Full-automatic classification detection method for peripheral blood cell morphology

Abstract

The invention relates to the technical field of cell classification, in particular to a full-automatic classification detection method for peripheral blood cell morphology. The method comprises the following steps: acquiring a cell region in a cell display image of peripheral blood; obtaining an overlapped cell region according to the form of the cell region and the curvature of edge pixel points in the cell region; according to the length of each edge line in the overlapped cell area, the curvature of edge pixel points on each edge line and the curvature of intersection point pixel points of each edge line and other edge lines, obtaining exposed edge lines of the covered cells; and determining a complete cell edge line of the covered cell according to the curvature and the end point of the exposed edge line, and carrying out full-automatic classification detection on the peripheral blood cell morphology. By acquiring the complete cell edge line of the covered cell, the form of the covered cell is determined, the situation of inaccurate automatic identification and classification of the cell due to cell overlapping is avoided, and the accuracy of full-automatic classification detection of the form of the peripheral blood cell is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of cell classification, and particularly to a fully automatic classification detection method for the morphology of peripheral blood cells. Background Art

[0002] The morphological examination of blood cells is an important diagnostic tool in clinical hematology. By observing the cell morphology in a blood sample under a microscope, it can provide key information about the patient's health status for doctors. This examination plays a crucial role in the early screening and diagnosis of various blood system diseases. Especially in the diagnosis of leukemia, anemia, infections, immune system diseases, and other blood diseases, it can help doctors make more accurate judgments. Traditional morphological examinations of blood cells usually rely on manual microscopic observation, which has many limitations, such as observer fatigue, subjective bias, slow analysis speed, and poor stability of detection results. To overcome these problems, in existing methods, a fully automatic classification detection of peripheral blood cell morphology is used. It uses fully automated equipment to identify and classify various cell types in peripheral blood, analyzes cell morphological characteristics, and helps doctors quickly and accurately evaluate the patient's health status, especially in the screening and diagnosis of blood diseases, infections, immune diseases, and some other diseases.

[0003] However, in actual situations, during the process of fully automatic classification detection of peripheral blood cell morphology, there is a problem that the cell distribution is too dense, resulting in cell overlap. The cell edges cannot be clearly separated, which makes it difficult for the automatic classification system to accurately extract the characteristics of each cell. It is easy to misidentify multiple cells as one cell, making the fully automatic classification detection of peripheral blood cell morphology inaccurate, and thus unable to accurately evaluate the patient's health status. Summary of the Invention

[0004] In order to solve the technical problem that the cell overlap caused by too dense cell distribution makes the fully automatic classification detection of peripheral blood cell morphology inaccurate, the purpose of the present invention is to provide a fully automatic classification detection method for the morphology of peripheral blood cells. The specific technical solution adopted is as follows: An embodiment of the present invention provides a fully automatic classification detection method for the morphology of peripheral blood cells. The method includes the following steps: Obtain the cell regions in the cell display image of peripheral blood; Obtain the overlapping cell regions according to the morphology of each cell region and the curvature fluctuation of the edge pixel points in each cell region; Obtain the exposed edge lines of the covered cells in the overlapping cell regions according to the length of each edge line in the overlapping cell regions, the curvature of the edge pixel points on each edge line, and the curvature of the intersection pixel points of each edge line with other edge lines; According to the curvature and end points of each exposed edge line, obtain the virtual covered edge line of the covered cells corresponding to each exposed edge line, determine the complete cell edge line of each covered cell, and perform automatic classification detection of peripheral blood cell morphology.

[0005] Further, the method for obtaining the overlapping cell region is as follows: According to the shape of each cell region, obtain the shape parameters of each cell region; According to the curvature fluctuation of the edge pixel points in each cell region, obtain the edge undulation degree of each cell region; According to the shape parameters and edge undulation degree of each cell region, obtain the overlapping degree of each cell region; wherein, the shape parameters and the overlapping degree are negatively correlated, and the edge undulation degree and the overlapping degree are positively correlated; When the overlapping degree is greater than the preset overlapping degree threshold, the corresponding cell region is used as the overlapping cell region.

[0006] Further, the method for obtaining the shape parameters is as follows: For any cell region, take the ratio of the area of the cell region to the square of the perimeter of the cell region as the first value; Take the product of a specified constant and the first value as the shape parameter of the cell region.

[0007] Further, the method for obtaining the edge undulation degree is as follows: For any cell region, obtain the curvature of each edge pixel point in the cell region as the reference curvature; Take the difference between the maximum reference curvature and the minimum reference curvature as the first fluctuation degree; Take the product of the variance of the reference curvature and the first fluctuation degree as the edge undulation degree of the cell region.

[0008] Further, the method for obtaining the exposed edge line is as follows: According to the length of each edge line, the curvature of the edge pixel points on each edge line, and the curvature of the intersection pixel points of each edge line and other edge lines in each overlapping cell region, obtain the exposure degree of each edge line in each overlapping cell region; When the exposure degree is greater than the preset exposure degree threshold, the corresponding edge line is used as the exposed edge line.

