括b)，在简化图像中，像素点矩阵的中心像素点的像素值为第二预设值，并且该像素点矩阵的连通像素大于2/3*k1*k2，即中心像素点的像素值大于临界值且连通像素大于矩阵的三分之二。这里，相邻的像素值都为第二预设值的两个或多个像素为所称的相连像素/连通像素(pixel connectivity)，例如，简化图像为二值化图像，第一预设值为0，第二预设值为1，如图2所示，加粗加大的表示所称的像素点矩阵的中心，粗线框表示像素点矩阵3*3，即k1＝k2＝3，该矩阵的中心像素点的像素值为1，连通像素为4(小于2/3*k1*k2＝6)，该像素点矩阵不满足条件b)，非候选亮斑。
在一个示例中，候选亮斑的判定需要满足的条件包括c)，在预处理图像中，g2为修正后的m1*m2范围的像素，即为修正后的m1*m2范围像素总和。在一个例子中，依据简化图像相应m1*m2范围中像素值为第二预设值的像素点所占的比例进行修正，例如，如图3所示，m1＝m2＝5，所称的简化图像相应m1*m2范围中像素值为第二预设值的像素点所占的比例为13/25(13个“1”)，修正后的g2为原来的13/25。如此，利于更准确的检测识别亮斑，利于后续亮斑信息的分析读取。
在某些具体实施方式中，如图4所示，该方法还包括S50确定候选亮斑是否为亮斑。在一个示例中S50包括：基于预处理后的图像确定第二亮斑检测阈值，以及判定像素值不小于第二亮斑检测阈值的候选亮斑为亮斑。在具体示例中，以候选亮斑的坐标所在的像素点的像素值作为该候选亮斑的像素值。通过利用基于预处理后的图像确定的第二亮斑检测阈值对候选亮斑的进一步筛选，能够排除掉至少一部分更可能是图像背景但亮度(强度)和/或形状表现为“亮斑”的亮斑，利于后续基于亮斑的序列的准确识别，提高下机数据的质量。
在一个示例中，可利用重心法获取候选亮斑的坐标，包括亚像素级坐标。利用双线性插值法计算候选亮斑的坐标位置的灰度值。
在某些具体示例中，S50包括：将预处理后的图像划分为预定大小的一组区域(block)，对该区域中的像素点的像素值进行排序，以确定该区域对应的第二亮斑检测阈值；对于位于区域的候选亮斑，判定像素值不小于该区域对应的第二亮斑检测阈值的候选亮斑为亮斑。如此，区分图像的不同 区域的差异比如光强的整体落差，分开进行亮斑的进一步检测识别，利于准确识别亮斑并且获得更多的亮斑。
所称的将预处理后的图像划分为预定大小的一组区域(block)，block之间可以有重叠也可以没有重叠。在一个示例中，block之间没有重叠。在一些实施例中，预处理后的图像的大小不小于512*512，例如为512*512、1024*1024、1800*1800或者2056*2056等，所称预定大小的区域可以设为为200*200。如此，利于快速计算判断识别亮斑。
在一些实施例中，确定该区域对应的第二亮斑检测阈值时，对每个block中的像素点的像素值按大小进行升序排列，取p10+(p10-p1)*4.1作为该block对应的第二亮斑检测阈值，即该block的背景，p1表示第百分之一分位的像素值，p10表示第百分之十分位的像素值。该阈值是发明人通过大量数据训练测试得出的较为稳定的阈值，能够消除大量背景上的亮斑。可以理解地，当光学系统调整，图像整体像素分布发生改变时，此阈值可能需要适当调整。图5为进行该S50前后的对比示意图，即排除掉区域背景前后的亮斑检测结果示意图，图5的上半部分为进行S50的亮斑检测结果、下半部分为不进行S50的亮斑检测结果，十字标记的为候选亮斑或亮斑。
