CN111398138A - Optical detection system and method of dry type blood cell analysis device - Google Patents
Optical detection system and method of dry type blood cell analysis device Download PDFInfo
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Abstract
The invention belongs to the technical field of cell analysis, and discloses an optical detection system and a method of a dry-type blood cell analysis device, wherein the optical detection system of the dry-type blood cell analysis device comprises: the blood sample collection module, the cell image collection module, the image correction module, the cell image enhancement module, the main control module, the optical detection module, the cell classification module, the cell counting module, the cell analysis module, the data storage module and the display module. The invention classifies the special types of the cells so as to improve the accuracy of cell classification; at the same time, the alternating operation of the microscope by means of the cell counting module, preferably in transmission mode, although the illumination time is very short, also enables sufficient illumination of the image for digital image analysis, detection of the arrangement of cells, cell structures and/or subcellular regions, and then recording of the fluorescence of the examined sub-regions, so that fluorescence quantification can be subsequently performed; improving the accuracy of cell counting.
Description
Technical Field
The invention belongs to the technical field of cell analysis, and particularly relates to an optical detection system and method of a dry type blood cell analysis device.
Background
Blood cells, also called "blood cells", are cells present in blood and can flow throughout the body with the blood. In mammals, blood cells are mainly composed of three types: red blood cell: the main function is to transport oxygen. White blood cell: mainly plays a role of immunity. When germs invade the human body, white blood cells can penetrate through the capillary wall and concentrate to the invaded part of the germs to be phagocytized after being surrounded by the germs. Platelets: plays an important role in the hemostasis process. Blood cells account for approximately 45% of the blood volume and include red blood cells, white blood cells, and platelets. Under normal physiological conditions, blood cells and platelets have a certain morphological structure and a relatively stable number. However, the optical detection system of the existing dry blood cell analysis apparatus does not accurately classify the cells; at the same time, the cells are not counted accurately.
In summary, the problems of the prior art are as follows: the optical detection system of the existing dry type blood cell analysis device can not accurately classify the cells; at the same time, the cells are not counted accurately.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides an optical detection system and method of a dry type blood cell analysis device.
The present invention is achieved as described above, and provides an optical detection method for a dry blood cell analyzer, the optical detection method including the steps of:
collecting a blood sample to be detected through a blood sample collecting device; collecting a blood cell microscopic image through microscopic imaging equipment; correcting the collected blood cell image through an image correction program; and carrying out enhancement processing on the acquired blood cell image through an image enhancement algorithm.
Step two, controlling the optical detection device through the main control computer to detect the collected blood sample: (I) the optical detection device sequentially emits blue light and red light through a blue light source and a red light source;
(II) sequentially irradiating the emitted blue light and red light on a capillary tube of the collected blood sample, imaging through a lens, and collecting by a color linear array CCD image sensor;
and (III) transmitting the collected color image of the blood sample to a main control computer for analysis through analog-digital conversion.
Step three, classifying the blood cell images through a classification program: (1) synthesizing the cell layer image into a cell synthesis image including an image of the cell by a synthesis program;
(2) screening a plurality of candidate cells from the cells in the cell synthesis image according to the cell synthesis image and screening conditions related to the cell size, and obtaining a plurality of candidate cell data respectively related to the image position and the size of the candidate cells;
(3) for each cell layer image related to the cell membrane of the cell, performing feature extraction on the cell layer image by using the candidate cell data to obtain a plurality of cell membrane feature values respectively related to the cell membrane of the candidate cell;
(4) for each cell layer image related to the cell nucleus of the cell, performing feature extraction on the cell layer image by using the candidate cell data to obtain a plurality of cell nucleus feature values respectively related to the cell nucleus of the candidate cell;
(5) and determining whether each candidate cell is a target cell or a non-target cell according to at least a plurality of upper limit values respectively corresponding to the cell layer images, the cell membrane characteristic values of the cell layer images and the cell nucleus characteristic values of the cell layer images.