[0009] Further, the method for obtaining the exposure degree is as follows: For any edge line in any overlapping cell region, obtain the mean value of the curvatures of all edge pixel points on the edge line as the curvature analysis value of the edge line; Obtain the average value of the curvatures of the intersection pixel points of this edge line and other edge lines in the overlapping cell region as the curvature reference value; Take the difference between the curvature analysis value and the curvature reference value as the first difference; Take the result of normalizing the ratio of the first difference to the number of edge pixels on this edge line as the exposure degree of this edge line.

[0010] Furthermore, the method for obtaining the exposed edge line further includes: Obtain the true exposed edge line according to the gray difference between each edge pixel on each exposed edge line and its preset neighborhood pixels.

[0011] Furthermore, the method for obtaining the true exposed edge line is: For any edge pixel on any exposed edge line, take the line perpendicular to the tangent of this edge pixel as the target line, and take the preset number of pixels closest to this edge pixel outside the covered cell corresponding to this exposed edge line and located on the target line as the preset neighborhood pixels of this edge pixel; Obtain the difference between the gray value of this edge pixel and the average gray value of its preset neighborhood pixels as the brightness mutation degree of this edge pixel; Take the result of negative correlation and normalization of the sum of the brightness mutation degrees of all edge pixels on this exposed edge line as the brightness smoothness degree of this exposed edge line; When the brightness smoothness degree is greater than the preset brightness smoothness degree threshold, take the corresponding exposed edge line as the true exposed edge line.

[0012] Furthermore, the method for obtaining the virtual covered edge line is: For any exposed edge line, take the pixels at both ends of this exposed edge line as target pixels; For any target pixel, take the eight-neighborhood pixels of this target pixel that are not passed by this exposed edge line as the adjacent pixels of this target pixel; Smoothly connect this exposed edge line with each adjacent pixel of this target pixel through Bezier curve technology to obtain the reference edge line corresponding to each adjacent pixel of this target pixel; Obtain the difference in curvature between the reference edge line corresponding to each adjacent pixel of this target pixel and this exposed edge line as the second difference; Take the adjacent pixel on the reference edge line corresponding to the smallest second difference as the first virtual edge pixel of this exposed edge line; Smoothly connect the exposed edge line with the first virtual edge pixel point through Bezier curve technology to obtain the first reference exposed edge line; Take the eight-neighborhood pixels of the first virtual edge pixel point not passed by the first reference exposed edge line as the adjacent pixels of the first virtual edge pixel point; Smoothly connect the first reference exposed edge line with each adjacent pixel of the first virtual edge pixel point through Bezier curve technology to obtain the reference edge line corresponding to each adjacent pixel of the first virtual edge pixel point; Obtain the difference in curvature between the reference edge line corresponding to each adjacent pixel of the first virtual edge pixel point and the first reference exposed edge line as the third difference; Take the adjacent pixel on the reference edge line corresponding to the smallest third difference as the second virtual edge pixel point of the exposed edge line; And so on, obtain each virtual edge pixel point of the exposed edge line until reaching the other target pixel point of the exposed edge line; Curve fit the virtual edge pixels obtained with each target pixel point as the starting point by the least squares method, and take the obtained curve as the virtual coverage edge line of the covered cell corresponding to the exposed edge line.

[0013] Furthermore, the method for obtaining the cell region is as follows: Obtain the cell region in the cell display image through the Canny edge detection algorithm and the connected component algorithm.

[0014] The present invention has the following beneficial effects: According to the morphology of each cell region and the curvature fluctuation of the edge pixels in each cell region, the present invention obtains the overlapping cell regions, accurately determines the overlapping cells, which is beneficial to the subsequent accurate and efficient analysis of the covered cells; in order to efficiently obtain the morphology of the covered cells, further according to the length of each edge line in the overlapping cell region, the curvature of the edge pixels on each edge line, and the curvature of the intersection pixels of each edge line with other edge lines, the exposed edge line of the covered cell in the overlapping cell region is obtained, and the edge line for the normal display of the covered cell is determined, which is beneficial to the subsequent acquisition of the complete morphology of the covered cell; furthermore, according to the curvature and endpoints of each exposed edge line, the virtual coverage edge line of the covered cell corresponding to each exposed edge line is accurately obtained, and the complete cell edge line of each covered cell is determined, effectively solving the problem that the cell morphology is incomplete due to cell overlap, resulting in inaccurate automatic recognition and classification of cells, improving the accuracy of the full-automatic classification detection of peripheral blood cell morphology, and further accurately evaluating the health status of patients. Description of the Drawings

[0015] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0016] Figure 1 Schematic flowchart of a fully automatic classification detection method for peripheral blood cell morphology provided by an embodiment of the present invention; Figure 2 Cell display image provided by an embodiment of the present invention; Figure 3 Flowchart of a method for obtaining an overlapping cell region provided by an embodiment of the present invention; Figure 4 Local image of cell overlap provided by an embodiment of the present invention; Figure 5 Flowchart of a method for obtaining a bare edge line provided by an embodiment of the present invention; Figure 6 Structural diagram of a fully automatic classification detection system for peripheral blood cell morphology provided by an embodiment of the present invention; Figure 7 Schematic diagram of a computer device provided by an embodiment of the present invention. Detailed implementation manners

[0017] To further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following will, in conjunction with the drawings and preferred embodiments, detail the specific implementation manners, structures, features, and effects of a fully automatic classification detection method for peripheral blood cell morphology proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0019] The following will specifically describe the specific solution of a fully automatic classification detection method for peripheral blood cell morphology provided by the present invention in conjunction with the drawings.

