CN112697658A - Memory, electron microscope particle geometric property determination method, device and apparatus - Google Patents

Memory, electron microscope particle geometric property determination method, device and apparatus Download PDF

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CN112697658A
CN112697658A CN201911008893.3A CN201911008893A CN112697658A CN 112697658 A CN112697658 A CN 112697658A CN 201911008893 A CN201911008893 A CN 201911008893A CN 112697658 A CN112697658 A CN 112697658A
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particle
matrix
gray
electron microscope
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王阔
张艳侠
黄玉洪
马蕊英
黄新露
桂兴华
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Sinopec Dalian Petrochemical Research Institute Co ltd
China Petroleum and Chemical Corp
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China Petroleum and Chemical Corp
Sinopec Dalian Research Institute of Petroleum and Petrochemicals
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Abstract

The invention discloses a memory, an electron microscope particle geometric property determination method, an electron microscope particle geometric property determination device and an electron microscope particle geometric property determination device, wherein the method comprises the following steps: respectively generating corresponding gray image information according to each particle image; establishing a corresponding relation between image pixels and an image observation scale according to the scale of the particle image and the scale of the gray matrix; converting the gray level image into a binary image according to a preset gray level image threshold value, and generating a corresponding binary image matrix; clustering separated points in the binary image to obtain pixel positions and pixel quantity information of particles in the gray level image; acquiring the geometric properties of particles in the gray level image; the method and the device have the advantages that the frequency distribution calculation is carried out on the equivalent radius and the NND of the obtained particle system to obtain the overall geometric information of the particle system observed by the material electron microscope, the instability of the judgment result caused by subjective judgment during manual screening is eliminated, and the measurement accuracy and the stability of the characteristics such as the granularity, the dispersion form and the like of the catalytic material are improved.

Description

Memory, electron microscope particle geometric property determination method, device and apparatus
Technical Field
The invention relates to the field of catalyst materials, in particular to a method, equipment and a device for measuring geometrical properties of particles of a memory and an electron microscope.
Background
With the development of modern materials science, various novel materials with specific functions are more endlessly developed. Wherein, the appearance of the novel catalytic material greatly improves the catalytic performance of the related catalyst. For catalytic materials, various characterization methods are increasingly becoming indispensable and necessary means for the research of catalytic materials.
The catalytic performance of a typical catalytic material is often closely related to its surface characteristics, particle size, and dispersion morphology. Particularly, the electron microscope method is the most intuitive and reliable method for analyzing and measuring the above characteristics of the catalytic material, that is, by comparing and analyzing the gray level image obtained by the electron microscope, researchers can often effectively compare and analyze the characteristics of the catalytic material, such as granularity, dispersion form and the like, so as to provide a powerful support for the research and development of novel materials.
The inventor finds that the measurement and analysis methods related to the characteristics of the catalytic material, such as particle size and dispersion morphology, in the prior art have at least the following defects:
first, to obtain and characterize the structure and distribution of the catalytic material, the area of imaging involved in electron microscopy imaging of the catalytic material may be relatively extensive. Therefore, the number of photos corresponding to the image files may be huge, and the information of the catalytic material contained in each image is also complicated, so that a large amount of manual observation and screening work is required, the workload is huge, and many 'subjective' factors are often brought into the manual screening work process, so that the observation and analysis results of different images of the same type of sample and even the same image have large difference, and the measurement accuracy and stability of the characteristics such as the granularity and the dispersion form of the catalytic material are influenced.
The information disclosed in this background section is only for enhancement of understanding of the general background of the invention and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art.
Disclosure of Invention
The invention aims to improve the measurement accuracy and stability of the characteristics of the catalytic material such as particle size, dispersion form and the like.
The invention provides a method for measuring geometrical properties of particles of an electron microscope, which comprises the following steps:
s11, respectively generating corresponding gray level image information including a gray level image described by the gray level matrix data according to each particle image obtained from the electron microscope;
s12, establishing a corresponding relation between image pixels and image observation scales according to scales of the particle images and the gray matrix scales;
s13, converting the gray level image into a binary image according to a preset gray level image threshold value, and generating a corresponding binary image matrix;
s14, clustering the separated points in the binary image to obtain the pixel position and the pixel quantity information of each particle in the gray level image;
s15, acquiring the geometric properties of the particles in the gray-scale image, including: calculating the gravity center position, area and equivalent radius of each particle; calculating the nearest neighbor near distance NND of the particle system according to the gravity center position of each particle;
and S16, performing frequency distribution calculation on the equivalent radius and the NND of the obtained particle system to obtain the overall geometric information of the particle system observed by the electron microscope.
