CN111768424B - Cell image extraction method applied to linear array detector flow cytometer - Google Patents
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Abstract
The invention discloses a cell image extraction method applied to a linear array detector flow cytometer, which comprises the following steps of 1, establishing an initial theoretical background model of a cell image; step 2, updating an initial theoretical background model to obtain an improved background model; step 3, separating the foreground cells from the background by improving the background model to obtain the change information of the foreground cells; and 4, extracting a linear array scanning image of the foreground cells, and finishing the extraction of the cell images. The invention can simplify and even eliminate the problems of poor imaging effect, complex background and the like caused by the influence of the quality of the light source and the micro-channel in the experimental operation process; effectively reduces the difficulty of analysis and treatment of cells in the later period.
Description
Technical Field
The invention belongs to the technical field of cell detection, and relates to a cell image extraction method applied to a linear array detector flow cytometer.
Background
Cell detection is important in medical research and in the monitoring of many diseases. However, conventional instruments for cell detection are relatively bulky and expensive and are only suitable for large medical institutions. The micro-fluidic technology and the image processing technology are combined, so that the miniaturization, low cost, easy operation and popularization of the cell detection system are possible. The cell image acquisition device based on the microfluidic technology and the linear array detector can solve the traditional problem, but the imaging effect is easily influenced by the quality of the light source and the micro-channel, so that the later cell analysis and processing difficulty is high, and therefore, the extraction of the cell image acquired by the linear array acquisition system is an important part in the later cell analysis and processing.
Disclosure of Invention
The invention aims to provide a cell image extraction method applied to a linear array detector flow cytometer, which solves the problem of poor cell image imaging recovery effect caused by complex background of an acquired cell image.
The technical scheme adopted by the invention is that the cell image extraction method applied to the linear array detector flow cytometer is implemented according to the following steps:
step 1, establishing an initial theoretical background model of a cell image;
step 2, performing foreground cell and background rough segmentation on the cell image, extracting a background image, and updating an initial theoretical background model to obtain an improved background model;
step 3, separating the foreground cells from the background by improving the background model to obtain the change information of the foreground cells;
and 4, linearly mapping the change information of the foreground cells to an acquisition image of a linear array scanning imaging system, extracting a linear array scanning image of the foreground cells, and completing cell image extraction.
The present invention is also characterized in that,
the cell image is obtained by collecting image data of cells flowing through a detection area at a constant speed by a linear array detector at a fixed frame frequency and splicing each frame of data of the image data according to time sequence.
Step 1 is specifically to model the mean value of the cell image without cell flow, and set F i To read the ith frame of the linear array scanning image, let P i Is F. Th i Pixel values of one row, P' i Is F. Th i-N Line to F i Mean model of rows, N<i and N and i are natural numbers other than 0, and the image data F is buffered in accordance with the frame stream i-N To F i The frame, wherein N is the current frame, the pixel points of N frames are added and averaged to obtain an initial theoretical background model P '' i 。
Initial theoretical background model P' i P with respect to pixel value i The functional expression is P' i ＝g(P i ) Initial theoretical background model P' i Extracted foreground cell variation information C 1 P i ＝P i -P’ i 。
The step 2 is specifically implemented according to the following steps:
step 2.1, according to the foreground cell variation information C 1 P i Setting a single threshold or a plurality of thresholds for threshold segmentation on a cell image with cells flowing through, realizing rough segmentation on a foreground cell image of the cell image with cells flowing through, and obtaining a Mask of a background image, wherein the Mask is recorded as: maP i ；
Step 2.2 Using mask MaP i Extraction of F i Background image of line, filtering out pixels with cells, and finding F-th image of cells flowing through i-N To F i Line mean background model P' i Mean background model P' i Namely, an improved background model, P' i Functional expression P' of " i ＝h(P’ I ,MaP i )。
Step 3 is specifically to model the initial theoretical background P' i Replacement by an improved background model P' i Then for an improved background model P' i The extracted foreground cell change information is C 2 P i ＝P i -P” i 。
Step 4 is specifically to refine the background model P' i Foreground cell variation information C of (C) 2 P i Mapping to the cell image acquired by the linear array detector scanning imaging system, and according to C 2 P i Compressing the background dynamic range and stretching the cell dynamic range, thereby obtaining a linear array scanning image of the foreground cells, and completing the extraction of the cell image.