Step four, performing cell counting on the collected blood sample through a cell counting program: 1) calibrating a microscope to continuously move the cell sample in a plane relative to an optical system of the microscope having at least one microscope camera;
2) during the movement of the cell sample, recording, with the at least one microscope camera, at least one or more images of a sub-region of the cell sample in the transmission mode or the fluorescence mode, and at least one or more images of the same sub-region of the cell sample in the fluorescence mode;
3) the at least one or more images of the same sub-region of the cell sample in the transmission mode or the fluorescence mode and the at least one or more images of the same sub-region of the cell sample in the fluorescence mode are positionally related to each other;
4) detecting the position and/or contour of cells or cellular components of the cellular sample in the image of the transmission mode and then analyzing the detected intensity of the image recorded in the fluorescence mode according to the detected position and/or contour of cells or cellular components in the cellular sample;
5) detecting the position and/or contour of cells or cellular components of the cellular sample in the image of the fluorescence mode and then analyzing the detected intensity of the image recorded in the transmission mode according to the detected position and/or contour of cells or cellular components in the cellular sample;
wherein the position and/or contour of cells or cell components of the cell sample is detected in the image of the fluorescence pattern and then the detected intensity of the image recorded in the second fluorescence pattern is analyzed in dependence on the detected position and/or contour of cells or cell components in the cell sample.
Analyzing the blood cell characteristics through a cell analysis program; storing the collected blood cell images, classification results, counting results and real-time data of analysis results through a storage chip; and displaying the collected blood cell image, the classification result, the counting result and the real-time data of the analysis result through a display.
Further, in the second step, the distance between the blue light source and the capillary tube is 20-25mm, the blue light source is positioned below the capillary tube, and the plane of the blue light source and the horizontal plane of the central axis of the capillary tube form an included angle of 45 degrees; the distance between the red light source and the capillary tube is 20-25mm, and the center line of the red light source and the central axis of the capillary tube are positioned on the same horizontal plane;
the color linear array CCD image sensor is an R, G, B three-color response CCD, which is distributed in a single row in sequence, each row at least contains 10550 pixels, and the resolution ratio is 48 lines/mm.
Further, in step three, the screening conditions in step (2) are as follows: the number of pixels of the image of the candidate cell is greater than a default value; the step (2) comprises the following substeps:
binarizing the cell composite image according to a plurality of pixels of a background of the cell composite image;
and obtaining the candidate cells and the candidate cell data according to the binarized cell synthetic image and a default value, wherein the pixel number of each candidate cell is greater than the default value.
Further, in step three, the following substeps are also included between steps (2) and (3):
acquiring cell membranes of the candidate cells in the cell layer images of the cell membranes of the cells according to the candidate cell data, and synthesizing the images of the cell membranes of the candidate cells into candidate cell membrane synthetic images;
for each candidate cell in the candidate cell membrane synthetic image, judging whether a candidate plexus cell can be separated by a distance conversion algorithm;
when the candidate cell is judged to be capable of separating the candidate Pleated cell by the distance conversion algorithm, obtaining candidate Pleated cell data related to the image position and size of the candidate Pleated cell according to the candidate cell membrane synthetic image, and using the candidate Pleated cell data as the candidate cell data.
Further, in step three, the step (7) comprises the following substeps:
for each candidate cell in the candidate cell membrane synthetic image, calculating a cell membrane critical value related to a pixel value by an adaptive algorithm according to the candidate cell membrane synthetic image;
for each candidate cell in the candidate cell membrane synthetic image, binarizing the candidate cell membrane synthetic image according to the cell membrane critical value;
for each candidate cell in the candidate cell membrane synthetic image, obtaining a candidate cell membrane image outline of the cell membrane related to the candidate cell according to the binarized candidate cell membrane synthetic image and candidate cell data related to the position and the size of the image of the candidate cell;
for each candidate cell in the candidate cell membrane synthesis image, calculating a cell membrane contour average length related to the candidate cell membrane image contour;
for each candidate cell in the candidate cell membrane synthetic image and for each pixel in the candidate cell membrane image contour, calculating a cell membrane contour shortest distance corresponding to the shortest distance between the pixel and the candidate cell membrane image contour to obtain a cell membrane contour ratio corresponding to the ratio of the cell membrane contour shortest distance to the cell membrane contour average length;
mapping each pixel value of the candidate cell membrane image contour to the maximum value of the pixel according to the cell membrane contour ratio;
normalizing the mapped candidate cell membrane image profile to obtain the number of cell membrane profile peaks; and judging whether the candidate plexus cells can be separated according to the number of the cell membrane profile wave peaks.
