WO2019015514A1 - 一种荧光图像的荧光强度确定方法和系统 - Google Patents

一种荧光图像的荧光强度确定方法和系统 Download PDF

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WO2019015514A1
WO2019015514A1 PCT/CN2018/095239 CN2018095239W WO2019015514A1 WO 2019015514 A1 WO2019015514 A1 WO 2019015514A1 CN 2018095239 W CN2018095239 W CN 2018095239W WO 2019015514 A1 WO2019015514 A1 WO 2019015514A1
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value
image
target
fluorescence
fluorescent
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PCT/CN2018/095239
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English (en)
French (fr)
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颜海波
黄海清
罗浦文
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上海睿钰生物科技有限公司
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Priority to US16/629,400 priority Critical patent/US11249005B2/en
Priority to JP2020523477A priority patent/JP6851670B2/ja
Priority to EP18835191.0A priority patent/EP3657154A4/en
Publication of WO2019015514A1 publication Critical patent/WO2019015514A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/60—Type of objects
    • G06V20/69—Microscopic objects, e.g. biological cells or cellular parts
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume, or surface-area of porous materials
    • G01N15/10—Investigating individual particles
    • G01N15/14—Electro-optical investigation, e.g. flow cytometers
    • G01N15/1434—Electro-optical investigation, e.g. flow cytometers using an analyser being characterised by its optical arrangement
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume, or surface-area of porous materials
    • G01N15/10—Investigating individual particles
    • G01N15/14—Electro-optical investigation, e.g. flow cytometers
    • G01N15/1429—Electro-optical investigation, e.g. flow cytometers using an analyser being characterised by its signal processing
    • G01N15/1431—Electro-optical investigation, e.g. flow cytometers using an analyser being characterised by its signal processing the electronics being integrated with the analyser, e.g. hand-held devices for on-site investigation
    • G01N15/1433—
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume, or surface-area of porous materials
    • G01N15/10—Investigating individual particles
    • G01N15/14—Electro-optical investigation, e.g. flow cytometers
    • G01N15/1434—Electro-optical investigation, e.g. flow cytometers using an analyser being characterised by its optical arrangement
    • G01N2015/144—Imaging characterised by its optical setup
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume, or surface-area of porous materials
    • G01N15/10—Investigating individual particles
    • G01N15/14—Electro-optical investigation, e.g. flow cytometers
    • G01N2015/1493—Particle size

Definitions

  • the invention relates to the technical field of cell detection, and in particular to a method and a system for determining the fluorescence intensity of a fluorescent image.
  • Flow cytometry is an important cell analysis technique.
  • the core principle is to statistically analyze the fluorescence intensity of target (cell or other detection particles) for cluster analysis.
  • the flow analysis technique expresses the fluorescence intensity by recording the voltage pulse value of the fluorescent signal.
  • embodiments of the present invention provide a method and system for determining a fluorescence intensity of a fluorescent image such that there is comparability between the fluorescence intensity of the image expression and the fluorescence intensity expressed by the stream.
  • the embodiment of the present invention provides the following technical solutions:
  • a method and a system for determining a fluorescence intensity of a fluorescence image obtain a fluorescence image by performing fluorescence imaging on a target sample by using a microscopic fluorescence imaging system; and a fluorescence image region of each detection target in the fluorescence image Edge extraction and segmentation are performed to obtain a fluorescence image region corresponding to each detection target in the fluorescence image; cumulative gray value, maximum gray value, average gray value, and each of the fluorescence image regions of each detection target are calculated.
  • the diameter value of the bright field image region of the detection target is detected, and the flow clustering analysis is performed based on the cumulative gray value, the maximum gray value, the average gray value, and the bright field diameter value. It can be developed or input to the flow clustering analysis software for analysis and results corresponding to and similar to flow analytics.
  • FIG. 1 is a schematic flow chart of a method for determining a fluorescence intensity of a fluorescent image disclosed in an embodiment of the present application
  • FIG. 2 is a schematic flow chart of an intelligent identification process in a method for determining a fluorescence intensity of a fluorescent image according to an embodiment of the present application
  • FIG. 3 is a schematic flow chart of an intelligent recognition process in a method for determining a fluorescence intensity of a fluorescent image according to another embodiment of the present application;
  • FIG. 4 is a schematic flow chart of an intelligent recognition process in a method for determining a fluorescence intensity of a fluorescent image according to still another embodiment of the present application;
  • FIG. 5 is a schematic structural diagram of a fluorescence intensity determining system for a fluorescent image according to another embodiment of the present application.
  • flow analytics provides a variety of indicators for customers to choose from, allowing customers to analyze target cells from different sides.
  • the main indicators are: 1. Corresponding lateral fluorescence channels acquired when cells pass through. The accumulated voltage pulse value is taken as the cumulative fluorescence intensity. 2. The highest voltage pulse of the corresponding lateral fluorescence channel acquired when the cells pass through is taken as the highest fluorescence intensity. 3. The average voltage pulse value of the corresponding lateral fluorescence channel collected when the cells pass through is taken as the average fluorescence intensity. 4. The voltage pulse value collected by the forward scatter channel when the cell passes is used as the target size value. .
  • the fluorescence pixel area, the fluorescence image diameter, the long axis of the fluorescence image, the short axis of the fluorescence image, and the perimeter of the fluorescent image can be analyzed and analyzed.
  • the applicant has obtained a large number of experimental verifications to obtain four indicators that can be aligned with the fluorescence intensity of flow cytometry. They are: the cumulative fluorescence gray value corresponds to the cumulative voltage pulse value of the flow corresponding fluorescence channel; the highest fluorescence gray value of the single cell corresponds to the strongest voltage pulse value of the flow corresponding fluorescence channel; the single cell average fluorescence gray value and The flow mode corresponds to the average voltage pulse value of the fluorescent channel; the single cell bright field diameter corresponds to the voltage pulse value collected by the flow forward scattering channel.
