CN103345654A - Method for differential counting of white blood cells based on morphology - Google Patents
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- 238000000034 method Methods 0.000 title claims abstract description 16
- 210000000265 leukocyte Anatomy 0.000 title abstract description 9
- 210000004027 cell Anatomy 0.000 claims abstract description 31
- 210000004698 lymphocyte Anatomy 0.000 claims abstract description 18
- 210000001616 monocyte Anatomy 0.000 claims abstract description 18
- 238000010586 diagram Methods 0.000 claims abstract description 16
- 210000003714 granulocyte Anatomy 0.000 claims abstract description 15
- 238000004458 analytical method Methods 0.000 claims abstract description 11
- 210000005259 peripheral blood Anatomy 0.000 claims abstract description 6
- 230000008033 biological extinction Effects 0.000 claims abstract description 5
- 239000011886 peripheral blood Substances 0.000 claims abstract description 5
- 230000001413 cellular effect Effects 0.000 claims description 10
- 230000000877 morphologic effect Effects 0.000 claims description 6
- 210000004493 neutrocyte Anatomy 0.000 claims description 5
- 238000010186 staining Methods 0.000 claims description 5
- 210000003979 eosinophil Anatomy 0.000 claims description 4
- 210000000222 eosinocyte Anatomy 0.000 abstract description 4
- 238000004820 blood count Methods 0.000 abstract description 2
- AXIKDPDWFVPGOD-UHFFFAOYSA-O [7-(dimethylamino)phenothiazin-3-ylidene]-dimethylazanium;2-(2,4,5,7-tetrabromo-3,6-dihydroxyxanthen-10-ium-9-yl)benzoic acid Chemical compound C1=CC(=[N+](C)C)C=C2SC3=CC(N(C)C)=CC=C3N=C21.OC(=O)C1=CC=CC=C1C1=C(C=C(Br)C(O)=C2Br)C2=[O+]C2=C1C=C(Br)C(O)=C2Br AXIKDPDWFVPGOD-UHFFFAOYSA-O 0.000 abstract 1
- 210000004940 nucleus Anatomy 0.000 description 8
- 210000000805 cytoplasm Anatomy 0.000 description 6
- 230000004907 flux Effects 0.000 description 4
- 210000002751 lymph Anatomy 0.000 description 3
- 210000002231 macronucleus Anatomy 0.000 description 3
- 239000000203 mixture Substances 0.000 description 3
- 238000003909 pattern recognition Methods 0.000 description 3
- 230000002159 abnormal effect Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000000527 lymphocytic effect Effects 0.000 description 2
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- 230000005856 abnormality Effects 0.000 description 1
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- 238000009534 blood test Methods 0.000 description 1
- 230000005859 cell recognition Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
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- 238000004043 dyeing Methods 0.000 description 1
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
- G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
- G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
- G01N33/569—Immunoassay; Biospecific binding assay; Materials therefor for microorganisms, e.g. protozoa, bacteria, viruses
- G01N33/56966—Animal cells
- G01N33/56972—White blood cells
-
- G01N15/1433—
Abstract
The invention discloses a method for differential counting of white blood cells based on morphology. The method includes the steps that after Wright stain is conducted on peripheral blood smears, a microscope is used for collecting an image; after the image is processed by a computer system, morphology parameter analysis is conducted on the white blood cells, and the white blood cells are divided into a lymphocyte area, a monocyte area, a granulocyte area and a cell extinction area; the morphology parameter analysis is conducted on the area cells which are divided in the previous step, and the cells are further divided into monocytes, lymphocytes, eosinophilic granulocytes and neutrophile granulocytes precisely; the differential counting statistics are carried out on the variety of the divided cells. The computer system is used for analyzing the processed image, several simple morphology parameters are used for making a two-dimensional scatter diagram, all the cells are presented in a statistical diagram mode, the white blood cells can be classified rapidly and precisely, white blood cell counting is automatic, compared with manual counting, the procedures are simple and fast, the diagnose speed is greatly increased, and accuracy is improved substantially.
