CN112504947A - Morphological analysis and counting method for peripheral blood cells - Google Patents
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Abstract
The invention belongs to the technical field of peripheral blood cell morphology detection, and particularly relates to a peripheral blood cell morphology analysis counting method, which comprises the following steps: s1: scanning a smear and preprocessing; s2: dividing a scanned image into a plurality of areas; s3: different cells are respectively set with different color value areas, and the color value distribution general of RGB three-color space of the counting area image is collected; s4: the a1 color value field in the count area is retained and the remaining pixels are set to black, resulting in a single a1 cell image b1 and stored, S5: respectively operating a C mean value clustering algorithm on the images, and segmenting the images; s6: counting cells of the image after the segmentation is finished; s7: and (3) obtaining data of various cells through calculation, comparing the data with a normal reference value, establishing association with the existing diseases, and performing association analysis. The method is used for solving the problem that the accuracy of the existing image analysis system for identifying and segmenting the overlapped cells is not high enough and often needs manual operation of a doctor for assistance.
Description
Technical Field
The invention belongs to the technical field of peripheral blood cell morphology detection, and particularly relates to a peripheral blood cell morphology analysis counting method.
Background
In peripheral blood samples, three types of tangible blood cells are contained: erythrocytes, leukocytes and platelets are classified into granulocytes, monocytes and lymphocytes according to the particles contained in the nucleus and the protoplasm, and granulocytes are classified into eosinophils, basophils and neutrophils according to the properties of the particles. The percentage of each kind of white blood cells and the total number of the whole white blood cells is an important item for peripheral blood examination, and after Reye's staining, the kinds of the white blood cells are distinguished according to the forms of nuclear staining, cytoplasmic staining, karyotype and the like of various mature white blood cells under a microscope.
Methods for qualitatively analyzing cell images through a microscope and by using a visual method and diagnosing the health condition of people have an important position in clinical pathology. With the development of computer technology, computer image processing and analyzing technology plays an increasingly important role in clinical diagnosis and treatment, a new image analyzing system is developed, immunohistochemical cell images are automatically processed by a computer for quantitative analysis, and doctors are assisted to make quick and accurate judgment, so that the method has an important application prospect in medical disease diagnosis.
However, the accuracy of the current image analysis system for identifying and segmenting the overlapped cells is not high enough, and manual operation of a doctor is often required for assistance.
Disclosure of Invention
In view of the above, the present invention provides a peripheral blood cell morphological analysis and counting method, which solves the problem that the image analysis system in the prior art is not high enough in accuracy of identifying and segmenting overlapped cells, and often needs manual operation of a doctor for assistance.
The invention solves the technical problems by the following technical means:
a peripheral blood cell morphology analysis counting method comprises the following steps:
s1: scanning the smear, and performing image preprocessing operation on the target; s2: dividing a scanned image into a plurality of regions, and randomly selecting a plurality of counting regions d1, d2 and d3 … … dn with the same size in the plurality of regions; s3: setting color value areas of A1, A2 and A3 … … An cells as a1, a2 and A3 … … An respectively, scanning a d1 counting area, and collecting color value distribution overview of RGB three-color space of An image in the counting area; s4: reserving a region of a1 color values in the counting region, setting the rest pixels of a2 and A3 … … an to be black, obtaining and storing a separate A1 cell image b1, and obtaining and storing b2 and b3 … … bn images in the same way; s5: and (3) running a C-means clustering algorithm on the RGB values of the images b1, b2 and b3 … … bn respectively to segment the images. Performing cell overlapping recognition on the segmented image, and performing cell overlapping segmentation if the segmented image is judged to be an overlapping cell; s6: counting the cells of the image after the segmentation is finished, counting the number of various cells in a d1 counting area, turning to S3, scanning a d2 counting area, counting until the dn counting area is finished, obtaining the total number c1 of A1 cells, the total numbers c2 and c3 … … cn of the rest various cells and the total area of the counting area, and obtaining the distribution ratio of the various cells through calculation; s7: and (3) obtaining data of various cells through calculation, comparing the data with a normal reference value, establishing association with the existing diseases, and performing association analysis.
Preferably, the method comprises the following steps: the method for identifying the cell overlap in the S5 comprises the following steps: b1: determining whether cells overlap by shape factor analysis, PE-4 pi A/L2Wherein A is the area of the judged cell and is obtained by judging the number of pixels in the cell; and L is the cell perimeter, the sum of the distances between adjacent pixels on the edge is used for representing, if the 8-adjacent-point distance d8 is adopted, the distances between 2 adjacent pixels fi, j and fm in the inclined direction are as follows:
the value range of the shape factor is 0< PE ≦ 1, when the image is judged to be close to a circle, the shape factor is close to 1, the threshold value P0 of the shape factor is determined, if PE > P0, the cell is judged not to be overlapped, and if PE < ═ P0, the cell is judged to be overlapped; b2: and calculating and extracting the number N of the core coordinates of the cells in the overlapped area and the core coordinates E of the whole overlapped area, further judging whether the cells are overlapped or not according to the number of the core coordinates N of the cells, and judging that the cells are not overlapped if N is less than 2.