  • a method for determining the fluorescence intensity of a fluorescent image see FIG. 1, the method comprising:
  • Step S101 performing fluorescence imaging on the target sample by using a microscopic fluorescence imaging system to obtain a fluorescence image
  • the target sample is first subjected to image flow-through fluorescence detection using a microscopic fluorescence imaging system to obtain a fluorescence image of the target sample;
  • Step S102 determining a fluorescent image area
  • the fluorescent image may be subjected to image processing and analysis using an intelligent recognition system to obtain a fluorescent image region of each cell (detection target) included in the fluorescent image.
  • image processing and analysis process by performing edge extraction and segmentation on the fluorescent image region of each detection target in the fluorescence image, a fluorescent image region corresponding to each detection target in the fluorescence image can be obtained;
  • Step S103 calculating an integrated gray value of a fluorescent image area of each detection target
  • the cumulative gray value of the fluorescent image region of each detection target is calculated by the image recognition technology, and the cumulative gray value is used as the streaming method.
  • the cumulative fluorescence intensity value expressed by the cumulative voltage pulse value collected by the photomultiplier tube in the clustering analysis; the cumulative fluorescence gray value is the sum of all the pixel gray values in the fluorescent image area of the single detection target;
  • Step S104 calculating a maximum gray value of a fluorescent image area of each detection target
  • the maximum gray value of the fluorescent image region of each detection target is calculated by the image recognition technology, and the maximum gray value is used as the streaming method.
  • Step S105 calculating an average gray value of a fluorescent image area of each detection target
  • the average gray value of the fluorescent image region of each detection target is calculated by the image recognition technology; the average gray value is used as a streaming The average fluorescence intensity value expressed by the average voltage pulse value collected by the photomultiplier tube in the clustering analysis; the average gray value is an average value of all the pixel points in the fluorescent image region of the single detection target;
  • Step S106 calculating a diameter value of a bright field image region of each detection target as a target size value
  • a bright field image of each detection target is acquired, data analysis is performed on the bright field image, and a diameter value of a bright field image area of each detection target is calculated as a target size value, and the value is determined as a development.
  • the corresponding substitute index of the size of the detection target expressed by the voltage pulse value collected by the photomultiplier tube in the forward scattering channel;
  • Step S107 performing flow clustering analysis based on the accumulated gray value, the maximum gray value, the average gray value, and the bright field diameter value;
  • the cumulative gray value is taken as the cumulative fluorescence intensity value
  • the maximum gray value is taken as the maximum fluorescence intensity value
  • the average gray value is taken as the average fluorescence intensity value
  • the diameter value of the region is used as the size of the detection target, and the cumulative gray value, the maximum gray value, the average gray value, and the bright field diameter value are used for stream cluster analysis, and the cumulative gray value, the maximum gray value, and The fluorescence detection waveform corresponding to one or more of the average gray value and the bright field diameter value directly compares the generated fluorescence detection waveform with the fluorescence detection waveform obtained by conventional flow analysis.
  • the cumulative voltage pulse value corresponding to the detection target, the maximum voltage purchase recharge value, the average voltage purchase recharge value, and the detection target size are not required to be obtained by the photomultiplier tube. Value, you can streamline the detection target with high accuracy.
  • the cumulative gray value when the flow clustering analysis is performed based on the cumulative gray value, the maximum gray value, the average gray value, and the bright field diameter value, the cumulative gray value may be adopted.
  • the maximum gray value, the average gray value, and the bright field diameter value are directly subjected to flow clustering analysis.
  • the cumulative gray value, the maximum gray value, the average gray value, and the bright field diameter value are used for streaming aggregation. Before class analysis, it is necessary to train the formula in the flow clustering analysis and adjust the weight value in the formula to improve the accuracy of the calculation result.
  • the cumulative fluorescence intensity value and the maximum fluorescence intensity value corresponding to the cumulative gray value, the maximum gray value, the average gray value, and the bright field diameter value may be searched according to a preset mapping table.
  • the average fluorescence intensity value and the fluorescence area value wherein the preset mapping table stores a mapping relationship between the preset cumulative gray value and the cumulative fluorescence intensity value, and a mapping between the maximum gray value and the maximum fluorescence intensity value.
  • the intelligent recognition system can be used for intelligently identifying the fluorescent image obtained by fluorescence imaging of the target sample, and performing edge extraction and segmentation on the fluorescent image region of each detection target in the fluorescence prediction.
  • the method obtains a fluorescent image area of each detection target.
  • the process may specifically include:
  • the Gaussian smoothing filtering is first performed on the fluorescent image, the isolated noise points in the fluorescent image are filtered out, and the edge of the image is smoothed to facilitate subsequent extraction and determination of the boundary;
  • the gradient edge detection is performed on the filtered fluorescence image, mainly by using the gray image corresponding to the filtered fluorescence image to calculate the gradient feature of the fluorescence image, and separating from the background target according to the strength of the gradient edge.
  • S1023 Perform fluorescence target foreground extraction on the fluorescent image according to the detection result of the gradient edge detection
  • step S202 the foreground detection area of step S202 is extracted, and the foreground target area to be identified is marked one by one according to the attribute of the connected area, and the position of the edge is recorded;
  • S1024 performing segmentation of the single fluorescent region on the fluorescent image according to the extracted target foreground, to obtain a fluorescent image region of each detection target;
  • step S1023 After the processing of step S1023 is completed, it is inevitable that there are a large number of overlapping detection targets, so it is also necessary to separate the overlapping detection targets.
  • This step can mark the edges by using a series of algorithms such as distance transformation, and finally to a single The detection target is separated to obtain a fluorescent image area of each detection target.
  • the microscopic fluorescence imaging system is adopted. Fluorescence imaging is performed on the target sample to obtain a fluorescence image, specifically: a fluorescence image and a bright field image of the target sample are obtained by using a microscopic fluorescence imaging system.
  • the method may further include:
  • Step S1011 Identifying the bright field target in the bright field image by the bright field recognition technology, and separating the agglomerate regions in the identified bright field target, and obtaining position information and size of each detection target in the bright field image. information;
  • the foreground image is separated from the background target for the bright field image captured by the bright field, and the specific method is to perform grayscale, brightness uniformity, image enhancement, and binarization operation on the bright field image.