Description
Technical field
The invention belongs to image processing field, be specifically related to a kind of Arneth's count method.
Background technology
In blood sample, quantity of leucocyte has only erythrocytic 1/800, gather in the visual field at a cell image, the leucocyte about 1-5 can occur, leucocyte is human defensive system's important component part, and nuclear is different with the particle in the protoplasm according to being included in, be divided into granulocyte, monocyte and lymphocyte, granulocyte are divided into eosinophil according to the different in kind of particle again, basophilic granulocyte and neutrophil leucocyte.All kinds of leucocytes are the big events of blood test with the percentage composition of all total white blood cellses, behind Wright's staining, distinguish leukocytic kind according to various eukocytes in forms such as microscopically nuclear staining, endochylema dyeing, karyomorphisms.
Behind the peripheral blood blood film Wright's staining wherein leucocyte being carried out manual sort's counting is routine work in the present clinical examination, because these testing amounts are big, the chemical examination time is long, process is loaded down with trivial details, inefficiency, the analysis of cell is subjected to the restriction of laboratory physician experience and vision addressability, and the too much subjective factor that mixes lacks objective standard.
Though the haemocyte automatic analyzer can carry out Arneth's count rapidly, the shortcoming that it is difficult to overcome below existing: 1. cell will pass through very thin aperture, results in blockage easily; 2. do not have ability identification for abnormal cell, be unfavorable for clinical diagnosis; 3. can not preserve the original physiologic state of sample.So necessaryly look for a kind of method that effectively substitutes it.
Pattern-recognition is as the most ripe application of computation vision, apply at present medical science and be the peripheral blood DIFF in detecting, by setting up the cellular morphology storehouse of standard, cell and the standard cell lines of sample to be checked are compared, sort out then, and reach the purpose of cell being carried out differential count.But pattern-recognition is in medical application, its limitation is arranged, what medical science detected main detection is up-set condition (abnormal conditions that occur under the disease change situation), and these abnormality thousand differ from into not, diversity extremely, and pattern-recognition is based on normal sample and compares a kind of non-measurement comparison algorithm, can't be to multifarious unusual as judging or identification.
Therefore, based on micro computer, use image to handle and the correlation technique of analyzing, determine cell by leukocytic various features, realize automatic identification and the quantitative test of leucocyte micro-image, have important Research Significance.
Summary of the invention
Goal of the invention: the objective of the invention is in order to solve the problems of the technologies described above, providing a kind of is to handle core with the microcomputer, based on the graphical analysis of morphology parameter, accomplishes quickly, efficiently and accurately quantitative classification leucocyte.
Technical scheme: of the present inventionly a kind ofly count leukocytic method based on morphological classification, said method comprising the steps of:
(1) peripheral blood film utilizes microscope to gather image behind Wright's staining;
(2) step (1) image input computer system is carried out the analysis of morphology parameter to leucocyte, and leucocyte is divided into lymphocyte district, monocyte district, granulocyte district and four zones of extinction cellular regions;
(3) the regional cell that further step (2) is marked off carries out the analysis of morphology parameter, and cell is further accurately distinguished monocyte, lymphocyte, eosinophil and neutrophil leucocyte;
(4) the leucocyte kind that distinguishes is carried out the differential count statistics.
In the step (2), be horizontal ordinate with the cell karyoplasmic ratio, nucleus circularity (the nuclear radius coefficient of variation) is ordinate, leukocytic these two morphology parameters are made two-dimentional scatter diagram, be divided into four zones, the MIcrosope image corresponding according to the zone is lymphocyte district, monocyte district, granulocyte district and extinction cellular regions with regional cell recognition.
In the step (3), be horizontal ordinate with nucleus brightness total amount, nucleus circularity (nuclear radius variance) is ordinate, these two morphology parameters to lymphocyte district and monocyte district cell are made two-dimentional scatter diagram, the MIcrosope image corresponding according to the zone further distinguished lymphocyte nuclear and monocyte.
