WO2019196019A1 - 荧光图像配准方法、基因测序仪及系统、存储介质 - Google Patents

荧光图像配准方法、基因测序仪及系统、存储介质 Download PDF

Info

Publication number
WO2019196019A1
WO2019196019A1 PCT/CN2018/082578 CN2018082578W WO2019196019A1 WO 2019196019 A1 WO2019196019 A1 WO 2019196019A1 CN 2018082578 W CN2018082578 W CN 2018082578W WO 2019196019 A1 WO2019196019 A1 WO 2019196019A1
Authority
WO
WIPO (PCT)
Prior art keywords
pixel
trajectory
pixel level
template
pixels
Prior art date
Application number
PCT/CN2018/082578
Other languages
English (en)
French (fr)
Inventor
李美
黎宇翔
刘逸文
Original Assignee
深圳华大智造科技有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority to PCT/CN2018/082578 priority Critical patent/WO2019196019A1/zh
Application filed by 深圳华大智造科技有限公司 filed Critical 深圳华大智造科技有限公司
Priority to AU2018418609A priority patent/AU2018418609B2/en
Priority to EP18914625.1A priority patent/EP3779868B1/en
Priority to JP2020555468A priority patent/JP7072081B2/ja
Priority to US17/043,226 priority patent/US11682125B2/en
Priority to BR112020020949-0A priority patent/BR112020020949A2/pt
Priority to KR1020207032386A priority patent/KR102504987B1/ko
Priority to SG11202010040YA priority patent/SG11202010040YA/en
Priority to CN201880005564.0A priority patent/CN111971711A/zh
Priority to CA3096723A priority patent/CA3096723C/en
Priority to RU2020134588A priority patent/RU2749893C1/ru
Publication of WO2019196019A1 publication Critical patent/WO2019196019A1/zh
Priority to US18/201,309 priority patent/US20230298188A1/en

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/30—Determination of transform parameters for the alignment of images, i.e. image registration
    • G06T7/33—Determination of transform parameters for the alignment of images, i.e. image registration using feature-based methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/30—Determination of transform parameters for the alignment of images, i.e. image registration
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/30—Determination of transform parameters for the alignment of images, i.e. image registration
    • G06T7/33—Determination of transform parameters for the alignment of images, i.e. image registration using feature-based methods
    • G06T7/337—Determination of transform parameters for the alignment of images, i.e. image registration using feature-based methods involving reference images or patches
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6869—Methods for sequencing
    • C—CHEMISTRY; METALLURGY
    • C40—COMBINATORIAL TECHNOLOGY
    • C40B—COMBINATORIAL CHEMISTRY; LIBRARIES, e.g. CHEMICAL LIBRARIES
    • C40B20/00—Methods specially adapted for identifying library members
    • C40B20/02—Identifying library members by their fixed physical location on a support or substrate
    • 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
    • G06T7/70—Determining position or orientation of objects or cameras
    • 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
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2563/00—Nucleic acid detection characterized by the use of physical, structural and functional properties
    • C12Q2563/107—Nucleic acid detection characterized by the use of physical, structural and functional properties fluorescence
    • 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
    • G06T2207/10—Image acquisition modality
    • G06T2207/10064—Fluorescence 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
    • G06T2207/20021—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
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30072—Microarray; Biochip, DNA array; Well plate
    • 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
    • G06T2207/30241—Trajectory

