CN114252385A - Portable leukocyte subclass detection device and method - Google Patents
Portable leukocyte subclass detection device and method Download PDFInfo
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Abstract
A portable leukocyte subset detection device and method, comprising: preparing a blood sample to be detected; placing the blood sample in the sample introduction unit to push the sample introduction unit into the portable leukocyte subset detection device; starting an illuminating unit to irradiate the test piece; collecting sample data amplified by an optical microscope unit in real time; and displaying a counting result after performing particle analysis and counting on the particle images acquired by the image acquisition unit.
Description
Technical Field
The invention relates to the technical field of detection, in particular to a portable leukocyte subset detection device and a method.
Background
Leukocyte analysis in blood routine is an indispensable means for clinically distinguishing bacterial infection from viral infection and the severity of infection, and patients are clinically guided to take medicine according to leukocyte counting results. The portable leucocyte counting instrument can obtain accurate diagnosis information in the shortest time in the place closest to a patient, is favorable for timely diagnosis, monitoring and treatment of diseases, and has the time and space advantages which are not possessed by large instruments.
For portable white blood cell counting systems, the prior art mainly includes the following: patents US5585246, secondly WO97/02482 disclose a method of determining the number of white blood cells by labeling white blood cells with a fluorescent dye and a coordination complex, exciting with a laser source, and measuring fluorescence; ③ patent CN105228749Leaf A proposes an image counting method based on chip and fluorescence. The method comprises the steps of firstly adding fluorescent dye into a blood sample, and obtaining leukocyte fluorescence image data by laser excitation. Finally, the data is transmitted back to the computer, and the computer acquires and counts cell areas by adopting a threshold conversion method and watershed segmentation on the acquired white blood cell images; and fourthly, Lab on a chip, vol 7, 2013, 13, page 1257, 1266, provides a method for counting and classifying leukocytes based on a microfluidic channel. The method designs a micro-fluid channel, wherein a sample is firstly uniformly mixed with fluorescent dye, and red and green fluorescence is generated by laser when the sample passes through the micro-fluid channel. Finally, the cell number is obtained by analyzing the red and green light characteristic quantities emitted by different white blood cells; US7086778B2 and US2014/0024107a1 propose a cell counting device based on an electrical impedance method, which forms a pulse signal by analyzing voltage change to obtain the number of cells; seventhly, CN101137904B and Cr 103471980B provide a chip type blood cell analysis device, which adopts the electric impedance, high-frequency conductance and laser scattering detection technology to count and classify the white blood cells; ninthly CA2772376A1, and R US20140024107A1
CN102216954 fuses cell information on a plurality of focal planes by improving an image focusing mode, so that the condition of missing detection caused by a single focal plane is reduced;
WO2016/050755a2 uses a hemolytic agent to lyse red blood cells, stain white blood cells, threshold image segmentation of white blood cell regions, and assessment of the white blood cell concentration of a blood sample by counting the number of white blood cell regions.
In summary, the fluorescent dye method is described. The number of the white blood cells is obtained mainly according to the fluorescent effect generated by the excitation of specific laser under the interaction of the white blood cells and certain fluorescent dyes; sixth, electrical impedance method. The sample cells pass through a charged pore, and pulse signals are formed by analyzing voltage changes to obtain the cell number; seventhly, adopting an electrical impedance and laser scattering method. First, red blood cells are removed by a hemolytic agent, and then the sample cells are passed through one or moreA plurality of laser beams, and a charged aperture. Finally, the number and the size of white blood cells are estimated by analyzing light scattering signals and impedance changes; ninthly hole
Optical microscopy. The number of cells was counted by taking a low power leukocyte microscopic image, and image processing segmented the cell area. The portable particle analyzer belongs to the microscopic image method, but compare with ninon
The method and apparatus are optimized and improved.