In the present invention, the format of the grain image includes:
one of BMP format, HDF format, PCX format, JPEG format and TIFF format and any mixture thereof.
In the present invention, the grayscale image described by the grayscale matrix data includes:
the data structure is a fluid 8 type two-dimensional matrix, and the data range is [0,255 ].
In the present invention, the grayscale image information includes:
the size of two dimensions of the fluid 8 type two-dimensional matrix and all the gray data values of the fluid 8 type two-dimensional matrix.
In the present invention, the establishing of the corresponding relationship between the image pixels and the image observation scale according to the scale and the gray matrix scale of the particle image includes:
and S21, measuring the length and width dimensions a and b of the gray-scale image.
S22, calculating the corresponding relation p between the image pixels of the gray-scale image and the image observation scale, wherein the corresponding relation p represents the observation scale represented by each pixel in the particle image; the calculation formula includes formula (1) or formula (2):
p ═ R × a/m formula (1);
p ═ R × b/n formula (2);
wherein R is the number of scales, and m and n are the number scales of two scales of the gray value matrix.
In the present invention, the converting the grayscale image into a binary image according to a preset grayscale image threshold and generating a corresponding binary image matrix includes:
s31, presetting a fluid 8 type data with the gray level image threshold value between 0 and 255;
s32, converting the gray image into a binary image, comprising: and when the gray value of the gray value matrix of the gray image at a certain point is smaller than the threshold value of the gray image, setting the matrix element at the point to be 0, otherwise setting the matrix element at the point to be 1.
In the present invention, the obtaining of the pixel position and the pixel number information of each particle in the grayscale image by clustering the points separated from the binary image includes:
s41, setting the binary matrix of the binary image matrix as Mm,n。
S42, traversing the twoElement matrix Mm,nIf its element is equal to 1, the position coordinate [ i, j ] of the matrix element is recorded]Generating a position matrix Np,2(ii) a Wherein p is the binary matrix Mm,nThe number of elements in the middle matrix element is equal to 1;
s43, according to the binary matrix Mm,nIs based on the position matrix Np,2Each row vector [ i, j ] of]Sequentially recording the coordinates of adjacent elements of the matrix element and recording; the determination rule of the adjacent element coordinate comprises the following steps:
1. when i is 1 and j is 1, the adjacent element coordinates are { [1,2], [2,1], [2,2] };
2. when i ═ m and j ═ 1, adjacent element coordinates are { [ m-1,1], [ m,2], [ m-1,2] };
3. when i is 1 and j is n, the adjacent element coordinates are { [1, n-1], [2, n ], [2, n-1] };
4. when i ═ m and j ═ n, the adjacent element coordinates are { [ m-1, n ], [ m, n-1], [ m-1, n-1] };
5. when i is 1 and j is not equal to n, the adjacent element coordinates are { [2, j-1], [2, j ], [2, j +1], [1, j-1], [1, j +1] };
6. when i ═ m and j ≠ 1 and j ≠ n, the adjacent element coordinates are { [ m-1, j-1], [ m-1, j ], [ m-1, j +1], [ m, j-1], [ m, j +1] };
7. when j is 1 and i is not equal to m, the adjacent element coordinates are { [ i-1,2], [ i,2], [ i +1,2], [ i-1,1], [ i +1,1] };
8. when j is equal to n, i is equal to 1, and i is equal to m, the adjacent element coordinates are { [ i-1, n-1], [ i, n-1], [ i +1, n-1], [ i-1, n ], [ i +1, n ] };
9. when i, j belong to other cases, the adjacent element coordinates are { [ i-1, j-1], [ i-1, j ], [ i-1, j +1], [ i, j-1], [ i, j +1], [ i +1, j-1], [ i +1, j ] };
s44, sequentially importing the position matrix Np,2N (i,1) and N (i,2), find and record [ N (i,1), N (i,2)]Adjacent element coordinates of (2); two sets are generated simultaneously, the first set being a set PP of position coordinates of the pointiOf the form { { N (i,1), N (i,2) }; the second set is a set of position coordinates QQ of the neighboring elements of the pointi；
S45、Sequentially importing the position matrix Np,2And N (i,1) and N (i,2) and performing this step until the position matrix N is completed for each pairp,2The introduction of all the line vectors and the updating of the system are carried out, and the position and pixel quantity information of all the particles in the gray level image is obtained;