Definitions

  • the invention relates to the field of gene sequencing, and in particular to a fluorescence image registration method, a gene sequencer, a gene sequencing system and a storage medium.
  • Gene sequencing refers to the analysis of the base sequence of a specific DNA fragment, ie, the arrangement of adenine (A), thymine (T), cytosine (C) and guanine (G).
  • A adenine
  • T thymine
  • C cytosine
  • G guanine
  • One of the commonly used sequencing methods is that the above four bases respectively carry four different fluorophores, and different fluorophores are excited to emit fluorescence of different wavelengths (colors), which can be identified by recognizing the fluorescence wavelength.
  • the type of the base to be synthesized is read, thereby reading the base sequence.
  • the second-generation sequencing technology uses a high-resolution microscopic imaging system to capture the fluorescent molecular images of DNA nanospheres (DNB, DNA Nanoballs) on a biochip (gene sequencing chip), and send the fluorescent molecular image to the base recognition software for decoding.
  • the image signal yields a base sequence.
  • the maps of the same scene need to be aligned by the positioning and registration methods, and then the corresponding signals are extracted by the corresponding algorithm, and the subsequent brightness information analysis processing is performed to obtain the alkali. Base sequence.
  • the sequencer products are equipped with real-time processing and analysis software for sequencing data, and most of them are equipped with registration and localization algorithms.
  • the existing registration techniques are based on the characteristics of the fluorescence image itself to perform content similarity matching, and feature extraction and registration according to different target features.
  • the signal is a point source under the high-resolution microscopic image.
  • the neighborhood centroid method is adopted, that is, the neighborhood pixel value of each point is extracted to find the center of gravity.
  • the bases of the second-generation sequencing - the fluorescence signal density is large, and there are unfocused target points, which is difficult to locate using the usual algorithm.
  • An embodiment of the present invention provides a fluorescence image registration method, which is applied to a biochip, wherein a pixel distance between the trace lines on the biochip is a template parameter, and the fluorescence image registration method includes:
  • the plurality of first template lines respectively traversing the pixels and features in the first direction and the second direction to find a minimum sum of pixels corresponding to the plurality of first template lines a position corresponding to the minimum value of the sum value is the position of the trajectory line;
  • the sub-pixel level position uniformly distributed on the surface of the biochip surface is obtained by the equalization grid method.
  • the step of acquiring pixels and features of the intermediate partial region of the fluorescent image in the first direction and the second direction includes:
  • the plurality of first template lines are selected according to the template parameter in the first direction and the second direction, respectively. Traversing the minimum value of the sum of the pixels corresponding to the plurality of first template lines in the feature includes:
  • the step of performing pixel-level correction on the trajectory line in a partial region of the trajectory position includes:
  • the step of acquiring the pixel level position of the trajectory line according to the position corresponding to the minimum value of the sum value includes:
  • a pixel level position of the trace line is acquired according to a pixel level position of the first trough.
  • the step of correcting the position of the pixel-level trajectory according to the center of gravity method includes:
  • a sub-pixel level position of the trajectory line is obtained according to the position of the center of gravity.
  • the step of acquiring a sub-pixel level position uniformly distributed on a surface of the biochip surface by the equalization grid method includes:
  • the sub-pixel level position of the locus on the block area is obtained by the equalization grid method.
  • the embodiment of the present invention further provides a gene sequencing system, which is applied to a biochip, wherein a pixel distance between the trajectory lines on the biochip is a template parameter, and the gene sequencing system includes:
  • An image acquisition module configured to acquire at least one fluorescent image of the biochip
  • a region selection module configured to select a middle local region of the fluorescent image
  • a pixel and an acquisition module configured to acquire pixels and features of the intermediate partial region of the fluorescent image in the first direction and the second direction, the first direction being perpendicular to the second direction;
  • a value minimum value finding module configured to select, according to the template parameter, a plurality of first template lines, respectively traversing the pixels and features in the first direction and the second direction to find the plurality of first template lines Corresponding pixel sum value minimum value, the position corresponding to the sum value minimum value is the trajectory line position;
  • a pixel level correction module configured to perform pixel level correction on the trajectory line in a local area of the trajectory line position, where an intersection of the trajectory line for performing pixel level correction is a pixel level trajectory intersection;
  • the other track intersection point acquiring module is configured to acquire other track intersection positions on the biochip according to the pixel level track intersection and perform pixel level correction on the other track intersections;
  • a gravity center correction module configured to correct a position of a pixel level trajectory line according to a center of gravity method, to obtain a sub-pixel level position of the trajectory line;
  • a sub-pixel level location acquisition module is configured to obtain a sub-pixel level position uniformly distributed on a surface of the biochip surface by a uniform grid method.
  • a further aspect of the embodiments of the present invention provides a gene sequencer, wherein the gene sequencer includes a processor, and the processor is configured to implement the fluorescence image registration method according to any one of the above items when the computer program stored in the memory is executed. A step of.
  • Still another aspect of the present invention provides a non-transitory computer readable storage medium having stored thereon a computer program, wherein the computer program is executed by a processor to implement the fluorescence image registration method according to any one of the above A step of.
  • the fluorescence image registration method, the gene sequencing system, the gene sequencer and the storage medium acquire at least one fluorescent image of the biochip; select an intermediate partial region of the fluorescent image; and obtain an intermediate portion of the fluorescent image a pixel and a feature of the region in the first direction and the second direction; selecting a plurality of first template lines according to the template parameter in the pixels and features in the first direction and the second direction, respectively Traversing a minimum value of the sum of the sums of the pixels corresponding to the plurality of first template lines, wherein the position corresponding to the minimum value of the sum is the position of the track line; in a partial area of the position of the track line,
  • the trajectory line performs pixel-level correction, and the intersection of the trajectory line for performing pixel-level correction is a pixel-level trajectory intersection; acquiring other trajectory intersection positions on the biochip according to the pixel-level trajectory intersection and the other trajectory Pixel-level correction at the intersection; correcting the position of the pixel-level trajectory according to the centroid method,
  • the positioning and registration operation of the fluorophore in the fluorescence image can be optimized by using the embodiment of the invention, and the embodiment of the invention has high accuracy and high efficiency for locating the target features of different sizes and different resolutions;
  • the embodiment of the invention can accurately and quickly locate the sub-pixel position of the point signal, and can be easily applied to different dot array images by setting parameters, has strong anti-interference ability and wide applicability.
  • FIG. 1 is a flow chart of a method for registering a fluorescent image according to an embodiment of the present invention.
  • FIG. 2 is a schematic view showing the structure of a gene sequencer according to an embodiment of the present invention.
  • FIG. 3 is an exemplary functional block diagram of the genetic sequencer shown in FIG.
  • 4A is a characteristic effect diagram of a local area in a fluorescent image according to an embodiment of the present invention.
  • Fig. 4B is an effect diagram in which a certain area in Fig. 4A is enlarged.
  • FIG. 5A is a partial partial area view of a fluorescent image according to an embodiment of the present invention.
  • FIG. 5B is a schematic diagram of pixels and features in the vertical direction of the intermediate partial area map provided in FIG. 5A.
  • FIG. 5B is a schematic diagram of pixels and features in the vertical direction of the intermediate partial area map provided in FIG. 5A.
  • FIG. 6A is a schematic diagram of searching for the pixels and features shown in FIG. 5B using three first template lines.
  • FIG. 6B is another schematic diagram of searching for the pixels and features shown in FIG. 5B using three first template lines.
  • FIG. 6C is another schematic diagram of searching for the pixels and features shown in FIG. 5B using three first template lines.
  • FIG. 7 is a schematic diagram showing the sum of three first template line pixels.
  • FIG. 8A is a partial area diagram of a trajectory line according to an embodiment of the present invention.
  • Fig. 8B is a diagram showing the pixels and features of the local area map of the trajectory in the vertical direction.
  • Fig. 8C is a schematic view showing an enlarged area of Fig. 8B.
  • FIG. 9 is a schematic diagram of the sum value of pixel sums obtained from the pixels and features shown in FIG. 8B.
  • FIG. 10 is a schematic diagram of deriving other trajectory intersections using pixel-level trajectory intersections according to an embodiment of the present invention.
  • Figure 11A is a partial area schematic view of a pixel level trajectory line.
  • FIG. 11B is a schematic diagram of pixels and features in a horizontal direction of a partial region of a pixel-level trajectory line.
  • Fig. 12A is a schematic view showing the position of a pixel-level trajectory line.
  • Fig. 12B is a schematic view showing the position of a sub-pixel level trajectory.
  • Figure 13 is a schematic illustration of sub-pixel level locations for acquiring a site using a mean-division grid method.
  • Genetic sequencer 1 Memory 10 Display 20 processor 30 Image acquisition module 11 Area selection module 12 Pixel and acquisition module 13 And value minimum finding module 14 Pixel level correction module 15 Other track intersection location acquisition module 16 Center of gravity correction module 17 Sub-pixel level location acquisition module 18
  • FIG. 1 is a flow chart of a method for registering a fluorescent image according to an embodiment of the present invention. As shown in FIG. 1, the fluorescent image registration method may include the following steps:
  • S101 Acquire at least one fluorescent image of the biochip.
  • the biochip may be a gene sequencing chip, and the fluorescent image may be a fluorescent signal image when sequencing.
  • the biochip can be photographed using a microscopic camera to obtain a fluorescent signal image.
  • the microscope's field of view is small, about 768.6 ⁇ m*648 ⁇ m, and can shoot hundreds of field of view (FOV) for a biochip.
  • An area formed between adjacent two horizontal and vertical trajectory lines in each field of view is referred to as a block, and the block is divided into an inner block and an outer block.
  • a plurality of sites are uniformly distributed in each block on the biochip, and the sites can adsorb DNA nanosphere molecules (DNB), which may be amplification products including DNA fragments.
  • DNB DNA nanosphere molecules
  • the DNA nanosphere molecule carries a fluorescent group when synthesizing a base, and a fluorescent signal is emitted when the fluorescent group is excited.
  • the first direction may be horizontal.
  • the part of the site is arranged according to a preset rule to form a first group of track lines arranged in parallel in the first direction and a second group of track lines distributed in parallel in the second direction.
  • the first direction may be horizontal.
  • the second direction may be a vertical direction.
  • the intersection between the first set of trajectory lines and the second set of trajectory lines is a track crossing.
  • S102 Select an intermediate partial region of the fluorescent image.
  • the fluorophores may be fixedly arranged on the biochip according to a preset rule, and by special design and processing, there is no site at some positions on the biochip, that is, no fluorophore The group exists.
  • the fluorophore is greater than 25% (adenine (A), thymine (T), cytosine (C) and guanine (G) four bases are equalized)
  • the position of the non-luminous boundary The wireframe is highlighted.
  • the highlighted boundary frame can be composed of three fluorophore positions, the fluorophores in the middle row have bright spots, and the fluorophores on both sides of the middle row are not bright.
  • the fluorophores in the middle row form a trajectory line
  • the fluorophores on both sides of the middle row form a dark line.
  • the highlighted boundary line frame can include the trajectory line and a dark line on both sides of the trajectory line. Imaging distortion can be ignored in the local range of the dark line frame.
  • the fluorescent image may be selected to have a width of 80% in the first direction, and a region of 10% of the length in the second direction is used as an intermediate partial region of the fluorescent image.
  • the intermediate partial region of the fluorescent image may include at least one trajectory line in the first direction and the second direction.
  • S103 Acquire pixels and features in the first direction and the second direction of the intermediate partial region of the fluorescent image, the first direction being perpendicular to the second direction.
  • the step of acquiring pixels and features in the first direction and the second direction of the intermediate partial region of the fluorescent image may include: selecting a plurality of second template lines, the number of the second template lines
  • the second template line may be sequentially translated in the middle portion of the fluorescent image in the first direction and the second direction, respectively.
  • Calculating a superposition of gray values of pixels covered by the position of the second template line on the local portion of the fluorescent image, and the superposition of the gray values is the gray of the pixels covered by the position of the second template line The sum of the degrees.
  • the intermediate portion of the fluorescent image may be acquired.
  • the position of the boundary wire frame corresponds to a pixel and a position corresponding to a pixel and a lowest value in the feature.
  • "pixel sum" means the sum of gray values of pixels.
  • S104 Select, according to the template parameter, a plurality of first template lines, respectively traversing the pixels and features in the first direction and the second direction to find a sum of pixels corresponding to the plurality of first template lines.
  • the minimum value, the position corresponding to the minimum value of the sum value is the position of the trajectory line.
  • a plurality of first template lines are selected according to template parameters, and the number of the first template lines may be 3.
  • the template parameter indicates that the pixel distance between the first template lines is fixed.
  • the pixel distance between the two first and second template lines may be the same or different.
  • the sum of the sum values of the pixels corresponding to the first template lines is described. It can be understood that when the positions of the plurality of first template lines are located near the pixel and the lowest value of the pixels and features, at this time, the sum of the pixels and the sum of the plurality of first template lines The smallest.
  • the position corresponding to the minimum value is the position of the trajectory line, and the position of the trajectory line acquired at this time is the approximate position of the trajectory line.
  • the template parameters referred to in the present invention refer to parameters for designing a biochip template.
  • S105 Perform pixel-level correction on the trajectory line in a local area of the trajectory position, where an intersection of the trajectory line for performing pixel-level correction is a pixel-level trajectory intersection.
  • pixels and features in the first direction and the second direction of the local area of the track line position are respectively acquired; and in the vertical direction, a plurality of third pieces are selected as the preset distance.
  • the template line traverses to find pixels and features of the local area of the track line location.
  • the W-shaped line features are included in the pixels and features of the local area of the trajectory position.
  • the positions corresponding to the two troughs in the W-shaped line feature are the positions corresponding to the dark lines on both sides of the track line, and the pixels and values of the positions corresponding to the dark lines are lower.
  • the position corresponding to the peak in the W-shaped line feature is the position corresponding to the trajectory line, and the pixel and the value of the position corresponding to the trajectory line are higher.
  • the position corresponding to the minimum value of the sum of the pixels corresponding to the plurality of third template lines is the position of the trough in the W-shaped line feature. Since the pixel distance between the valley and the peak is fixed, the position of the peak can be obtained according to the position of the trough. It can be understood that the position of the peak corresponds to the position of the trajectory line, and thus the pixel level position of the trajectory line can be obtained according to the position corresponding to the minimum value of the sum value, and the intersection point of the trajectory line for performing pixel level correction is a pixel. Level track intersection.
  • the intersection of the trajectory is a virtual point, and the actual position is not necessarily set at the position of this point, and the position does not necessarily illuminate.
  • the first template line, the second template line, and the third template line are also not actually existing lines, but are virtual lines that are provided to facilitate the description of the present invention.
  • S106 Acquire other trajectory intersection position on the biochip according to the pixel level trajectory intersection and perform pixel level correction on the other trajectory intersection.
  • a part of the sites are arranged according to a preset rule to form a first group of trajectories parallelly distributed in a first direction and a second group of trajectories parallelly distributed in a second direction.
  • the arrangement between the first set of trajectory lines and the second set of trajectory lines is regular, and the arrangement of the trajectory intersections is also regular.
  • a width of three pixels and a region of a length of 50 pixels may be selected as the pixel-level trajectory local region.
  • the sub-pixel level position of the trajectory line in the horizontal direction is obtained, and the intersection of the sub-pixel level trajectory lines is a sub-pixel level trajectory line intersection.
  • a block area composed of two sub-pixel level track intersections in the first direction and the second direction is acquired, and the block area is arranged according to a preset rule.
  • the sub-pixel level position of all the spots on the block area can be obtained by the equalization grid method.