  • the detection target image of the foreground is extracted from the background target; the detected target image of the extracted foreground may be in a large number of agglomerations, etc., so that the plurality of detection target images are overlapped, and the detection target image must be separated to obtain a single Detecting a target image, obtaining an edge of a single detection target image, and recording edge information;
  • Step S1012 comparing the position information and the size information of the detection target acquired by the bright field recognition technology with the corresponding area in the fluorescence image to identify the fluorescence image; and determining the corresponding bright field image detected in the fluorescence image. Whether the location information of each detection target, the area of the size information is empty, if not, step S1013 is performed, and if yes, step S1014 is performed;
  • Step S1013 determining, according to the position corresponding to the position and size information of each detection target detected by the obtained bright field image recognition technology, the corresponding region in the fluorescence image, and determining the fluorescence of each corresponding detection target from the fluorescence image.
  • Step S1014 Perform Gaussian smoothing filtering on the fluorescent image and perform subsequent operations if the region of the fluorescence image corresponding to the position and size information of the detection target in the bright field image is empty;
  • the fluorescent image is identified, specifically, for the captured fluorescent image, the region where the fluorescent target is located is separated from the background target, and the specific method is graying, brightness uniformity, edge enhancement, and binary value of the image. Operation, and then extract the edges of the image, and store the area where the fluorescent target is located;
  • the bright field identification is specifically: marking an area (a single detection target edge and recording edge information) divided by each detection target in the bright field image as a mark, and using the segmentation result of the bright field image technology to segment the fluorescent image mark, The region where the single detection target is in the fluorescence image is extracted, that is, there is a bright field target there, then the position is also marked in the fluorescent target, and the fluorescence target separation is performed by using the bright field target one by one.
  • the fluorescence image and the bright field image of the target sample are collected by using a microscopic fluorescence imaging system, and the specific process is as follows:
  • the fluorescence image of the target sample is obtained by using a microscopic fluorescence imaging system to determine whether the fluorescent image area of the obtained target sample satisfies a preset condition. If not, a bright field image of the target sample is obtained by using a microscopic fluorescence imaging system.
  • the system will automatically use the method in Figure 3 for correction identification.
  • a processing method combining the intelligent recognition processing methods disclosed in the above two embodiments is also disclosed, and the method is as shown in FIG. 2 .
  • the processing method disclosed in the above is mainly, and the processing method disclosed in FIG. 3 is supplemented. Specifically, see FIG. 4,
  • the method further includes:
  • Step S401 determining whether the obtained fluorescent image area of the detection target satisfies a preset condition, if not, executing step S402;
  • Step S402 Acquire a bright field image of the target sample by using a microscopic fluorescence imaging system, and perform step S1011.
  • the present application also discloses a fluorescence intensity determining system for a fluorescent image, and the method and system can learn from each other.
  • the system may include:
  • the intelligent recognition system 200 is configured to perform edge extraction and segmentation on the fluorescent image region of each detection target in the fluorescent image to obtain a fluorescent image region corresponding to each detection target in the fluorescent image;
  • the data processing system 300 includes: a first calculation unit 310, a second calculation unit 320, a third calculation unit 330, a fourth calculation unit and 340, and a flow cluster analysis unit 350;
  • the first calculating unit 310 is configured to calculate an integrated gray value of the fluorescent image region of each detection target, and use the cumulative gray value as a cumulative amount collected by the photomultiplier tube in the corresponding fluorescent channel in the flow clustering analysis.
  • the cumulative fluorescence intensity value expressed by the voltage pulse value;
  • the second calculating unit 320 is configured to calculate a maximum gray value of the fluorescent image region of each detection target, and use the maximum gray value as the maximum value of the corresponding fluorescent channel collected by the photomultiplier tube in the flow clustering analysis.
  • the third calculating unit 330 is configured to calculate an average gray value of the fluorescent image region of each detection target, and use the average gray value as an average of the corresponding fluorescent channels collected by the photomultiplier tube in the flow clustering analysis.
  • the fourth calculating unit 340 is configured to calculate a diameter value of a bright field image region of each detection target as a target size value, and determine the value as a voltage collected by the photomultiplier tube in the forward scattering channel in the flow clustering analysis. Corresponding surrogate index of the size of the detection target expressed by the pulse value
  • the flow clustering analysis unit 350 is configured to perform flow clustering analysis based on the cumulative gray value, the maximum gray value, the average gray value, and the bright field diameter value.
  • the first calculating unit 310, the second calculating unit 320, the third calculating unit 330, and the fourth calculating unit 340 in the system may alternatively exist, that is, the system may be selectively A computing unit 310, a second computing unit 320, a third computing unit 330, and/or a fourth computing unit 340.
  • the smart identification system 200 is configured to perform intelligent recognition processing on the fluorescent image to extract an edge of a single detection target fluorescent image, and all pixels in the edge region are fluorescent image regions of a single detection target. .
  • the intelligent identification system 200 includes at least a fluorescent target extraction unit, and the fluorescent target extraction unit includes:
  • a filtering unit configured to perform Gaussian smoothing filtering on the fluorescent image
  • a gradient detecting unit configured to perform gradient edge detection on the smooth filtered fluorescent image
  • a foreground extraction unit configured to perform fluorescence target foreground extraction on the fluorescent image according to the detection result of the gradient edge detection
  • a segmentation unit configured to perform segmentation of the single fluorescent region on the fluorescent image according to the extracted target foreground to obtain a fluorescent image region of each detection target.
  • the microscopic fluorescence imaging system 100 is further configured to acquire a bright field image of a target sample
  • the smart identification system 200 may further include: a bright field cell separation system, and the bright field cell separation system may include:
  • a bright field identification unit for identifying a bright field target in the bright field image, and separating the agglomerate regions in the identified bright field target to obtain position and size information of each detection target in the bright field image
  • a bright field cell separation unit configured to determine whether a region of the fluorescence image corresponding to the position and size information of each detection target in the bright field image is empty, and if not, the position of each detection target in the obtained bright field image is used. And an area corresponding to the size information, wherein the corresponding fluorescent image area of each detection target is determined from the fluorescent image, and if so, a trigger signal for triggering the fluorescent target motion is output to the fluorescent target.
