CN115170555A - Counting method and system based on images - Google Patents
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Abstract
The invention relates to a counting method and a counting system based on an image, wherein the method avoids adverse effects of air bubbles, residual water and other pollutants on the surface of a micro-pit array on a calculation result by eliminating noise and uneven background and utilizing image enhancement, adaptively distinguishes negative micro-pits and positive micro-pits, and directly calculates the absolute number of molecules to be measured. The invention does not adopt a preset gray threshold value to distinguish the positive target and the negative target in the image, can carry out accurate absolute quantitative detection without depending on a reference standard sample and a standard curve, and the sensitivity of the invention can not be reduced along with the reduction of the concentration of the biological molecules.
Description
[ technical field ] A method for producing a semiconductor device
The invention belongs to the field of digital image processing, and particularly relates to a counting method and system based on images.
[ background of the invention ]
The core process of digital detection is to detect the sample (for exampleProtein molecules) are uniformly distributed to a plurality of sample distribution units (for example, protein molecules are adsorbed to micrometer-scale magnetic beads, each magnetic bead can adsorb 1 or a plurality of protein molecules), then a plurality of magnetic beads are diffused in a reaction detection unit (for example, a micro-pit array precisely processed on the surface of silicon, glass or polymer, the diameter of each micro-pit is a plurality of micrometers), the samples to be detected are subjected to biochemical reaction in a plurality of reaction units at the same time, and finally, the result detection is carried out. For example: simoa of Quanterix corporation TM (Single-molecular array) by using digital ELISA technology, dividing a biomolecule sample into tens of thousands of small reaction systems (namely a micro-pit array), carrying out enzymatic reaction to amplify a fluorescence signal, and then collecting the fluorescence signal by using a CCD (charge coupled device), wherein a fluorescence signal reaction unit higher than a set threshold is judged to be positive, and otherwise, the fluorescence signal reaction unit is judged to be negative. Thus, the absolute concentration of protein molecules is obtained through image analysis, and quantitative detection is realized. The counting method is that a threshold value is set for the luminescence signal detected by each reaction detection unit, the reaction unit of the luminescence signal judges as 1 (positive) when the threshold value is higher, and the reaction detection unit of the luminescence signal judges as 0 (negative) when the threshold value is lower. Since background fluorescence of a certain intensity is present in all the micro-pits, the intensity of background fluorescence in negative micro-pits does not increase with time, while there is increasingly enhanced reaction fluorescence in positive micro-pits. Therefore, the two images are shot at intervals for each area of the micro-pit array, the two images are compared, and if the increase amplitude of the fluorescence signal in the same pit is large, the result is judged to be positive; otherwise, the judgment is negative. However, in different batches of experiments, background fluorescence in the micro-pits may fluctuate; moreover, when the concentration of the biomolecules is low, the sensitivity of the method in the prior art is greatly reduced along with the reduction of the concentration of the biomolecules; how to improve the detection sensitivity of low-pair-concentration biomolecules is a technical problem to be solved.
According to the method, a preset gray threshold value is not adopted to distinguish positive targets and negative targets in the image, but noise and uneven background are eliminated, the adverse effect of pollutants such as bubbles and residual water on the surface of the micro-pit array on a calculation result is avoided by utilizing the image enhancement technology and the like, and negative micro-pits and positive micro-pits are distinguished in a self-adaptive manner, so that the absolute number of molecules to be detected is directly calculated, and accurate absolute quantitative detection can be carried out without depending on a control standard sample and a standard curve;
[ summary of the invention ]
In order to solve the above problems in the prior art, the present invention provides an image-based counting method and system, wherein the method comprises:
step S1: uniformly distributing the sample to be detected containing low-concentration biomolecules to a large number of sample distribution units and then diffusing the sample to be detected in the micro-pit array;
step S2: taking a picture under a microscope to obtain a micro-pit array image;
and step S3: reading the micro-pit array image, and adjusting the gray level according to the gray level histogram to enhance the image; then, performing opening operation on the image to obtain an image to be processed;
and step S4: adjusting the image to be processed according to the distribution condition of light spots in the micro-pit array, and performing pixel value compensation on the inertial characteristic of the image to be processed;
step S5: filtering the image to be processed to obtain an uneven illumination background;
step S6: performing difference processing on the image to be processed and the uneven illumination background to obtain a fourth image;
step S7: adjusting the gray level of the fourth image, and carrying out binarization on the fourth image to obtain a binarized image;
step S8: eliminating miscellaneous points in the binary image to obtain a result image; and identifying and counting the number of light spots in the result image, and taking the number of the light spots as a counting value.