In the prior art, the first step and the second step are fluorescence excitation methods. The white blood cell number is obtained by utilizing the interaction of the white blood cells and certain fluorescent dyes and the fluorescence effect generated by specific laser excitation, but the following defects exist: 1) the blood sample is analyzed to be less than 10u l, the blood sample is diluted by a diluent during analysis, and the sample size is too small to accurately represent the whole condition; 2) fluorescent dyes are complex, and exciting light is harmful to human bodies; 3) the counting can be carried out only when the white blood cells are positioned at the bottom of the capillary tube, the waiting time for counting is long, and the speed is low; 4) the instrument is not automatic enough: the patent III needs to be used together with a computer, the real-time detection of the white blood cells cannot be realized, and an image analysis module of the patent III is positioned in computer software. Sixth, electrical impedance method. The sample cell passes through a charged pore, and the cell number is obtained by analyzing the voltage change to form a pulse signal. The method needs to manually separate the white blood cells and count the white blood cells, although the equipment is portable and low in cost, the equipment cannot be operated by non-professional personnel, and the previous white blood cell separation operation is complicated. Seventhly, detecting and counting white blood cells by using a combination of a fluorescence signal, a light scattering signal and impedance change (Coulter effect) through an analyzer by adopting an electrical impedance + laser scattering method, wherein a blood sample needs to be mixed with a special reagent, and a precise photomultiplier and a flow channel need to be configured to enhance the detection precision of the analyzer. Although the patent (c) simplifies the device constitution as much as possible to enhance the portability of the instrument, the instrument is not portable enough andthe method has complex technology and high cost, and is not suitable for household use. Ninthly, although the image method is adopted, white blood cells need to be separated out firstly as with the fifth method, and the operation is complicated.
Adding a hemolytic agent and a staining agent into the blood sample, cracking red blood cells, and obtaining a stained leukocyte low-power microscope image; with the difference that
The automatic focusing device acquires cells at a focus position, and realizes the recognition of the sub-types of white blood cells, but the device has complex structure and high equipment cost;
a single slice image under a 4x microscope is acquired and the number of leukocytes is acquired by a fixed threshold algorithm. Although the white blood cell image is obviously enhanced, the fixed threshold algorithm cannot segment the aggregated cells, and the white blood cell counting error is large.
Disclosure of Invention
The present invention is directed to a portable leukocyte subset detection device and method to solve the above problems.
The embodiment of the invention is realized by the following steps:
in one aspect of the embodiments of the present invention, a portable leukocyte subset detection device is provided, including:
the sample introduction unit is used for bearing a test piece filled with a blood sample;
an illumination unit configured to illuminate the test strip after the sample introduction unit is pushed into the portable leukocyte subset detection device;
an optical microscope unit comprising a microscope capable of moving up and down, the microscope being capable of acquiring planar images of a first number of layers within the suspension vessel;
the image acquisition unit is used for acquiring sample data amplified by the microscope in real time;
and the image processing unit is used for analyzing and counting the particles of the particle images acquired by the image acquisition unit.
And the result display unit is used for displaying the counting result of the image processing unit.
Optionally, the optical microscope unit comprises a step motor device, and the motor drives the microscope to move up and down to obtain at least 300 layers of plane images in the suspension vessel; the motor is fixed on the linear guide rail, and the motor rotates to drive the gear to move on the rack, so that the linear motion of the sample adding test module is realized; the optical module with the lens is vertically fixed on the linear guide rail, a first eccentric wheel is arranged in the optical module, a second eccentric wheel is arranged in the second motor set, the first eccentric wheel is in line contact with the second eccentric wheel, and the second motor rotates to drive the first eccentric wheel in the optical module to move, so that the optical module vertically moves from top to bottom and then back and forth from bottom to top.
Optionally, the optical microscope unit has an optical magnification of 1.5 to 10 times, and the lens elements are <5 pieces.
Optionally, the image acquisition unit is a CCD or a CMOS.
Optionally, the effective pixels of the image acquisition unit are not lower than 2M.
Optionally, the image processing unit is configured to perform image region enhancement, particle region focus identification, focused particle segmentation, ROI region feature sequence construction, particle unsupervised cluster identification counting, and concentration correction.
Optionally, the result display unit is an LED display screen.
In another aspect of the embodiments of the present invention, there is also provided a portable leukocyte subset detection method, including:
preparing a blood sample to be detected;
placing the blood sample in the sample introduction unit to push the sample introduction unit into the portable leukocyte subset detection device;
starting an illuminating unit to irradiate the test piece;
collecting sample data amplified by an optical microscope unit in real time;
and displaying a counting result after performing particle analysis and counting on the particle images acquired by the image acquisition unit.