The beneficial effects of the invention are as follows:
the invention relates to a cell image extraction method applied to a linear array detector flow cytometer, which is characterized in that on the basis of a mean value background model, foreground threshold segmentation is carried out, the model is updated in real time, and the change information of foreground cells is mapped to an image for foreground extraction, so that a linear array scanning image of the foreground cells is obtained; therefore, the problems of poor imaging effect, complex background and the like caused by the influence of the quality of a light source and a micro-channel in the experimental operation process can be simplified and even removed; the method effectively reduces the difficulty of analysis and treatment of the later cells, and is an extremely important step for analysis and treatment of the later cell images.
Drawings
FIG. 1 is a schematic diagram of a flow cytometer based on a linear array detector;
FIG. 2 is a side view of the basic structure of a linear array detector based flow cytometer;
FIG. 3 is a cell image of a cell image extraction method of the present invention applied to a linear array detector flow cytometer;
FIG. 4 is a diagram of the change information of foreground cells obtained by separating the foreground and background in the cell image extraction method applied to the linear array detector flow cytometer;
FIG. 5 is a perspective view of a line detector flow cytometer of the present invention showing a foreground cell map after separating the foreground and background based on one dimension;
fig. 6 is a line scan image of a foreground cell in a cell image extraction method of the present invention applied to a line detector flow cytometer.
In the figure, a microfluidic chip 1, a linear array detector 2, a data processing device 3, a parallel light source 4, a microchannel 6, a microfluidic channel inlet a, a microfluidic channel inlet b, and a microfluidic channel outlet 8.
Detailed Description
The invention will be described in detail below with reference to the drawings and the detailed description.
The invention relates to a cell image extraction method applied to a linear array detector flow cytometer, which is shown in fig. 1-2, and comprises a microfluidic chip 1, a linear array detector 2, a data processing device 3, a parallel light source 4, a microchannel 5, a microfluidic channel inlet a6, a microfluidic channel inlet b7 and a microfluidic channel outlet 8, wherein the method is implemented according to the following steps:
step 1, establishing an initial theoretical background model of a cell image
Mean modeling is performed on the cell image without cell flow, and F is set i To read the ith frame of the linear array scanning image, let P i Is F. Th i Pixel values of one row, P' i Is F. Th i-N Line to F i Mean model of rows, N<i and N and i are natural numbers other than 0, and the image data F is buffered in accordance with the frame stream i-N To F i The frame, wherein N is the current frame, the pixel points of N frames are added and averaged to obtain an initial theoretical background model P '' i ；
Initial theoretical background model P' i P with respect to pixel value i The functional expression is P' i ＝g(P i ) Initial theoretical background model P' i Extracted foreground cell variation information C 1 P i ＝P i -P’ i 。
Step 2, performing foreground cell and background rough segmentation on the cell image, extracting a background image, updating an initial theoretical background model, and obtaining an improved background model
The cell image is shown in fig. 3, which is obtained by collecting image data of cells flowing through a detection area at a constant speed by a linear array detector at a fixed frame frequency, and splicing each frame of data of the image data according to time sequence;
step 2.1, according to the foreground cell variation information C 1 P i Setting a single threshold or a plurality of thresholds for threshold segmentation on a cell image with cells flowing through, realizing rough segmentation on a foreground cell image of the cell image with cells flowing through, and obtaining a Mask of a background image, wherein the Mask is recorded as: maP i ；
Step 2.2 Using mask MaP i Extraction of F i Background image of line, filtering out pixels with cells, and finding F-th image of cells flowing through i-N To F i Line mean background model P' i Mean background model P' i Namely, an improved background model, P' i Functional expression P' of " i ＝h(P’ I ,MaP i )。
Step 3, separating the foreground cells from the background by improving the background model to obtain the change information of the foreground cells;
will be the initial theoretical background model P' i Replacement by an improved background model P' i Then for an improved background model P' i The extracted foreground cell change information is C 2 P i ＝P i -P” i The foreground cell change information is shown in FIGS. 4-5.