[0020] Embodiment 1: The present invention proposes a fully automatic classification detection method for peripheral blood cell morphology. Please refer to Figure 1, which shows a schematic flowchart of a fully automatic classification detection method for peripheral blood cell morphology provided by an embodiment of the present invention. The method includes the following steps: Step S1: Obtain the cell regions in the cell display image of the peripheral blood.

[0021] Specifically, in order to analyze the health status of a patient through peripheral blood, in this embodiment, a blood smear is first obtained from the patient's peripheral blood sample and subjected to a staining process. Commonly used staining methods include Giemsa staining and edge staining methods, etc.; then a high-resolution microscope or a scanning microscope is used to observe the blood smear, and at the same time, a cell display image of the peripheral blood is obtained. For the convenience of subsequent analysis, in this embodiment, the cell display image is grayscale processed as Figure 2 shown. It should be noted that all the cell display images that appear subsequently are grayscale cell display images. Among them, Giemsa staining, edge staining method, and grayscale processing are all well-known techniques and will not be elaborated here.

[0022] In order to accurately and efficiently analyze peripheral blood cells, in this embodiment, the cell regions in the cell display image are obtained through the Canny edge detection algorithm and the connected component algorithm. Among them, the Canny edge detection algorithm and the connected component algorithm are all well-known techniques and will not be elaborated here.

[0023] Step S2: Obtain the overlapping cell regions according to the morphology of each cell region and the curvature fluctuation of the edge pixel points in each cell region.

[0024] It is known that during the process of fully automatic classification detection of peripheral blood cell morphology, there is a situation where the cells in the cell display image are too densely distributed, resulting in overlapping between cells. As a result, it is difficult for the automatic classification system to accurately extract the features of each cell. The automatic classification system may misidentify multiple overlapping cells as one cell, resulting in incorrect automatic classification, directly affecting the accuracy of peripheral blood cell classification, and thus unable to accurately analyze the health status of the patient. Therefore, in this embodiment, it is necessary to analyze the overlapping cells to eliminate the inaccurate situation of peripheral blood cell classification. Usually, the shape of a single cell in the blood is round, but when cells overlap, the shape of the overlapping cells becomes irregular. For example, it may be curved, elongated, or have sharp edges, etc., and the shape significantly deviates from a circle. Therefore, in this embodiment, according to the morphology of each cell region, it is analyzed whether each cell region is an overlapping cell region.

[0025] Considering that the cell morphology is not perfectly close to a circle, simply based on the morphology of each cell region, some cell regions may be misjudged as overlapping cell regions, and thus further analysis of each cell region is required. It is known that when cells overlap, obvious undulations will appear at the edge of the overlapping part, that is, the edge is significantly irregular. Therefore, there will be obvious fluctuations in the curvature of the edge pixel points in the cell regions with cell overlap, and the curvature fluctuations of the edge pixel points in the cell region corresponding to a single cell are relatively small. Among them, the method for obtaining the curvature of the edge pixel points is a well-known technology and will not be elaborated here. Therefore, in this embodiment, according to the morphology of each cell region and the curvature fluctuation of the edge pixel points in each cell region, the overlapping cell regions with cell overlap are obtained, which is beneficial to the subsequent accurate and efficient processing of overlapping cells.

[0026] Preferably, in a feasible implementation manner of this embodiment, for the method of obtaining the overlapping cell regions, please refer to Figure 3 , which shows a flowchart of a method for obtaining an overlapping cell region provided in this embodiment. The method includes the following steps: Step S201: According to the shape of each cell region, obtain the shape parameter of each cell region.

[0027] Through the shape parameters proposed by Zeng Qingnan in the research on the automatic classification technology of red and white blood cell images that can identify overlapping cells, the method for obtaining the shape parameters is as follows: For any cell region, the ratio of the area of the cell region to the square of the perimeter of the cell region is used as the first value; the product of a specified constant and the first value is used as the shape parameter of the cell region; where the specified constant is . It should be noted that the research on the automatic classification technology of red and white blood cell images that can identify overlapping cells is well-known content and will not be elaborated here. Among them, the methods for obtaining the area and perimeter of the cell region are well-known technologies and will not be elaborated here.

[0028] Among them, the calculation formula for the shape parameter is: ; in the formula, is the shape parameter of the i-th cell region; is the area of the i-th cell region; is the perimeter of the i-th cell region.

[0029] It should be noted that the closer it is to 1, the closer the morphology of the i-th cell region is to a circle, and the less likely the i-th cell region is an overlapping cell region; the closer it is to 0, the less likely the morphology of the i-th cell region is a circle, and the more likely the i-th cell region is an overlapping cell region.

[0030] So far, the shape parameters of each cell region are obtained.

[0031] Step S202: Obtain the edge undulation degree of each cell region according to the curvature fluctuation of the edge pixel points in each cell region.