  • the fluorescence image registration method acquires at least one fluorescent image of the biochip; selects an intermediate partial region of the fluorescent image; and acquires an intermediate partial region of the fluorescent image in the first direction and the first Pixels and features in two directions; selecting a plurality of first template lines according to template parameters, respectively traversing the pixels and features in the first direction and the second direction to find the plurality of first template lines a minimum value of the sum of the sums of the pixels, the position corresponding to the minimum value of the sum is the position of the trajectory line; and in the local area of the position of the trajectory line, performing pixel-level correction on the trajectory line, the performing pixel
  • the intersection of the level-corrected trajectory lines is a pixel-level trajectory intersection; the other trajectory intersection positions on the biochip are acquired according to the pixel-level trajectory intersections, and pixel-level correction is performed on the other trajectory intersections; Position of the pixel-level trajectory line, obtaining a sub-pixel level position of the trajectory line; acquiring the biochip upper level by
  • An embodiment of the present invention further provides a gene sequencer, including a memory, a processor, and a computer program stored on the memory and operable on the processor, wherein the processor executes the program to implement any of the above embodiments.
  • the gene sequencer may include a chip platform, an optical system, and a liquid path system.
  • the chip platform can be used to load a biochip
  • the optical system can be used to acquire a fluorescent image
  • the liquid path system can be used for biochemical reaction using a preset reagent.
  • the gene sequencer 1 includes a memory 10 in which a gene sequencing system 100 is stored.
  • the gene sequencing system 100 can acquire at least one fluorescent image of the biochip; select an intermediate partial region of the fluorescent image; and acquire pixels in the first direction and the second direction of the intermediate partial region of the fluorescent image Selecting a plurality of first template lines according to the template parameters, respectively traversing the pixels and features in the first direction and the second direction to find a sum of pixels corresponding to the plurality of first template lines a minimum value, a position corresponding to the minimum value of the sum is a position of the trajectory line; a pixel level correction is performed on the trajectory line in a partial region of the position of the trajectory line, and the trajectory line for performing pixel level correction
  • the intersection point is a pixel-level trajectory intersection; acquiring other trajectory intersection points on the biochip according to the pixel-level trajectory intersection and performing pixel-level correction on the other trajectory intersections; and correct
  • the genetic sequencer 1 may further include a display screen 20 and a processor 30.
  • the memory 10 and the display screen 20 can be electrically connected to the processor 30, respectively.
  • the memory 10 can be a different type of storage device for storing various types of data.
  • it may be the memory of the genetic sequencer 1 or the memory, or may be a memory card externally connected to the genetic sequencer 1, such as a flash memory, a SIM card (Smart Media Card), an SD card (Secure Digital Card, Secure digital card) and so on.
  • the memory 10 may include a high-speed random access memory, and may also include a non-volatile memory such as a hard disk, a memory, a plug-in hard disk, a smart memory card (SMC), and a secure digital (Secure Digital, SD).
  • SMC smart memory card
  • SD Secure Digital
  • Card flash card, at least one disk storage device, flash device, or other volatile solid state storage device.
  • the memory 10 is used to store various types of data, for example, various types of applications installed in the genetic sequencer 1, information set and acquired by applying the above-described fluorescent image registration method, and the like.
  • the display screen 20 is mounted to the genetic sequencer 1 for displaying information.
  • the processor 30 is configured to execute the fluorescent image registration method and various types of software installed in the genetic sequencer 1, such as an operating system and application display software.
  • the processor 30 includes, but is not limited to, a Central Processing Unit (CPU), a Micro Controller Unit (MCU), and the like for interpreting computer instructions and processing data in the computer software.
  • CPU Central Processing Unit
  • MCU Micro Controller Unit
  • the genetic sequencing system 100 can include one or more modules that are stored in the memory 10 of the genetic sequencer 1 and configured to be comprised by one or more processors (this embodiment is A processor 30) executes to perform embodiments of the present invention.
  • the gene sequencing system 100 can include an image acquisition module 11, a region selection module 12, a pixel and acquisition module 13, a value minimum value finding module 14, a pixel level correction module 15, and other track intersections.
  • the module referred to in the embodiment of the present invention may be a program segment that performs a specific function, and is more suitable than the program to describe the execution process of the software in the processor.
  • the genetic sequencer 1 may include some or all of the functional modules shown in FIG. 3, and the functions of the respective modules will be specifically described below. It should be noted that the same noun related terms and their specific explanations in the respective embodiments of the above fluorescent image registration method can also be applied to the following functions for each module. In order to save space and avoid duplication, we will not repeat them here.
  • the image acquisition module 11 can be configured to acquire at least one fluorescent image of the biochip.
  • the region selection module 12 can be used to select an intermediate local region of the fluorescent image.
  • the pixel and acquisition module 13 can be configured to acquire pixels and features in the first direction and the second direction of the intermediate partial region of the fluorescent image, the first direction being perpendicular to the second direction.
  • the value minimum value finding module 14 may be configured to select, according to the template parameter, a plurality of first template lines, respectively traversing the pixels and features in the first direction and the second direction to find the plurality of first templates.
  • the minimum value of the sum of the pixels corresponding to the line, and the position corresponding to the minimum value of the sum is the position of the track line.
  • the pixel level correction module 15 can be configured to perform pixel level correction on the trajectory line in a local area of the trajectory line position, where the intersection of the trajectory lines for pixel level correction is a pixel level trajectory intersection.
  • the other track intersection location acquiring module 16 may be configured to acquire other track intersection locations on the biochip according to the pixel level track intersection and perform pixel level correction on the other track intersections.
  • the centroid correction module 17 can be configured to correct the position of the pixel-level trajectory according to the centroid method to obtain the sub-pixel level position of the trajectory.
  • the sub-pixel level location acquisition module 18 can be used to obtain sub-pixel level locations uniformly distributed on the surface of the biochip surface by the equalization grid method.
  • Embodiments of the present invention also provide a non-transitory computer readable storage medium having stored thereon a computer program that, when executed by a processor, implements the steps of the fluorescent image registration method of any of the above embodiments.
  • the gene sequencing system/gene sequencer/computer device integrated module/unit can be stored in a computer readable storage medium if it is implemented in the form of a software functional unit and sold or used as a standalone product. Based on such understanding, the present invention implements all or part of the processes in the foregoing embodiments, and may also be completed by a computer program to instruct related hardware.
  • the computer program may be stored in a computer readable storage medium. The steps of the various method embodiments described above may be implemented when the program is executed by the processor.
  • the computer program comprises computer program code, which may be in the form of source code, object code form, executable file or some intermediate form.
  • the computer readable storage medium may include any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a removable hard disk, a magnetic disk, an optical disk, a computer memory, a read only memory (ROM, Read-Only Memory). ), random access memory (RAM), electrical carrier signals, telecommunications signals, and software distribution media.
  • the so-called processor can be a central processing unit (CPU), or other general-purpose processor, digital signal processor (DSP), application specific integrated circuit (ASIC), ready-made Field-Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, etc.
  • the general purpose processor may be a microprocessor or the processor or any conventional processor or the like, which is the control center of the gene sequencing system/gene sequencer, and connects the entire gene sequencing using various interfaces and lines. Various parts of the system/gene sequencer.
  • the memory is for storing the computer program and/or module, the processor implementing the gene by running or executing a computer program and/or module stored in the memory, and calling data stored in the memory Various functions of the sequencing system/gene sequencer.
  • the memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application required for at least one function (such as a sound playing function, an image playing function, etc.), and the like.
  • the memory may include a high-speed random access memory, and may also include non-volatile memory such as a hard disk, a memory, a plug-in hard disk, a smart memory card (SMC), and a Secure Digital (SD) card. , Flash Card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.
  • FIG. 4A is a characteristic effect diagram of a local area in a fluorescent image according to an embodiment of the present invention
  • FIG. 4B is an effect diagram of enlarging an area in FIG. 4A.
  • the fluorophores are distributed on the biochip in a predetermined regularity, and by special design and processing, some positions on the biochip can be made free of fluorescence.
  • Fig. 4A in the case where the fluorophore is more than 25% (four bases balanced) at a random position, the position of the boundary frame of the non-illumination is highlighted.
  • FIG. 4A in the case where the fluorophore is more than 25% (four bases balanced) at a random position, the position of the boundary frame of the non-illumination is highlighted.
  • the number of boundary wire frames in the horizontal direction is 2 (the position pointed by the black arrow is the position where the boundary wire frame is located), and the number of boundary wire frames in the vertical direction is 3.
  • the boundary frame in the horizontal direction and the vertical direction are respectively enlarged, as shown in FIG. 4B.
  • the boundary frame highlighted may be composed of three fluorophore positions, and the fluorescence of the middle row There are bright spots in the group, and the fluorophores on both sides of the middle row are not bright.
  • the fluorophores in the middle row form a trajectory line, and the fluorophores on both sides of the middle row form a dark line.
  • the highlighted boundary line frame can include the trajectory line and a dark line on both sides of the trajectory line.
  • FIG. 5A is a partial view of a central portion of a fluorescent image according to an embodiment of the present invention
  • FIG. 5B is a schematic diagram of pixels and features of the intermediate partial region image provided in FIG. 5A in a vertical direction.
  • a rectangular area in which the fluorescent image is 80% in the horizontal direction and 10% in the vertical direction (the white rectangular frame selection portion) is selected.
  • the middle partial region of the fluorescent image there are four boundary wire frames in the vertical direction and two boundary wire frames in the horizontal direction. Taking the vertical direction as an example, pixels and features in the vertical direction of the intermediate partial region of the fluorescent image are calculated.
  • FIG. 5B the horizontal axis in FIG.
  • FIG. 5B represents the pixel position coordinates of the local partial region of the fluorescent image on the biochip, and the vertical axis represents the sum of the pixel gradation values.
  • the position of four pixels and the lowest value appears in Fig. 5B.
  • the positions of the four pixels and the lowest value correspond to four boundary wireframe positions on the local area in the middle of the fluorescent image in the vertical direction.
  • the position of the black ellipse mark is the position of the second line (from left to right) of the boundary line frame in the vertical direction of the middle portion of the fluorescent image provided in FIG. 5A.
  • FIG. 6A is a schematic diagram of searching for the pixels and features shown in FIG. 5B by using three first template lines
  • FIG. 6B is searching for the pixels shown in FIG. 5B by using three first template lines.
  • FIG. 6C is yet another schematic diagram of searching for the pixels and features shown in FIG. 5B using three first template lines.
  • Each of the three first template lines is fixedly searched for and searched for, and the distance between the three first template lines is fixed but different, for example, between the first first template line and the second first template line. The distance is less than the distance between the second first template line and the third first template line.
  • FIG. 7 is a schematic diagram showing the sum of three first template line pixels. As shown in FIG. 7, the selected portion of the black circle corresponds to the sum of the sum of the three first template line pixels corresponding to the position, and the position with the smallest value corresponds to the approximate position of the track line.
  • FIG. 8A is a partial area diagram of a trajectory line according to an embodiment of the present invention
  • FIG. 8B is a schematic diagram of pixels and features of a trajectory line partial area diagram in a vertical direction
  • FIG. 8C is a diagram of FIG. 8B.
  • the white solid-line frame selection portion is a partial region of the selected trajectory line, and traverses the pixel sum of the local region of the trajectory line in a single direction (horizontal direction or vertical direction). Taking the vertical direction as an example, as shown in FIG.
  • a W-shaped line characteristic curve (selected portion of the black elliptical circle) appears.
  • the W-shaped line characteristic curve is enlarged, as shown in FIG. 8C.
  • the W-shaped line features there are two valley positions and one peak position. It can be understood that the two trough positions are positions corresponding to dark lines on both sides of the track line on the biochip, and the pixels and values of the positions corresponding to the dark lines are low.
  • the position corresponding to the peak in the W-shaped line feature is the position corresponding to the trajectory line, and the pixel and the value of the position corresponding to the trajectory line are higher.
  • FIG. 9 is a schematic diagram showing the sum value of pixel sums obtained according to the pixel and the feature shown in FIG. 8B.
  • two third template lines with a preset distance of 4 are selected to traverse the pixels and features of the local area of the position of the trajectory line. Let the distance "4" be selected based on the biochip template parameters.
  • the interval on the horizontal axis has a sum of the sums of the sums of the pixels between (40, 60).
  • the position corresponding to the minimum value is the pixel level position of the trough in the W-line feature.
  • the pixel level position of the trace line can be obtained according to the pixel level position of the trough.
  • FIG. 10 is a schematic diagram of deriving other trajectory intersections by using pixel-level trajectory intersections according to an embodiment of the present invention.
  • the light gray dots indicate the acquired pixel level track intersection positions.
  • the arrangement between the first set of trajectory lines and the second set of trajectory lines is regular, and the arrangement of the trajectory intersections is also regular.
  • the positions of other trajectory intersections on the biochip are obtained by using corresponding rules.
  • the positions of the other track intersections obtained at this time are approximate positions, and pixel-level corrections are needed for other track intersection points to obtain pixel-level positions of other track intersection points.
  • FIG. 11A is a partial area diagram of a pixel-level trajectory line
  • FIG. 11B is a schematic diagram of pixels and features of a local area of a pixel-level trajectory line in a horizontal direction.
  • a width of 3 pixels and a region of length of 50 pixels may be selected as the partial region of the pixel-level trajectory line (the selected portion of the white line frame is a pixel-level trajectory local region).
  • Obtaining a position of a center of gravity of a local region of the pixel-level trajectory line, and a trajectory line passing through the center of gravity position in a vertical direction is a sub-pixel level trajectory line.
  • FIG. 11A is a partial area diagram of a pixel-level trajectory line
  • FIG. 11B is a schematic diagram of pixels and features of a local area of a pixel-level trajectory line in a horizontal direction.
  • a width of 3 pixels and a region of length of 50 pixels may be selected as the partial region of the pixel-level trajectory line (the selected portion of
  • the horizontal axis represents the pixel position coordinates of the local region of the pixel-level trajectory line in the horizontal direction
  • the vertical axis represents the sum of the pixel gradation values.
  • the horizontal axis coordinate interval is (0, 2).
  • the pixel sum value reaches a maximum value when the pixel position coordinate is 1, the pixel sum value is 0 when the pixel position coordinate is 2, and the pixel sum value is between the interval (1000, 1200) when the pixel position coordinate is 0.
  • the approximate position of the position of the center of gravity on the partial area of the pixel-level trajectory line can be determined, for example, the position of the center of gravity at the left-left position of the local area of the pixel-level trajectory line.
  • FIG. 12A is a schematic diagram of the position of the pixel-level trajectory line
  • FIG. 12B is a schematic diagram of the position of the sub-pixel level trajectory line.
  • the selected black frame area represents the pixel level position of a certain track line
  • the pixel is further subdivided (the position of the pixel level track line is corrected according to the center of gravity method, and the track line is obtained.
  • Pixel-level position as shown in FIG. 12B, a sub-pixel level position is obtained, wherein a position of a center of gravity on a local area of a certain trajectory line is represented by a light gray dot. It can be understood that a straight line passing through the black dot is a trajectory.
  • a line, and the trace line is a sub-pixel level trace line.
  • Figure 13 is a schematic illustration of sub-pixel level locations for acquiring a site using a mean-division grid method.
  • the black dots represent intersections of sub-pixel level trace lines in the first direction and sub-pixel level trace lines in the second direction, the intersection locations being sub-pixel level locations.
  • the grid method acquires the sub-pixel level position of the locus on the block area.
  • the disclosed terminal and method may be implemented in other manners.
  • the system implementations described above are merely illustrative.
  • the division of the modules is only a logical function division, and the actual implementation may have another division manner.