when the set { { N (i,1), N (i,2) } } is associated with the existing set QQiWhen all the intersections are empty sets, generating corresponding sets PP in turn according to the method of step S44iAnd set QQi；
When the set { { N (i,1), N (i,2) } } is associated with the existing set QQiIf there is a non-empty set for all intersections, all k sets QQ that are not empty with the set { { N (i,1), N (i,2) } } intersection will bejAnd the PP aggregatejSequentially extracting; all the QQQ setsjAnd PPjCalculating to generate a new position coordinate set NPP and a new adjacent element position coordinate set NQQ; adding a new position coordinate set PP and a position coordinate set QQ of a new adjacent element to the particle position and pixel information respectively by using the generated set NPP and the set NQQ to realize the QQ of the original setjAnd the PP aggregatejUpdating of (1); the operation rules of the set NPP and the set NQQ are shown in formula (3) and formula (4):
Figure BDA0002243580890000051
Figure BDA0002243580890000052
in the present invention, before the acquiring the geometric property of the particle in the grayscale image, the method further includes:
extracting position coordinates of all points of each particle contained in the set PP to obtain a corresponding abscissa { xn } and ordinate { yn }, and extracting an enclosure for each particle, including:
s51, sequencing all the points in the particles, wherein the sequencing principle is as follows: firstly, sorting all points from small to large according to an abscissa x; secondly, sorting all the points according to the ordinate y from small to large under the same abscissa condition; finally, forming an ordered sequence of points { p1, p2, …, pn } for all points, and defining a point O; the horizontal and vertical coordinates of the O point are defined as shown in formula (5) and formula (6), and include:
Figure BDA0002243580890000053
Figure BDA0002243580890000054
wherein n is the number of particles contained in each particle;
s52, forming initial set pi of upper enclosureup_set＝{p1,p2}；
S53, selecting a point p3 from the set { p3, p4, …, pn } to define two vectors; the definition is shown in formula (7) and formula (8), and includes:
Figure BDA0002243580890000061
Figure BDA0002243580890000062
the vector product of two vectors is calculated in the manner shown in equation (9), and includes:
Figure BDA0002243580890000063
if the product of scalar quantities
Figure BDA0002243580890000064
Defining the points p1, p2 and p3 as a right turn;
if p1, p2 and p3 make a right turn, p3 is added to IIup_setIn which a new set Π is formedup_set＝{p1，p2，p3}；
If p1, p2 and p3 do not form a right turn, p2 is moved from piup_setDeleting to form new set piup_set＝{p1，p3}；
S54, sequentially carrying out the operations of the steps S53 on the points P4 to Pn, and continuously updating the pi setup_setWhen all the point-aligning operations are completed, a set pi of the upper half enclosure is formedup_set；
S55, forming an initial set pi of lower enclosuredown_set＝{p(n),p(n-1)}；
S56, selecting a point p (n-2) from the set { p (n-2), p (n-3), …, p1} to define two vectors; the definition mode is shown as formula (10) and formula (11), and includes:
Figure BDA0002243580890000065
Figure BDA0002243580890000066
the vector product of two vectors is calculated in the manner shown in equation (12), and includes:
Figure BDA0002243580890000067
if the product of scalar quantities
Figure BDA0002243580890000071
Defining the points p (n), p (n-1) and p (n-2) to form a right turn;
if p (n), p (n-1) and p (n-2) form a right turn, then p (n-2) is added to the set Πdown_setIn which a new set Π is formedup_set＝{p(n),p(n-1),p(n-2)}；
If p (n), p (n-1) and p (n-2) do not form a right turn, p (n-1) is taken from the set Πdown_setDeleting to form new set pidown_set＝{p(n),p(n-2)}；
S57, moving points P (n-3) to P1Continuously updating the set pi in the step S56down_setWhen all the point-aligning operations are completed, a set pi of the lower half enclosure is formeddown_set；
S58, IIdown_setConnected to a set Πup_setAt the back, a new set pi is formedsetThe point corresponding to the set is the enclosure corresponding to the particle;
s59, repeating the following steps until the NPP set and NQQ set at the point are updated:
judging all points in NQQ set corresponding to each particle, if the point is in II set corresponding to the particlesetIf so, the point in the NQQ set is drawn into the NPP set of the point, and the point is deleted from the NQQ set; otherwise, the operation is not carried out on the set to which the point belongs.