  • the microscopic fluorescence imaging system is further configured to acquire a bright field image of the target sample
  • the intelligent identification system 300 includes: a fluorescent target extraction unit and a bright field cell separation system. Although the embodiment also includes a fluorescent target and a bright field cell separation system, the working timing of the two is the same as the fluorescent target in the above embodiment. The working sequence of the bright field cell separation system is different, specifically:
  • the fluorescent target further includes: a determining unit, configured to determine whether the fluorescent image area of the obtained cell meets a preset condition, and if not, control the microscopic fluorescence imaging system to acquire a bright field image of the target sample. And outputting a trigger signal to the bright field cell separation system.
  • the steps of a method or algorithm described in connection with the embodiments disclosed herein can be implemented directly in hardware, a software module executed by a processor, or a combination of both.
  • the software module can be placed in random access memory (RAM), memory, read only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or technical field. Any other form of storage medium known.

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Abstract

一种荧光图像的荧光强度确定方法和系统，方法包括：采用显微荧光成像系统对目标样品进行明场成像和荧光成像得到明场图像和荧光图像；对荧光图像中每个检测目标的荧光图像区域进行边缘提取和分割，得到所述荧光图像中每个检测目标对应的荧光图像区域；计算得到每个检测目标的荧光图像区域的累计灰度值、最大灰度值、平均灰度值、每个检测目标的明场图像区域的直径值，基于所述累计灰度值、最大灰度值、平均灰度值和明场直径值进行流式聚类分析。可以开展或输入到流式聚类分析软件进行分析，并获得与流式分析术对应和类似的结果。

Description

一种荧光图像的荧光强度确定方法和系统
本申请要求于2017年07月20日提交中国专利局、申请号为CN201710596449.2、发明名称为“一种荧光图像的荧光强度确定方法和系统”的国内申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本发明涉及细胞检测技术领域，具体涉及一种荧光图像的荧光强度确定方法和系统。
背景技术
流式分析技术是一种重要的细胞分析技术，其核心的原理就是统计检测目标(细胞或其他检测颗粒)的荧光强度进行聚类分析。流式分析技术是通过记录荧光信号的电压脉冲值来表达荧光强度的。现在，随着显微荧光成像技术和智能识别技术的发展，通过对大量检测目标进行荧光成像，并通过图像识别方法进行分析，可以实现基于荧光成像技术的聚类分析，这种以显微成像分析为基础融合流式聚类分析的方法(图像类流式分析方法)，成为一种新的分析方法。
但是，基于显微荧光成像和智能识别的图像分析是以像素和灰度为基础来表达荧光强度的，与流式分析以电压脉冲来表达荧光强度的方式完全不同。如何建立者两者之间的对应关系，让图像表达的荧光强度与流式表达的荧光强度具有可比性，并保证进行类流式分析结果的准确性，成为图像类流式分析方法中亟待解决的关键点和难点。
发明内容
有鉴于此，本发明实施例提供一种荧光图像的荧光强度确定方法和系统，以使得图像表达的荧光强度与流式表达的荧光强度之间具有可比性。
为实现上述目的，本发明实施例提供如下技术方案：
对应权利要求内容；
基于上述技术方案，本发明实施例提供的荧光图像的荧光强度确定方法和系统，通过采用显微荧光成像系统对目标样品进行荧光成像得到荧光图像；对荧光图像中每个检测目标的荧光图像区域进行边缘提取和分割，得到所述荧光 图像中每个检测目标对应的荧光图像区域；计算得到每个检测目标的荧光图像区域的累计灰度值、最大灰度值、平均灰度值、每个检测目标的明场图像区域的直径值作，基于所述累计灰度值、最大灰度值、平均灰度值和明场直径值进行流式聚类分析。可以开展或输入到流式聚类分析软件进行分析，并获得与流式分析术对应和类似的结果。
附图说明
为了更清楚地说明本发明实施例或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本发明的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1为本申请实施例公开的一种荧光图像的荧光强度确定方法的流程示意图；