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • General Physics & Mathematics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Chemical & Material Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Organic Chemistry (AREA)
  • General Health & Medical Sciences (AREA)
  • Proteomics, Peptides & Aminoacids (AREA)
  • Molecular Biology (AREA)
  • Medical Informatics (AREA)
  • Genetics & Genomics (AREA)
  • Biophysics (AREA)
  • Biotechnology (AREA)
  • Zoology (AREA)
  • Wood Science & Technology (AREA)
  • Analytical Chemistry (AREA)
  • Biochemistry (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Quality & Reliability (AREA)
  • Radiology & Medical Imaging (AREA)
  • Evolutionary Biology (AREA)
  • Immunology (AREA)
  • Microbiology (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Chemical & Material Sciences (AREA)
  • Chemical Kinetics & Catalysis (AREA)
  • Medicinal Chemistry (AREA)
  • Investigating, Analyzing Materials By Fluorescence Or Luminescence (AREA)
  • Image Analysis (AREA)

Abstract

一种荧光图像配准方法，获取生物芯片的至少一荧光图像(S101)；选取所述荧光图像的中间区域中间局部区域(S102)；获取所述荧光图像的中间局部区域在第一方向与第二方向上的像素和特征，所述第一方向垂直于所述第二方向(S103)；根据模板参数选取若干条第一模板线分别在所述第一方向与所述第二方向上的所述像素和特征遍历寻找所述若干条第一模板线对应的像素和的和值最小值，所述和值最小值对应的位置为所述轨迹线位置(S104)；在所述轨迹线位置的局部区域内，对所述轨迹线进行像素级修正，所述进行像素级修正的轨迹线的交点为像素级轨迹交叉点(S105)；根据所述像素级轨迹交叉点获取生物芯片上其他轨迹交叉点位置并对所述其他轨迹交叉点进行像素级修正(S106)；根据重心法修正像素级轨迹线的位置，得到所述轨迹线的亚像素级位置(S107)；通过均分画网格方法获取均匀分布于所述生物芯片表面上位点的亚像素级位置(S108)。还提供一种基因测序系统、基因测序仪与存储介质，可优化荧光图像中荧光基团的定位与配准操作。