In the step (3), cell colourity is divided into red flux and blue flux, is horizontal ordinate with the red flux, and blue flux is that ordinate makes up two-dimentional scatter diagram, the cell that red component is many is eosinophilic granulocyte, therefore the granulocyte district is further accurately divided into eosinophilic granulocyte and neutrophil leucocyte.
The implication of morphology parameter,
Nuclear area: the pixel summation in leucocyte karyon zone.
Cytoplasm area: the pixel summation in leucocyte endochylema zone.
Cell karyoplasmic ratio: nuclear area (the nuclear area pixel is formed area)/cytoplasm area (the cytoplasm area pixel is formed area).
Nucleus circularity: nuclear circularity is the rotund key character parameter of characterize cells stone grafting.Nucleus circularity of the present invention refers to represent nuclear circularity with the size of the coefficient of variation (CV) of nuclear radius (focus point is to the length of frontier point line) or variance (Var).
Blue component (B): the amount of blue composition in the cytoplasm.
Red component (R): the amount of red composition in the cytoplasm.
Beneficial effect: image is handled in the machine systematic analysis as calculated, use several simple morphology parameters to make two-dimentional scatter diagram, the mode of all cells with statistical graph presented, the MIcrosope image feature corresponding according to the zone realized the quick exact classification of leucocyte, the white blood cell count(WBC) robotization, compare with the artificial counting method, overcome subjective factor, step is simply quick, and diagnosis speed and accuracy significantly improve.
Description of drawings
Fig. 1 is leucocyte of the present invention zone cellular morphology mathematic(al) parameter two dimension scatter diagram.
Fig. 2 is lymphocyte of the present invention district cellular morphology mathematic(al) parameter two dimension scatter diagram.
Fig. 3 is monocyte of the present invention district cellular morphology mathematic(al) parameter two dimension scatter diagram.
Fig. 4 is granulocyte of the present invention district cellular morphology mathematic(al) parameter two dimension scatter diagram.
Fig. 5 is the monocytic MIcrosope image of lymph sample of the present invention.
Fig. 6 is lymphocytic MIcrosope image of the present invention.
Fig. 7 is monocytic MIcrosope image of the present invention.
Fig. 8 is the lymphocytic MIcrosope image of macronucleus of the present invention.
Fig. 9 is the MIcrosope image of granulocyte of the present invention district cell.
Embodiment
In order to deepen the understanding of the present invention, the invention will be further described below in conjunction with embodiment and accompanying drawing, and this embodiment only is used for explaining the present invention, does not constitute the restriction to protection domain of the present invention.
Embodiment
(1) peripheral blood film utilizes microscope to gather image behind Wright's staining;
(2) image is imported computer system processor, referring to Fig. 1, leucocyte is carried out the analysis of morphology parameter, be horizontal ordinate with the cell karyoplasmic ratio, make up two-dimentional scatter diagram, nucleus circularity (the nuclear radius coefficient of variation) is ordinate, makes up two-dimentional scatter diagram, the MIcrosope image corresponding according to regional cell is divided into lymphocyte district R1, monocyte district R2, granulocyte district R3 and four zones of extinction cellular regions R4 with the leucocyte partition;
(3) the regional cell lymphocyte district R1 that further step (2) is marked off, monocyte district R2, granulocyte district R3 carry out the analysis of morphology parameter;
Referring to Fig. 2 and 3, be horizontal ordinate with nucleus brightness total amount, nucleus circularity (nuclear radius variance) is ordinate, make up two-dimentional scatter diagram, the MIcrosope image corresponding according to regional cell further accurately divided into lymph sample monocyte R5, lymphocyte R7, monocyte R6, macronucleus lymphocyte R8 with lymphocyte district R1 and monocyte district R2;
Referring to Fig. 4, be horizontal ordinate with red component (R), blue component (B) ordinate makes up two-dimentional scatter diagram, and the cell that red component is many is eosinophilic granulocyte, and granulocyte district R3 is further accurately divided into eosinophil R9 and neutrophil leucocyte R10;
Fig. 5, Fig. 6, Fig. 7, Fig. 8, Fig. 9 correspond respectively to the MIcrosope image of lymph sample monocyte R5, lymphocyte R7, monocyte R6, macronucleus lymphocyte R8, granulocyte district R3 zone cell, according to the characteristics of MIcrosope image, can identify the leucocyte kind.