In another aspect of the present invention, there is provided an electron microscope particle geometry measuring apparatus comprising:
a gradation information generating unit for generating corresponding gradation image information including a gradation image described by the gradation value matrix data, respectively, from each particle image acquired from the electron microscope;
the corresponding unit is used for establishing the corresponding relation between image pixels and the image observation scale according to the scale and the gray matrix scale of the particle image; the gray matrix scale is the size of two dimensions of the gray matrix; the scale is the ratio relation of the microscopic scale of the particle image and the indication length;
the matrix generation unit is used for converting the gray level image into a binary image according to a preset gray level image threshold value and generating a corresponding binary image matrix;
the clustering unit is used for clustering the points separated from the binary image to obtain the pixel position and the pixel quantity information of each particle in the gray level image;
a property calculation unit for obtaining geometric properties of particles in the grayscale image, comprising: respectively calculating the gravity center position of each particle according to the pixel position of each particle; calculating the area of each particle according to the pixel position and the pixel quantity information of each particle; calculating the equivalent radius of each particle according to the area of each particle and the corresponding relation between the image pixel and the image observation scale; calculating the nearest neighbor near distance NND of the particle system according to the gravity center position of each particle;
and the information statistical unit is used for carrying out frequency distribution calculation on the equivalent radius and the NND of the obtained particle system to obtain the overall geometric information of the particle system observed by the electron microscope.
In another aspect of the embodiments of the present invention, there is also provided a memory including a software program adapted to execute the steps of the above-mentioned electron microscope particle geometry determination method by a processor.
In another aspect of the embodiments of the present invention, there is also provided an electron microscope particle geometry measuring apparatus including a computer program stored on a memory, the computer program including program instructions which, when executed by a computer, cause the computer to perform the method of the above aspects and achieve the same technical effects.
Has the advantages that:
in the invention, a corresponding binary image matrix is generated by graying and binarizing a particle image, and then, the pixel position and the pixel quantity information of each particle in the grayscale image are obtained by clustering separated points in the binary image; then, the geometric property data of each particle is obtained by calculating the gravity center position, the area, the equivalent radius and the nearest neighbor close range of the particle system of each particle; and finally, under the condition of considering the scale, performing frequency distribution calculation on the equivalent radius and the NND of the obtained particle system to obtain the overall geometric information of the particle system observed by the material electron microscope.
Therefore, by using the technical scheme in the embodiment of the invention, manual observation and screening can be replaced, so that a large amount of manual workload is effectively reduced, and the instability of a judgment result caused by subjective judgment during manual screening is eliminated through a unified algorithm, thereby improving the measurement accuracy and stability of the characteristics such as the granularity, the dispersion form and the like of the catalytic material.
Furthermore, in the embodiment of the invention, in the process of clustering the separated points in the binary image to obtain the pixel position and the pixel quantity information of each particle in the gray level image, the separation of all the particles in the binary image is realized, so that when the geometric properties of the particles are calculated subsequently, a more accurate result can be obtained, and the measurement accuracy of the characteristics such as the particle size, the dispersion form and the like of the catalytic material is further improved.
The foregoing description is only an overview of the technical solutions of the present invention, and in order to make the technical means of the present invention more clearly understood and to make the technical means implementable in accordance with the contents of the description, and to make the above and other objects, technical features, and advantages of the present invention more comprehensible, one or more preferred embodiments are described below in detail with reference to the accompanying drawings.