Optionally, after the image obtaining unit obtains the particle image, the image is sent to an image processing unit for particle analysis and counting, and the image processing unit mainly includes: the image processing unit comprises image area enhancement, particle area focusing identification, focusing particle segmentation and ROI area feature extraction, particle unsupervised clustering identification counting and concentration correction;
the image enhancement comprises: according to the characteristics of an instrument image, Gaussian filtering and morphological open operation reconstruction are adopted to complete the removal of interference substances of the image to be analyzed and the enhancement of an ROI (region of interest);
particle region focus identification and segmentation includes: after the image is enhanced, firstly removing the image without the ROI area by using a gray mean threshold value; dividing an ROI (region of interest) region by taking an image plane as an x-y axis and an image depth as a longitudinal axis and combining a maximum inter-class variance method, and constructing an ROI region image sequence; extracting the peak value of each depth ROI image, and establishing a depth-gray value Gaussian curve model by taking the depth of the image layer as a variable and the size of the peak value as a dependent variable; the depth corresponding to the curve peak value is the layer where the ROI focuses, and the width of the curve peak value is the longitudinal height of the ROI; acquiring an image at the ROI focus and the longitudinal height of the ROI to complete particle region focus identification and height feature acquisition; for the identified focusing region, fuzzy C-means clustering is carried out to complete the accurate extraction of the ROI region boundary;
the ROI region feature extraction and subclass counting comprises the following steps:
obtaining the area and the circularity of an ROI (region of interest) according to ROI boundary information, constructing 3-dimensional space vector points by combining with the height characteristics extracted in the last step, and finally completing the counting of leukocyte 3 subclasses by a Density-Based Spatial Clustering of Applications with Noise (DBSCAN) Clustering algorithm:
for a given data set D ═ x(1),x(2),...,x(m)For pair x(j)E.g. D, its epsilon neighborhood contains the sum x of D(j)All samples with a distance of not more than epsilon:
Nε(x(j))＝{x(i)∈D|dist(x(i),x(j))≤ε}
minimum number of samples MinPts in ε neighborhood, if x(j)Contains at least MinPts samples, | Nε(x(j)) | is not less than MinPts, then x(j)Is a core object; if x(j)At x(i)In the neighborhood of epsilon, and x(i)Is a core object, then called x(j)From x(i)The density is direct; density-through relationships generally do not satisfy symmetry unless x(j)Is also a core object; for x(i)And x(j)If a sample sequence p is present1,p2,...,pnWherein p is1＝x(i)，pn＝x(j)，p1,p2,...,pn-1Are all core objects and pi+1From piWhen the density is up to, it is called x(j)From x(i)The density can be reached; the density reachable relation satisfies the transitivity, but does not satisfy the symmetry; for x(i)And x(j)If x is present(k)So that x(i)And x(j)Are all x(k)When the density is up, it is called x(i)And x(j)Connecting the densities; the density connectivity satisfies symmetry and the largest set of density-connected samples C derived from the density reachability relationship, the cluster C satisfies the following two properties:
x(i)∈C，x(j)∈C→x(i)and x(j)Connecting the densities;
x(i)∈C，x(j)from x(i)Density up to → x(j)∈C；
The specific implementation process is as follows:
a, constructing three-dimensional empty point characteristics according to the area, the circularity and the height of an ROI (region of interest), initializing characteristic point data, and assigning 0 attributes to all points to indicate that the point is not accessed;
b, acquiring the coordinates of the central points of the intervals of the neutral cells, the lymphocytes and other cell points according to the data of the white blood cell sample, and initializing a cluster core object p1，p2And p3；
c creating and clustering core objects p1，p2And p3Corresponding clusters C1, C2, C3；
d for C1If there is an unlabeled class, then label as C1；
e for C1Each object in the system is marked as a class C, the points in the neighborhood are not marked, and the density of the density connected point set can reach the points1；
f repeating d-e until C1All categories in are labeled;
g returns to c and starts finding the next cluster until all points are marked.
Alternatively, after the number of particles is obtained, the concentration L of each type of particles may be obtained by the following formula:
wherein Ncell is the number of various particles obtained by clustering DBSCAN, omega is the optical magnification, A is the number of image pixels, H is the height of the microfluidic chip, and M is the actual length of each pixel of the image.