Further, in step four, at least one time shifted image of a sub-region of the cell sample in the transmission mode is modified with respect to a predetermined first image of the sub-region of the cell sample in the transmission mode as a function of a time shift or as a function of a relative displacement of the cell sample with respect to the at least one microscope camera between the predetermined first image and the time shifted image, such that the first predetermined image and the time shifted image are associated with the same sub-region of the cell sample, wherein the time shift is in particular later or earlier.
Further, in step four, at least one time shifted image of a sub-region of the cell sample in the fluorescence mode is modified with respect to a predetermined first image of the sub-region of the cell sample in the fluorescence mode as a function of a time shift or as a function of a relative displacement of the cell sample with respect to the at least one microscope camera between the predetermined first image and the time shifted image, such that the first predetermined image and the time shifted image are associated with the same sub-region of the cell sample, wherein the time shift is in particular later or earlier.
Another object of the present invention is to provide an optical detection system of a dry blood cell analyzer to which the optical detection method of a dry blood cell analyzer is applied, the optical detection system of a dry blood cell analyzer including:
the blood sample collection module, the cell image collection module, the image correction module, the cell image enhancement module, the main control module, the optical detection module, the cell classification module, the cell counting module, the cell analysis module, the data storage module and the display module.
The blood sample collection module is connected with the main control module and is used for collecting a blood sample to be detected through the blood sample collection device;
the cell image acquisition module is connected with the image correction module and is used for acquiring a blood cell microscopic image through microscopic imaging equipment;
the image correction module is connected with the cell image acquisition module and the main control module and is used for correcting the acquired blood cell image through an image correction program;
the cell image enhancement module is connected with the main control module and is used for enhancing the collected blood cell image through an image enhancement algorithm;
the main control module is connected with the blood sample acquisition module, the cell image acquisition module, the image correction module, the cell image enhancement module, the optical detection module, the cell classification module, the cell counting module, the cell analysis module, the data storage module and the display module and is used for controlling the normal work of each module through a host;
the optical detection module is connected with the main control module and is used for detecting the collected blood sample through the optical detection device;
the cell classification module is connected with the main control module and is used for classifying the blood cell images through a classification program;
the cell counting module is connected with the main control module and is used for counting the cells of the collected blood sample through a cell counting program;
the cell analysis module is connected with the main control module and is used for analyzing the blood cell characteristics through a cell analysis program;
the data storage module is connected with the main control module and used for storing the collected blood cell images, classification results, counting results and real-time data of analysis results through the storage chip;
and the display module is connected with the main control module and used for displaying the acquired blood cell images, classification results, counting results and real-time data of analysis results through a display.
It is another object of the present invention to provide a computer program product stored on a computer readable medium, comprising a computer readable program for providing a user input interface to implement the optical detection method of the dry blood cell analysis device when executed on an electronic device.
Another object of the present invention is to provide a computer-readable storage medium storing instructions which, when executed on a computer, cause the computer to perform the optical detection method of the dry blood cell analysis apparatus.
The invention has the advantages and positive effects that: according to the invention, the cell classification module utilizes the candidate cell data to perform feature capture on the cell layer image so as to obtain the cell membrane feature value and the cell nucleus feature value, and classifies the candidate cells into target cells and non-target cells according to at least the upper limit value, the cell membrane feature value and the cell nucleus feature value, so that the classification is performed according to the specific types of the cells so as to improve the accuracy of cell classification; at the same time, the alternating operation of the microscope by means of the cell counting module, preferably in transmission mode, although the illumination time is very short, also enables (sufficient) illumination of the image for digital image analysis, detection of the arrangement of cells, cell structures and/or subcellular regions, and then recording of the fluorescence of the examined sub-regions, so that fluorescence quantification can be subsequently performed; improving the accuracy of cell counting.
Drawings
FIG. 1 is a flowchart of an optical detection method of a dry blood cell analyzer according to an embodiment of the present invention.
FIG. 2 is a block diagram showing the structure of an optical detection system of the dry blood cell analyzer according to the embodiment of the present invention;
in the figure: 1. a blood sample collection module; 2. a cell image acquisition module; 3. an image correction module; 4. a cell image enhancement module; 5. a main control module; 6. an optical detection module; 7. a cell sorting module; 8. a cell counting module; 9. a cell analysis module; 10. a data storage module; 11. and a display module.