[0032] When the fluctuation degree of the curvature of the edge pixel points in a certain cell region is greater, it is more likely that there is cell overlapping in this cell region. Therefore, in this embodiment, the edge undulation degree of each cell region is obtained according to the curvature fluctuation of the edge pixel points in each cell region. The greater the edge undulation degree, the more likely the corresponding cell region is an overlapping cell region.

[0033] Preferably, in a feasible implementation manner of this embodiment, the method for obtaining the edge undulation degree is as follows: for any cell region, obtain the curvature of each edge pixel point in this cell region as the reference curvature; take the difference between the maximum reference curvature and the minimum reference curvature as the first fluctuation degree; the greater the first fluctuation degree, the more irregular the edge in this cell region; in order to accurately characterize the undulation degree of the edge line in this cell region, then take the product of the variance of the reference curvature and the first fluctuation degree as the edge undulation degree of this cell region.

[0034] Thus, the edge undulation degree of each cell region is obtained.

[0035] Step S203: Obtain the overlapping degree of each cell region according to the shape parameter and the edge undulation degree of each cell region; wherein, the shape parameter and the overlapping degree are negatively correlated, and the edge undulation degree and the overlapping degree are positively correlated.

[0036] It is known that the greater the shape parameter, the less likely the corresponding cell region is an overlapping cell region; the greater the edge undulation degree, the more likely the corresponding cell region is an overlapping cell region. Therefore, in this embodiment, the overlapping degree of each cell region is obtained according to the shape parameter and the edge undulation degree of each cell region; wherein, the shape parameter and the overlapping degree are negatively correlated, and the edge undulation degree and the overlapping degree are positively correlated. The greater the overlapping degree, the more likely the corresponding cell region is an overlapping cell region. Among them, the calculation formula of the overlapping degree is: ; in the formula, is the overlapping degree of the i-th cell region; is the shape parameter of the i-th cell region; is the edge undulation degree of the i-th cell region; norm is the normalization function. In other embodiments, it can be obtained through obtain , which is not limited herein.

[0037] Thus, the overlapping degree of each cell region is obtained.

[0038] Step S204: When the overlapping degree is greater than the preset overlapping degree threshold, the corresponding cell region is taken as the overlapping cell region.

[0039] It is known that the greater the overlapping degree, the more likely the corresponding cell region is the overlapping cell region. Therefore, in this embodiment, the preset overlapping degree threshold is set to 0.5. The implementer can set the size of the preset overlapping degree threshold according to the actual situation, which is not limited here. When the overlapping degree is greater than the preset overlapping degree threshold, the corresponding cell region is taken as the overlapping cell region.

[0040] So far, the overlapping cell regions in the cell display image are screened out.

[0041] Step S3: According to the length of each edge line in the overlapping cell region, the curvature of the edge pixels on each edge line, and the curvature of the intersection pixels of each edge line with other edge lines, obtain the exposed edge lines of the covered cells in the overlapping cell region.

[0042] In the overlapping cell region, one cell will be partially covered by another cell. As Figure 4 shown in the local image of cell overlap, it can be seen from Figure 4 that a part of the edge line of the covered cell, that is, the exposed edge line, is normally displayed in the cell display image. The length of the exposed edge line is shorter than that of the complete edge line of the uncovered cell. At the same time, since the covered cell is covered by another cell on the surface, the degree of undulation of the edge line at the junction of the exposed edge line of the covered cell and other edge lines is relatively large, that is, the curvature of the intersection pixels of the exposed edge line of the covered cell and other edge lines is significantly greater than the curvature of the edge pixels on the exposed edge line of the covered cell (excluding the intersection pixels of the exposed edge line of the covered cell and other edge lines). Therefore, in this embodiment, according to the length of each edge line in the overlapping cell region, the curvature of the edge pixels on each edge line, and the curvature of the intersection pixels of each edge line with other edge lines, the exposed edge lines of the covered cells in the overlapping cell region are obtained.

[0043] Preferably, in a feasible implementation manner of this embodiment, for the method of obtaining the exposed edge line, please refer to Figure 5 which shows a flowchart of a method for obtaining an exposed edge line provided in this embodiment. The method includes the following steps: Step S301: According to the length of each edge line in each overlapping cell region, the curvature of the edge pixels on each edge line, and the curvature of the intersection pixels of each edge line with other edge lines, obtain the exposure degree of each edge line in each overlapping cell region.

[0044] When the length of an edge line in a certain overlapping cell region is short, and the curvature of the intersection pixel points of this edge line with other edge lines is significantly greater than the curvature of the edge pixel points on this edge line, it indicates that this edge line is more likely to be the exposed edge line of the covered cell. It should be noted that the edge pixel points on this edge line do not include the intersection pixel points of this edge line with other edge lines. Therefore, in this embodiment, according to the length of each edge line, the curvature of the edge pixel points on each edge line, and the curvature of the intersection pixel points of each edge line with other edge lines in each overlapping cell region, the exposure degree of each edge line in each overlapping cell region is obtained. The greater the exposure degree, the more likely the corresponding edge line is to be the exposed edge line of the covered cell.