上述在流程图中表示或在此以其他方式描述的逻辑和/或步骤，例如，可以被认为是用于实现逻辑功能的可执行指令的序列表，可以具体实现在任何计算机可读存储介质中，以供指令执行系统、装置或设备(如基于计算机的系统、包括处理器的系统或其他可以从指令执行系统、装置或设备取指令并执行指令的系统)使用，或结合这些指令执行系统、装置或设备而使用。就本说明书而言，"计算机可读存储介质"可以是任何可以包含、存储、通信、传播或传输程序以供指令执行系统、装置或设备或结合这些指令执行系统、装置或设备而使用的装置。计算机可读存储介质的更具体的示例(非穷尽性列表)包括以下：具有一个或多个布线的电连接部(电子装置)，便携式计算机盘盒(磁装置)，随机存取存储器(RAM)，只读存储器(ROM)，可擦除可编辑只读存储器(EPROM或闪速存储器)，光纤装置，以及便携式光盘只读存储器(CDROM)。另外，计算机可读存储介质甚至可以是可在其上打印所述程序的纸或其他合适的介质，因为可以例如通过对纸或其他介质进行光学扫描，接着进行编辑、解译或必要时以其他合适方式进行处理来以电子方式获得所述程序，然后将其存储在计算机存储器中。
请参阅图6，本发明实施方式的一种检测图像上的亮斑的装置1000，该装置用以实现上述本发明任一实施例中的检测图像上的亮斑的方法，所称的图像采集自发生碱基延伸反应的一个视野，发生碱基延伸反应的该视野上存在多个带有光学可检测标记的核酸分子，至少一部分核酸分子在图像上表现为亮斑，装置包括：预处理单元100，用于预处理图像，获得预处理后的图像；简化单元200，用于确定临界值以简化来自预处理单元的预处理后的图像，包括对小于临界值的预处理后的图像上的像素点的像素值赋值为第一预设值，对不小于临界值的预处理后的图像上的像素点的像素值赋值为第二预设值，以获得简化图像；第一阈值确定单元300，用于基于来自预处理单元的预处理后的图像确定第一亮斑检测阈值c1；候选亮斑确定单元400，用于基于来自预处理单元的预处理后的图像和来自简化单元的简化图像识别图像上的候选亮斑，包括判定满足以下条件的像素点矩阵为一个候选亮斑，a)在预处理后的图像中，像素点矩阵的中心像素点的像素值为最大，像素点矩阵可表示 为k1*k2，k1和k2均为大于1的奇数，k1*k2像素点矩阵包含k1*k2个像素点，b)在简化图像中，像素点矩阵的中心像素点的像素值为第二预设值并且像素点矩阵的连通像素大于2/3*k1*k2，以及c)在预处理后的图像中的像素点矩阵的中心像素点的像素值大于第三预设值，并且满足g1*g2>c1，g1为以像素点矩阵的中心像素点为中心的m1*m2范围的二维高斯分布的相关系数，g2为m1*m2范围的像素值，m1和m2均为大于1的奇数，m1*m2范围包含m1*m2个像素点。
上述对本发明任一实施方式中的图像上的亮斑的检测方法的优点和技术特征的描述，同样适用本发明这一实施方式中的亮斑检测装置，在此不再赘述。
例如，如图7所示，在一些示例中，该装置1000还包括第二阈值确定单元500和亮斑确定单元600，第二阈值确定单元500用于基于来自预处理单元的预处理后的图像确定第二亮斑检测阈值，亮斑确定单元600用于判定像素值不小于第二亮斑检测阈值的候选亮斑为亮斑。
在一些示例中，所称的候选亮斑的像素值为该候选亮斑的坐标所在的像素点的像素值。
在一些示例中，第二阈值确定单元500用于将预处理后的图像划分为预定大小的一组区域，对该区域中的像素点的像素值进行排序，以确定该区域对应的第二亮斑检测阈值；亮斑确定单元用于对于位于区域的候选亮斑，判定像素值不小于该区域对应的第二亮斑检测阈值的候选亮斑为亮斑。
在一些示例中，预处理单元100用于进行以下：利用开运算确定图像的背景，基于背景，利用顶帽运算将图像转化为第一图像，对第一图像进行高斯模糊处理，获得第二图像，对第二图像进行锐化，获得预处理后的图像。
在一些示例中，简化单元200用于，基于背景和预处理后的图像，确定临界值，比较预处理后的图像上的像素点的像素值与临界值，以获得简化图像。
在一些示例中，g2为修正后的m1*m2范围的像素，依据简化图像相应m1*m2范围中像素值为第二预设值的像素点所占的比例以修正m1*m2范围的像素。
本发明的实施方式还提供一种计算机程序产品，该产品包括实现检测图像上的亮斑的指令，指令在计算机执行程序时，使计算机执行上述任一实施例中的检测图像上的亮斑的方法的全部或部分步骤。