Detailed Description
In order to further understand the contents, features and effects of the present invention, the following embodiments are illustrated and described in detail with reference to the accompanying drawings.
The structure of the present invention will be described in detail below with reference to the accompanying drawings.
As shown in fig. 1, the optical detection method of the dry blood cell analyzer according to the embodiment of the present invention includes the following steps:
s101, collecting a blood sample to be detected through a blood sample collecting device; and acquiring a blood cell microscopic image through a microscopic imaging device.
S102, correcting the collected blood cell image through an image correction program; and carrying out enhancement processing on the acquired blood cell image through an image enhancement algorithm.
S103, controlling the normal work of an optical detection system of the dry type blood cell analysis device through a host; the collected blood sample is detected by an optical detection device.
S104, classifying the blood cell images through a classification program; the collected blood samples were subjected to cell counting by a cell counting procedure.
S105, analyzing the blood cell characteristics through a cell analysis program; and storing the collected blood cell images, classification results, counting results and real-time data of analysis results by a storage chip.
And S106, displaying the collected blood cell image, the classification result, the counting result and the real-time data of the analysis result through a display.
As shown in fig. 2, an optical detection system of a dry blood cell analyzer according to an embodiment of the present invention includes: the system comprises a blood sample collection module 1, a cell image collection module 2, an image correction module 3, a cell image enhancement module 4, a main control module 5, an optical detection module 6, a cell classification module 7, a cell counting module 8, a cell analysis module 9, a data storage module 10 and a display module 11.
The blood sample collection module 1 is connected with the main control module 5 and is used for collecting a blood sample to be detected through a blood sample collection device;
the cell image acquisition module 2 is connected with the image correction module 3 and is used for acquiring a blood cell microscopic image through microscopic imaging equipment;
the image correction module 3 is connected with the cell image acquisition module 2 and the main control module and is used for correcting the acquired blood cell image through an image correction program;
the cell image enhancement module 4 is connected with the main control module 5 and is used for enhancing the collected blood cell image through an image enhancement algorithm;
the main control module 5 is connected with the blood sample acquisition module 1, the cell image acquisition module 2, the image correction module 3, the cell image enhancement module 4, the optical detection module 6, the cell classification module 7, the cell counting module 8, the cell analysis module 9, the data storage module 10 and the display module 11, and is used for controlling the normal work of each module through a host;
the optical detection module 6 is connected with the main control module 5 and is used for detecting the collected blood sample through an optical detection device;
the cell classification module 7 is connected with the main control module 5 and is used for classifying the blood cell images through a classification procedure;
the cell counting module 8 is connected with the main control module 5 and is used for counting the cells of the collected blood sample through a cell counting program;
the cell analysis module 9 is connected with the main control module 5 and is used for analyzing the blood cell characteristics through a cell analysis program;
the data storage module 10 is connected with the main control module 5 and used for storing the collected blood cell images, classification results, counting results and real-time data of analysis results through a storage chip;
and the display module 11 is connected with the main control module 5 and is used for displaying the acquired blood cell images, classification results, counting results and real-time data of analysis results through a display.
The invention is further described with reference to specific examples.
Example 1
As shown in fig. 1, the optical detection method of the dry blood cell analyzer according to the embodiment of the present invention is a method for detecting a collected blood sample by an optical detection device, as a preferred embodiment, including:
(I) the optical detection device sequentially emits blue light and red light through the blue light source and the red light source.
(II) the emitted blue light and red light are sequentially irradiated on the capillary of the collected blood sample, imaged by a lens and collected by a color linear array CCD image sensor.
And (III) transmitting the collected color image of the blood sample to a main control computer for analysis through analog-digital conversion.
The distance between the blue light source and the capillary tube is 20-25mm, the blue light source is positioned below the capillary tube, and the plane of the blue light source and the horizontal plane of the central axis of the capillary tube form an included angle of 45 degrees; the distance between the red light source and the capillary tube is 20-25mm, and the center line of the red light source and the central axis of the capillary tube are positioned on the same horizontal plane;
the color linear array CCD image sensor is an R, G, B three-color response CCD, which is distributed in a single row in sequence, each row at least contains 10550 pixels, and the resolution ratio is 48 lines/mm.