[0045] Preferably, in a feasible implementation manner of this embodiment, the method for obtaining the exposure degree is as follows: For any edge line in any overlapping cell region, obtain the average value of the curvatures of all edge pixel points on this edge line as the curvature analysis value of this edge line; obtain the average value of the curvatures of the intersection pixel points of this edge line with other edge lines in this overlapping cell region as the curvature reference value; when the difference between the curvature analysis value and the curvature reference value is greater, this edge line is more likely to be an exposed edge line. Furthermore, obtain the absolute value of the difference between the curvature analysis value and the curvature reference value as the first difference; the greater the first difference, the more likely this edge line is to be an exposed edge line. At the same time, when this edge line is shorter, that is, the number of edge pixel points on this edge line is smaller, it also indicates that this edge line is more likely to be an exposed edge line. Furthermore, in this embodiment, the result of normalizing the ratio of the first difference to the number of edge pixel points on this edge line is used as the exposure degree of this edge line. Among them, the calculation formula for the exposure degree is: ; In the formula, is the exposure degree of the b-th edge line in the a-th overlapping cell region; is the curvature analysis value of the b-th edge line in the a-th overlapping cell region; is the curvature reference value corresponding to the b-th edge line in the a-th overlapping cell region; is the number of edge pixel points on the b-th edge line in the a-th overlapping cell region; norm is the normalization function; is the first difference; is the absolute value function.

[0046] Thus, the exposure degree of each edge line in each overlapping cell region is obtained.

[0047] Step S302: When the exposure degree is greater than the preset exposure degree threshold, the corresponding edge line is used as the exposed edge line.

[0048] It is known that the greater the degree of exposure, the more likely the corresponding edge line is the exposed edge line of the covered cell. Therefore, in this embodiment, the preset exposure degree threshold is set to 0.6. The implementer can set the size of the preset exposure degree threshold according to the actual situation, which is not limited here. When the exposure degree is greater than the preset exposure degree threshold, the corresponding edge line is regarded as the exposed edge line.

[0049] So far, the exposed edge lines of the covered cells in each overlapping cell region are determined.

[0050] In another embodiment, considering that due to the instability of the environmental light conditions, there will be noise in the cell display image. The noise usually appears as irregular bright spots or dark patches, and there will also be corresponding edge lines. Therefore, the exposed edge line may be a pseudo-edge line formed by the noise. In this embodiment, in order to accurately classify the cells in the cell display image, it is necessary to determine the morphology of each cell in the cell display image. Therefore, it is necessary to further analyze the exposed edge line to avoid the exposed edge line being a pseudo-edge line formed by the noise, which affects the accuracy of the subsequent automatic classification and detection of the morphology of peripheral blood cells. It is known that the noise is randomly distributed bright spots or dark patches, and the brightness change between the region corresponding to the noise and the surrounding background region is abrupt. Therefore, there is an obvious difference between the gray values of the edge pixel points on the pseudo-edge line corresponding to the noise and the gray values of other pixel points in its local background region. Because the cell surface usually has a certain transparency or film structure, the gray value difference between the edge pixel points on the cell edge line and the pixel points in its local background region is smoothly gradual. Therefore, in this embodiment, according to the gray difference between each edge pixel point on each exposed edge line and its preset neighborhood pixel points, the true exposed edge line is obtained.

[0051] Preferably, in a feasible implementation manner of this embodiment, the method for obtaining the true exposed edge line is as follows: for any edge pixel point on any exposed edge line, the straight line perpendicular to the tangent line of the edge pixel point is used as the target straight line, and the preset number of pixel points located on the target straight line and closest to the edge pixel point outside the covered cell corresponding to the exposed edge line are used as the preset neighborhood pixel points of the edge pixel point. In this embodiment, the preset number is set to 20, and the implementer can set the size of the preset number according to the actual situation, which is not limited here. The absolute value of the difference between the gray value of the edge pixel point and the average gray value of its preset neighborhood pixel points is used as the degree of brightness mutation of the edge pixel point; the greater the degree of brightness mutation, the more likely the edge pixel point is generated by noise. In order to comprehensively analyze whether the exposed edge line is a true exposed edge line, the result of negative correlation and normalization of the sum of the degrees of brightness mutation of all edge pixel points on the exposed edge line is used as the brightness smoothness of the exposed edge line; the greater the brightness smoothness, the more likely the exposed edge line is the true exposed edge line corresponding to the cell. Therefore, in this embodiment, the preset brightness smoothness threshold is set to 0.7, and the implementer can set the size of the preset brightness smoothness threshold according to the actual situation, which is not limited here. When the brightness smoothness is greater than the preset brightness smoothness threshold, the corresponding exposed edge line is used as the true exposed edge line. Thus, the true exposed edge line in the overlapping cell region is determined, effectively avoiding the interference of noise.

[0052] Among them, the calculation formula for the brightness smoothness is: ; in the formula, is the brightness smoothness of the m-th exposed edge line in the v-th overlapping cell region; is the number of edge pixel points on the m-th exposed edge line in the v-th overlapping cell region; is the gray value of the r-th edge pixel point on the m-th exposed edge line in the v-th overlapping cell region; is the average gray value of the preset neighborhood pixel points of the r-th edge pixel point on the m-th exposed edge line in the v-th overlapping cell region; is the degree of brightness mutation of the r-th edge pixel point on the m-th exposed edge line in the v-th overlapping cell region; is the absolute value function; exp is the exponential function with the natural constant as the base.