Preferably, the method comprises the following steps: the cell overlap segmentation in the S5 comprises the following steps: c1: judging the concave-convex property of the peak of the overlapped cell image by a vector product method to obtain concave points; c2: according to the relation between the number of concave points N1 and the number of cell core coordinates N2, if N1 is equal to N2, the cells are judged to be in parallel, and if N1 is greater than N2, the cells are judged to be in series; c3: if the cells are connected in series, the pits in a pair are connected to the linear separation overlap region, and if the cells are connected in parallel, the pits and the core coordinates E of the overlap region are connected to the linear separation overlap region.
Preferably, the method comprises the following steps: when the cells are counted in the S6, A is used as the target amount of A1 cell counting, when A is larger than c1, the counting of the next counting area is carried out, and when A < ═ c1, the counting is stopped after counting of the counting area is completed.
Preferably, the method comprises the following steps: the preprocessing operation in S1 includes gray scale transformation, histogram modification, spatial filtering, and frequency filtering.
Preferably, the method comprises the following steps: the S7 comprises the following steps of D1: associating the data of various cells with the existing diseases in the database respectively; d2: comparing the similarity and the dissimilarity of each associated disease, and when the similarity is greater than the dissimilarity, classifying the disease into a suspected disease; d3: multiplying the similarity and the dissimilarity of the matched suspected diseases to finally obtain the similarity and the dissimilarity of the overall detection result on the diseases; d4: and (4) sorting the similarity from high to low, and completing the analysis of the data and the association with the disease.
Preferably, the method comprises the following steps: the method for calculating and extracting the number N of the core coordinates of the cells in the overlapping area and the core coordinates E of the whole overlapping area in the B2 comprises the following steps: the cell boundaries are eroded layer by a mathematical morphological method until being separated into single cells, and then the cores of the individual cells in the overlapped cell region are extracted.
The invention has the beneficial effects that:
1. according to the method, the image is divided into a plurality of regions, the cells in the regions are counted respectively, and when the C-means clustering algorithm is used for calculation, the calculation amount of single calculation is reduced, and the requirement on the calculation speed of a computer is relieved.
2. This application is different through the region of all kinds of cell RGB three-colour spatial colour values, stores calculation alone all kinds of cell images, interference between all kinds of cells when having avoided calculating.
3. Judging whether cells overlap or not by a shape factor analysis method, extracting the core coordinate number N of the cells in the overlapping area and the core coordinate E of the whole overlapping area, and further judging whether the cells overlap or not according to the number of the core coordinate number N of the cells.
4. The method comprises the steps of judging the concave-convex property of the top points of the overlapped cell images through a vector product method to obtain concave points, judging the overlapped type of the overlapped cells through the relation between the number of the concave points N1 and the number of cell core coordinates N2, connecting the concave points in pairs with the core coordinates E of the overlapped regions to form a straight line separating overlapped region if the cells are connected in parallel, and realizing the segmentation of the overlapped cells.
Drawings
FIG. 1 is a flow chart of a method for the morphological analysis and counting of peripheral blood cells according to the present invention;
FIG. 2 is a flow chart of the overlapping cell segmentation of the method for analyzing and counting the morphology of peripheral blood cells according to the present invention.
Detailed Description
The invention will be described in detail below with reference to the following figures and specific examples:
as shown in fig. 1-2, the method for analyzing and counting peripheral blood cell morphology according to the present invention comprises the following steps: s1: scanning smear, and performing image preprocessing operation on the target, wherein the preprocessing operation comprises gray scale transformation, histogram correction, spatial filtering and frequency filtering. S2: dividing a scanned image into a plurality of regions, and randomly selecting a plurality of counting regions d1, d2 and d3 … … dn with the same size in the plurality of regions; s3: setting color value areas of A1, A2 and A3 … … An cells as a1, a2 and A3 … … An respectively, scanning a d1 counting area, and collecting color value distribution overview of RGB three-color space of An image in the counting area; s4: reserving a region of a1 color values in the counting region, setting the rest pixels of a2 and A3 … … an to be black, obtaining and storing a separate A1 cell image b1, and obtaining and storing b2 and b3 … … bn images in the same way; s5: and (3) running a C-means clustering algorithm on the RGB values of the images b1, b2 and b3 … … bn respectively to segment the images. Performing cell overlapping recognition on the segmented image, and performing cell overlapping segmentation if the segmented image is judged to be an overlapping cell; s6: counting the cells of the image after the segmentation is finished, counting the number of various cells in a d1 counting area, turning to S3, scanning a d2 counting area, counting until the dn counting area is finished, obtaining the total number c1 of A1 cells, the total numbers c2 and c3 … … cn of the rest various cells and the total area of the counting area, and obtaining the distribution ratio of the various cells through calculation; s7: and (3) obtaining data of various cells through calculation, comparing the data with a normal reference value, establishing association with the existing diseases, and performing association analysis.