Description

荧光图像配准方法、基因测序仪及系统、存储介质 技术领域
本发明涉及基因测序领域，具体的，涉及一种荧光图像配准方法、基因测序仪、基因测序系统以及存储介质。
背景技术
本部分旨在为权利要求书及具体实施方式中陈述的本发明实施例的实施方式提供背景或上下文。此处的描述不因为包括在本部分中就承认是现有技术。
基因测序是指分析特定DNA片段的碱基序列，即腺嘌呤(A),胸腺嘧啶(T)，胞嘧啶(C)与鸟嘌呤(G)的排列方式。目前常用的测序方法之一是：上述四种碱基分别携带四种不同的荧光基团，不同的荧光基团受激发后发射出不同波长(颜色)的荧光，通过识别该荧光波长就能够识别出被合成碱基的类型，从而读取碱基序列。二代测序技术采用高分辨显微成像系统，拍照采集生物芯片(基因测序芯片)上的DNA纳米球分子(即DNB，DNA Nanoballs)的荧光分子图像，将荧光分子图像送入碱基识别软件解码图像信号得到碱基序列。在实际测序过程中，若存在同个场景多张图，首先就需要通过定位和配准方法将同一场景的图进行对齐，再通过相应的算法提取点信号，进行后续的亮度信息分析处理得到碱基序列。随着二代测序技术的发展，测序仪产品均配套有测序数据实时处理分析软件，其中大部分配套有配准和定位算法。
现有的配准技术大多是基于荧光图像本身的特征进行内容相似度匹配，根据不同的目标特征进行特征提取和配准。然而，对于荧光分子的信号，在高分辨率显微图像下均为点光源信号，一般情况下采用的是邻域重心法，即提取每个点的邻域像素值求重心。但二代测序 的碱基——荧光信号密度大，且存在不亮的目标点，难以采用通常的算法进行定位。
发明内容
鉴于此，有必要提供一种荧光图像配准方法、基因测序仪、基因测序系统以及存储介质，可优化荧光图像中荧光基团的定位与配准操作。
本发明实施例一方面提供一种荧光图像配准方法，应用于生物芯片，所述生物芯片上轨迹线之间的像素距离为模板参数，所述荧光图像配准方法包括：
获取生物芯片的至少一荧光图像；
选取所述荧光图像的中间局部区域；
获取所述荧光图像的中间局部区域在第一方向与第二方向上的像素和特征；
根据模板参数选取若干条第一模板线分别在所述第一方向与所述第二方向上的所述像素和特征中遍历寻找所述若干条第一模板线对应的像素和的和值最小值，所述和值最小值对应的位置为所述轨迹线位置；
在所述轨迹线位置的局部区域内，对所述轨迹线进行像素级修正，所述进行像素级修正的轨迹线的交点为像素级轨迹交叉点；
根据所述像素级轨迹交叉点获取所述生物芯片上其他轨迹交叉点位置并对所述其他轨迹交叉点进行像素级修正；
根据重心法修正像素级轨迹线的位置，得到所述轨迹线的亚像素级位置；
通过均分画网格方法获取均匀分布于所述生物芯片表面上位点的亚像素级位置。
进一步的，在本发明实施例提供的上述的荧光图像配准方法中，所述获取所述荧光图像的中间局部区域在第一方向与第二方向上的 像素和特征的步骤包括：
选取若干条第二模板线；
分别在所述第一方向与所述第二方向上依次将所述第二模板线在所述荧光图像中间局部区域进行平移操作；
计算荧光图像中间局部区域上第二模板线所在位置覆盖到的像素的灰度值的叠加和，所述灰度值的叠加和即为所述第二模板线所在位置覆盖到的像素的灰度值之和。
进一步的，在本发明实施例提供的上述的荧光图像配准方法中，所述根据模板参数选取若干条第一模板线分别在所述第一方向与所述第二方向上的所述像素和特征中遍历寻找所述若干条第一模板线对应的像素和的和值最小值包括：
根据模板参数选取若干条第一模板线；
利用所述第一方向与所述第二方向上的像素和特征计算所述若干条第一模板线对应像素和的和值；
获取所述像素和的和值的最小值。
进一步的，在本发明实施例提供的上述的荧光图像配准方法中，所述在所述轨迹线位置的局部区域内，对所述轨迹线进行像素级修正的步骤包括：
获取所述轨迹线位置的局部区域在所述第一方向与所述第二方向上的像素和特征；
选取相距为预设距离的若干条第三模板线遍历寻找所述轨迹线位置的局部区域的像素和特征；
获取所述相距为预设距离的若干条第三模板线对应的像素和的和值最小值；
根据所述和值最小值对应的位置获取所述轨迹线的像素级位置。
进一步的，在本发明实施例提供的上述的荧光图像配准方法中，所述根据所述和值最小值对应的位置获取所述轨迹线的像素级位置的步骤包括：
根据所述和值最小值对应的位置获取W型线特征中的第一波谷 的像素级位置，所述轨迹线位置的局部区域的像素和特征中包含所述W型线特征；
根据所述第一波谷的像素级位置获取所述轨迹线的像素级位置。
进一步的，在本发明实施例提供的上述的荧光图像配准方法中，所述根据重心法修正像素级轨迹线的位置的步骤包括：
选取像素级轨迹线的局部区域；
获取所述像素级轨迹线的局部区域的重心位置；
根据所述重心位置获取所述轨迹线的亚像素级位置。
进一步的，在本发明实施例提供的上述的荧光图像配准方法中，所述通过均分画网格方法获取均匀分布于所述生物芯片表面上位点的亚像素级位置的步骤包括：
获取在所述第一方向与所述第二方向上相邻两个亚像素级轨迹交叉点组成的区块区域，所述区块区域以预设规则排布所述位点；
通过均分画网格方法获取所述区块区域上位点的亚像素级位置。
本发明实施例再以方面还提供一种基因测序系统，应用于生物芯片，所述生物芯片上轨迹线之间的像素距离为模板参数，所述基因测序系统包括：
图像获取模块，用于获取生物芯片的至少一荧光图像；
区域选取模块，用于选取所述荧光图像的中间局部区域；
像素和获取模块，用于获取所述荧光图像的中间局部区域在所述第一方向与所述第二方向上的像素和特征，所述第一方向垂直于所述第二方向；
和值最小值寻找模块，用于根据模板参数选取若干条第一模板线分别在所述第一方向与所述第二方向上的所述像素和特征中遍历寻找所述若干条第一模板线对应的像素和的和值最小值，所述和值最小值对应的位置为所述轨迹线位置；
像素级修正模块，用于在所述轨迹线位置的局部区域内，对所述轨迹线进行像素级修正，所述进行像素级修正的轨迹线的交点为像素级轨迹交叉点；
其他轨迹交叉点位置获取模块，用于根据所述像素级轨迹交叉点获取所述生物芯片上其他轨迹交叉点位置并对所述其他轨迹交叉点进行像素级修正；
重心法修正模块，用于根据重心法修正像素级轨迹线的位置，得到所述轨迹线的亚像素级位置；
亚像素级位点获取模块，用于通过均分画网格方法获取均匀分布于所述生物芯片表面上位点的亚像素级位置。
本发明实施例再一方面还提供一种基因测序仪，所述基因测序仪包括处理器，所述处理器用于执行存储器中存储的计算机程序时实现上述任意一项所述的荧光图像配准方法的步骤。
本发明实施例再一方面还提供一种非易失性计算机可读存储介质，其上存储有计算机程序，所述计算机程序被处理器执行时实现上述任意一项所述的荧光图像配准方法的步骤。
本发明实施例提供的荧光图像配准方法、基因测序系统、基因测序仪以及存储介质，获取生物芯片的至少一荧光图像；选取所述荧光图像的中间局部区域；获取所述荧光图像的中间局部区域在所述第一方向与所述第二方向上的像素和特征；根据模板参数选取若干条第一模板线分别在所述第一方向与所述第二方向上的所述像素和特征中遍历寻找所述若干条第一模板线对应的像素和的和值最小值，所述和值最小值对应的位置为所述轨迹线位置；在所述轨迹线位置的局部区域内，对所述轨迹线进行像素级修正，所述进行像素级修正的轨迹线的交点为像素级轨迹交叉点；根据所述像素级轨迹交叉点获取所述生物芯片上其他轨迹交叉点位置并对所述其他轨迹交叉点进行像素级修正；根据重心法修正像素级轨迹线的位置，得到所述轨迹线的亚像素级位置；通过均分画网格方法获取所述生物芯片上位点的亚像素级位置。利用本发明实施例，可优化荧光图像中荧光基团的定位与配准操作，本发明实施例对于不同大小、不同分辨率下的目标特征的定位具有较高的准确性和高效性；利用本发明实施例可以准确快速的定位点信号的亚像素级位置，且能通过设置参数，简便的适用于不同的点 阵列图像，抗干扰能力强，适用性广。
附图说明
图1是本发明实施例提供荧光图像配准方法的流程图。
图2是本发明一实施方式的基因测序仪的结构示意图。
图3是图2所示的基因测序仪的示例性的功能模块图。
图4A是本发明实施例提供的荧光图像中局部区域的特征效果图。
图4B是将图4A中某一区域进行放大后的效果图。
图5A是本发明实施例提供的荧光图像中间局部区域图。
图5B是图5A中提供的中间局部区域图在垂直方向上的像素和特征示意图。
图6A是利用三条第一模板线搜索图5B所示的像素和特征的一示意图。
图6B是利用三条第一模板线搜索图5B所示的像素和特征的另一示意图。
图6C是利用三条第一模板线搜索图5B所示的像素和特征的又一示意图。
图7是三条第一模板线像素和的和特征示意图。
图8A是本发明实施例提供轨迹线局部区域图。
图8B是轨迹线局部区域图在垂直方向上的像素和特征示意图。
图8C是将图8B中选中的区域进行放大后的示意图。
图9是根据图8B所示像素和特征获取的像素和的和值特征示意图。
图10是本发明实施例提供的利用像素级轨迹交叉点推导出其他轨迹交叉点的示意图。
图11A是像素级轨迹线的局部区域示意图。
图11B是像素级轨迹线的局部区域在水平方向的像素和特征示意图。
图12A是像素级轨迹线的位置示意图。
图12B是亚像素级轨迹线的位置示意图。
图13是利用均分画网格方法获取位点的亚像素级位置的示意图。
主要元件符号说明
基因测序仪 1
存储器 10
显示屏 20
处理器 30
图像获取模块 11
区域选取模块 12
像素和获取模块 13
和值最小值寻找模块 14
像素级修正模块 15
其他轨迹交叉点位置获取模块 16
重心法修正模块 17
亚像素级位点获取模块 18
如下具体实施方式将结合上述附图进一步说明本发明实施例。
具体实施方式
为了能够更清楚地理解本发明实施例的上述目的、特征和优点，下面结合附图和具体实施方式对本发明进行详细描述。需要说明的是，在不冲突的情况下，本申请的实施方式中的特征可以相互组合。
在下面的描述中阐述了很多具体细节以便于充分理解本发明实施例，所描述的实施方式仅仅是本发明一部分实施方式，而不是全部 的实施方式。基于本发明中的实施方式，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施方式，都属于本发明实施例保护的范围。
除非另有定义，本文所使用的所有的技术和科学术语与属于本发明实施例的技术领域的技术人员通常理解的含义相同。本文中在本发明的说明书中所使用的术语只是为了描述具体的实施方式的目的，不是旨在于限制本发明实施例。
图1是本发明实施例提供荧光图像配准方法的流程图。如图1所示，所述荧光图像配准方法可以包括如下步骤：
S101：获取生物芯片的至少一荧光图像。
在一实施方式中，所述生物芯片可以是基因测序芯片，所述荧光图像可以是测序时拍摄荧光信号图像。在测序过程中，可以利用显微相机对所述生物芯片进行拍摄获取荧光信号图像。显微相机的视场较小，约为768.6μm*648μm，对一张生物芯片可以拍摄几百幅视场(FOV，field of view)。每个视场中相邻2条水平和垂直方向上的轨迹线之间形成的区域称为一个区块(block),区块分为内部区块和外部区块。所述生物芯片上每个区块内均匀分布有若干个位点，所述位点可以吸附DNA纳米球分子(DNB)，所述DNA纳米球分子可以是包括DNA片段的扩增产物。所述DNA纳米球分子在合成碱基时携带有荧光基团，荧光基团受激发时会发出荧光信号。部分所述位点按照预设规则排列形成在第一方向上平行分布的第一组轨迹线(trackline)及在第二方向上平行分布的第二组轨迹线，所述第一方向可以是水平方向，所述第二方向可以是垂直方向。所述第一组轨迹线与所述第二组轨迹线之间的交叉点为轨迹交叉点(trackcross)。
S102：选取所述荧光图像的中间局部区域。
在一实施方式中，所述荧光基团可以按照预设规则固定排列在所述生物芯片上，通过特殊的设计和处理，所述生物芯片上某些位置没有位点存在，也即没有荧光基团存在。在所述荧光基团大于25％(腺嘌呤(A),胸腺嘧啶(T)，胞嘧啶(C)与鸟嘌呤(G)四种碱基均衡) 随机位置发光的情况下，不发光的边界线框就凸显出来。凸显出来的所述边界线框可以由三个荧光基团位置组成，中间排的荧光基团存在亮点，中间排的两边位置的荧光基团均不亮。中间排的荧光基团形成轨迹线，中间排的两边位置的荧光基团形成暗线。可以理解的是，凸显出来的所述边界线框可以包括所述轨迹线和所述轨迹线两侧的暗线。在暗线线框的局部范围内，可以忽略成像畸变。
在一实施方式中，可以选取所述荧光图像在所述第一方向上80％的宽大小，在所述第二方向上10％的长大小的区域作为所述荧光图像的中间局部区域。所述荧光图像的中间局部区域可以在所述第一方向与所述第二方向上包含至少一条轨迹线。
S103：获取所述荧光图像的中间局部区域在第一方向与第二方向上的像素和特征，所述第一方向垂直于所述第二方向。
本实施方式中，所述获取所述荧光图像的中间局部区域在第一方向与第二方向上的像素和特征的步骤可以包括：选取若干条第二模板线，所述第二模板线的数量可以为1，分别在所述第一方向与所述第二方向上依次将所述第二模板线在所述荧光图像中间局部区域进行平移操作。计算荧光图像中间局部区域上的第二模板线所在位置覆盖到的像素的灰度值的叠加和，所述灰度值的叠加和即为所述第二模板线所在位置覆盖到的像素的灰度值之和。可以理解的是，当所述第二模板线分别在所述第一方向与所述第二方向上沿着所述荧光图像中间局部区域平移操作结束之后，可以获取所述荧光图像中间局部区域在所述第一方向与所述第二方向上的像素和特征。所述边界线框所在的位置对应像素和特征中像素和最低值对应的位置。在本发明中，为了便于表述，“像素和”是指像素的灰度值之和。
S104：根据模板参数选取若干条第一模板线分别在所述第一方向与所述第二方向上的所述像素和特征中遍历寻找所述若干条第一模板线对应的像素和的和值最小值，所述和值最小值对应的位置为所述轨迹线位置。
本实施方式中，根据模板参数选取若干条第一模板线，所述第一 模板线的数量可以为3。所述模板参数表明所述第一模板线之间的像素距离固定。所述第一模板线两两之间的像素距离可以相同，也可以不相同。根据所述荧光图像的中间局部区域在所述第一方向与所述第二方向上的像素和特征，利用选取的若干条第一模板线固定依序在所述像素和特征中搜索，获取所述若干条第一模板线对应的像素和的和值特征。可以理解的是，当若干条第一模板线的位置都位于所述像素和特征中的像素和最低值所在位置附近时，此时，所述若干条第一模板线对应的像素和的和值最小。和值最小值对应的位置为所述轨迹线的位置，此时获取的所述轨迹线的位置为所述轨迹线的大致位置。本发明所称的模板参数是指设计生物芯片模板的参数。
S105：在所述轨迹线位置的局部区域内，对所述轨迹线进行像素级修正，所述进行像素级修正的轨迹线的交点为像素级轨迹交叉点。
本实施方式中，分别获取所述轨迹线位置的局部区域在所述第一方向与所述第二方向上的像素和特征；以垂直方向为例，选取相距为预设距离的若干条第三模板线遍历寻找所述轨迹线位置的局部区域的像素和特征。所述轨迹线位置的局部区域的像素和特征中包含W型线特征。W型线特征中两个波谷对应的位置为所述轨迹线两侧暗线对应的位置，暗线对应的位置的像素和值较低。W型线特征中波峰对应的位置为所述轨迹线对应的位置，所述轨迹线对应的位置的像素和值较高。获取所述相距为预设距离的若干条第三模板线对应的像素和的和值最小值。若干条第三模板线对应的像素和的和值最小值对应的位置为W型线特征中波谷的位置，由于波谷与波峰之间的像素距离固定，因而可以根据波谷的位置得到波峰的位置。可以理解的是，波峰的位置则对应所述轨迹线的位置，因而可以根据所述和值最小值对应的位置获取所述轨迹线的像素级位置，进行像素级修正的轨迹线的交点为像素级轨迹交叉点。应当理解的是，轨迹交叉点是一个虚拟的点，在这个点的位置并不一定设置了实际位点，该位点也不一定发光。第一模板线、第二模板线和第三模板线，也并不是实际存在的线条，而是为了便于本发明的描述才设置的虚拟线。
S106：根据所述像素级轨迹交叉点获取所述生物芯片上其他轨迹交叉点位置并对所述其他轨迹交叉点进行像素级修正。
本实施方式中，在所述生物芯片上，部分所述位点按照预设规则排列形成在第一方向上平行分布的第一组轨迹线及在第二方向上平行分布的第二组轨迹线。可以理解的是，所述第一组轨迹线与所述第二组轨迹线之间的排列是有规则的，所述轨迹交叉点的排列也是有规则的。已知一个轨迹交叉点的像素级位置，可以根据相应的规则得到所述生物芯片上其他轨迹交叉点的大致位置，再对所述其他轨迹线交叉点进行像素级修正，得到其他轨迹交叉点的像素级位置。
S107：根据重心法修正像素级轨迹线的位置，得到所述轨迹线的亚像素级位置。
本实施方式中，以垂直方向为例，获取所述像素级轨迹线的局部区域，可以选取3个像素的宽度，50个像素的长度的区域作为所述像素级轨迹线局部区域。获取所述像素级轨迹线的局部区域的重心位置，在垂直方向上通过所述重心位置的轨迹线为亚像素级轨迹线，即根据所述重心位置获取所述轨迹线的亚像素级位置。同理，获取在水平方向上所述轨迹线的亚像素级位置，亚像素级轨迹线的交点为亚像素级轨迹线交叉点。
S108：通过均分画网格方法获取均匀分布于所述生物芯片表面上位点的亚像素级位置。