本领域技术人员知晓，除了以纯计算机可读程序代码方式实现控制器/处理器外，完全可以通过将方法步骤进行逻辑变成来使得控制器以逻辑门、开关、专用集成电路、可编辑逻辑控制器和嵌入微控制器等的形式来实现相同的功能。因此，这种控制器/处理器可以被认为是一种硬件部件，而对其内包括的用于实现各种功能的装置也可以视为硬件部件内的结构。或者甚至，可以将用于实现各种功能的装置视为既可以是实现方法的的软件模块又可以是硬件部件内的结构。
在本说明书的描述中，一个实施方式、一些实施方式、一个或一些具体实施方式、一个或一些实施例、示例等的描述意指结合该实施方式或示例描述的具体特征、结构或者特点包含于本发明的至少一个实施例或示例中。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例或示例。而且，描述的具体特征、结构等特点可以在任何的一个或多个实施例或示例中以合适的方式结合。
尽管已经示出和描述了本发明的实施例，本领域的普通技术人员可以理解：在不脱离本发明的 原理和宗旨的情况下可以对这些实施例进行多种变化、修改、替换和变型，本发明的范围由权利要求及其等同限定。

Claims (15)

  1. 一种对图像上的亮斑进行检测的方法，其特征在于，所述图像采集自发生碱基延伸反应的一个视野，发生碱基延伸反应的该视野上存在多个带有光学可检测标记的核酸分子，至少一部分所述核酸分子在所述图像上表现为亮斑，所述方法包括：
    预处理所述图像，获得预处理后的图像；
    确定临界值以简化所述预处理后的图像，包括对小于所述临界值的所述预处理后的图像上的像素点的像素值赋值为第一预设值，对不小于所述临界值的所述预处理后的图像上的像素点的像素值赋值为第二预设值，以获得简化图像；
    基于所述预处理后的图像确定第一亮斑检测阈值c1；
    基于所述预处理后的图像和所述简化图像识别所述图像上的候选亮斑，包括判定满足以下a)-c)中至少两个条件的像素点矩阵为一个所述候选亮斑，
    a)在所述预处理后的图像中，像素点矩阵的中心像素点的像素值为最大，所述像素点矩阵可表示为k1*k2，k1和k2均为大于1的奇数，k1*k2像素点矩阵包含k1*k2个像素点，
    b)在所述简化图像中，所述像素点矩阵的中心像素点的像素值为第二预设值并且所述像素点矩阵的连通像素大于2/3*k1*k2，以及
    c)在所述预处理后的图像中的所述像素点矩阵的中心像素点的像素值大于第三预设值，并且满足g1*g2>c1，g1为以所述像素点矩阵的中心像素点为中心的m1*m2范围的二维高斯分布的相关系数，g2为该m1*m2范围的像素，m1和m2均为大于1的奇数，m1*m2范围包含m1*m2个像素点。
  2. 权利要求1的方法，其特征在于，还包括判定所述候选亮斑是否为所述亮斑，包括：
    基于所述预处理后的图像确定第二亮斑检测阈值，以及判定像素值不小于所述第二亮斑检测阈值的候选亮斑为所述亮斑。
  3. 权利要求2的方法，其特征在于，所述候选亮斑的像素值为该候选亮斑的坐标所在的像素点的像素值。
  4. 权利要求2或3的方法，其特征在于，所述基于预处理后的图像确定第二亮斑检测阈值，判定像素值不小于所述第二亮斑检测阈值的候选亮斑为所述亮斑，包括：
    将所述预处理后的图像划分为预定大小的一组区域，
    对该区域中的像素点的像素值进行排序，以确定该区域对应的第二亮斑检测阈值，
    对于位于所述区域的候选亮斑，判定像素值不小于该区域对应的第二亮斑检测阈值的候选亮斑为所述亮斑。
  5. 权利要求1-4任一方法，其特征在于，预处理所述图像，包括：
    利用开运算确定所述图像的背景，
    基于所述背景，利用顶帽运算将所述图像转化为第一图像，
    对所述第一图像进行高斯模糊处理，获得第二图像，
    对所述第二图像进行锐化，获得所述预处理后的图像。
  6. 权利要求5的方法，其特征在于，所述确定临界值以简化所述预处理后的图像，获得简化图像，包括：