Cell overlap recognition in S5The method comprises the following steps: b1: determining whether cells overlap by shape factor analysis, PE-4 pi A/L2Wherein A is the area of the judged cell and is obtained by judging the number of pixels in the cell; and L is the cell perimeter, the sum of the distances between adjacent pixels on the edge is used for representing, if the 8-adjacent-point distance d8 is adopted, the distances between 2 adjacent pixels fi, j and fm in the inclined direction are as follows:
the value range of the shape factor is 0< PE ≦ 1, when the image is judged to be close to a circle, the shape factor is close to 1, the threshold value P0 of the shape factor is determined, if PE > P0, the cell is judged not to be overlapped, and if PE < ═ P0, the cell is judged to be overlapped; b2: and calculating and extracting the number N of the core coordinates of the cells in the overlapped area and the core coordinates E of the whole overlapped area, further judging whether the cells are overlapped or not according to the number of the core coordinates N of the cells, and judging that the cells are not overlapped if N is less than 2.
The method for cell overlap segmentation in S5 comprises: c1: judging the concave-convex property of the peak of the overlapped cell image by a vector product method to obtain concave points; c2: according to the relation between the number of concave points N1 and the number of cell core coordinates N2, if N1 is equal to N2, the cells are judged to be in parallel, and if N1 is greater than N2, the cells are judged to be in series; c3: if the cells are connected in series, the pits in a pair are connected to the linear separation overlap region, and if the cells are connected in parallel, the pits and the core coordinates E of the overlap region are connected to the linear separation overlap region.
In the cell counting in S6, a is set as a target amount of a1 cell counting, and when a > c1, the next counting area is counted, and when a < ═ c1, counting is stopped after counting of the counting area is completed.
S7 includes the steps of D1: associating the data of various cells with the existing diseases in the database respectively; d2: comparing the similarity and the dissimilarity of each associated disease, and when the similarity is greater than the dissimilarity, classifying the disease into a suspected disease; d3: multiplying the similarity and the dissimilarity of the matched suspected diseases to finally obtain the similarity and the dissimilarity of the overall detection result on the diseases; d4: and (4) sorting the similarity from high to low, and completing the analysis of the data and the association with the disease.
The method for calculating and extracting the number N of the core coordinates of the cells in the overlapping area and the core coordinates E of the whole overlapping area in the step B2 comprises the following steps: the cell boundaries are eroded layer by a mathematical morphological method until being separated into single cells, and then the cores of the individual cells in the overlapped cell region are extracted.
The invention takes 200 rows of peripheral blood examination pictures in the hospital as an example, the counting method is used for counting, the counting speed is far higher than the speed of manual examination according to the comparison index which is the data of physician oil-scope examination counting. And the segmentation effect on the overlapped cells and the accuracy are almost negligible compared with the manual test.
According to the method, the image is divided into a plurality of regions, the cells in the regions are counted respectively, and when the C-means clustering algorithm is used for calculation, the calculation amount of single calculation is reduced, and the requirement on the calculation speed of a computer is relieved. This application is different through the region of all kinds of cell RGB three-colour spatial colour values, stores all kinds of cell images alone and calculates, interference between all kinds of cells when having avoided calculating. The method judges whether cells overlap or not by a shape factor analysis method, extracts the cell core coordinate number N of an overlapping area and the core coordinate E of the whole overlapping area, and further judges whether the cells overlap or not according to the number of the cell core coordinate number N. The method comprises the steps of judging the concave-convex of the top point of an overlapped cell image through a vector product method to obtain concave points, judging the overlapped type of the overlapped cells through the relation between the number of the concave points N1 and the number N2 of core coordinates of the cells, connecting the concave points in pairs with the core coordinates E of the overlapped regions to form a straight line separation overlapped region if the cells are connected in parallel, and realizing the segmentation of the overlapped cells.
Although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted without departing from the spirit and scope of the invention as defined in the appended claims. The techniques, shapes, and configurations not described in detail in the present invention are all known techniques.