本实施方式中，获取在所述第一方向与所述第二方向上相邻两个亚像素级轨迹交叉点组成的区块区域，所述区块区域以预设规则排布所述位点；通过均分画网格方法可以获取所述区块区域上所有位点的亚像素级位置。
本发明实施例提供的荧光图像配准方法，获取生物芯片的至少一荧光图像；选取所述荧光图像的中间局部区域；获取所述荧光图像的中间局部区域在所述第一方向与所述第二方向上的像素和特征；根据模板参数选取若干条第一模板线分别在所述第一方向与所述第二方向上的所述像素和特征中遍历寻找所述若干条第一模板线对应的像 素和的和值最小值，所述和值最小值对应的位置为所述轨迹线位置；在所述轨迹线位置的局部区域内，对所述轨迹线进行像素级修正，所述进行像素级修正的轨迹线的交点为像素级轨迹交叉点；根据所述像素级轨迹交叉点获取所述生物芯片上其他轨迹交叉点位置并对所述其他轨迹交叉点进行像素级修正；根据重心法修正所述像素级轨迹线的位置，得到所述轨迹线的亚像素级位置；通过均分画网格方法获取所述生物芯片上位点的亚像素级位置。利用本发明实施例，可优化荧光图像中荧光基团的定位与配准操作。
以上是对本发明实施例所提供的方法进行的详细描述。下面对本发明实施例所提供的基因测序仪进行描述。
本发明实施例还提供一种基因测序仪，包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，所述处理器执行所述程序时实现上述任一实施方式中所述的荧光图像配准方法的步骤。需要说明的是，所述基因测序仪可以包括芯片平台、光学系统、液路系统。其中，所述芯片平台可以用于装载生物芯片，所述光学系统可以用于获取荧光图像，所述液路系统可以用于利用预设的试剂进行生化反应。
图2是本发明一实施方式的基因测序仪的结构示意图。如图2所示，基因测序仪1包括存储器10，存储器10中存储有基因测序系统100。所述基因测序系统100可以获取生物芯片的至少一荧光图像；选取所述荧光图像的中间局部区域；获取所述荧光图像的中间局部区域在所述第一方向与所述第二方向上的像素和特征；根据模板参数选取若干条第一模板线分别在所述第一方向与所述第二方向上的所述像素和特征中遍历寻找所述若干条第一模板线对应的像素和的和值最小值，所述和值最小值对应的位置为所述轨迹线位置；在所述轨迹线位置的局部区域内，对所述轨迹线进行像素级修正，所述进行像素级修正的轨迹线的交点为像素级轨迹交叉点；根据所述像素级轨迹交叉点获取所述生物芯片上其他轨迹交叉点位置并对所述其他轨迹交叉点进行像素级修正；根据重心法修正像素级轨迹线的位置，得到所 述轨迹线的亚像素级位置；通过均分画网格方法获取均匀分布于所述生物芯片表面上位点的亚像素级位置。利用本发明实施例，可优化荧光图像中荧光基团的定位与配准操作。
本实施方式中，基因测序仪1还可以包括显示屏20及处理器30。
存储器10、显示屏20可以分别与处理器30电连接。
所述的存储器10可以是不同类型存储设备，用于存储各类数据。例如，可以是基因测序仪1的存储器、内存，还可以是可外接于该基因测序仪1的存储卡，如闪存、SM卡(Smart Media Card，智能媒体卡)、SD卡(Secure Digital Card，安全数字卡)等。此外，存储器10可以包括高速随机存取存储器，还可以包括非易失性存储器，例如硬盘、内存、插接式硬盘，智能存储卡(Smart Media Card,SMC)，安全数字(Secure Digital,SD)卡，闪存卡(Flash Card)、至少一个磁盘存储器件、闪存器件、或其他易失性固态存储器件。存储器10用于存储各类数据，例如，所述基因测序仪1中安装的各类应用程序(Applications)、应用上述荧光图像配准方法而设置、获取的数据等信息。
显示屏20安装于基因测序仪1，用于显示信息。
处理器30用于执行所述荧光图像配准方法以及所述基因测序仪1内安装的各类软件，例如操作系统及应用显示软件等。处理器30包含但不限于处理器(Central Processing Unit，CPU)、微控制单元(Micro Controller Unit，MCU)等用于解释计算机指令以及处理计算机软件中的数据的装置。
所述的基因测序系统100可以包括一个或多个的模块，所述一个或多个模块被存储在基因测序仪1的存储器10中并被配置成由一个或多个处理器(本实施方式为一个处理器30)执行，以完成本发明实施例。例如，参阅图3所示，所述基因测序系统100可以包括图像获取模块11、区域选取模块12、像素和获取模块13、和值最小值寻找模块14、像素级修正模块15、其他轨迹交叉点位置获取模块16、重心法修正模块17、亚像素级位点获取模块18。本发明实施例所称的 模块可以是完成一特定功能的程序段，比程序更适合于描述软件在处理器中的执行过程。
可以理解的是，对应上述荧光图像配准方法中的各实施方式，基因测序仪1可以包括图3中所示的各功能模块中的一部分或全部，各模块的功能将在以下具体介绍。需要说明的是，以上荧光图像配准方法的各实施方式中相同的名词相关名词及其具体的解释说明也可以适用于以下对各模块的功能介绍。为节省篇幅及避免重复起见，在此就不再赘述。
图像获取模块11可以用于获取生物芯片的至少一荧光图像。
区域选取模块12可以用于选取所述荧光图像的中间局部区域。
像素和获取模块13可以用于获取所述荧光图像的中间局部区域在第一方向与第二方向上的像素和特征，所述第一方向垂直于所述第二方向。
和值最小值寻找模块14可以用于根据模板参数选取若干条第一模板线分别在所述第一方向与所述第二方向上的所述像素和特征中遍历寻找所述若干条第一模板线对应的像素和的和值最小值，所述和值最小值对应的位置为所述轨迹线位置。
像素级修正模块15可以用于在所述轨迹线位置的局部区域内，对所述轨迹线进行像素级修正，所述进行像素级修正的轨迹线的交点为像素级轨迹交叉点。
其他轨迹交叉点位置获取模块16可以用于根据所述像素级轨迹交叉点获取所述生物芯片上其他轨迹交叉点位置并对所述其他轨迹交叉点进行像素级修正。
重心法修正模块17可以用于根据重心法修正所述像素级轨迹线的位置，得到所述轨迹线的亚像素级位置。
亚像素级位点获取模块18可以用于通过均分画网格方法获取均匀分布于所述生物芯片表面上位点的亚像素级位置。
本发明实施例还提供一种非易失性计算机可读存储介质，其上存储有计算机程序，所述计算机程序被处理器执行时实现上述任一实施 方式中的荧光图像配准方法的步骤。
所述基因测序系统/基因测序仪/计算机设备集成的模块/单元如果以软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储介质中。基于这样的理解，本发明实现上述实施方式方法中的全部或部分流程，也可以通过计算机程序来指令相关的硬件来完成，所述的计算机程序可存储于一计算机可读存储介质中，该计算机程序在被处理器执行时，可实现上述各个方法实施例的步骤。其中，所述计算机程序包括计算机程序代码，所述计算机程序代码可以为源代码形式、对象代码形式、可执行文件或某些中间形式等。所述计算机可读存储介质可以包括：能够携带所述计算机程序代码的任何实体或装置、记录介质、U盘、移动硬盘、磁碟、光盘、计算机存储器、只读存储器(ROM，Read-Only Memory)、随机存取存储器(RAM，Random Access Memory)、电载波信号、电信信号以及软件分发介质等。
所称处理器可以是中央处理单元(Central Processing Unit，CPU)，还可以是其他通用处理器、数字信号处理器(Digital Signal Processor，DSP)、专用集成电路(Application Specific Integrated Circuit，ASIC)、现成可编程门阵列(Field-Programmable Gate Array，FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件等。通用处理器可以是微处理器或者该处理器也可以是任何常规的处理器等，所述处理器是所述基因测序系统/基因测序仪的控制中心，利用各种接口和线路连接整个基因测序系统/基因测序仪的各个部分。
所述存储器用于存储所述计算机程序和/或模块，所述处理器通过运行或执行存储在所述存储器内的计算机程序和/或模块，以及调用存储在存储器内的数据，实现所述基因测序系统/基因测序仪的各种功能。所述存储器可主要包括存储程序区和存储数据区，其中，存储程序区可存储操作系统、至少一个功能所需的应用程序(比如声音播放功能、图像播放功能等)等。此外，存储器可以包括高速随机存取存储器，还可以包括非易失性存储器，例如硬盘、内存、插接式硬 盘，智能存储卡(Smart Media Card,SMC)，安全数字(Secure Digital,SD)卡，闪存卡(Flash Card)、至少一个磁盘存储器件、闪存器件、或其他易失性固态存储器件。
请参阅图4A、图4B，图4A是本发明实施例提供的荧光图像中局部区域的特征效果图，图4B是将图4A中某一区域进行放大后的效果图。所述荧光基团以预设规则分布在所述生物芯片上，通过特殊的设计和处理，可以使得所述生物芯片上的某些位置没有荧光存在。如图4A所示，在荧光基团大于25％(四种碱基均衡)随机位置发光的情况下，不发光的边界线框位置就会凸显出来。图4A中，在水平方向上的边界线框数量为2(黑色箭头指向的位置为边界线框所在的位置)，在垂直方向上边界线框数量为3。分别将水平方向与垂直方向上的边界线框进行放大处理，如图4B所示，以垂直方向为例，凸显出来的所述边界线框可以由三个荧光基团位置组成，中间排的荧光基团存在亮点，中间排的两边位置的荧光基团均不亮。中间排的荧光基团形成轨迹线，中间排的两边位置的荧光基团形成暗线。可以理解的是，凸显出来的所述边界线框可以包括所述轨迹线和所述轨迹线两侧的暗线。对于生物芯片上位点布局方式的理解，以及对于轨迹线布局方式的理解，可以参考PCT专利申请PCT/US2011/050047所公开的生化阵列芯片。
请参阅图5A、图5B，图5A是本发明实施例提供的荧光图像中间局部区域图，图5B是图5A中提供的中间局部区域图在垂直方向上的像素和特征示意图。如图5A所示，选取所述荧光图像在水平方向80％的宽大小、在垂直方向10％的长大小的矩形区域(白色矩形框框选部分)。在荧光图像的中间局部区域内，垂直方向存在四条边界线框，水平方向存在两条边界线框。以垂直方向为例，计算所述荧光图像的中间局部区域在垂直方向上的像素和特征。如图5B所示，图5B中横轴表示所述生物芯片上荧光图像中间局部区域的像素位置坐标，纵轴表示像素灰度值的和值。图5B中出现四个像素和最低值的位置。四个像素和最低值的位置对应为在垂直方向上所述荧光图像中 间局部区域上的四个边界线框位置。其中，黑色椭圆标记的位置即为图5A提供的荧光图像中间局部区域垂直方向上第二条(从左往右顺序)边界线框所在的位置。
请参阅图6A、图6B、图6C，图6A是利用三条第一模板线搜索图5B所示的像素和特征的一示意图，图6B是利用三条第一模板线搜索图5B所示的像素和特征的另一示意图，图6C是利用三条第一模板线搜索图5B所示的像素和特征的又一示意图。每三条第一模板线固定依次去和特征中搜索，所述三条第一模板线之间的距离固定但不相同，例如，第一条第一模板线与第二条第一模板线之间的距离小于第二条第一模板线与第三条第一模板线之间的距离。如图6A、图6C所示，三条第一模板线位置不同时位于所述像素和特征中和值最低值所在的位置。如图6B所示，三条第一模板线位置同时位于像素和特征中前三条和值最低值所在的位置(从左到右的顺序)，此时，这三个第一模板线位置的像素和的和值最小。像素和的和值最小值对应的位置为轨迹线的大致位置。关于这个步骤(利用三条第一模板线搜索图5B所示的像素和特征)，可以这么理解，3条虚拟的第一模板线，它们之间可以按照固定的间距，从左往右移动“滚压”像素和特征的坐标曲线(如图5B所示)，分别读取它们所“压到”的位置的像素和的值(也就是所谓的遍历)，再将这3个值相加，然后将该相加的值画入图7的坐标图中，每一次相加获得的值标至第一条第一模板线所在位置的纵坐标上。
请参阅图7，图7是三条第一模板线像素和的和特征示意图。如图7所示，黑色圆圈选中的部分对应该位置的三条第一模板线像素和的和值最小，和值最小的位置对应所述轨迹线的大致位置。
请参阅图8A、图8B、图8C，图8A是本发明实施例提供的轨迹线局部区域图，图8B是轨迹线局部区域图在垂直方向上的像素和特征示意图，图8C是将图8B中选中的区域进行放大后的示意图。如图8A所示，白色实线框框选部分即为选取轨迹线的局部区域，遍历获取轨迹线的局部区域在单方向(水平方向或垂直方向)上的像素和。 以垂直方向为例，如图8B所示，轨迹线局部区域在垂直方向上的像素和特征示意图上，出现了W型线特征曲线(黑色椭圆圈选中部分)。将W型线特征曲线放大处理，如图8C所示，W型线特征中，有两处波谷位置以及一处波峰位置。可以理解的是，两处波谷位置为所述生物芯片上所述轨迹线两侧暗线对应的位置，暗线对应的位置的像素和值较低。W型线特征中波峰对应的位置为所述轨迹线对应的位置，所述轨迹线对应的位置的像素和值较高。
请参阅图9，图9是根据图8B所示像素和特征获取的像素和的和值特征示意图。对于图8B所示的轨迹线局部区域图在垂直方向上的像素和特征示意图，选取预设距离为4的两条第三模板线遍历寻找所述轨迹线位置的局部区域的像素和特征，预设距离“4”是根据生物芯片模板参数选定的。获取预设距离为4的两条第三模板线对应的像素和的和值特征，得到和值特征中的最小值。如图9所示，横轴上区间在(40,60)之间存在像素和的和值最小值。和值最小值对应的位置为W型线特征中波谷的像素级位置。根据所述波谷的像素级位置可以获取所述轨迹线的像素级位置。
请参阅图10，图10是本发明实施例提供的利用像素级轨迹交叉点推导出其他轨迹交叉点的示意图。如图10所示，浅灰色圆点表示的是已获取的像素级轨迹交叉点位置。在所述生物芯片上，所述第一组轨迹线与所述第二组轨迹线之间的排列是有规则的，所述轨迹交叉点的排列也是有规则的。根据已获取的像素级轨迹交叉点位置，利用相应的规则得到所述生物芯片上其他轨迹交叉点(深灰色圆点表示的是其他轨迹交叉点)的位置。此时得到的其他轨迹交叉点的位置为大致位置，需对其他轨迹交叉点进行像素级修正，得到其他轨迹交叉点的像素级位置。
请参阅图11A、图11B，图11A是像素级轨迹线的局部区域示意图，图11B是像素级轨迹线的局部区域在水平方向的像素和特征示意图。如图11A所示，可以选取3个像素的宽度，50个像素的长度的区域作为所述像素级轨迹线局部区域(白色线框选中部分为像素级 轨迹线局部区域)。获取所述像素级轨迹线的局部区域的重心位置，在垂直方向上通过所述重心位置的轨迹线为亚像素级轨迹线。如图11B所示，横轴表示像素级轨迹线的局部区域在水平方向上的像素位置坐标，纵轴表示像素灰度值的和值。横轴坐标区间为(0,2)。像素和值在像素位置坐标为1时达到最大值，像素和值在像素位置坐标为2时为0，在像素位置坐标为0时，像素和值在区间(1000,1200)之间。利用图11B所示的像素级轨迹线的局部区域示意图，可以判断像素级轨迹线的局部区域上的重心位置的大致位置，例如，重心位置在像素级轨迹线的局部区域偏左的位置。
请参阅图12A、图12B，图12A是像素级轨迹线的位置示意图，图12B是亚像素级轨迹线的位置示意图。如图12A所示，其中选中的黑色框区域表示某一轨迹线的像素级位置，将所述像素再向下细分(根据重心法修正像素级轨迹线的位置，得到所述轨迹线的亚像素级位置)，如图12B所示，得到亚像素级位置，其中用浅灰色圆点表示某一轨迹线局部区域上重心的位置，可以理解的是，通过该黑色圆点的直线即为轨迹线，且所述轨迹线为亚像素级轨迹线。
图13是利用均分画网格方法获取位点的亚像素级位置的示意图。如图所示，黑色圆点表示在所述第一方向上亚像素级轨迹线与所述第二方向上亚像素级轨迹线的交叉点，所述交叉点位置为亚像素级的位置。获取在所述第一方向与所述第二方向上相邻两个亚像素级轨迹交叉点组成的区块区域，所述区块区域以预设规则排布所述位点；通过均分画网格方法获取所述区块区域上位点的亚像素级位置。
在本发明所提供的几个具体实施方式中，应该理解到，所揭露的终端和方法，可以通过其它的方式实现。例如，以上所描述的系统实施方式仅仅是示意性的，例如，所述模块的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式。
对于本领域技术人员而言，显然本发明实施例不限于上述示范性实施例的细节，而且在不背离本发明实施例的精神或基本特征的情况下，能够以其他的具体形式实现本发明实施例。因此，无论从哪一点 来看，均应将实施例看作是示范性的，而且是非限制性的，本发明实施例的范围由所附权利要求而不是上述说明限定，因此旨在将落在权利要求的等同要件的含义和范围内的所有变化涵括在本发明实施例内。不应将权利要求中的任何附图标记视为限制所涉及的权利要求。系统、装置或终端权利要求中陈述的多个单元、模块或装置也可以由同一个单元、模块或装置通过软件或者硬件来实现。
以上实施方式仅用以说明本发明实施例的技术方案而非限制，尽管参照以上较佳实施方式对本发明实施例进行了详细说明，本邻域的普通技术人员应当理解，可以对本发明实施例的技术方案进行修改或等同替换都不应脱离本发明实施例的技术方案的精神和范围。