[0053] It should be noted that in this embodiment, the subsequent exposed edge lines are all defaulted to be true exposed edge lines, effectively avoiding the influence of noise generation.

[0054] Step S4: Based on the curvature and endpoints of each exposed edge line, obtain the virtual covered edge line of the covered cell corresponding to each exposed edge line, determine the complete cell edge line of each covered cell, and perform automatic classification detection of peripheral blood cell morphology.

[0055] It is known that the automatic classification detection of peripheral blood cell morphology needs to be classified according to the complete morphology of each cell. Therefore, in this embodiment, it is necessary to obtain the edge line covered by the covered cell corresponding to each exposed edge line. The edge line covered by the covered cell should be consistent with the curvature change trend of the exposed edge line of the covered cell. Therefore, in this embodiment, based on the curvature and endpoints of each exposed edge line, the virtual covered edge line of the covered cell corresponding to each exposed edge line, that is, the edge line covered by the covered cell, is obtained. Among them, the method of obtaining the curvature of the edge line is a well-known technology and will not be elaborated.

[0056] Preferably, in a realizable manner of this embodiment, the method for obtaining the virtual covered edge line is as follows: for any exposed edge line, the pixel points at both endpoints of the exposed edge line are used as target pixel points; for any target pixel point, the eight-neighborhood pixel points of the target pixel point that the exposed edge line does not pass through are used as the adjacent pixel points of the target pixel point, ensuring that the adjacent pixel points of the target pixel point must not be passed by the exposed edge line; further, the exposed edge line is smoothly connected to each adjacent pixel point of the target pixel point through Bezier curve technology to obtain the reference edge line corresponding to each adjacent pixel point of the target pixel point; among them, Bezier curve technology is a well-known technology and will not be elaborated. The absolute value of the difference in curvature between the reference edge line corresponding to each adjacent pixel point of the target pixel point and the exposed edge line is used as the second difference; the smaller the second difference, the more consistent the curvature change trend of the corresponding reference edge line and the exposed edge line. Therefore, in this embodiment, the adjacent pixel points on the reference edge line corresponding to the smallest second difference are used as the first virtual edge pixel points of the exposed edge line; it should be noted that there must be only one smallest second difference; The exposed edge line is smoothly connected to the first virtual edge pixel point through Bezier curve technology to obtain the first reference exposed edge line; the eight-neighborhood pixels of the first virtual edge pixel point not passed by the first reference exposed edge line are used as the adjacent pixels of the first virtual edge pixel point; the first reference exposed edge line is smoothly connected to each adjacent pixel of the first virtual edge pixel point through Bezier curve technology to obtain the reference edge line corresponding to each adjacent pixel of the first virtual edge pixel point; the absolute value of the difference in curvature between the reference edge line corresponding to each adjacent pixel of the first virtual edge pixel point and the first reference exposed edge line is used as the third difference; the smaller the third difference, the more consistent the curvature change trend of the corresponding reference edge line and the first reference exposed edge line. Therefore, the adjacent pixel on the reference edge line corresponding to the smallest third difference is used as the second virtual edge pixel point of the exposed edge line; it should be noted that there must be only one smallest third difference; and so on, each virtual edge pixel point of the exposed edge line is obtained until the other target pixel point of the exposed edge line is reached; finally, the virtual edge pixels obtained with each target pixel point as the starting point are curve-fitted by the least squares method, and the obtained curve is used as the virtual coverage edge line of the covered cell corresponding to the exposed edge line. Among them, the least squares method is a well-known technology and will not be elaborated here.

[0057] So far, the virtual coverage edge line of the covered cell corresponding to each exposed edge line is obtained.

[0058] The exposed edge line and the virtual coverage edge line of each covered cell are combined into a closed edge line to determine the complete cell edge line of each covered cell, that is, the morphology of each covered cell is determined, which is beneficial to accurately identify and classify the cells in the cell display image subsequently, and effectively solves the problem of inaccurate cell classification due to cell overlap. Finally, using the convolutional neural network deep learning model, according to the morphology of each cell in the cell display image, the cells in the cell display image are automatically identified and classified, and the full-automatic classification detection of peripheral blood cell morphology is accurately completed. Among them, the convolutional neural network deep learning model is a well-known technology and will not be elaborated here.

[0059] In summary, in this embodiment, the cell region in the cell display image of the peripheral blood is obtained; according to the morphology of the cell region and the curvature of the edge pixel points in the cell region, the overlapping cell region is obtained; according to the length of each edge line in the overlapping cell region, the curvature of the edge pixel points on each edge line, and the curvature of the intersection pixel points of each edge line with other edge lines, the exposed edge line of the covered cell is obtained; according to the curvature and endpoints of the exposed edge line, the complete cell edge line of the covered cell is determined, and the full-automatic classification detection of the peripheral blood cell morphology is carried out. By obtaining the complete cell edge line of the covered cell, the present invention determines the morphology of the covered cell, avoids the inaccurate automatic recognition and classification of cells caused by cell overlap, and ensures the accuracy of the full-automatic classification detection of the peripheral blood cell morphology.