Further, the miscellaneous point is a light spot with a communication area smaller than a preset value or a light spot in a preset range.
Further, the preset value is 30.
Further, each dimple in the dimple array has a diameter of 1 to 10 micrometers.
Further, a typical size of silicon, glass, polymer, 3mm x 3mm.
An image-based counting system, the system comprising:
a distribution module: the sample distribution unit is used for uniformly distributing a sample to be detected containing low-concentration biomolecules to a large number of sample distribution units and then diffusing the sample to be detected in the micro-pit array;
an acquisition module: the micro-pit array is used for taking a picture under a microscope to obtain a micro-pit array image;
a to-be-processed image acquisition module: the micro-pit array image processing device is used for reading a micro-pit array image and adjusting the gray level according to the gray level histogram to enhance the image; then, performing opening operation on the image to obtain an image to be processed;
a compensation module: the micro-pit array is used for adjusting the image to be processed according to the distribution condition of light spots in the micro-pit array and performing pixel value compensation on the inertial characteristic of the image to be processed;
a background image acquisition module: the system is used for filtering an image to be processed to obtain an uneven illumination background;
a difference module: the image processing device is used for carrying out difference processing on the image to be processed and the uneven illumination background to obtain a fourth image;
a binarization module: the image processing device is used for adjusting the gray scale of the fourth image, and obtaining a binary image after binaryzation is carried out on the fourth image;
a result determination module: eliminating the miscellaneous points in the binary image to obtain a result image; and identifying and counting the number of light spots in the result image, and taking the number of the light spots as a counting value.
Further, each dimple in the dimple array has a diameter of 2 to 5 micrometers.
A processor for running a program, wherein the program is run to perform the image-based counting method.
A computer-readable storage medium comprising a program which, when run on a computer, causes the computer to execute the image-based counting method.
An execution device comprising a processor coupled to a memory, the memory storing program instructions that, when executed by the processor, implement the image-based counting method.
The beneficial effects of the invention include:
(1) Positive targets and negative targets in the image are distinguished without adopting a preset gray threshold, but by eliminating noise and uneven background and utilizing image enhancement and other technologies, the adverse effect of pollutants such as bubbles, residual water and the like on the surface of the micro-pit array on a calculation result is avoided, negative micro-pits and positive micro-pits are distinguished in a self-adaptive manner, the absolute number of molecules to be detected is directly calculated, and accurate absolute quantitative detection can be carried out without depending on a reference standard sample and a standard curve; (2) The image adjustment based on the significant feature matrix and the scale feature matrix can effectively overcome the directional influence on the detection result caused by the solidification change or inertia maintenance of the detection environment along with the increase of the detection times; effective information is captured through scale change, so that compensatory adjustment such as targeted regional enhancement and the like is performed on the micro-pit array image, and the accuracy of biological counting based on the image is further improved.
[ description of the drawings ]
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, and are not to be considered limiting of the invention, in which:
FIG. 1 is a schematic diagram of the method for detecting low concentration biomolecules according to the present invention.
FIG. 2 is a schematic diagram of the image of the micro-pit array after image enhancement according to the present invention.
FIG. 3 is a schematic diagram of the background of non-uniform illumination according to the present invention.
FIG. 4 is a schematic diagram of a differential processed image of the micro-pit array of the present invention.
FIG. 5 is a schematic diagram of a micropit array image after the binarization processing according to the present invention.
FIG. 6 is a schematic diagram of a micro-pit array image after the speckle reduction process according to the present invention.