(4) the leucocyte kind that distinguishes is carried out the differential count statistics.
The above only is preferred embodiment of the present invention, and is in order to limit the present invention, within the spirit and principles in the present invention not all, any modification of doing, is equal to replacement, improvement etc., all should be included within protection scope of the present invention.
Claims (4)
1. count leukocytic method based on morphological classification for one kind, it is characterized in that, may further comprise the steps:
(1) peripheral blood film utilizes microscope to gather image behind Wright's staining;
The image input computer system of (2) step (1) being gathered is carried out the analysis of morphology parameter to leucocyte, and leucocyte is divided into lymphocyte district, monocyte district, granulocyte district and four zones of extinction cellular regions;
(3) the regional cell that step (2) is marked off carries out the analysis of morphology parameter, and cell is further accurately distinguished monocyte, lymphocyte, eosinophil and neutrophil leucocyte;
(4) the leucocyte kind that distinguishes is carried out the differential count statistics.
2. according to claim 1ly a kind ofly count leukocytic method based on morphological classification, it is characterized in that the morphology parameter described in the step (2) is cell karyoplasmic ratio and nucleus circularity.
3. according to claim 1ly a kind ofly count leukocytic method based on morphological classification, it is characterized in that the morphology parameter described in the step (3) is nucleus brightness total amount, nucleus circularity, red component, blue component.
4. a kind ofly count leukocytic method based on morphological classification according to claim 1-3 is described, it is characterized in that, be horizontal stroke, ordinate carry out two-dimentional scatter diagram to leucocyte drafting with described morphology parameter, according to the corresponding MIcrosope image feature in scatter diagram zone, from figure, judge to mark off different types of cellular regions.
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| Application Number | Priority Date | Filing Date | Title |
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| CN201310254872.6A CN103345654B (en) | 2013-06-25 | 2013-06-25 | Method for differential counting of white blood cells based on morphology |
| PCT/CN2014/082868 WO2014206376A1 (en) | 2013-06-25 | 2014-07-24 | Method of classifying and counting white blood cells on basis of morphology |
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Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103745210A (en) * | 2014-01-28 | 2014-04-23 | 爱威科技股份有限公司 | Method and device for classifying white blood cells |
| WO2014206376A1 (en) * | 2013-06-25 | 2014-12-31 | 苏州创继生物科技有限公司 | Method of classifying and counting white blood cells on basis of morphology |
| CN104408738A (en) * | 2014-12-15 | 2015-03-11 | 爱威科技股份有限公司 | Image processing method and system |
| CN106248559A (en) * | 2016-07-14 | 2016-12-21 | 中国计量大学 | A kind of leukocyte five sorting technique based on degree of depth study |
| CN107492088A (en) * | 2016-06-11 | 2017-12-19 | 青岛华晶生物技术有限公司 | Leucocyte automatic identification and statistical method in a kind of gynaecology's micro-image |
| CN107686859A (en) * | 2017-07-18 | 2018-02-13 | 天津师范大学 | A kind of method counted suitable for Haemocytes of Fishes Fast Classification and application |