[0060] Embodiment 2: The present invention also provides a full-automatic classification detection system for peripheral blood cell morphology. Please refer to Figure 6 , which shows the structural diagram of a full-automatic classification detection system for peripheral blood cell morphology provided by an embodiment of the present invention. The system includes: an acquisition module 10, an overlapping cell region acquisition module 20, an exposed edge line acquisition module 30, and a detection module 40.

[0061] The acquisition module 10 is configured to acquire the cell region in the cell display image of the peripheral blood.

[0062] The overlapping cell region acquisition module 20 is configured to acquire the overlapping cell region according to the morphology of each cell region and the curvature fluctuation of the edge pixel points in each cell region.

[0063] The exposed edge line acquisition module 30 is configured to acquire the exposed edge line of the covered cell in the overlapping cell region according to the length of each edge line in the overlapping cell region, the curvature of the edge pixel points on each edge line, and the curvature of the intersection pixel points of each edge line with other edge lines.

[0064] The detection module 40 is configured to obtain the virtual covered edge line of the covered cell corresponding to each exposed edge line according to the curvature and endpoints of each exposed edge line, determine the complete cell edge line of each covered cell, and perform the full-automatic classification detection of the peripheral blood cell morphology.

[0065] It should be noted that: For the system provided in the above embodiments, only the division of the above functional modules is used as an example for illustration. In practical applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the computer device is divided into different functional modules to complete all or part of the functions described above. In addition, an automatic full - scale classification detection system for peripheral blood cell morphology and an embodiment of an automatic full - scale classification detection method for peripheral blood cell morphology provided in the above embodiments belong to the same concept. For the specific implementation process, please refer to the method embodiments and will not be elaborated here.

[0066] Embodiment 3: The present invention also proposes an automatic full - scale classification detection device for peripheral blood cell morphology. The device includes a memory and a processor. Among them, an executable program code is stored in the memory, and the processor is used to call and execute the executable program code to execute an automatic full - scale classification detection method for peripheral blood cell morphology provided in an embodiment of the present application. The device may specifically be a chip, a component or a module. The chip may include a connected processor and a memory; among them, the memory is used to store instructions. When the processor calls and executes the instructions, the chip can execute an automatic full - scale classification detection method for peripheral blood cell morphology provided in the above embodiments.

[0067] In addition, embodiments of the present application also protect a computer device. Please refer to Figure 7 , the computer device includes a memory 401, a processor 402, and a computer program 403 stored in the memory 401 and running on the processor 402. Among them, when the processor 402 executes the computer program 403, the computer device can execute any of the automatic full - scale classification detection methods for peripheral blood cell morphology introduced above.

[0068] Embodiment 4: This embodiment also provides a computer - readable storage medium. Computer program code is stored in the computer - readable storage medium. When the computer program code runs on a computer, the computer is enabled to execute the above - related method steps to implement an automatic full - scale classification detection method for peripheral blood cell morphology provided in the above embodiments.

[0069] Embodiment 5: This embodiment also provides a computer program product. When the computer program product runs on a computer, the computer is enabled to execute the above - related steps to implement an automatic full - scale classification detection method for peripheral blood cell morphology provided in the above embodiments.

[0070] Among them, the device, computer-readable storage medium, computer program product or chip provided in this embodiment are all used to execute the corresponding method provided above. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method provided above, and will not be elaborated here.

[0071] It should be noted that the above sequence of embodiments of the present invention is only for description and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the particular order or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0072] Each embodiment in this specification is described in a progressive manner, and the same or similar parts among the embodiments can be referred to each other. The key point of each embodiment is to illustrate the differences from other embodiments.

Claims

1. An automatic classification detection method for peripheral blood cell morphology, characterized in that, The method includes the following steps: Obtain the cell regions in the cell display image of peripheral blood; Obtain the overlapping cell regions according to the morphology of each cell region and the curvature fluctuation of the edge pixel points in each cell region; Obtain the exposed edge lines of the covered cells in the overlapping cell regions according to the length of each edge line in the overlapping cell regions, the curvature of the edge pixel points on each edge line, and the curvature of the intersection pixel points of each edge line with other edge lines; Obtain the virtual covered edge lines corresponding to the covered cells according to the curvature and endpoints of each exposed edge line, determine the complete cell edge lines of each covered cell, and perform automatic full-classification detection of the morphology of peripheral blood cells.

2. The automatic classification detection method for peripheral blood cell morphology according to claim 1, characterized in that, The method for obtaining the overlapping cell regions is as follows: Obtain the shape parameters of each cell region according to the shape of each cell region; Obtain the edge undulation degree of each cell region according to the curvature fluctuation of the edge pixel points in each cell region; Obtain the overlapping degree of each cell region according to the shape parameters and edge undulation degree of each cell region; wherein, the shape parameter and the overlapping degree are negatively correlated, and the edge undulation degree and the overlapping degree are positively correlated; When the overlapping degree is greater than the preset overlapping degree threshold, the corresponding cell region is used as the overlapping cell region.