[ detailed description ] embodiments
The invention will be described in detail with reference to the drawings and specific embodiments, wherein the exemplary embodiments and the description are only for the purpose of illustrating the invention, and are not to be construed as limiting the invention
As shown in fig. 1, the present invention provides an image-based counting method, which includes the following steps:
step S1: uniformly distributing the sample to be detected containing low-concentration biomolecules to a large number of sample distribution units and then diffusing the sample to be detected in the micro-pit array;
as shown in fig. 1, the step S1 specifically includes: placing a silicon, glass and polymer substrate for biochemical reaction at the bottom of a container, and adding water and a plurality of micro-magnetic beads above the glass sheet to enable the micro-magnetic beads to be suspended on the glass sheet; due to gravity or magnetic attraction, magnetic beads may fall into the interior of the micro-pits of the micro-pit array or stay on the surface of the glass sheet, but generally two magnetic beads cannot fall into the same micro-pit; removing the micro magnetic beads on the surface of the glass sheet by cleaning, and only leaving the magnetic beads in the micro pits;
preferably, the following components: each dimple in the dimple array has a diameter of 1 to 10 microns;
preferably: size of glass sheet 3mm; processing a micro-pit array on the surface of the glass sheet, wherein the diameter of each micro-pit is 5 micrometers, the depth of each micro-pit is 5 micrometers, and the interval between the centers of the micro-pits is 10 micrometers; each magnetic bead was 3 microns in diameter;
step S2: taking a picture under a microscope to obtain a micro-pit array image; the micro pits containing micro magnetic beads in the micro pit array image can present larger light spots, and the micro pits without magnetic beads can present a smaller light spot under the common condition; because the detection environment is relatively fixed, the glass sheet deformation, uneven illumination, fixing operation methods, cleaning and stain retention and the like in the environment can cause the inertia maintenance of the falling position of the micro magnetic beads on the glass sheet and the unevenness of the illumination background, so that the detection result has directional influence, and if the directional influence can be eliminated or reduced in the image-based counting, the counting accuracy can be further improved; for example: FIG. 2 shows a star-cloud like non-uniform lighting background, regardless of the cause but once such non-uniformity is regular, it creates an environmentally dependent directional effect;
and step S3: reading the micro-pit array image, and adjusting the gray level according to the gray level histogram to enhance the image; then, performing opening operation on the image to obtain an image to be processed; the obtained image to be processed is shown in figure 2; in such a way, the small light spot corresponding to the micro pit without magnetic beads is changed into a darker light spot, and the small light spot corresponding to the micro pit with magnetic beads is changed into a brighter light spot;
preferably: the opening operation is firstly corrosion and then expansion, and the used structural element is disk circle; at the moment, the opening operation can eliminate or smooth small light spots and large object boundaries, but the area of the boundary is not obviously changed;
and step S4: adjusting the image to be processed according to the distribution condition of the light spots in the micro-pit array, and performing pixel value compensation on the inertial characteristic of the image to be processed; wherein: the distribution condition of light spots in the micro-pit array indicates the inherent attribute characteristics of the micro-pit array indicated by the distribution position of the magnetic beads corresponding to the current image to be processed;
the step S4 specifically includes the following steps:
step S41: construction of micro-pit distribution matrix RT = [ RT ] i,j ]I =1 to N, j =1 to M; each element in the matrix corresponds to a micro-pit position; n is the number of rows and M is the number of columns;
preferably: initializing the element rt in the micro-pit distribution matrix i,j = -1; i.e. an invalid value;
step S42: when light spots with the communication areas larger than a specified value exist at the micro-pit positions (i, j), setting the element value of the micro-pit distribution matrix corresponding to the micro-pit positions to be 1, otherwise, setting the element value corresponding to the micro-pit positions to be 0;
step S43: computing a multiscale location feature matrix CRT _ S u，v (ii) a Wherein: s is the dynamic scale; (u, v) are the position coordinates of the elements in the position feature matrix;
the pit distribution matrix is gridded according to the size of the scale, so that elements in the position characteristic matrix correspond to grids, and the element value is equal to the sum of all element values in the pit distribution matrix corresponding to the grids;
preferably: setting S =3, 9 or 27;
step S44: judging whether the position feature matrix is a significant feature matrix under each scale; the method specifically comprises the following steps: sequentially determining whether element relations in the position feature matrix conform to any relation in a relation set corresponding to each scale aiming at the position feature matrix under each scale, and if so, determining the position feature matrix as a significant feature matrix; acquiring a scale feature matrix corresponding to the coincidence relation; wherein: each scale corresponds to a relationship set, and the relationship set comprises one or more element relationships; the element relation defines a relation which needs to be satisfied between element values of one element and/or a plurality of elements in the position characteristic matrix; for example: e1+ E2>25, E1, E2 are elemental values; the relations are obtained based on the light spot appearance position rules in historical micro-pit array images accumulated in the same or different counting environments (including experimenters, image acquisition light, experimental equipment and experimental temperature); the obtained mode can be fitting and the like; because the environment has a plurality of determining elements, the presented relationship has great difference, and effective relationship information can be captured through scale change, so that compensatory adjustments such as targeted enhancement and the like can be made on the micro-pit array image;