Optionally, the counting result is displayed on an LED display screen on a display unit.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
FIG. 1 is a flow chart of a portable leukocyte subset detection method according to the present invention;
FIG. 2 is a schematic view of a portable leukocyte subset detection device;
FIG. 3 is a peak Gaussian fit;
fig. 4 shows the three-dimensional DBSCAN clustering result.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1 to 4, the present invention provides a portable leukocyte subset detection method, system and device for rapidly, accurately and conveniently counting the number of leukocyte subsets in peripheral blood of a human body. The technical route is shown in fig. 1, and the specific implementation example is shown in fig. 2. The technical scheme is mainly explained as follows:
sample preparation
10 mul of fingertip blood of a patient is sucked by the disposable capillary vessel and is added into a container with the reagent to be uniformly mixed with the reagent. The reagent is composed of hemolytic agent and staining agent, and is stored in the reagent micro-tube in a dry state. The hemolytic agent is used for cracking red blood cells in the blood sample, reducing interference of the red blood cells and simplifying distinguishing and identifying white blood cells in the sample; the staining agent is used for staining the white blood cells, so that the difference between the white blood cells and the background of impurities is enhanced, and the subsequent detection precision is improved. After the blood sample and the reagent are fully mixed, the blood sample and the reagent are added into the test piece sample adding groove by the liquid transferring gun, and the mixed liquid is uniformly distributed in the inner cavity of the test piece under the capillary action.
Image acquisition
The test strip containing the blood sample is placed in the sample introduction unit and pushed into the device, and the illumination unit is turned on to irradiate the test strip. The image sensor is initialized and configured dynamically, software sends instructions to carry out real-time acquisition, ISP processing and automatic image exposure on sample data amplified by an optical microscope unit through a CCD (charge coupled device) or a CMOS (complementary metal oxide semiconductor), and an automatically adjusted high-quality digital image is obtained and used as input data of a following algorithm module:
motor 1 fixes on linear guide, and motor 1 rotates and drives the gear and move on the rack, realizes the linear motion of application of sample test module, and the test piece is by advancing the sample unit push device. When the sample adding unit module moves to a certain position, the position sensor senses and sends an instruction, and the motor stops at the specified position; the carrier frame with the test piece is provided with a magnet device, when the motor stops at a designated position, the magnet plays a role, so that the carrier frame with the test piece is suspended and adsorbed on a corresponding test seat, and the sample injection is completed;
an optical module provided with a lens is vertically fixed on a linear guide rail 2, an eccentric wheel 1 is arranged in the optical module, and an eccentric wheel 2 is arranged in a motor 2 set. The eccentric wheel 1 is in line contact with the eccentric wheel 2, the motor 2 rotates to drive the eccentric wheel 1 in the optical module to move, the optical module vertically moves from top to bottom and then from bottom to top, images are shot at equal time intervals by cmos, sample images of different depths in the height range of the sample adding test piece are obtained, and the number of the obtained images is more than 200;
the LED module is responsible for supplying a light source for the test sheet, so that illumination required by image acquisition is ensured, and white blood cells are clearly shown on the image.
Wherein, the sample introduction unit is used for semi-automatic sample introduction; the lighting unit is used for lighting an LED, and the LED is provided with a light homogenizing sheet to ensure the uniform distribution of light intensity; the optical magnification of the optical microscope unit is 1.5-10 times, preferably 10 times, and the lens elements are less than 5 sheets so as to ensure that stained white blood cells are clearly visible on an image; the image acquisition process of the image sensor is completed in the image acquisition unit, and the effective pixel of the acquired image is not lower than 2M.
Image processing and particle recognition
After the image acquisition unit acquires the particle image, the image is sent to the image processing unit for particle analysis and counting. The image processing unit mainly includes: the image processing unit comprises image area enhancement, particle area focusing identification, focusing particle segmentation and ROI area feature extraction, particle unsupervised clustering identification counting and concentration correction.
Image enhancement
And according to the characteristics of the instrument image, adopting Gaussian filtering and morphological open operation reconstruction to complete the removal of interference substances of the image to be analyzed and the enhancement of the ROI area.