3. The automatic classification detection method for peripheral blood cell morphology according to claim 2, characterized in that, The method for obtaining the shape parameters is as follows: For any cell region, take the ratio of the area of the cell region to the square of the perimeter of the cell region as the first value; Take the product of a specified constant and the first value as the shape parameter of the cell region.

4. The automatic classification detection method for peripheral blood cell morphology according to claim 2, characterized in that, The method for obtaining the edge undulation degree is as follows: For any cell region, obtain the curvature of each edge pixel point in the cell region, and all of them are used as the reference curvatures; Take the difference between the maximum reference curvature and the minimum reference curvature as the first fluctuation degree; Take the product of the variance of the reference curvatures and the first fluctuation degree as the edge undulation degree of the cell region.

5. The automatic classification detection method for peripheral blood cell morphology according to claim 1, characterized in that, The method for obtaining the exposed edge lines is as follows: Obtain the exposure degree of each edge line in each overlapping cell region according to the length of each edge line in each overlapping cell region, the curvature of the edge pixel points on each edge line, and the curvature of the intersection pixel points of each edge line with other edge lines; When the exposure degree is greater than the preset exposure degree threshold, the corresponding edge line is used as the exposed edge line.

6. The automatic classification detection method for peripheral blood cell morphology according to claim 5, characterized in that, The method for obtaining the exposure degree is as follows: For any edge line in any overlapping cell region, obtain the mean value of the curvatures of all the edge pixel points on the edge line as the curvature analysis value of the edge line; Obtain the mean value of the curvatures of the intersection pixel points of the edge line with other edge lines in the overlapping cell region as the curvature reference value; Take the difference between the curvature analysis value and the curvature reference value as the first difference; Take the result of normalizing the ratio of the first difference to the number of edge pixel points on the edge line as the exposure degree of the edge line.

7. The automatic classification detection method for peripheral blood cell morphology according to claim 1, characterized in that, The method for obtaining the exposed edge lines further includes: Obtain the true exposed edge lines according to the gray-scale difference between each edge pixel point on each exposed edge line and its preset neighborhood pixel points.

8. The automatic classification detection method for peripheral blood cell morphology according to claim 7, characterized in that,The method for obtaining the true exposed edge lines is: For any edge pixel point on any exposed edge line, the straight line perpendicular to the tangent line of the edge pixel point is taken as the target line, and the preset number of pixel points located on the target line and closest to the edge pixel point outside the covered cell corresponding to the exposed edge line are taken as the preset neighborhood pixel points of the edge pixel point; Obtain the difference between the gray value of the edge pixel point and the average gray value of its preset neighborhood pixel points as the degree of brightness mutation of the edge pixel point; Take the result of negative correlation and normalization of the sum of the degrees of brightness mutation of all edge pixel points on the exposed edge line as the brightness smoothness of the exposed edge line; When the brightness smoothness is greater than the preset brightness smoothness threshold, the corresponding exposed edge line is taken as the true exposed edge line.

9. The full-automatic classification detection method for the morphology of peripheral blood cells according to claim 1, characterized in that, The method for obtaining the virtual covered edge line is as follows: For any exposed edge line, the pixel points at both ends of the exposed edge line are taken as target pixel points; For any target pixel point, the eight-neighborhood pixel points of the target pixel point not passed by the exposed edge line are taken as the adjacent pixel points of the target pixel point; Smoothly connect the exposed edge line with each adjacent pixel point of the target pixel point through Bezier curve technology to obtain the reference edge line corresponding to each adjacent pixel point of the target pixel point; Obtain the difference in curvature between the reference edge line corresponding to each adjacent pixel point of the target pixel point and the exposed edge line as the second difference; Take the adjacent pixel points on the reference edge line corresponding to the smallest second difference as the first virtual edge pixel point of the exposed edge line; Smoothly connect the exposed edge line with the first virtual edge pixel point through Bezier curve technology to obtain the first reference exposed edge line; Take the eight-neighborhood pixel points of the first virtual edge pixel point not passed by the first reference exposed edge line as the adjacent pixel points of the first virtual edge pixel point; Smoothly connect the first reference exposed edge line with each adjacent pixel point of the first virtual edge pixel point through Bezier curve technology to obtain the reference edge line corresponding to each adjacent pixel point of the first virtual edge pixel point; Obtain the difference in curvature between the reference edge line corresponding to each adjacent pixel point of the first virtual edge pixel point and the first reference exposed edge line as the third difference; Take the adjacent pixel points on the reference edge line corresponding to the smallest third difference as the second virtual edge pixel point of the exposed edge line; And so on, obtain each virtual edge pixel point of the exposed edge line until reaching the other target pixel point of the exposed edge line; Curve fit the virtual edge pixel points obtained with each target pixel point as the starting point through the least squares method, and take the obtained curve as the virtual covered edge line of the covered cell corresponding to the exposed edge line.

10. The full-automatic classification detection method for the morphology of peripheral blood cells according to claim 1, characterized in that, The method for obtaining the cell region is as follows: Obtain the cell region in the cell display image through the Canny edge detection algorithm and the connected component algorithm.

Images