Example 2
As shown in fig. 1, the optical detection method of the dry blood cell analyzer according to the embodiment of the present invention is a method for classifying blood cell images by a classification procedure, as a preferred embodiment, including:
(1) synthesizing the cell layer image into a cell synthesis image including an image of the cell by a synthesis program.
(2) And screening a plurality of candidate cells from the cells in the cell synthesis image according to the cell synthesis image and screening conditions related to the cell size, and obtaining a plurality of candidate cell data respectively related to the image positions and the sizes of the candidate cells.
(3) For each cell layer image related to the cell membrane of the cell, feature extraction is performed on the cell layer image by using the candidate cell data to obtain a plurality of cell membrane feature values respectively related to the cell membrane of the candidate cell.
(4) For each cell layer image related to the cell nucleus of the cell, performing feature extraction on the cell layer image by using the candidate cell data to obtain a plurality of cell nucleus feature values respectively related to the cell nucleus of the candidate cell.
(5) And determining whether each candidate cell is a target cell or a non-target cell according to at least a plurality of upper limit values respectively corresponding to the cell layer images, the cell membrane characteristic values of the cell layer images and the cell nucleus characteristic values of the cell layer images.
The screening conditions in the step (2) provided by the embodiment of the invention are as follows: the number of pixels of the image of the candidate cell is greater than a default value; the step (2) comprises the following substeps:
binarizing the cell composite image according to a plurality of pixels of a background of the cell composite image;
and obtaining the candidate cells and the candidate cell data according to the binarized cell synthetic image and a default value, wherein the pixel number of each candidate cell is greater than the default value.
The method provided by the embodiment of the invention also comprises the following substeps between the steps (2) and (3):
acquiring cell membranes of the candidate cells in the cell layer images of the cell membranes of the cells according to the candidate cell data, and synthesizing the images of the cell membranes of the candidate cells into candidate cell membrane synthetic images;
for each candidate cell in the candidate cell membrane synthetic image, judging whether a candidate plexus cell can be separated by a distance conversion algorithm;
when the candidate cell is judged to be capable of separating the candidate Pleated cell by the distance conversion algorithm, obtaining candidate Pleated cell data related to the image position and size of the candidate Pleated cell according to the candidate cell membrane synthetic image, and using the candidate Pleated cell data as the candidate cell data.
The step (7) provided by the embodiment of the invention comprises the following substeps:
for each candidate cell in the candidate cell membrane synthetic image, calculating a cell membrane critical value related to a pixel value by an adaptive algorithm according to the candidate cell membrane synthetic image;
for each candidate cell in the candidate cell membrane synthetic image, binarizing the candidate cell membrane synthetic image according to the cell membrane critical value;
for each candidate cell in the candidate cell membrane synthetic image, obtaining a candidate cell membrane image outline of the cell membrane related to the candidate cell according to the binarized candidate cell membrane synthetic image and candidate cell data related to the position and the size of the image of the candidate cell;
for each candidate cell in the candidate cell membrane synthesis image, calculating a cell membrane contour average length related to the candidate cell membrane image contour;
for each candidate cell in the candidate cell membrane synthetic image and for each pixel in the candidate cell membrane image contour, calculating a cell membrane contour shortest distance corresponding to the shortest distance between the pixel and the candidate cell membrane image contour to obtain a cell membrane contour ratio corresponding to the ratio of the cell membrane contour shortest distance to the cell membrane contour average length;
mapping each pixel value of the candidate cell membrane image contour to the maximum value of the pixel according to the cell membrane contour ratio;
normalizing the mapped candidate cell membrane image profile to obtain the number of cell membrane profile peaks; and judging whether the candidate plexus cells can be separated according to the number of the cell membrane profile wave peaks.
Example 3
As shown in fig. 1, the optical detection method of the dry blood cell analyzer according to the embodiment of the present invention includes, as a preferred embodiment, a method of performing cell counting on a collected blood sample by a cell counting program, including:
1) calibrating a microscope to continuously move the cell sample in a plane relative to an optical system of the microscope having at least one microscope camera.