    基于所述背景和所述预处理后的图像，确定所述临界值，
    比较所述预处理后的图像上的像素点的像素值与所述临界值，以获得所述简化图像。
  7. 权利要求1-6任一方法，其特征在于，g2为修正后的m1*m2范围的像素，依据所述简化图像相应m1*m2范围中像素值为第二预设值的像素点所占的比例进行所述修正。
  8. 一种检测图像上的亮斑的装置，其特征在于，所述图像采集自发生碱基延伸反应的一个视野，发生碱基延伸反应的该视野上存在多个带有光学可检测标记的核酸分子，至少一部分所述核酸分子在所述图像上表现为亮斑，所述装置包括：
    预处理单元，用于预处理所述图像，获得预处理后的图像；
    简化单元，用于确定临界值以简化来自所述预处理单元的预处理后的图像，包括对小于所述临界值的所述预处理后的图像上的像素点的像素值赋值为第一预设值，对不小于所述临界值的所述预处理后的图像上的像素点的像素值赋值为第二预设值，以获得简化图像；
    第一阈值确定单元，用于基于来自所述预处理单元的预处理后的图像确定第一亮斑检测阈值c1；
    候选亮斑确定单元，用于基于来自所述预处理单元的预处理后的图像和来自所述简化单元的简化图像识别所述图像上的候选亮斑，包括判定满足以下a)-c)中至少两个条件的像素点矩阵为一个所述候选亮斑，
    a)在所述预处理后的图像中，像素点矩阵的中心像素点的像素值为最大，所述像素点矩阵可表示为k1*k2，k1和k2均为大于1的奇数，k1*k2像素点矩阵包含k1*k2个像素点，
    b)在所述简化图像中，所述像素点矩阵的中心像素点的像素值为第二预设值并且所述像素点矩阵的连通像素大于2/3*k1*k2，以及
    c)在所述预处理后的图像中的所述像素点矩阵的中心像素点的像素值大于第三预设值，并且满足g1*g2>c1，g1为以所述像素点矩阵的中心像素点为中心的m1*m2范围的二维高斯分布的相关系数，g2为m1*m2范围的像素值，m1和m2均为大于1的奇数，m1*m2范围包含m1*m2个像素点。
  9. 权利要求8的装置，其特征在于，还包括第二阈值确定单元和亮斑确定单元，
    所述第二阈值确定单元用于基于来自所述预处理单元的预处理后的图像确定第二亮斑检测阈值，
    所述亮斑确定单元用于判定像素值不小于所述第二亮斑检测阈值的候选亮斑为所述亮斑。
  10. 权利要求9的装置，其特征在于，所述候选亮斑的像素值为该候选亮斑的坐标所在的像素点的像素值。
  11. 权利要求9或10的装置，其特征在于，所述第二阈值确定单元用于将所述预处理后的图像划分为预定大小的一组区域，对该区域中的像素点的像素值进行排序，以确定该区域对应的第二亮斑检测阈值；
    所述亮斑确定单元用于对于位于所述区域的候选亮斑，判定像素值不小于该区域对应的第二亮斑检测阈值的候选亮斑为所述亮斑。
  12. 权利要求8-11任一装置，其特征在于，所述预处理单元用于进行以下：
    利用开运算确定所述图像的背景，
    基于所述背景，利用顶帽运算将所述图像转化为第一图像，
    对所述第一图像进行高斯模糊处理，获得第二图像，
    对所述第二图像进行锐化，获得所述预处理后的图像。
  13. 权利要求12的装置，其特征在于，所述简化单元用于，
    基于所述背景和所述预处理后的图像，确定所述临界值，
    比较所述预处理后的图像上的像素点的像素值与所述临界值，以获得所述简化图像。
  14. 权利要求8-13任一装置，其特征在于，g2为修正后的m1*m2范围的像素，依据所述简化图像相应m1*m2范围中像素值为第二预设值的像素点所占的比例以修正m1*m2范围的像素。
  15. 一种计算机程序产品，该产品包括实现检测图像上的亮斑的指令，所述指令在所述计算机执行所述程序时，使所述计算机执行如权利要求1-7任一项所述的方法。
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