Claims (7)
1. A peripheral blood cell morphology analysis counting method is characterized by comprising the following steps:
s1: scanning the smear, and performing image preprocessing operation on the target;
s2: dividing a scanned image into a plurality of regions, and randomly selecting a plurality of counting regions d1, d2 and d3 … … dn with the same size in the plurality of regions;
s3: setting color value areas of A1, A2 and A3 … … An cells as a1, a2 and A3 … … An respectively, scanning a d1 counting area, and collecting color value distribution overview of RGB three-color space of An image in the counting area;
s4: reserving a region of a1 color values in the counting region, setting the rest pixels of a2 and A3 … … an to be black, obtaining and storing a separate A1 cell image b1, and obtaining and storing b2 and b3 … … bn images in the same way;
s5: and (3) running a C-means clustering algorithm on the RGB values of the images b1, b2 and b3 … … bn respectively to segment the images. Performing cell overlapping recognition on the segmented image, and performing cell overlapping segmentation if the segmented image is judged to be an overlapping cell;
s6: counting the cells of the image after the segmentation is finished, counting the number of various cells in a d1 counting area, turning to S3, scanning a d2 counting area, counting until the dn counting area is finished, obtaining the total number c1 of A1 cells, the total numbers c2 and c3 … … cn of the rest various cells and the total area of the counting area, and obtaining the distribution ratio of the various cells through calculation;
s7: and (3) obtaining data of various cells through calculation, comparing the data with a normal reference value, establishing association with the existing diseases, and performing association analysis.
2. The method for morphologically counting peripheral blood cells according to claim 1, wherein: the method for identifying the cell overlap in the S5 comprises the following steps:
b1: determining whether cells overlap by shape factor analysis, PE-4 pi A/L2Wherein A is the area of the judged cell and is obtained by judging the number of pixels in the cell; and L is the cell perimeter, the sum of the distances between adjacent pixels on the edge is used for representing, if the 8-adjacent-point distance d8 is adopted, the distances between 2 adjacent pixels fi, j and fm in the inclined direction are as follows:
the value range of the shape factor is 0< PE ≦ 1, when the image is judged to be close to a circle, the shape factor is close to 1, the threshold value P0 of the shape factor is determined, if PE > P0, the cell is judged not to be overlapped, and if PE < ═ P0, the cell is judged to be overlapped;
b2: and calculating and extracting the number N of the core coordinates of the cells in the overlapped area and the core coordinates E of the whole overlapped area, further judging whether the cells are overlapped or not according to the number of the core coordinates N of the cells, and judging that the cells are not overlapped if N is less than 2.
3. The method for morphologically counting peripheral blood cells according to claim 1, wherein: the cell overlap segmentation in the S5 comprises the following steps:
c1: judging the concave-convex property of the peak of the overlapped cell image by a vector product method to obtain concave points;
c2: according to the relation between the number of concave points N1 and the number of cell core coordinates N2, if N1 is equal to N2, the cells are judged to be in parallel, and if N1 is greater than N2, the cells are judged to be in series;
c3: if the cells are connected in series, the pits in a pair are connected to the linear separation overlap region, and if the cells are connected in parallel, the pits and the core coordinates E of the overlap region are connected to the linear separation overlap region.
4. The method for morphologically counting peripheral blood cells according to claim 1, wherein: when the cells are counted in the S6, A is used as the target amount of A1 cell counting, when A is larger than c1, the counting of the next counting area is carried out, and when A < ═ c1, the counting is stopped after counting of the counting area is completed.
5. The method for morphologically counting peripheral blood cells according to claim 1, wherein: the preprocessing operation in S1 includes gray scale transformation, histogram modification, spatial filtering, and frequency filtering.
6. The method for morphologically counting peripheral blood cells according to claim 1, wherein: the S7 includes the steps of:
d1: associating the data of various cells with the existing diseases in the database respectively;
d2: comparing the similarity and the dissimilarity of each associated disease, and when the similarity is greater than the dissimilarity, classifying the disease into a suspected disease;
d3: multiplying the similarity and the dissimilarity of the matched suspected diseases to finally obtain the similarity and the dissimilarity of the overall detection result on the diseases;
d4: and (4) sorting the similarity from high to low, and completing the analysis of the data and the association with the disease.
7. The method according to claim 2, wherein the method comprises: the method for calculating and extracting the number N of the core coordinates of the cells in the overlapping area and the core coordinates E of the whole overlapping area in the B2 comprises the following steps: the cell boundaries are eroded layer by a mathematical morphological method until being separated into single cells, and then the cores of the individual cells in the overlapped cell region are extracted.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114004851A (en) * | 2021-11-26 | 2022-02-01 | 广州市艾贝泰生物科技有限公司 | Cell image segmentation method and device and cell counting method |
| CN115201092A (en) * | 2022-09-08 | 2022-10-18 | 珠海圣美生物诊断技术有限公司 | Method and device for acquiring cell scanning image |
| CN116503859A (en) * | 2023-06-27 | 2023-07-28 | 成都云芯医联科技有限公司 | Data enhancement three-classification leukemia algorithm based on deep learning |
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