Drawings
FIG. 1 is a diagram of the steps of the electron microscopy particle geometry determination method of the present invention;
FIG. 2 is a schematic diagram of an original image of a grain of an electron microscope according to the present invention;
FIG. 3 is a schematic diagram of a binary image converted from a grain image in the present invention;
FIG. 4 is a schematic diagram of an image obtained by clustering separated clusters of binary images in accordance with the present invention;
FIG. 5 is a schematic representation of an extracted graph of the appearance of the 3754 th particle in the present invention;
FIG. 6 is a schematic representation of a boundary inclusion extraction plot for the 3754 th particle in the present invention;
FIG. 7 is a schematic diagram of the filling of the internal cavity of the 3754 th particle in the present invention;
FIG. 8 is a schematic illustration of a graph showing the center of gravity of each particle in the present invention;
FIG. 9 is a schematic representation of the particle equivalent radius distribution of the present invention;
FIG. 10 is a schematic representation of a close proximity map of particles in the present invention;
FIG. 11 is a schematic view of the structure of an apparatus for measuring geometrical properties of electron microscope particles according to the present invention;
FIG. 12 is a schematic structural view of an apparatus for measuring geometrical properties of electron microscope particles according to the present invention.
Detailed Description
The following detailed description of the present invention is provided in conjunction with the accompanying drawings, but it should be understood that the scope of the present invention is not limited to the specific embodiments.
Throughout the specification and claims, unless explicitly stated otherwise, the word "comprise", or variations such as "comprises" or "comprising", will be understood to imply the inclusion of a stated element or component but not the exclusion of any other element or component.
Spatially relative terms, such as "below," "lower," "upper," "above," "upper," and the like, may be used herein for ease of description to describe one element or feature's relationship to another element or feature in the figures. It will be understood that the spatially relative terms are intended to encompass different orientations of the object in use or operation in addition to the orientation depicted in the figures. For example, if the items in the figures are turned over, elements described as "below" or "beneath" other elements or features would then be oriented "above" the elements or features. Thus, the exemplary term "below" can encompass both an orientation of below and above. The article may be otherwise oriented (rotated 90 degrees or at other orientations) and the spatially relative terms used herein should be interpreted accordingly.
In this document, the terms "first", "second", etc. are used to distinguish two different elements or portions, and are not used to define a particular position or relative relationship. In other words, the terms "first," "second," and the like may also be interchanged with one another in some embodiments.
Example one
In order to improve the accuracy and stability of measurement of the characteristics of catalytic material such as particle size and dispersion morphology, as shown in fig. 1, the embodiment of the present invention provides a method for measuring geometric properties of particles by an electron microscope, comprising the steps of:
s11, respectively generating corresponding gray level image information including a gray level image described by the gray level matrix data according to each particle image obtained from the electron microscope;
the technical scheme of the embodiment of the invention is illustrated by taking a catalyst used in a petrochemical enterprise as an example; that is, embodiments of the present invention may be used to analyze the surface characteristics, particle size, and dispersion morphology of a catalyst image to characterize and analyze the performance characteristics of the catalyst.
Specifically, after a particle image of the catalyst is acquired by an electron microscope, it is necessary to first perform gradation on the particle image and generate gradation image information corresponding to the particle image, that is, gradation value matrix data is included in the gradation image information. Fig. 2 shows a schematic representation of an original image of a grain by an electron microscope.
In practical applications, the format of the electron microscope or the collected grain image may be one of BMP format, HDF format, PCX format, JPEG format, and TIFF format, or any mixture thereof.
In an embodiment of the present invention, the data structure of the gray-scale image described by the gray-scale value matrix data may be a fluid 8 type two-dimensional matrix having a data range of [0,255 ]. The grayscale image information may specifically include the size of two dimensions of a fluid 8 type two-dimensional matrix and numerical information of all grayscale data of the matrix.
S12, establishing a corresponding relation between image pixels and image observation scales according to scales of the particle images and the gray matrix scales; the gray matrix scale is the size of two dimensions of the gray matrix; the scale is the ratio relation of the microscopic scale of the particle image and the indication length;
the specific way of establishing the corresponding relationship between the image pixels and the image observation scale may include:
the scale in the embodiment of the present invention is a ratio of the microscopic scale of the particle image to the indicated length, for example, when R is 1.5um/cm, it means that the image length of each 1cm represents the microscopic scale of 1.5 um.
The grayscale matrix scale in the embodiment of the present invention refers to the size of two dimensions of the grayscale matrix; such as may be represented as: the size of the grayscale matrix is mxn.
S21, when the length and width dimensions a and b of the gray scale image are measured.