Particle area focus identification and segmentation
After the image is enhanced, firstly removing the image without the ROI area by using a gray mean threshold value; dividing an ROI (region of interest) region by taking an image plane as an x-y axis and an image depth as a longitudinal axis and combining a maximum inter-class variance method, and constructing an ROI region image sequence; and extracting the peak value of each depth ROI image, and establishing a depth-gray value Gaussian curve model by taking the depth of the image layer as a variable and the size of the peak value as a dependent variable. The depth corresponding to the curve peak value is the layer where the ROI is focused, and the width of the curve peak value is the longitudinal height of the ROI. And acquiring an image at the ROI focus and the vertical height of the ROI to complete particle region focus identification and height feature acquisition. And for the identified focusing region, performing fuzzy C-means clustering to finish the accurate extraction of the ROI region boundary.
ROI regional feature extraction and subclass counting
Obtaining the area and the circularity of an ROI (region of interest) according to ROI boundary information, constructing 3-dimensional space vector points by combining with the height characteristics extracted in the last step, and finally completing the counting of leukocyte 3 subclasses by a Density-Based Spatial Clustering of Applications with Noise (DBSCAN) Clustering algorithm:
for a given data set D ═ x(1),x(2),...,x(m)For pair x(j)E.g. D, its epsilon neighborhood contains the sum x of D(j)All samples with a distance of not more than epsilon:
Nε(x(j))＝{x(i)∈D|dist(x(i),x(j))≤ε}
minimum number of samples MinPts in ε neighborhood, if x(j)Contains at least MinPts samples, | Nε(x(j)) | is not less than MinPts, then x(j)Is a core object. If x(j)At x(i)In the neighborhood of epsilon, and x(i)Is a core object, then called x(j)From x(i)The density is up to. Density-through relationships generally do not satisfy symmetry unless x(j)Is also a core object; for x(i)And x(j)If a sample sequence p is present1,p2,...,pnWherein p is1＝x(i)，pn＝x(j)，p1,p2,...,pn-1Are all core objects and pi+1From piWhen the density is up to, it is called x(j)From x(i)The density can be reached. The density reachable relation satisfies the transitivity, but does not satisfy the symmetry; for x(i)And x(j)If x is present(k)So that x(i)And x(j)Are all x(k)When the density is up, it is called x(i)And x(j)The densities are connected. The density connectivity satisfies symmetry and the largest set of density-connected samples C derived from the density reachability relationship, the cluster C satisfies the following two properties:
x(i)∈C，x(j)∈C→x(i)and x(j)Connecting the densities;
x(i)∈C，x(j)from x(i)Density up to → x(j)∈C。
The specific implementation process is as follows:
establishing three-dimensional empty point characteristics according to the area, the circularity and the height of the aRII region, initializing characteristic point data, and assigning 0 attribute to all points to indicate that the point is not accessed;
b, acquiring the coordinates of the central points of the intervals of the neutral cells, the lymphocytes and other cell points according to the data of the white blood cell sample, and initializing a cluster core object p1，p2And p3；
c creating and clustering core objects p1，p2And p3Corresponding clusters C1, C2, C3；
d for C1If there is an unlabeled class, then label as C1；
e for C1Density of each object whose points in the neighborhood are unmarkedThe connected point set density reachable points are all marked as class C1；
f repeating d-e until C1All categories in are labeled;
g returns to c and starts finding the next cluster until all points are marked.
And finally counting the number of the points in the Ci to complete the counting of the leukocyte subclasses.
1) Leukocyte subset concentration calculation
After the number of particles is obtained, the concentration L of each type of particles can be obtained by the following formula:
wherein N iscellThe number of various particles obtained by clustering DBSCAN is omega, the optical magnification is, A is the number of image pixels, H is the height of the microfluidic chip, and M is the actual length of each image pixel.
2) Results display
After the particles are counted and corrected by the image processing unit, the counting result is displayed on the display unit. The display unit is composed of an LED display screen.
In order to verify the feasibility and the effectiveness of the invention, the method provided by the invention is adopted to collect 10 mul of haemolytic staining leukocyte images and identify the focusing area, 200 collected image sequences are obtained, the sequence depth is 150 mu m, the size of each sequence image is 2130 multiplied by 2120, and the image magnification is 10 multiplied by 10.