2) During the movement of the cell sample, recording, with the at least one microscope camera, at least one or more images of a sub-region of the cell sample in the transmission mode or the fluorescence mode, and at least one or more images of the same sub-region of the cell sample in the fluorescence mode.
3) The at least one or more images of the same sub-region of the cell sample in the transmission mode or the fluorescence mode and the at least one or more images of the same sub-region of the cell sample in the fluorescence mode are positionally related to each other.
4) Detecting the position and/or contour of cells or cellular components of the cellular sample in the image of the transmission mode and then analyzing the detected intensity of the image recorded in the fluorescence mode in dependence on the detected position and/or contour of cells or cellular components in the cellular sample.
5) Detecting the position and/or contour of cells or cellular components of the cellular sample in the image of the fluorescence mode and then analyzing the detected intensity of the image recorded in the transmission mode in dependence on the detected position and/or contour of cells or cellular components in the cellular sample.
Wherein the position and/or contour of cells or cell components of the cell sample is detected in the image of the fluorescence pattern and then the detected intensity of the image recorded in the second fluorescence pattern is analyzed in dependence on the detected position and/or contour of cells or cell components in the cell sample.
Embodiments of the invention provide that at least one time shifted image of a sub-region of the cell sample in the transmission mode is modified with respect to a predetermined first image of the sub-region of the cell sample in the transmission mode in dependence on a time shift, or in dependence on a relative displacement of the cell sample with respect to the at least one microscope camera between the predetermined first image and the time shifted image, such that the first predetermined image and the time shifted image are associated with the same sub-region of the cell sample, wherein the time shift is in particular later or earlier.
Embodiments of the invention provide that at least one time shifted image of a sub-region of the cell sample in the fluorescence mode is modified with respect to a predetermined first image of the sub-region of the cell sample in the fluorescence mode as a function of a time shift, or as a function of a relative displacement of the cell sample with respect to the at least one microscope camera between the predetermined first image and the time shifted image, such that the first predetermined image and the time shifted image are associated with the same sub-region of the cell sample, wherein the time shift is in particular later or earlier.
The computer instructions may be stored in or transmitted from one computer-readable storage medium to another computer-readable storage medium, e.g., from one website site, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DS L) or wireless (e.g., infrared, wireless, microwave, etc.) means to another website site, computer, server, or data center via a solid state storage medium, such as a solid state storage medium, such as a DVD, a solid state disk, a solid state storage medium, a digital versatile disk, a digital video disk.
The above description is only for the preferred embodiment of the present invention, and is not intended to limit the present invention in any way, and all simple modifications, equivalent changes and modifications made to the above embodiment according to the technical spirit of the present invention are within the scope of the technical solution of the present invention.
Claims (10)
1. An optical detection method for a dry blood cell analyzer, comprising:
collecting a blood sample to be detected through a blood sample collecting device; collecting a blood cell microscopic image through microscopic imaging equipment; correcting the collected blood cell image through an image correction program; enhancing the collected blood cell image by an image enhancement algorithm;
step two, controlling the optical detection device through the main control computer to detect the collected blood sample: (I) the optical detection device sequentially emits blue light and red light through a blue light source and a red light source;
(II) sequentially irradiating the emitted blue light and red light on a capillary tube of the collected blood sample, imaging through a lens, and collecting by a color linear array CCD image sensor;
(III) transmitting the collected color image of the blood sample to a main control computer for analysis through analog-digital conversion;
step three, classifying the blood cell images through a classification program: (1) synthesizing the cell layer image into a cell synthesis image including an image of the cell by a synthesis program;
(2) screening a plurality of candidate cells from the cells in the cell synthesis image according to the cell synthesis image and screening conditions related to the cell size, and obtaining a plurality of candidate cell data respectively related to the image position and the size of the candidate cells;
(3) for each cell layer image related to the cell membrane of the cell, performing feature extraction on the cell layer image by using the candidate cell data to obtain a plurality of cell membrane feature values respectively related to the cell membrane of the candidate cell;
(4) for each cell layer image related to the cell nucleus of the cell, performing feature extraction on the cell layer image by using the candidate cell data to obtain a plurality of cell nucleus feature values respectively related to the cell nucleus of the candidate cell;
(5) determining whether each candidate cell is a target cell or a non-target cell according to at least a plurality of upper limit values respectively corresponding to the cell layer image, the cell membrane characteristic value of the cell layer image and the cell nucleus characteristic value of the cell layer image;
step four, performing cell counting on the collected blood sample through a cell counting program: 1) calibrating a microscope to continuously move the cell sample in a plane relative to an optical system of the microscope having at least one microscope camera;