preferably: the scale feature matrix under one scale corresponds to one or more relations; the relation is obtained by fitting analysis data of a historical micro-pit array image; the scale characteristic matrix is a preset value and is set according to experience or big data statistical results;
preferably, the following components: setting the position characteristic matrix, the relation and/or the scale through an artificial neural network, and predicting the relation between the position characteristic matrix and the scale characteristic matrix; training an artificial neural network model through sample data obtained under the same environment, and predicting a scale feature matrix based on the model;
alternatively: each relation corresponds to a scale feature matrix; guiding the adjustment of the element values based on the scale feature matrix so as to discretize the relationship between the adjusted element values and overcome the counting difficulty influence of the inherent environment on the counting image;
preferably: when the significant feature matrix is multiple, namely the multiple position feature matrices under the scale all present significance, selecting one significant feature matrix from the multiple significant feature matrices; for example: selecting a salient feature matrix with the minimum scale; at this time, the granularity of image enhancement is minimum;
step S45: local compensation of the image to be processed is carried out based on the obtained scale characteristic matrix; the method specifically comprises the following steps: sequentially reading elements in the scale characteristic matrix, and adjusting pixel values in the image to be processed corresponding to the element positions based on the element values of the elements;
preferably: the adjusting of the pixel value in the image to be processed corresponding to the element position based on the element value of the element specifically includes: when the element value is 0, not adjusting the pixel value in the image to be processed corresponding to the position; when the element value is 1, adjusting the pixel value in the image to be processed corresponding to the position; further, when the element value is larger than 1, adjusting the pixel value in the image to be processed corresponding to the position according to the size of the element value; when the element value is larger, the adjustment amount is larger, and on the contrary, when the element value is smaller, the adjustment amount is smaller; because the scale characteristic matrix is obtained by gridding based on the distribution of the micro-pits, the elements in one scale characteristic matrix correspond to one area in one image to be processed; for example: when the scale is 3, one element may correspond to an image area containing 9 micro pits, that is, the adjustment of the image to be processed is a piece-by-piece adjustment, and in fact, such an adjustment is also in accordance with a rule, and the influence of a tiny inherent characteristic on the counting accuracy is also small;
preferably: the adjustment is an increase or decrease;
step S5: filtering the image to be processed to obtain a non-uniform illumination background as shown in fig. 3; the uneven illumination background image obtained after filtering becomes fuzzy, and partial areas cannot be distinguished;
preferably, the following components: removing abnormal points in a local range by a small-scale window median filtering method, and then removing interference and noise points in a global range by a large-scale window mean filtering method; the global scope is greater than the local scope;
step S6: performing difference processing on the image to be processed and the uneven illumination background to obtain a fourth image, and reducing the influence of the uneven illumination background on counting; as shown in fig. 4;
step S7: adjusting the gray scale of the fourth image, and carrying out binarization on the fourth image to obtain a binarized image; as shown in fig. 5;
step S8: eliminating the miscellaneous points in the binary image to obtain a result image; identifying and counting the number of light spots in a result image, and taking the number of the light spots as a count value; as shown in fig. 6;
preferably: the miscellaneous points are light spots with communicating areas smaller than a preset value;
preferably, the following components: the preset value is 30;
preferably: the identification and statistics are gray levels of the adjustment result image and a target is marked;
based on the same inventive concept, the invention provides a counting system based on images; the system comprises:
a distribution module: the sample distribution unit is used for uniformly distributing a sample to be detected containing low-concentration biomolecules to a large number of sample distribution units and then diffusing the sample to be detected in the micro-pit array;
an acquisition module: the micro-pit array is used for taking a picture under a microscope to obtain a micro-pit array image;
a to-be-processed image acquisition module: the micro-pit array image processing system is used for reading a micro-pit array image and adjusting the gray level according to the gray level histogram to enhance the image; then, performing opening operation on the image to obtain an image to be processed;
a compensation module: the micro-pit array is used for adjusting the image to be processed according to the distribution condition of light spots in the micro-pit array and performing pixel value compensation on the inertial characteristic of the image to be processed;
a background image acquisition module: the device is used for filtering an image to be processed to obtain an uneven illumination background;
a difference module: the image processing device is used for carrying out difference processing on the image to be processed and the uneven illumination background to obtain a fourth image;
a binarization module: the image processing device is used for adjusting the gray scale of the fourth image, and obtaining a binary image after the fourth image is binarized;
a result determination module: eliminating the miscellaneous points in the binary image to obtain a result image; and identifying and counting the number of light spots in the result image, and taking the number of the light spots as a counting value.