S22, calculating the corresponding relation p between the image pixels of the gray-scale image and the image observation scale, wherein the corresponding relation p represents the observation scale represented by each pixel in the particle image; the calculation formula includes formula 1 or formula 2:
p ═ R × a/m (formula 1);
p ═ R × b/n (formula 2);
wherein R is the scale number, and m and n are the sizes of two dimensions of the gray matrix respectively;
s13, converting the gray level image into a binary image according to a preset gray level image threshold value, and generating a corresponding binary image matrix;
fig. 3 is a schematic diagram of a binary image obtained by conversion according to the original image in fig. 2, in which the scale of the gray-scale matrix is 1024x692x3, and the correspondence between the image pixels and the image observation scale is that the corresponding length of each pixel point is 0.011 um.
The specific steps can be realized through the following substeps:
s31, presetting a fluid 8 type data with the gray level image threshold value between 0 and 255;
the gray threshold of the set gray image of the electron microscope is a fluid 8 type data between 0 and 255, and the specific data is determined by the specific situation of the available image.
S32, converting the gray image into a binary image, comprising: and when the gray value of the gray value matrix of the gray image at a certain point is smaller than the threshold value of the gray image, setting the matrix element at the point to be 0, otherwise setting the matrix element at the point to be 1.
The conversion principle for converting the corresponding gray level image into the binary image in the embodiment of the invention comprises the following steps: when the gray data of the gray value matrix of the original gray image at a certain point is smaller than a preset gray image threshold value, setting the value of the matrix element at the point to be 0, otherwise setting the value of the matrix element at the point to be 1.
S14, clustering the separated points in the binary image to obtain the pixel position and the pixel quantity information of each particle in the gray level image;
in the present invention, the obtaining of the pixel position and the pixel number information of each particle in the grayscale image by clustering the points separated from the binary image includes:
s41, setting the binary matrix of the binary image matrix as Mm,n。
In a binary image matrix for representing the whole grain image comprising the grain and the background, wherein an element 0 represents the background part of the grain image and an element 1 represents the part of the grain in the grain image.
S42, traversing the binary matrix Mm,nIf its element is equal to 1, the position coordinate [ i, j ] of the matrix element is recorded]Generating a position matrix Np,2(ii) a Wherein p is the binary matrix Mm,nThe number of elements in the middle matrix element is equal to 1;
through traversal, the part of the binary image matrix representing the particle can be selected, and the position information of the part in the particle image can be recorded.
S43, according to the binary matrix Mm,nIs based on the position matrix Np,2Each row vector [ i, j ] of]Sequentially recording the coordinates of adjacent elements of the matrix element and recording; the determination rule of the adjacent element coordinate comprises the following steps:
1. when i is 1 and j is 1, the adjacent element coordinates are { [1,2], [2,1], [2,2] };
2. when i ═ m and j ═ 1, adjacent element coordinates are { [ m-1,1], [ m,2], [ m-1,2] };
3. when i is 1 and j is n, the adjacent element coordinates are { [1, n-1], [2, n ], [2, n-1] };
4. when i ═ m and j ═ n, the adjacent element coordinates are { [ m-1, n ], [ m, n-1], [ m-1, n-1] };
5. when i is 1 and j is not equal to n, the adjacent element coordinates are { [2, j-1], [2, j ], [2, j +1], [1, j-1], [1, j +1] };
6. when i ═ m and j ≠ 1 and j ≠ n, the adjacent element coordinates are { [ m-1, j-1], [ m-1, j ], [ m-1, j +1], [ m, j-1], [ m, j +1] };
7. when j is 1 and i is not equal to m, the adjacent element coordinates are { [ i-1,2], [ i,2], [ i +1,2], [ i-1,1], [ i +1,1] };
8. when j is equal to n, i is equal to 1, and i is equal to m, the adjacent element coordinates are { [ i-1, n-1], [ i, n-1], [ i +1, n-1], [ i-1, n ], [ i +1, n ] };
9. when i, j belong to other cases, the adjacent element coordinates are { [ i-1, j-1], [ i-1, j ], [ i-1, j +1], [ i, j-1], [ i, j +1], [ i +1, j-1], [ i +1, j ] };
in this step, the position information of each particle adjacency point can be given and recorded, and the number of the adjacency points corresponding to the particle points at different positions is different, and at most 8, and at least 2.