2) during the movement of the cell sample, recording, with the at least one microscope camera, at least one or more images of a sub-region of the cell sample in the transmission mode or the fluorescence mode, and at least one or more images of the same sub-region of the cell sample in the fluorescence mode;
3) the at least one or more images of the same sub-region of the cell sample in the transmission mode or the fluorescence mode and the at least one or more images of the same sub-region of the cell sample in the fluorescence mode are positionally related to each other;
4) detecting the position and/or contour of cells or cellular components of the cellular sample in the image of the transmission mode and then analyzing the detected intensity of the image recorded in the fluorescence mode according to the detected position and/or contour of cells or cellular components in the cellular sample;
5) detecting the position and/or contour of cells or cellular components of the cellular sample in the image of the fluorescence mode and then analyzing the detected intensity of the image recorded in the transmission mode according to the detected position and/or contour of cells or cellular components in the cellular sample;
wherein the position and/or contour of cells or cell components of the cell sample is detected in the image of the fluorescence pattern and then the detected intensity of the image recorded in the second fluorescence pattern is analyzed in dependence on the detected position and/or contour of cells or cell components in the cell sample;
analyzing the blood cell characteristics through a cell analysis program; storing the collected blood cell images, classification results, counting results and real-time data of analysis results through a storage chip; and displaying the collected blood cell image, the classification result, the counting result and the real-time data of the analysis result through a display.
2. The optical detection method of a dry blood cell analyzer as set forth in claim 1, wherein in the second step, the blue light source is located at a distance of 20-25mm from the capillary tube and below the capillary tube, and the plane of the blue light source forms an angle of 45 ° with the horizontal plane of the central axis of the capillary tube; the distance between the red light source and the capillary tube is 20-25mm, and the center line of the red light source and the central axis of the capillary tube are positioned on the same horizontal plane;
the color linear array CCD image sensor is an R, G, B three-color response CCD, which is distributed in a single row in sequence, each row at least contains 10550 pixels, and the resolution ratio is 48 lines/mm.
3. The optical detection method for a dry blood cell analyzer according to claim 1, wherein in step (iii), the screening conditions in step (2) are: the number of pixels of the image of the candidate cell is greater than a default value; the step (2) comprises the following substeps:
binarizing the cell composite image according to a plurality of pixels of a background of the cell composite image;
and obtaining the candidate cells and the candidate cell data according to the binarized cell synthetic image and a default value, wherein the pixel number of each candidate cell is greater than the default value.
4. The optical detection method for a dry blood cell analyzer according to claim 1, further comprising the following substeps between the steps (2) and (3):
acquiring cell membranes of the candidate cells in the cell layer images of the cell membranes of the cells according to the candidate cell data, and synthesizing the images of the cell membranes of the candidate cells into candidate cell membrane synthetic images;
for each candidate cell in the candidate cell membrane synthetic image, judging whether a candidate plexus cell can be separated by a distance conversion algorithm;
when the candidate cell is judged to be capable of separating the candidate Pleated cell by the distance conversion algorithm, obtaining candidate Pleated cell data related to the image position and size of the candidate Pleated cell according to the candidate cell membrane synthetic image, and using the candidate Pleated cell data as the candidate cell data.
5. The optical detection method for a dry blood cell analyzer according to claim 1, wherein the step (7) comprises the following substeps:
for each candidate cell in the candidate cell membrane synthetic image, calculating a cell membrane critical value related to a pixel value by an adaptive algorithm according to the candidate cell membrane synthetic image;
for each candidate cell in the candidate cell membrane synthetic image, binarizing the candidate cell membrane synthetic image according to the cell membrane critical value;
for each candidate cell in the candidate cell membrane synthetic image, obtaining a candidate cell membrane image outline of the cell membrane related to the candidate cell according to the binarized candidate cell membrane synthetic image and candidate cell data related to the position and the size of the image of the candidate cell;
for each candidate cell in the candidate cell membrane synthesis image, calculating a cell membrane contour average length related to the candidate cell membrane image contour;
for each candidate cell in the candidate cell membrane synthetic image and for each pixel in the candidate cell membrane image contour, calculating a cell membrane contour shortest distance corresponding to the shortest distance between the pixel and the candidate cell membrane image contour to obtain a cell membrane contour ratio corresponding to the ratio of the cell membrane contour shortest distance to the cell membrane contour average length;
mapping each pixel value of the candidate cell membrane image contour to the maximum value of the pixel according to the cell membrane contour ratio;
normalizing the mapped candidate cell membrane image profile to obtain the number of cell membrane profile peaks; and judging whether the candidate plexus cells can be separated according to the number of the cell membrane profile wave peaks.