图2为本申请实施例公开的一种荧光图像的荧光强度确定方法中智能识别处理的流程示意图；
图3为本申请另一实施例公开的一种荧光图像的荧光强度确定方法中智能识别处理的流程示意图；
图4为本申请再一实施例公开的一种荧光图像的荧光强度确定方法中智能识别处理的流程示意图；
图5为本申请又一实施例公开的一种荧光图像的荧光强度确定系统的结构示意图。
具体实施方式
下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，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例，都属于本发明保护的范围。
在实际的应用中，流式分析术提供了多种指标供客户选择，让客户可以从不同侧面去对目标细胞进行分析，主要指标有：1、以细胞通过时采集到的对 应侧向荧光通道的累计电压脉冲值作为累计荧光强度。2、以细胞通过时采集到的对应侧向荧光通道的最强电压脉冲作为最高荧光强度。3、以细胞通过时采集到的对应侧向荧光通道的平均电压脉冲值作为平均荧光强度。4、细胞通过时的正向散射通道采集到的电压脉冲值作为目标的大小值。。
在细胞荧光图像分析中，对目标细胞的荧光图像进行边缘提取和分割后，可以分析统计出该细胞的荧光像素面积、荧光图像直径、荧光图像长轴、荧光图像短轴、荧光图像周长、荧光图像圆度、荧光图像最大灰度值、最小灰度值、平均灰度值、累计灰度值、中值灰度值、灰度值落差值(最大灰度值减最小灰度值)……等几十种特征指标。
但是，细胞荧光图像分析虽然提取了非常多的图像特征值，哪种特征值可以与上述流式细胞术中的累计电压脉冲值、高度和宽度具有对应关系，其统计分析数据可以得到类比于流式分析的结果和精度，是现有图像分析方法没有解决的问题。这阻碍了图像分析方法与流式分析方法结果的可比性。
针对于此，申请人通过进行大量实验验证得到4个可以与流式细胞术荧光强度对接的指标。分别是：累计荧光灰度值与流式对应荧光通道的累计电压脉冲值对应；单细胞最高荧光灰度值与流式对应荧光通道的最强电压脉冲值对应；单细胞平均荧光灰度值与流式对应荧光通道的平均电压脉冲值对应；单细胞明场直径与流式正向散射通道采集到的电压脉冲值对应。
针对于现有技术中图像表达的荧光强度与流式表达的荧光强度不具有可比性，导致用户无法更好地判断检测结果的准确性的问题，本申请公开了一种基于上述四种对应关系的荧光图像的荧光强度确定方法，参见图1，该方法包括：
步骤S101：采用显微荧光成像系统对目标样品进行荧光成像得到荧光图像；
在本步骤中，首先采用显微荧光成像系统对目标样品进行图像类流式荧光检测，获得目标样品的荧光图像；
步骤S102：确定荧光图像区域；
在得到目标样品的荧光图像之后，还可以采用智能识别系统对所述荧光图像进行图像处理与分析，得到所述荧光图像中所包含的每个细胞(检测目标)的荧光图像区域，具体的，在图像处理与分析过程中通过对荧光图像中每个检 测目标的荧光图像区域进行边缘提取和分割，即可得到所述荧光图像中每个检测目标对应的荧光图像区域；
步骤S103：计算得到每个检测目标的荧光图像区域的累计灰度值；
在本步骤中，依据得到的单个检测目标的荧光图像区域的区域范围，通过图像识别技术，计算得到每个检测目标的荧光图像区域的累计灰度值，将所述累计灰度值作为流式聚类分析中对应荧光通道以光电倍增管采集到的累计电压脉冲值表达的累计荧光强度值；所述累计荧光灰度值为单个检测目标的荧光图像区域内所有的像素灰度值的总和；
步骤S104：计算得到每个检测目标的荧光图像区域的最大灰度值；
在本步骤中，依据得到的单个检测目标的荧光图像区域的区域范围，通过图像识别技术，计算得到每个检测目标的荧光图像区域的最大灰度值，将所述最大灰度值作为流式聚类分析中对应荧光通道以光电倍增管采集到的最大电压脉冲值表达的最大荧光强度值；所述最大灰度值为单个检测目标的荧光图像区域内灰度值最高的像素点的灰度值；
步骤S105：计算得到每个检测目标的荧光图像区域的平均灰度值；
在本步骤中，依据得到的单个检测目标的荧光图像区域的区域范围，通过图像识别技术，计算得到每个检测目标的荧光图像区域的平均灰度值；将所述平均灰度值作为流式聚类分析中对应荧光通道以光电倍增管采集到的平均电压脉冲值表达的平均荧光强度值；所述平均灰度值为单个检测目标的荧光图像区域内所有像素点的平均值；
步骤S106：计算每个检测目标的明场图像区域的直径值作为目标大小值；
在本步骤中，获取每个检测目标的明场图像，对所述明场图像进行数据分析，计算得到每个检测目标的明场图像区域的直径值作为目标大小值，并确定该值作为开展流式聚类分析中正向散射通道以光电倍增管采集到的电压脉冲值表达的检测目标的大小值的对应替代指标；
上述方法中，步骤S103-106之间的顺序可以依据用户需求自行调整；
步骤S107：基于所述累计灰度值、最大灰度值、平均灰度值和明场直径值进行流式聚类分析；
在本步骤中，将所述累计灰度值作为累计荧光强度值、将所述最大灰度值作为最大荧光强度值、将所述平均灰度值作为平均荧光强度值；将所述明场图 像区域的直径值作为检测目标的大小值，采用累计灰度值、最大灰度值、平均灰度值和明场直径值进行流式聚类分析，生成与累计灰度值、最大灰度值、平均灰度值和明场直径值中的一项或多项对应的荧光检测波形图，将生成的荧光检测波形图可直接与采用常规流式分析术得到的荧光检测波形图进行直接对比，依据对比结果，调整流式聚类分析中检测目标的大小值，采用累计灰度值、最大灰度值、平均灰度值和明场直径值的权重值，从而使得其借测结果更接近于现有的采用常规流式分析术得到的荧光检测波形，从而提高检测结果的准确性。
具体的，在实际使用过程中，在本申请上述实施例公开的方法中无需通过光电倍增管获得所述检测目标对应的累计电压脉冲值、最大电压买充值、平均电压买充值和检测目标的大小值，即可对检测目标进行流式分析，且具有较高的精确度。