Step 4, linearly mapping the change information of the foreground cells to an acquisition image of a linear array scanning imaging system, wherein the linear array scanning image of the extracted foreground cells is shown in fig. 6, and the cell image extraction is completed
Will improve the background model P' i Foreground cell variation information C of (C) 2 P i Mapping to the cell image acquired by the linear array detector scanning imaging system, and according to C 2 P i Compressing the background dynamic range and stretching the cell dynamic range, thereby obtaining a linear array scanning image of the foreground cells, and completing the extraction of the cell image.
The linear array scanning image of the foreground cells shown in fig. 6, which is obtained by the linear array scanning image of the cell images shown in fig. 3 and the cell image extraction method based on the linear array detector flow cytometer, can be obviously seen, the foreground cell information is more clear, the problems of complex background and the like caused by the influence of the quality of a light source and a micro-channel in the experimental operation process are simplified and even eliminated, and the difficulty of later cell analysis and processing is reduced.
Claims (2)
1. The cell image extraction method applied to the linear array detector flow cytometer is characterized by comprising the following steps of:
step 1, establishing an initial theoretical background model of a cell image;
the cell image is obtained by collecting image data of cells flowing through a detection area at a constant speed by a linear array detector at a fixed frame frequency and splicing each frame of data of the image data according to time sequence;
said step 1Specifically, the mean value modeling is performed on the cell image when no cell flows through, and F is set i To read the ith frame of the linear array scanning image, let P i Is F. Th i Pixel values of a row and a line, P ’ i Is F. Th i-N Line to F i Mean model of rows, N<i and N and i are natural numbers other than 0, and the image data F is buffered in accordance with the frame stream i-N To F i Frame, wherein N is the current frame, pixel points of N frames are added and averaged to obtain an initial theoretical background model P ’ i ；
The initial theoretical background model P ’ i P with respect to pixel value i The function expression is P ’ i ＝g(P i ) Initial theoretical background model P ’ i Extracted foreground cell variation information C 1 P i ＝P i -P ’ i ；
Step 2, performing foreground cell and background rough segmentation on the cell image, extracting a background image, and updating an initial theoretical background model to obtain an improved background model;
step 2.1, according to the foreground cell variation information C 1 P i Setting a single threshold or a plurality of thresholds for threshold segmentation on a cell image with cells flowing through, realizing rough segmentation on a foreground cell image of the cell image with cells flowing through, and obtaining a Mask of a background image, wherein the Mask is recorded as: maP i ；
Step 2.2 Using mask MaP i Extraction of F i Background image of line, filtering out pixels with cells, and finding F-th image of cells flowing through i-N To F i Line mean background model P ” i Mean background model P ” i Namely, an improved background model, P ” i Is a functional expression P of (2) ” i ＝h(P ’ I ,MaP i )；
Step 3, separating the foreground cells from the background by improving the background model to obtain the change information of the foreground cells;
step 4, linearly mapping the change information of the foreground cells to an acquisition image of a linear array scanning imaging system, extracting a linear array scanning image of the foreground cells, and completing cell image extraction;
said step 4 is specifically to modify the background model P' i Foreground cell variation information C of (C) 2 P i Mapping to the cell image acquired by the linear array detector scanning imaging system, and according to C 2 P i Compressing the background dynamic range and stretching the cell dynamic range, thereby obtaining a linear array scanning image of the foreground cells, and completing the extraction of the cell image.
2. The method for extracting a cell image applied to a linear array detector flow cytometer as described in claim 1, wherein said step 3 is specifically to use an initial theoretical background model P' i Replacement by an improved background model P' i Then for an improved background model P' i The extracted foreground cell change information is C 2 P i ＝P i -P” i 。
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