The terms "data processing apparatus", "data processing system", "user equipment" or "computing device" encompass all kinds of apparatus, devices and machines for processing data, including by way of example a programmable processor, a computer, a system on a chip, or a plurality or combination of the above. The apparatus can comprise special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application-specific integrated circuit). The apparatus can include, in addition to hardware, code that creates an execution environment for the computer program, e.g., code that constitutes processor firmware, a protocol stack, a database management system, an operating system, a cross-platform execution environment, a virtual machine, or a combination of one or more of the above. The apparatus and execution environment may implement a variety of different computing model infrastructures, such as web services, distributed computing, and grid computing infrastructures.
A computer program (also known as a program, software application, script, or code) can be written in any form of programming language, including compiled or interpreted languages, declarative or procedural languages, and it can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, object, or other unit suitable for use in a computing environment. The computer program may, but need not, correspond to a file in a file system. A program can be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that store one or more modules, subroutines, or portions of code). A computer program can be deployed to be executed on one computer or on multiple computers that are located at one site or distributed across multiple sites and interconnected by a communication network.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (10)
1. An image-based counting method, comprising:
step S1: uniformly distributing a sample to be detected containing low-concentration biomolecules to a large number of sample distribution units, and then diffusing the sample to be detected in the micro-pit array;
step S2: taking a picture under a microscope to obtain a micro-pit array image;
and step S3: reading the micro-pit array image, and adjusting the gray level according to the gray level histogram to enhance the image; then, performing opening operation on the image to obtain an image to be processed;
and step S4: adjusting the image to be processed according to the distribution condition of light spots in the micro-pit array, and performing pixel value compensation on the inertial characteristic of the image to be processed;
step S5: filtering the image to be processed to obtain an uneven illumination background;
step S6: performing difference processing on the image to be processed and the uneven illumination background to obtain a fourth image;
step S7: adjusting the gray scale of the fourth image, and carrying out binarization on the fourth image to obtain a binarized image;
step S8: eliminating the miscellaneous points in the binary image to obtain a result image; and identifying and counting the number of light spots in the result image, and taking the number of the light spots as a counting value.
2. The image-based counting method according to claim 1, wherein the outlier is a spot having a connected area smaller than a preset value or a spot located in a preset range.
3. The image-based counting method according to claim 2, wherein said preset value is 30.
4. The image-based counting method according to claim 3, wherein each micro-pit in the micro-pit array has a diameter of 1 to 10 μm.
5. The image-based counting method according to claim 4, characterized in that the typical size of silicon, glass, polymer is 3mm.
6. An image-based counting system, comprising:
a distribution module: the sample distribution unit is used for uniformly distributing a sample to be tested containing low-concentration biomolecules to a large number of sample distribution units and then diffusing the sample to be tested in the micro-pit array;
an acquisition module: the micro-pit array is used for taking a picture under a microscope to obtain a micro-pit array image;
a to-be-processed image acquisition module: the micro-pit array image processing system is used for reading a micro-pit array image and adjusting the gray level according to the gray level histogram to enhance the image; then, performing opening operation on the image to obtain an image to be processed;
a compensation module: the micro-pit array is used for adjusting the image to be processed according to the distribution condition of light spots in the micro-pit array and performing pixel value compensation on the inertial characteristic of the image to be processed;
a background image acquisition module: the device is used for filtering an image to be processed to obtain an uneven illumination background;
a difference module: the image processing device is used for carrying out difference processing on the image to be processed and the uneven illumination background to obtain a fourth image;
a binarization module: the image processing device is used for adjusting the gray scale of the fourth image, and obtaining a binary image after binaryzation is carried out on the fourth image;
a result determination module: eliminating the miscellaneous points in the binary image to obtain a result image; and identifying and counting the number of light spots in the result image, and taking the number of the light spots as a counting value.
7. The image-based counting system of claim 6, wherein each micropit in the array of micropits has a diameter of 1 to 10 microns.
8. A processor, characterized in that the processor is configured to run a program, wherein the program when running performs the image based counting method of any of claims 1-5.
9. A computer-readable storage medium, characterized by comprising a program which, when run on a computer, causes the computer to carry out the image-based counting method according to any one of claims 1 to 5.
10. An execution device comprising a processor coupled to a memory, the memory storing program instructions that, when executed by the processor, implement the image-based counting method of any one of claims 1-5.
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