Claims (10)

  1. 一种荧光图像配准方法，应用于生物芯片，所述生物芯片上轨迹线之间的像素距离为模板参数，其特征在于，所述荧光图像配准方法包括：
    获取生物芯片的至少一荧光图像；
    选取所述荧光图像的中间局部区域；
    获取所述荧光图像的中间局部区域在第一方向与第二方向上的像素和特征，所述第一方向垂直于所述第二方向；
    根据模板参数选取若干条第一模板线分别在所述第一方向与所述第二方向上的所述像素和特征中遍历寻找所述若干条第一模板线对应的像素和的和值最小值，所述和值最小值对应的位置为所述轨迹线位置；
    在所述轨迹线位置的局部区域内，对所述轨迹线进行像素级修正，所述进行像素级修正的轨迹线的交点为像素级轨迹交叉点；
    根据所述像素级轨迹交叉点获取所述生物芯片上其他轨迹交叉点位置并对所述其他轨迹交叉点进行像素级修正；
    根据重心法修正像素级轨迹线的位置，得到所述轨迹线的亚像素级位置；
    通过均分画网格方法获取均匀分布于所述生物芯片表面上位点的亚像素级位置。
  2. 根据权利要求1所述的荧光图像配准方法，其特征在于，所述获取所述荧光图像的中间局部区域在第一方向与第二方向上的像素和特征的步骤包括：
    选取若干条第二模板线；
    分别在所述第一方向与所述第二方向上依次将所述第二模板线在所述荧光图像中间局部区域进行平移操作；
    计算荧光图像中间局部区域上第二模板线所在位置覆盖到的像素的灰度值的叠加和，所述灰度值的叠加和即为所述第二模板线所在位置覆盖到的像素的灰度值之和。
  3. 根据权利要求2所述的荧光图像配准方法，其特征在于，所述根据模板参数选取若干条第一模板线分别在所述第一方向与所述第二方向上的所述像素和特征中遍历寻找所述若干条第一模板线对应的像素和的和值最小值的步骤包括：
    根据模板参数选取若干条第一模板线；
    利用所述第一方向与所述第二方向上的像素和特征计算所述若干条第一模板线对应像素和的和值；
    获取所述像素和的和值的最小值。
  4. 根据权利要求3所述的荧光图像配准方法，其特征在于，所述在所述轨迹线位置的局部区域内，对所述轨迹线进行像素级修正的步骤包括：
    获取所述轨迹线位置的局部区域在所述第一方向与所述第二方向上的像素和特征；
    选取相距为预设距离的若干条第三模板线遍历寻找所述轨迹线位置的局部区域的像素和特征；
    获取所述相距为预设距离的若干条第三模板线对应的像素和的和值最小值；
    根据所述和值最小值对应的位置获取所述轨迹线的像素级位置。
  5. 根据权利要求4所述的荧光图像配准方法，其特征在于，所述根据所述和值最小值对应的位置获取所述轨迹线的像素级位置的步骤包括：
    根据所述和值最小值对应的位置获取W型线特征中的波谷的像素级位置，所述轨迹线位置的局部区域的像素和特征中包含所述W型线特征；
    根据所述波谷的像素级位置获取所述轨迹线的像素级位置。
  6. 根据权利要求5所述的荧光图像配准方法，其特征在于，所述根据重心法修正像素级轨迹线的位置的步骤包括：
    选取像素级轨迹线的局部区域；
    获取所述像素级轨迹线的局部区域的重心位置；
    根据所述重心位置获取所述轨迹线的亚像素级位置。
  7. 根据权利要求6所述的荧光图像配准方法，其特征在于，所述通过均分画网格方法获取均匀分布于所述生物芯片表面上位点的亚像素级位置的步骤包括：
    获取在所述第一方向与所述第二方向上相邻两个亚像素级轨迹交叉点组成的区块区域，所述区块区域以预设规则排布所述位点；
    通过均分画网格方法获取所述区块区域上位点的亚像素级位置。
  8. 一种基因测序系统，应用于生物芯片，所述生物芯片上轨迹线之间的像素距离为模板参数，其特征在于，所述基因测序系统包括：
    图像获取模块，用于获取生物芯片的至少一荧光图像；
    区域选取模块，用于选取所述荧光图像的中间局部区域；
    像素和获取模块，用于获取所述荧光图像的中间局部区域在所述第一方向与所述第二方向上的像素和特征，所述第一方向垂直于所述第二方向；
    和值最小值寻找模块，用于根据模板参数选取若干条第一模板线分别在所述第一方向与所述第二方向上的所述像素和特征中遍历寻找所述若干条第一模板线对应的像素和的和值最小值，所述和值最小值对应的位置为所述轨迹线位置；
    像素级修正模块，用于在所述轨迹线位置的局部区域内，对所述轨迹线进行像素级修正，所述进行像素级修正的轨迹线的交点为像素级轨迹交叉点；
    其他轨迹交叉点位置获取模块，用于根据所述像素级轨迹交叉点获取所述生物芯片上其他轨迹交叉点位置并对所述其他轨迹交叉点进行像素级修正；
    重心法修正模块，用于根据重心法修正像素级轨迹线的位置，得到所述轨迹线的亚像素级位置；
    亚像素级位点获取模块，用于通过均分画网格方法获取均匀分布于所述生物芯片表面上位点的亚像素级位置。
  9. 一种基因测序仪，其特征在于，所述基因测序仪包括处理器， 所述处理器用于执行存储器中存储的计算机程序时实现如权利要求1-7任意一项所述的荧光图像配准方法的步骤。
  10. 一种非易失性计算机可读存储介质，其上存储有计算机程序，其特征在于，所述计算机程序被处理器执行时实现如权利要求1-7任意一项所述的荧光图像配准方法的步骤。
PCT/CN2018/082578 2018-04-10 2018-04-10 荧光图像配准方法、基因测序仪及系统、存储介质 WO2019196019A1 (zh)