S44, sequentially importing the position matrix Np,2N (i,1) and N (i,2), find and record [ N (i,1), N (i,2)]Adjacent element coordinates of (2); two sets are generated simultaneously, the first set being a set PP of position coordinates of the pointiOf the form { { N (i,1), N (i,2) }; the second set is a set of position coordinates QQ of the neighboring elements of the pointi；
S45, sequentially importing the position matrix Np,2And N (i,1) and N (i,2) and performing this step until the position matrix N is completed for each pairp,2The introduction of all the line vectors and the updating of the system are carried out, and the position and pixel quantity information of all the particles in the gray level image is obtained;
when the set { { N (i,1), N (i,2) } } is associated with the existing set QQiWhen all the intersections are empty sets, generating corresponding sets PP in turn according to the method of step S44iAnd set QQi；
When the set { { N (i,1), N (i,2) } } is associated with the existing set QQiIf there is a non-empty set for all intersections, all k sets QQ that are not empty with the set { { N (i,1), N (i,2) } } intersection will bejAnd the PP aggregatejAre carried out in sequenceExtracting; all the QQQ setsjAnd PPjCalculating to generate a new position coordinate set NPP and a new adjacent element position coordinate set NQQ; adding a new position coordinate set PP and a position coordinate set QQ of a new adjacent element to the particle position and pixel information respectively by using the generated set NPP and the set NQQ to realize the QQ of the original setjAnd the PP aggregatejUpdating of (1); the operation rules of the set NPP and the set NQQ are shown in formula (3) and formula (4):
Figure BDA0002243580890000141
Figure BDA0002243580890000142
steps S44 and S45 in the embodiment of the present invention implement a clustering process based on the data obtained in steps S42 and S43, wherein step S44 is used to initialize the process, and step S45 is performed to perform the whole classification operation until all non-adjacent particles are separated from each other.
In the embodiment of the present invention, in order to obtain more accurate results when the geometric properties of the particles are calculated subsequently, the particle points missing due to various reasons in the binary image matrix may be filled by performing bounding volume extraction on each particle, in a manner as shown in steps S51 to S59, specifically:
extracting position coordinates of all points of each particle contained in the set PP to obtain a corresponding abscissa { xn } and ordinate { yn }, and extracting an enclosure for each particle, including:
s51, sequencing all the points in the particles, wherein the sequencing principle is as follows: firstly, sorting all points from small to large according to an abscissa x; secondly, sorting all the points according to the ordinate y from small to large under the same abscissa condition; finally, forming an ordered sequence of points { p1, p2, …, pn } for all points, and defining a point O; the horizontal and vertical coordinates of the O point are defined as shown in formula (5) and formula (6), and include:
Figure BDA0002243580890000151
Figure BDA0002243580890000152
wherein n is the number of particles contained in each particle;
s52, forming initial set pi of upper enclosureup_set＝{p1,p2}；
S53, selecting a point p3 from the set { p3, p4, …, pn } to define two vectors; the definition is shown in formula (7) and formula (8), and includes:
Figure BDA0002243580890000153
Figure BDA0002243580890000154
the vector product of two vectors is calculated in the manner shown in equation (9), and includes:
Figure BDA0002243580890000155
if the product of scalar quantities
Figure BDA0002243580890000156
Defining the points p1, p2 and p3 as a right turn;
if p1, p2 and p3 make a right turn, p3 is added to IIup_setIn which a new set Π is formedup_set＝{p1，p2，p3}；
If p1, p2 and p3 do not form a right turn, p2 is moved from piup_setDeleting to form new set piup_set＝{p1，p3}；
S54And sequentially carrying out the operation of step S53 on the points P4 to Pn, and continuously updating the set piup_setWhen all the point-aligning operations are completed, a set pi of the upper half enclosure is formedup_set；
S55, forming an initial set pi of lower enclosuredown_set＝{p(n),p(n-1)}；
S56, selecting a point p (n-2) from the set { p (n-2), p (n-3), …, p1} to define two vectors; the definition mode is shown as formula (10) and formula (11), and includes:
Figure BDA0002243580890000161
Figure BDA0002243580890000162
the vector product of two vectors is calculated in the manner shown in equation (12), and includes:
Figure BDA0002243580890000163
if the product of scalar quantities
Figure BDA0002243580890000164
Defining the points p (n), p (n-1) and p (n-2) to form a right turn;
if p (n), p (n-1) and p (n-2) form a right turn, then p (n-2) is added to the set Πdown_setIn which a new set Π is formedup_set＝{p(n),p(n-1),p(n-2)}；
If p (n), p (n-1) and p (n-2) do not form a right turn, p (n-1) is taken from the set Πdown_setDeleting to form new set pidown_set＝{p(n),p(n-2)}；
S57, sequentially carrying out the operations of the steps S56 on the points P (n-3) to P1, and continuously updating the pi setdown_setWhen all the point-aligning operations are completed, a set pi of the lower half enclosure is formeddown_set；
S58, mixingCollection IIdown_setConnected to a set Πup_setAt the back, a new set pi is formedsetThe point corresponding to the set is the enclosure corresponding to the particle;
s59, repeating the following steps until the NPP set and NQQ set at the point are updated:
judging all points in NQQ set corresponding to each particle, if the point is in II set corresponding to the particlesetIf so, the point in the NQQ set is drawn into the NPP set of the point, and the point is deleted from the NQQ set; otherwise, the operation is not carried out on the set to which the point belongs.