By adopting the method provided by the invention, FIG. 3 is a sequence image of a part of white blood cells after lysis and staining, and FIG. 4 is a sparse matrix image after sparsification in FIG. 3; other cells (suspension cells such as red blood cells and platelets), suspension particle autofocus recognition: the particle automatic focusing method and system based on the sparse matrix can be used for white blood cell focusing identification, and can automatically focus suspended cell particles such as red blood cells, platelets and the like in a certain volume: by obtaining multi-depth cell/particle sequence images (images can be 4x, 5x, 8x, 10x and the like, and the method is effective for 100x microscope images), after multi-depth images corresponding to suspension cells/particles such as red blood cells and platelets are obtained, the area, circularity and height characteristics of the same particles/cells (platelets) are extracted, and three-dimensional DBSCAN clustering is carried out to realize focusing and identification of each particle. Because the sizes of all cells/particles are different, the corresponding target identification counting can be completed for the three-dimensional DBSCAN core center object in the method.
Compared with the prior art, the invention has the following advantages:
the multi-depth scanning of the micro particles with specific height is realized through the eccentric wheel device, the device is simple in structure, the device cost is low, and the portability is high;
the three-dimensional DBSCAN cluster recognition method of the designated initial center is constructed, the problem of calculation time brought by high-dimensional recognition and multi-dimensional features is avoided, and leukocyte subclass recognition and counting are completed quickly, effectively and concisely;
the method does not need to wait for the natural sedimentation of the cells, avoids the problem that the detection precision of the high-value sample is influenced by the aggregation of multiple cells with the same x-y plane coordinate and different z depths caused by the sedimentation of the cells, can quickly and automatically acquire the cell image at the focus position in the natural sedimentation process, and provides a basis for the high-precision classification and identification of the subsequent cells.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A portable leukocyte subset detection method, comprising:
preparing a blood sample to be detected;
placing the blood sample in the sample introduction unit to push the sample introduction unit into the portable leukocyte subset detection device;
starting an illuminating unit to irradiate the test piece;
collecting sample data amplified by an optical microscope unit in real time;
and displaying a counting result after performing particle analysis and counting on the particle images acquired by the image acquisition unit.
2. The method for detecting a subset of leukocytes according to claim 1, wherein the image obtaining unit obtains the particle image and sends the image to the image processing unit for particle analysis and counting, and the image processing unit mainly comprises: the image processing unit comprises image area enhancement, particle area focusing identification, focusing particle segmentation and ROI area feature extraction, particle unsupervised clustering identification counting and concentration correction;
the image enhancement comprises: according to the characteristics of an instrument image, Gaussian filtering and morphological open operation reconstruction are adopted to complete the removal of interference substances of the image to be analyzed and the enhancement of an ROI (region of interest);
particle region focus identification and segmentation includes: after the image is enhanced, firstly removing the image without the ROI area by using a gray mean threshold value; dividing an ROI (region of interest) region by taking an image plane as an x-y axis and an image depth as a longitudinal axis and combining a maximum inter-class variance method, and constructing an ROI region image sequence; extracting the peak value of each depth ROI image, and establishing a depth-gray value Gaussian curve model by taking the depth of the image layer as a variable and the size of the peak value as a dependent variable; the depth corresponding to the curve peak value is the layer where the ROI focuses, and the width of the curve peak value is the longitudinal height of the ROI; acquiring an image at the ROI focus and the longitudinal height of the ROI to complete particle region focus identification and height feature acquisition; for the identified focusing region, fuzzy C-means clustering is carried out to complete the accurate extraction of the ROI region boundary;
the ROI region feature extraction and subclass counting comprises the following steps:
obtaining the area and the circularity of an ROI (region of interest) according to ROI boundary information, constructing 3-dimensional space vector points by combining with the height characteristics extracted in the last step, and finally completing the counting of leukocyte 3 subclasses by a Density-Based Spatial Clustering of Applications with Noise (DBSCAN) Clustering algorithm:
for a given data set D ═ x(1),x(2),...,x(m)For pair x(j)E.g. D, its epsilon neighborhood contains the sum x of D(j)All samples with a distance of not more than epsilon:
Nε(x(j))＝{x(i)∈D|dist(x(i),x(j))≤ε}
minimum number of samples MinPts in ε neighborhood, if x(j)Contains at least MinPts samples, | Nε(x(j)) | is not less than MinPts, then x(j)Is a core object; if x(j)At x(i)In the neighborhood of epsilon, and x(i)Is a core object, then called x(j)From x(i)The density is direct; density-through relationships generally do not satisfy symmetry unless x(j)Is also a core object; for x(i)And x(j)If a sample sequence p is present1,p2,...,pnWherein p is1＝x(i)，pn＝x(j)，p1,p2,...,pn-1Are all core objects and pi+1From piWhen the density is up to, it is called x(j)From x(i)The density can be reached; the density reachable relation satisfies the transitivity, but does not satisfy the symmetry; for x(i)And x(j)If x is present(k)So that x(i)And x(j)Are all x(k)When the density is up, it is called x(i)And x(j)Connecting the densities; the density connectivity satisfies symmetry and the largest set of density-connected samples C derived from the density reachability relationship, the cluster C satisfies the following two properties:
x(i)∈C，x(j)∈C→x(i)and x(j)Connecting the densities;
x(i)∈C，x(j)from x(i)Density up to → x(j)∈C；
The specific implementation process is as follows:
establishing three-dimensional empty point characteristics according to the area, the circularity and the height of the aRII region, initializing characteristic point data, and assigning 0 attribute to all points to indicate that the point is not accessed;
b, acquiring the coordinates of the central points of the intervals of the neutral cells, the lymphocytes and other cell points according to the data of the white blood cell sample, and initializing a cluster core object p1，p2And p3；
c creating and clustering core objects p1，p2And p3Corresponding cluster C1、C2、C3；
d for C1If there is an unlabeled class, then label as C1；
e for C1Each object in the system is marked as a class C, the points in the neighborhood are not marked, and the density of the density connected point set can reach the points1；
f repeating d-e until C1All categories in are labeled;
g returns to c and starts finding the next cluster until all points are marked.
3. The portable leukocyte subset detection method according to claim 1, wherein after the number of particles is obtained, the concentration L of each type of particles is obtained by the following formula:
wherein Ncell is the number of various particles obtained by clustering DBSCAN, omega is the optical magnification, A is the number of image pixels, H is the height of the microfluidic chip, and M is the actual length of each pixel of the image.
4. A portable leukocyte subset detection apparatus, comprising:
the sample introduction unit is used for bearing a test piece filled with a blood sample;
an illumination unit configured to illuminate the test strip after the sample introduction unit is pushed into the portable leukocyte subset detection device;
an optical microscope unit comprising a microscope capable of moving up and down, the microscope being capable of acquiring planar images of a first number of layers within the suspension vessel;
the image acquisition unit is used for acquiring sample data amplified by the microscope in real time;
the image processing unit is used for carrying out particle analysis and counting on the particle images acquired by the image acquisition unit;
and the result display unit is used for displaying the counting result of the image processing unit.
5. The portable leukocyte subset detection device of claim 4, wherein the optical microscope unit comprises a motor, and the motor drives the microscope to move up and down to obtain at least 300 planar images of the interior of the suspension vessel; the motor is fixed on the linear guide rail, and the motor rotates to drive the gear to move on the rack, so that the linear motion of the sample adding test module is realized; the optical module with the lens is vertically fixed on the linear guide rail, a first eccentric wheel is arranged in the optical module, a second eccentric wheel is arranged in the second motor set, the first eccentric wheel is in line contact with the second eccentric wheel, and the second motor rotates to drive the first eccentric wheel in the optical module to move, so that the optical module vertically moves from top to bottom and then back and forth from bottom to top.
6. The portable leukocyte subset detection apparatus according to claim 4, wherein the optical magnification of the optical microscope unit is 1.5 to 10 times and the lens element is <5 pieces.
7. The portable leukocyte subset detection apparatus of claim 4, wherein said image acquisition unit is a CCD or a CMOS.
8. The portable leukocyte subset detection apparatus of claim 7 wherein the effective pixels of said image acquisition unit are not less than 2M.
9. The portable leukocyte subset detection apparatus according to claim 4, wherein the image processing unit is configured to perform image region enhancement, particle region focusing identification, focused particle segmentation, ROI region feature sequence construction, particle unsupervised cluster identification counting, and concentration correction.
10. The portable leukocyte subset detection apparatus of claim 4, wherein said result display unit is an LED display screen.
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