在本申请上述实施例公开的技术方案中，基于所述累计灰度值、最大灰度值、平均灰度值和明场直径值进行流式聚类分析时，可采用所述累计灰度值、最大灰度值、平均灰度值和明场直径值直接进行流式聚类分析，当然，在采用累计灰度值、最大灰度值、平均灰度值和明场直径值进行流式聚类分析之前，需要对流式聚类分析中的公式进行训练，调整公式中的权重值，以提高计算结果的精准度。当然在本步骤中也可以依据预设的映射表查找得到与所述累计灰度值、最大灰度值、平均灰度值和明场直径值一一对应的累计荧光强度值、最大荧光强度值、平均荧光强度值和荧光面积值，所述预设映射表中存储有预设的累计灰度值与累计荧光强度值之间的映射关系、最大灰度值与最大荧光强度值之间的映射关系、平均灰度值与平均荧光强度值之间的映射关系、明场直径值与荧光面积值之间的映射关系。
在本申请上述实施例公开的技术方案中，可以采用智能识别系统对目标样品进行荧光成像得到的荧光图像进行智能识别，通过对荧光预想中每个检测目标的荧光图像区域进行边缘提取和分割的方式得到每个检测目标的荧光图像区域，参见图2，其过程具体可以包括：
S1021：对得到的荧光图像进行高斯平滑滤波；
在本步骤中，首先对荧光图像进行高斯平滑滤波，滤除荧光图像中的孤立 噪声点，平滑图像边缘，以方便后续的边界的提取和确定；
S1022：对平滑滤波后的荧光图像进行梯度边缘检测；
本步骤中，对滤波后的荧光图像进行梯度边缘的检测，主要是利用滤波后的荧光图像对应的灰度图像，计算出荧光图像的梯度特征，按照梯度边缘的强弱，从背景目标中分离出来待识别的前景目标区域；
S1023：依据梯度边缘检测的检测结果对所述荧光图像进行荧光目标前景提取；
本步骤中，对步骤S202的前景检测区域进行目标进行提取，利用边缘的跟踪，把待识别的前景目标区域按照连通区域的属性逐一标记出来，记录边缘的位置；
S1024：依据提取到的目标前景对所述荧光图像进行单个荧光区域的分割，得到每个检测目标的荧光图像区域；
步骤S1023处理完成以后，必然会发现存在大量的交叠的检测目标，因此还需要对交叠的检测目标进行分离，本步骤可通过利用距离变换等一系列算法，对边缘进行标记，最终对单个检测目标进行分离得到每个检测目标的荧光图像区域。
在本申请另一实施例公开的技术方案中除了采用荧光成像系统采集到目标样品的荧光图像之外，还需要采集得到其明场图像，因此，上述步骤中，所述采用显微荧光成像系统对目标样品进行荧光成像得到荧光图像，具体为：采用显微荧光成像系统采集得到目标样品的荧光图像和明场图像，此时，在对荧光图像进行高斯平滑滤波之前还可以包括：
步骤S1011：通过明场识别技术对明场图像中的明场目标进行识别，并且对识别出的明场目标中的团块区域进行分离，得到明场图像中每个检测目标的位置信息以及尺寸信息；
具体的，本步骤中，针对明场拍摄的明场图像，从背景目标中分离出前景，具体的方法是对明场图像进行灰度化、亮度均一化、图像增强、二值化操作，从背景目标中提取出前景的检测目标图像；上述提取到的前景的检测目标图像会在大量的结团等情况，使得多个检测目标图像重叠在一起，必须对检测目标图像进行分离，获得单个的检测目标图像，求取单个检测目标图像的边缘，并且记录边缘信息；
步骤S1012：将所述明场识别技术获取到的检测目标的位置信息以及尺寸信息与荧光图像中对应的区域进行对比，对荧光图像进行识别；判断所述荧光图像中对应明场图像检测到的各个检测目标的位置信息、尺寸信息的区域是否为空，如果否，执行步骤S1013，如果是，执行步骤S1014；
步骤S1013：依据采用得到的明场图像识别技术检测到的每个检测目标的位置、尺寸信息在所述荧光图像中相对应的区域，由所述荧光图像中确定对应的每个检测目标的荧光图像区域；
步骤S1014：如果所述述荧光图像中对应明场图像中检测目标的位置、尺寸信息的区域为空，对所述荧光图像进行高斯平滑滤波并执行后续操作；
所述对荧光图像进行识别，具体为：针对拍摄的荧光图像，从背景目标中分离出荧光目标所在的区域，具体的方法是图像进行灰度化、亮度均一化、边缘增强以及图像的二值化操作，然后进行图像边缘的提取，把荧光目标所在的区域都标记存储下来；
所述明场识别具体为：是以明场图像中各个检测目标分割的区域(单个检测目标的边缘，并且记录边缘信息)为标记，利用明场图像技术的分割结果对荧光图像标进行分割，提取得到单个检测目标在荧光图像中的区域，也就是该处存在明场目标，那么在荧光目标中也标记出这个位置，使用明场目标一一对应进行荧光目标分离。
上述方案中，采用显微荧光成像系统采集得到目标样品的荧光图像和明场图像，具体过程为：
采用显微荧光成像系统采集得到目标样品的荧光图像，判定得到的目标样品的荧光图像区域是否满足预设条件，如果否，采用显微荧光成像系统采集得到目标样品的明场图像。
如果该处荧光目标用图2中公开的方法明显分割不合理，比如边缘分割碎裂化，或者边缘弥散开来导致荧光的区域明显不正确，系统会自动采用图3中方法进行校正识别，也就是用明场目标的识别结果为依据来操作，对此在本实施例中，还公开了一种将上述两种实施例公开的智能识别处理方法相结合的处理方式，该方式，以图2中公开的处理方式为主，以图3公开的处理方式为辅，具体的，参见图4，
得到每个检测目标的荧光图像区域之后，还包括：
步骤S401：判定得到的检测目标的荧光图像区域是否满足预设条件，如果否执行步骤S402；
步骤S402：采用显微荧光成像系统采集得到目标样品的明场图像，执行步骤S1011。
与上述方法相对应，本申请还公开了一种荧光图像的荧光强度确定系统，所述方法和系统可以相互借鉴，参见图5，该系统可以包括：
显微荧光成像系统100，用于对目标样品进行荧光成像；；
智能识别系统200，用于对荧光图像中每个检测目标的荧光图像区域进行边缘提取和分割，得到所述荧光图像中每个检测目标对应的荧光图像区域；
数据处理系统300，所述数据处理系统300包括：第一计算单元310、第二计算单元320、第三计算单元330、第四计算单元以及340以及流式聚类分析单元350；
所述第一计算单元310用于计算得到每个检测目标的荧光图像区域的累计灰度值，将所述累计灰度值作为流式聚类分析中对应荧光通道以光电倍增管采集到的累计电压脉冲值表达的累计荧光强度值；
所述第二计算单元320用于计算得到每个检测目标的荧光图像区域的最大灰度值，将所述最大灰度值作为流式聚类分析中对应荧光通道以光电倍增管采集到的最大电压脉冲值表达的最大荧光强度值；