6. The optical detection method of a dry blood cell analyzer according to claim 1, wherein in step four at least one time shifted image of a sub-region of the cell sample in the transmission mode is modified with respect to a predetermined first image of the sub-region of the cell sample in the transmission mode according to a time shift, in particular later or earlier, or according to a relative displacement of the cell sample with respect to the at least one microscope camera between the predetermined first image and the time shifted image, such that the first predetermined image and the time shifted image are associated with the same sub-region of the cell sample.
7. The optical detection method of a dry blood cell analyzer according to claim 1, wherein in step four at least one time shifted image of a sub-region of the cell sample in the fluorescence mode is modified with respect to a predetermined first image of the sub-region of the cell sample in the fluorescence mode as a function of a time shift, or as a function of a relative displacement of the cell sample with respect to the at least one microscope camera between the predetermined first image and the time shifted image, such that the first predetermined image and the time shifted image are associated with the same sub-region of the cell sample, wherein the time shift is in particular later or earlier.
8. An optical detection system of a dry blood cell analyzer to which the optical detection method of the dry blood cell analyzer according to any one of claims 1 to 7 is applied, the optical detection system comprising:
the blood sample collection module is connected with the main control module and is used for collecting a blood sample to be detected through the blood sample collection device;
the cell image acquisition module is connected with the image correction module and is used for acquiring a blood cell microscopic image through microscopic imaging equipment;
the image correction module is connected with the cell image acquisition module and the main control module and is used for correcting the acquired blood cell image through an image correction program;
the cell image enhancement module is connected with the main control module and is used for enhancing the collected blood cell image through an image enhancement algorithm;
the main control module is connected with the blood sample acquisition module, the cell image acquisition module, the image correction module, the cell image enhancement module, the optical detection module, the cell classification module, the cell counting module, the cell analysis module, the data storage module and the display module and is used for controlling the normal work of each module through a host;
the optical detection module is connected with the main control module and is used for detecting the collected blood sample through the optical detection device;
the cell classification module is connected with the main control module and is used for classifying the blood cell images through a classification program;
the cell counting module is connected with the main control module and is used for counting the cells of the collected blood sample through a cell counting program;
the cell analysis module is connected with the main control module and is used for analyzing the blood cell characteristics through a cell analysis program;
the data storage module is connected with the main control module and used for storing the collected blood cell images, classification results, counting results and real-time data of analysis results through the storage chip;
and the display module is connected with the main control module and used for displaying the acquired blood cell images, classification results, counting results and real-time data of analysis results through a display.
9. A computer program product stored on a computer readable medium, comprising a computer readable program for providing a user input interface to implement the optical detection method of the dry blood cell analysis device according to any one of claims 1 to 7 when executed on an electronic device.
10. A computer-readable storage medium storing instructions which, when executed on a computer, cause the computer to execute the optical detection method of the dry blood cell analysis device according to any one of claims 1 to 7.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112037217A (en) * | 2020-09-09 | 2020-12-04 | 南京诺源医疗器械有限公司 | Intraoperative blood flow imaging method based on fluorescence imaging |
| CN113671024A (en) * | 2021-07-01 | 2021-11-19 | 诸暨市人民医院 | B10 cell detection device |
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2020
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112037217A (en) * | 2020-09-09 | 2020-12-04 | 南京诺源医疗器械有限公司 | Intraoperative blood flow imaging method based on fluorescence imaging |
| CN113671024A (en) * | 2021-07-01 | 2021-11-19 | 诸暨市人民医院 | B10 cell detection device |
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