Priority Applications (12)

Application Number Priority Date Filing Date Title
BR112020020949-0A BR112020020949A2 (pt) 2018-04-10 2018-04-10 método de registro de imagem de fluorescência, instrumento e sistema de sequenciamento genético e meio de armazenagem
AU2018418609A AU2018418609B2 (en) 2018-04-10 2018-04-10 Fluorescence image registration method, gene sequencing instrument and system, and storage medium
EP18914625.1A EP3779868B1 (en) 2018-04-10 2018-04-10 Fluorescence image registration method, gene sequencing instrument and system, and storage medium
JP2020555468A JP7072081B2 (ja) 2018-04-10 2018-04-10 蛍光画像位置合わせ方法、遺伝子シーケンサ及びシステム、記憶媒体
US17/043,226 US11682125B2 (en) 2018-04-10 2018-04-10 Fluorescence image registration method, gene sequencing instrument, and storage medium
PCT/CN2018/082578 WO2019196019A1 (zh) 2018-04-10 2018-04-10 荧光图像配准方法、基因测序仪及系统、存储介质
KR1020207032386A KR102504987B1 (ko) 2018-04-10 2018-04-10 형광 이미지 정합 방법, 유전자 시퀀서 및 시스템, 저장 매체
CA3096723A CA3096723C (en) 2018-04-10 2018-04-10 Fluorescence image registration method, gene sequencing instrument and system, and storage medium
CN201880005564.0A CN111971711A (zh) 2018-04-10 2018-04-10 荧光图像配准方法、基因测序仪及系统、存储介质
SG11202010040YA SG11202010040YA (en) 2018-04-10 2018-04-10 Fluorescence image registration method, gene sequencing instrument and system, and storage medium
RU2020134588A RU2749893C1 (ru) 2018-04-10 2018-04-10 Способ совмещения изображений флуоресценции, прибор и система секвенирования генов и запоминающая среда
US18/201,309 US20230298188A1 (en) 2018-04-10 2023-05-24 Fluorescence image registration method, gene sequencing instrument and system

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/CN2018/082578 WO2019196019A1 (zh) 2018-04-10 2018-04-10 荧光图像配准方法、基因测序仪及系统、存储介质

Related Child Applications (2)

Application Number Title Priority Date Filing Date
US17/043,226 A-371-Of-International US11682125B2 (en) 2018-04-10 2018-04-10 Fluorescence image registration method, gene sequencing instrument, and storage medium
US18/201,309 Continuation US20230298188A1 (en) 2018-04-10 2023-05-24 Fluorescence image registration method, gene sequencing instrument and system

Publications (1)

Publication Number Publication Date
WO2019196019A1 true WO2019196019A1 (zh) 2019-10-17

Family

ID=68162773

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2018/082578 WO2019196019A1 (zh) 2018-04-10 2018-04-10 荧光图像配准方法、基因测序仪及系统、存储介质

Country Status (11)

Country Link
US (2) US11682125B2 (zh)
EP (1) EP3779868B1 (zh)
JP (1) JP7072081B2 (zh)
KR (1) KR102504987B1 (zh)
CN (1) CN111971711A (zh)
AU (1) AU2018418609B2 (zh)
BR (1) BR112020020949A2 (zh)
CA (1) CA3096723C (zh)
RU (1) RU2749893C1 (zh)
SG (1) SG11202010040YA (zh)
WO (1) WO2019196019A1 (zh)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2024001051A1 (zh) * 2022-06-29 2024-01-04 深圳华大生命科学研究院 空间组学单细胞数据获取方法及装置和电子设备

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113255649B (zh) * 2021-06-21 2023-09-19 北博（厦门）智能科技有限公司 一种基于图像识别的图像分割框选方法及终端
CN115331735B (zh) * 2022-10-11 2023-03-17 青岛百创智能制造技术有限公司 芯片解码方法及装置
CN117237441B (zh) * 2023-11-10 2024-01-30 湖南科天健光电技术有限公司 亚像素定位方法、系统、电子设备和介质

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103025927A (zh) * 2010-04-30 2013-04-03 考利达基因组股份有限公司 精确地比对与配准用于dna测序的阵列的方法及系统
CN105303187A (zh) * 2015-12-10 2016-02-03 北京中科紫鑫科技有限责任公司 一种dna测序的图像识别方法及装置
CN105427328A (zh) * 2015-12-10 2016-03-23 北京中科紫鑫科技有限责任公司 一种dna测序的互信息图像配准方法及装置
CN105550990A (zh) * 2015-12-10 2016-05-04 北京中科紫鑫科技有限责任公司 一种dna测序的傅里叶配准方法及装置
CN107730541A (zh) * 2016-08-12 2018-02-23 广州康昕瑞基因健康科技有限公司 图像配准方法和系统及图像拍摄对位方法和系统

Family Cites Families (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR100463336B1 (ko) * 2001-10-11 2004-12-23 (주)가이아진 바이오칩 이미지 분석 시스템 및 그 방법
AU2003298655A1 (en) * 2002-11-15 2004-06-15 Bioarray Solutions, Ltd. Analysis, secure access to, and transmission of array images
US20050281462A1 (en) * 2004-06-16 2005-12-22 Jayati Ghosh System and method of automated processing of multiple microarray images
US9671344B2 (en) * 2010-08-31 2017-06-06 Complete Genomics, Inc. High-density biochemical array chips with asynchronous tracks for alignment correction by moiré averaging
JP5822664B2 (ja) * 2011-11-11 2015-11-24 株式会社Pfu 画像処理装置、直線検出方法及びコンピュータプログラム
CN102821238B (zh) * 2012-03-19 2015-07-22 北京泰邦天地科技有限公司 宽视场超高分辨率成像系统
US9481903B2 (en) * 2013-03-13 2016-11-01 Roche Molecular Systems, Inc. Systems and methods for detection of cells using engineered transduction particles
EP3027773B1 (en) 2013-07-30 2020-03-25 President and Fellows of Harvard College Quantitative dna-based imaging and super-resolution imaging
CN105427327B (zh) * 2015-12-10 2017-05-31 北京中科紫鑫科技有限责任公司 一种dna测序的图像配准方法及装置
JP6687039B2 (ja) * 2016-02-05 2020-04-22 株式会社リコー 物体検出装置、機器制御システム、撮像装置、物体検出方法、及びプログラム

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103025927A (zh) * 2010-04-30 2013-04-03 考利达基因组股份有限公司 精确地比对与配准用于dna测序的阵列的方法及系统
CN105303187A (zh) * 2015-12-10 2016-02-03 北京中科紫鑫科技有限责任公司 一种dna测序的图像识别方法及装置
CN105427328A (zh) * 2015-12-10 2016-03-23 北京中科紫鑫科技有限责任公司 一种dna测序的互信息图像配准方法及装置
CN105550990A (zh) * 2015-12-10 2016-05-04 北京中科紫鑫科技有限责任公司 一种dna测序的傅里叶配准方法及装置
CN107730541A (zh) * 2016-08-12 2018-02-23 广州康昕瑞基因健康科技有限公司 图像配准方法和系统及图像拍摄对位方法和系统

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2024001051A1 (zh) * 2022-06-29 2024-01-04 深圳华大生命科学研究院 空间组学单细胞数据获取方法及装置和电子设备

Also Published As

Publication number Publication date
AU2018418609B2 (en) 2021-10-07
CN111971711A (zh) 2020-11-20
KR20200142040A (ko) 2020-12-21
JP7072081B2 (ja) 2022-05-19
CA3096723C (en) 2023-10-24
RU2749893C1 (ru) 2021-06-18
KR102504987B1 (ko) 2023-02-28
US20210019895A1 (en) 2021-01-21
EP3779868B1 (en) 2024-05-08
US20230298188A1 (en) 2023-09-21
AU2018418609A1 (en) 2020-11-12
JP2021517701A (ja) 2021-07-26
BR112020020949A2 (pt) 2021-03-02
SG11202010040YA (en) 2020-11-27
EP3779868A1 (en) 2021-02-17
EP3779868A4 (en) 2021-11-17
CA3096723A1 (en) 2019-10-17
US11682125B2 (en) 2023-06-20

Similar Documents

Publication Publication Date Title
US11972583B2 (en) Fluorescence image registration method, gene sequencing instrument, and storage medium
WO2019196019A1 (zh) 荧光图像配准方法、基因测序仪及系统、存储介质
Ren et al. gSLICr: SLIC superpixels at over 250Hz
WO2018068600A1 (zh) 图像处理方法及系统
CN114868010A (zh) 用于多重荧光原位杂化图像的获取及处理的系统及方法
WO2021030952A1 (zh) 碱基识别方法、系统、计算机程序产品和测序系统
WO2020037573A1 (zh) 检测图像上的亮斑的方法、装置和计算机程序产品
CN110310334B (zh) 生物芯片定位方法、基因测序仪及系统、存储介质
JP2016186703A (ja) 画像認識方法、画像認識装置および画像認識プログラム
CN110111842A (zh) 图像清晰度分析及对焦方法、测序仪、系统与存储介质
CN112289381B (zh) 基于图像构建测序模板的方法、装置和计算机产品
WO2024000288A1 (zh) 图像拼接方法、基因测序系统及相应的基因测序仪
CN112285070B (zh) 检测图像上的亮斑的方法和装置、图像配准方法和装置
WO2020037570A1 (zh) 图像配准方法、装置和计算机程序产品
WO2020037571A1 (zh) 基于图像构建测序模板的方法、装置和计算机程序产品
ES2773152T3 (es) Método para determinar las posiciones de una pluralidad de objetos en una imagen digital
US12008775B2 (en) Method and device for image registration, and computer program product
CN112288783B (zh) 基于图像构建测序模板的方法、碱基识别方法和装置
US11170506B2 (en) Method for constructing sequencing template based on image, and base recognition method and device
JP4720805B2 (ja) 画像処理装置及びプログラム
WO2014129016A1 (ja) 文字認識装置、文字認識方法及び記録媒体
CN112288781A (zh) 图像配准方法、装置和计算机程序产品

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 18914625

Country of ref document: EP

Kind code of ref document: A1

ENP Entry into the national phase

Ref document number: 3096723

Country of ref document: CA

Ref document number: 2020555468

Country of ref document: JP

Kind code of ref document: A

NENP Non-entry into the national phase

Ref country code: DE

REG Reference to national code

Ref country code: BR

Ref legal event code: B01A

Ref document number: 112020020949

Country of ref document: BR

ENP Entry into the national phase

Ref document number: 20207032386

Country of ref document: KR

Kind code of ref document: A

WWE Wipo information: entry into national phase

Ref document number: 2020134588

Country of ref document: RU

ENP Entry into the national phase

Ref document number: 2018418609

Country of ref document: AU

Date of ref document: 20180410

Kind code of ref document: A

ENP Entry into the national phase

Ref document number: 2018914625

Country of ref document: EP

Effective date: 20201110

ENP Entry into the national phase

Ref document number: 112020020949

Country of ref document: BR

Kind code of ref document: A2

Effective date: 20201013