所述第三计算单元330用于计算得到每个检测目标的荧光图像区域的平均灰度值，将所述平均灰度值作为流式聚类分析中对应荧光通道以光电倍增管采集到的平均电压脉冲值表达的平均荧光强度值；
所述第四计算单元340用于计算每个检测目标的明场图像区域的直径值作为目标大小值，并确定该值作为开展流式聚类分析中正向散射通道以光电倍增管采集到的电压脉冲值表达的检测目标的大小值的对应替代指标
所述流式聚类分析单元350，用于用于基于所述累计灰度值、最大灰度值、平均灰度值和明场直径值进行流式聚类分析。
与上述方法相对应，本系统中所述第一计算单元310、第二计算单元320、第三计算单元330、第四计算单元340可以择一存在，即，本系统中可以选择性的有第一计算单元310、第二计算单元320、第三计算单元330和/或第四计 算单元340。
与上述方法相对应，所述智能识别系统200被配置为：对所述荧光图像进行智能识别处理，提取单个检测目标荧光图像的边缘，边缘区域内的所有像素即为单个检测目标的荧光图像区域。
与上述方法相对应，所述智能识别系统200，包括至少荧光目标提取单元，所述荧光目标提取单元包括：
滤波单元，用于对荧光图像进行高斯平滑滤波；
梯度检测单元，用于对平滑滤波后的荧光图像进行梯度边缘检测；
前景提取单元，用于依据梯度边缘检测的检测结果对所述荧光图像进行荧光目标前景提取；
分割单元，用于依据提取到的目标前景对所述荧光图像进行单个荧光区域的分割，得到每个检测目标的荧光图像区域。
与上述方法相对应，所述显微荧光成像系统100还用于采集得到目标样品的明场图像；
所述智能识别系统200，还可以包括：明场细胞分离系统，所述明场细胞分离系统可以包括：
明场识别单元，用于对明场图像中的明场目标进行识别，并且对识别出的明场目标中的团块区域进行分离，得到明场图像中每个检测目标的位置、尺寸信息；
明场细胞分离单元，用于判断所述述荧光图像中对应明场图像中各个检测目标的位置、尺寸信息的区域是否为空，如果否，采用得到的明场图像中每个检测目标的位置、尺寸信息相对应的区域，由所述荧光图像中确定对应的每个检测目标的荧光图像区域，如果是，向荧光目标输出用于触发荧光目标动作的触发信号。
与上述方法相对应，在本申请另一实施例公开的技术方案中，所述显微荧光成像系统还用于采集得到目标样品的明场图像；
所述智能识别系统300，包括：荧光目标提取单元和明场细胞分离系统，本实施例虽然也包含荧光目标和明场细胞分离系统，但是两者的工作时序与上述实施例中的荧光目标和明场细胞分离系统的工作时序不同，具体的：
在本实施例中，所述荧光目标，还包括：判断单元，用于判定得到的细胞的荧光图像区域是否满足预设条件，如果否，控制显微荧光成像系统采集得到目标样品的明场图像，向所述明场细胞分离系统输出触发信号。
为了描述的方便，描述以上系统时以功能分为各种模块分别描述。当然，在实施本申请时可以把各模块的功能在同一个或多个软件和/或硬件中实现。
本说明书中的各个实施例均采用递进的方式描述，各个实施例之间相同相似的部分互相参见即可，每个实施例重点说明的都是与其他实施例的不同之处。尤其，对于系统或系统实施例而言，由于其基本相似于方法实施例，所以描述得比较简单，相关之处参见方法实施例的部分说明即可。以上所描述的系统及系统实施例仅仅是示意性的，其中所述作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部模块来实现本实施例方案的目的。本领域普通技术人员在不付出创造性劳动的情况下，即可以理解并实施。
专业人员还可以进一步意识到，结合本文中所公开的实施例描述的各示例的单元及算法步骤，能够以电子硬件、计算机软件或者二者的结合来实现，为了清楚地说明硬件和软件的可互换性，在上述说明中已经按照功能一般性地描述了各示例的组成及步骤。这些功能究竟以硬件还是软件方式来执行，取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能，但是这种实现不应认为超出本发明的范围。
结合本文中所公开的实施例描述的方法或算法的步骤可以直接用硬件、处理器执行的软件模块，或者二者的结合来实施。软件模块可以置于随机存储器(RAM)、内存、只读存储器(ROM)、电可编程ROM、电可擦除可编程ROM、寄存器、硬盘、可移动磁盘、CD-ROM、或技术领域内所公知的任意其它形式的存储介质中。
还需要说明的是，在本文中，诸如第一和第二等之类的关系术语仅仅用来将一个实体或者操作与另一个实体或操作区分开来，而不一定要求或者暗示这些实体或操作之间存在任何这种实际的关系或者顺序。而且，术语“包括”、 “包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、物品或者设备不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、物品或者设备所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括所述要素的过程、方法、物品或者设备中还存在另外的相同要素。
对所公开的实施例的上述说明，使本领域专业技术人员能够实现或使用本发明。对这些实施例的多种修改对本领域的专业技术人员来说将是显而易见的，本文中所定义的一般原理可以在不脱离本发明的精神或范围的情况下，在其它实施例中实现。因此，本发明将不会被限制于本文所示的这些实施例，而是要符合与本文所公开的原理和新颖特点相一致的最宽的范围。

Claims (10)

  1. 一种荧光图像的荧光强度确定方法，其特征在于，包括：
    采用显微荧光成像系统对目标样品进行荧光成像得到荧光图像；
    对荧光图像中每个检测目标的荧光图像区域进行边缘提取和分割，得到所述荧光图像中每个检测目标对应的荧光图像区域；
    计算得到每个检测目标的荧光图像区域的累计灰度值，将所述累计灰度值作为流式聚类分析中对应荧光通道中以光电倍增管采集到的累计电压脉冲值表达的累计荧光强度值；
    计算得到每个检测目标的荧光图像区域的最大灰度值，将所述最大灰度值作为流式聚类分析中对应荧光通道中以光电倍增管采集到的最大电压脉冲值表达的最大荧光强度值；