| CN108885682A (en) * | 2016-02-26 | 2018-11-23 | 谷歌有限责任公司 | Use Processing with Neural Network cell image |
| CN111047577A (en) * | 2019-12-12 | 2020-04-21 | 太原理工大学 | Abnormal urine red blood cell classification statistical method and system |
| CN112150466A (en) * | 2020-11-26 | 2020-12-29 | 北京小蝇科技有限责任公司 | Method and system for detecting abnormality of white blood cell scatter diagram |
| CN112508909A (en) * | 2020-12-03 | 2021-03-16 | 中国人民解放军陆军军医大学第二附属医院 | Disease association method of peripheral blood cell morphology automatic detection system |
| CN112595654A (en) * | 2020-10-28 | 2021-04-02 | 宁夏医科大学总医院 | Cerebrospinal fluid cell image feature library and establishing method thereof |
| WO2021109029A1 (en) * | 2019-12-04 | 2021-06-10 | 深圳迈瑞生物医疗电子股份有限公司 | Target cell statistical method, device, system, and storage medium |
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|---|---|---|---|---|
| WO2014206376A1 (en) * | 2013-06-25 | 2014-12-31 | 苏州创继生物科技有限公司 | Method of classifying and counting white blood cells on basis of morphology |
| CN103745210B (en) * | 2014-01-28 | 2018-02-06 | 爱威科技股份有限公司 | A kind of leucocyte classification method and device |
| CN103745210A (en) * | 2014-01-28 | 2014-04-23 | 爱威科技股份有限公司 | Method and device for classifying white blood cells |
| CN104408738A (en) * | 2014-12-15 | 2015-03-11 | 爱威科技股份有限公司 | Image processing method and system |
| CN104408738B (en) * | 2014-12-15 | 2018-08-17 | 爱威科技股份有限公司 | A kind of image processing method and system |
| CN108885682B (en) * | 2016-02-26 | 2022-03-22 | 谷歌有限责任公司 | Processing cell images using neural networks |
| US11915134B2 (en) | 2016-02-26 | 2024-02-27 | Google Llc | Processing cell images using neural networks |
| US11443190B2 (en) | 2016-02-26 | 2022-09-13 | Google Llc | Processing cell images using neural networks |
| CN108885682A (en) * | 2016-02-26 | 2018-11-23 | 谷歌有限责任公司 | Use Processing with Neural Network cell image |
| CN107492088A (en) * | 2016-06-11 | 2017-12-19 | 青岛华晶生物技术有限公司 | Leucocyte automatic identification and statistical method in a kind of gynaecology's micro-image |
| CN106248559B (en) * | 2016-07-14 | 2018-10-23 | 中国计量大学 | A kind of five sorting technique of leucocyte based on deep learning |
| CN106248559A (en) * | 2016-07-14 | 2016-12-21 | 中国计量大学 | A kind of leukocyte five sorting technique based on degree of depth study |
| CN107686859A (en) * | 2017-07-18 | 2018-02-13 | 天津师范大学 | A kind of method counted suitable for Haemocytes of Fishes Fast Classification and application |
| WO2021109029A1 (en) * | 2019-12-04 | 2021-06-10 | 深圳迈瑞生物医疗电子股份有限公司 | Target cell statistical method, device, system, and storage medium |
| CN111047577A (en) * | 2019-12-12 | 2020-04-21 | 太原理工大学 | Abnormal urine red blood cell classification statistical method and system |
| CN112595654A (en) * | 2020-10-28 | 2021-04-02 | 宁夏医科大学总医院 | Cerebrospinal fluid cell image feature library and establishing method thereof |
| CN112150466A (en) * | 2020-11-26 | 2020-12-29 | 北京小蝇科技有限责任公司 | Method and system for detecting abnormality of white blood cell scatter diagram |
| CN112508909A (en) * | 2020-12-03 | 2021-03-16 | 中国人民解放军陆军军医大学第二附属医院 | Disease association method of peripheral blood cell morphology automatic detection system |
| CN112508909B (en) * | 2020-12-03 | 2023-08-25 | 中国人民解放军陆军军医大学第二附属医院 | Disease association method of peripheral blood cell morphology automatic detection system |
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| WO2014206376A1 (en) | 2014-12-31 |
| CN103345654B (en) | 2017-02-08 |
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