    计算得到每个检测目标的荧光图像区域的平均灰度值，将所述平均灰度值作为流式聚类分析中对应荧光通道中以光电倍增管采集到的平均电压脉冲值表达的平均荧光强度值；
    计算每个检测目标的明场图像区域的直径值作为目标大小值，并确定该值作为开展流式聚类分析中正向散射通道中以光电倍增管采集到的电压脉冲值表达的检测目标的大小值的对应替代指标
    基于所述累计灰度值、最大灰度值、平均灰度值和明场直径值进行流式聚类分析。
  2. 根据权利要求1所述的荧光图像的荧光强度确定方法，其特征在于，基于所述累计灰度值、最大灰度值、平均灰度值和像素面积值进行流式聚类分析，包括：
    依据预设映射表查找得到分别与所述累计灰度值、最大灰度值、平均灰度值和明场直径值一一对应的累计荧光强度值、最大荧光强度值、平均荧光强度值和目标大小，基于查表得到的累计荧光强度值、最大荧光强度值、平均荧光强度值和目标大小值进行流式聚类分析。
  3. 根据权利要求1所述的荧光图像的荧光强度确定方法，其特征在于，对荧光图像中每个检测目标的荧光图像区域进行边缘提取和分割，包括：
    对荧光图像进行高斯平滑滤波；
    对平滑滤波后的荧光图像进行梯度边缘检测；
    依据梯度边缘检测的检测结果对所述荧光图像进行荧光目标前景提取；
    依据提取到的目标前景对所述荧光图像进行单个荧光区域的分割，得到每个检测目标的荧光图像区域。
  4. 根据权利要求3所述的荧光图像的荧光强度确定方法，其特征在于，
    所述采用显微荧光成像系统对目标样品进行荧光成像得到荧光图像，具体为：采用显微荧光成像系统采集得到目标样品的荧光图像和明场图像；所述对荧光图像进行高斯平滑滤波之前还包括：
    对明场图像中的明场目标进行识别，并且对识别出的明场目标中的团块区域进行分离，得到明场图像中每个检测目标的位置、尺寸信息；
    对荧光图像进行识别，判断所述述荧光图像中对应明场图像中各个检测目标的位置、尺寸信息的区域是否为空，如果否，采用得到的明场图像中每个检测目标的位置、尺寸信息相对应的区域，由所述荧光图像中确定对应的每个检测目标的荧光图像区域；
    如果所述述荧光图像中对应明场图像中检测目标的位置、尺寸信息的区域为空，对荧光图像进行高斯平滑滤波并执行后续操作。
  5. 根据权利要求4所述的荧光图像的荧光强度确定方法，其特征在于，采用显微荧光成像系统采集得到目标样品的荧光图像和明场图像，具体为：
    采用显微荧光成像系统采集得到目标样品的荧光图像，判定得到的目标样品的荧光图像区域是否满足预设条件，如果否，采用显微荧光成像系统采集得到目标样品的明场图像。
  6. 一种荧光图像的荧光强度确定系统，其特征在于，包括：
    显微荧光成像系统，用于对目标样品进行荧光成像；
    智能识别系统，用于对荧光图像中每个检测目标的荧光图像区域进行边缘提取和分割，得到所述荧光图像中每个检测目标对应的荧光图像区域；
    数据处理系统，所述数据处理系统包括：第一计算单元、第二计算单元、第三计算单元、第四计算单元和流式聚类分析单元；
    所述第一计算单元用于计算得到每个检测目标的荧光图像区域的累计灰度值，将所述累计灰度值作为流式聚类分析中对应荧光通道以光电倍增管采集到的累计电压脉冲值表达的累计荧光强度值；
    所述第二计算单元用于计算得到每个检测目标的荧光图像区域的最大灰度值，将所述最大灰度值作为流式聚类分析中对应荧光通道以光电倍增管采集到的最大电压脉冲值表达的最大荧光强度值；
    所述第三计算单元用于计算得到每个检测目标的荧光图像区域的平均灰度值，将所述平均灰度值作为流式聚类分析中对应荧光通道以光电倍增管采集到的平均电压脉冲值表达的平均荧光强度值；
    所述第四计算单元用于计算每个检测目标的明场图像区域的直径值作为目标大小值，并确定该值作为开展流式聚类分析中正向散射通道以光电倍增管采集到的电压脉冲值表达的检测目标的大小值的对应替代指标；
    所述流式聚类分析单元，用于基于所述累计灰度值、最大灰度值、平均灰度值和明场直径值进行流式聚类分析。
  7. 根据权利要求6所述的荧光图像的荧光强度确定系统，其特征在于，所述流式聚类分析单元具体用于：依据预设映射表查找得到分别与所述累计灰度值、最大灰度值、平均灰度值和明场大小值一一对应的累计荧光强度值、最大荧光强度值、平均荧光强度值和目标大小值，基于查表得到的累计荧光强度值、最大荧光强度值、平均荧光强度值和目标大小值进行流式聚类分析。
  8. 根据权利要求1所述的荧光图像的荧光强度确定系统，其特征在于，智能识别系统，包括至少荧光目标提取单元，所述荧光目标提取单元包括：
    滤波单元，用于对荧光图像进行高斯平滑滤波；
    梯度检测单元，用于对平滑滤波后的荧光图像进行梯度边缘检测；
    前景提取单元，用于依据梯度边缘检测的检测结果对所述荧光图像进行荧光目标前景提取；
    分割单元，用于依据提取到的目标前景对所述荧光图像进行单个荧光区域的分割，得到每个检测目标的荧光图像区域。
  9. 根据权利要求8所述的荧光图像的荧光强度确定系统，其特征在于，所述显微荧光成像系统还用于采集得到目标样品的明场图像；
    所述智能识别系统，还包括：明场细胞分离系统，所述明场细胞分离系统包括：
    明场识别单元，用于对明场图像中的明场目标进行识别，并且对识别出的明场目标中的团块区域进行分离，得到明场图像中每个检测目标的位置、尺寸 信息；
    明场细胞分离单元，用于判断所述述荧光图像中对应明场图像中各个检测目标的位置、尺寸信息的区域是否为空，如果否，采用得到的明场图像中每个检测目标的位置、尺寸信息相对应的区域，由所述荧光图像中确定对应的每个检测目标的荧光图像区域，如果是，向荧光目标输出用于触发荧光目标动作的触发信号。
  10. 根据权利要求9所述的荧光图像的荧光强度确定系统，其特征在于，
    所述荧光目标分离系统，还包括：判断单元，用于判定得到的细胞的荧光图像区域是否满足预设条件，如果否，控制显微荧光成像系统采集得到目标样品的明场图像，向所述明场细胞分离系统输出触发信号。
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