FIG. 4 is a schematic diagram of an image obtained after clustering of the separated clusters of the grayscale image according to the embodiment of the present invention; in the embodiment, the clustering calculation obtains 6809 independent particles, and different particles are marked by different gray scales. Taking the particle No. 3754 as an example, the position coordinates of the origin are shown in FIG. 5, and the boundary of the convex inclusion is shown in FIG. 6. However, due to some problems in the image capturing process, such that the inside of the inclusion has a hole, the convex inclusion after filling the hole can be obtained by the implementation of the above steps S51 to S59 as shown in fig. 7.
S15, acquiring the geometric properties of the particles in the gray-scale image, including: respectively calculating the gravity center position of each particle according to the pixel position of each particle; calculating the area of each particle according to the pixel position and the pixel quantity information of each particle; calculating the equivalent radius of each particle according to the area of each particle and the corresponding relation between the image pixel and the image observation scale; calculating the nearest neighbor near distance NND of the particle system according to the gravity center position of each particle;
since the particle structure obtained by a typical electron microscopy system is irregular, the particle morphology may differ greatly from the circular shape. The equivalent radius in the embodiment of the present invention means that each particle is equivalent to a circle, and the corresponding radius obtained by calculating the area of the circle through a circular area calculation formula is used to describe the size of the particle.
The particle system in the embodiment of the present invention is the nearest neighbor of the particle system included in the whole particle image.
In the embodiment of the present invention, acquiring the geometric property of the particle in the grayscale image may specifically include:
1) collecting the position coordinates of all pixel points of each particle Nk,ix,Nk,iy]In which N isk,ix,Nk,iyRespectively representing the abscissa and ordinate of the ith pixel point of the kth particle.
2) The formula for calculating the barycentric coordinates of particles is shown in equations (13) and (14):
Figure BDA0002243580890000171
Figure BDA0002243580890000181
wherein G isk,xAnd Gk,yRespectively, the abscissa and ordinate of the center of gravity of the kth particle. N is a radical ofkRepresenting the number of pixel points contained in the present particle.
Calculating the area of all particles based on the position and pixel number information of all particles, including:
1) the observation area of the whole image is calculated as shown in equation 15:
ST＝R2ab (15)
2) the area of each particle is calculated as shown in equation 16:
Figure BDA0002243580890000182
calculating an equivalent radius for each particle based on the area of each particle and the correspondence of image pixels to image observation dimensions, comprising:
1) the equivalent radius of each particle is calculated as shown in equation 17:
Figure BDA0002243580890000183
calculating the NND of the system based on the position of the center of gravity of each particle, comprising:
1) extracting barycentric location coordinates G of each particlek,xAnd Gk,y；
2) Calculating the distance between the gravity center of each particle and the gravity centers of all the particles except the particle in the system, and recording the distances in a vector form;
3) sorting the vectors generated in the step 2) from small to large, and selecting the minimum value as the NND of the particle;
4) repeating the steps 1) -3) to calculate the NND of each particle in turn;
and S16, performing frequency distribution calculation on the equivalent radius and the NND of the obtained particle system to obtain the overall geometric information of the particle system observed by the electron microscope.
Thus, geometric information of a catalyst or molecular sieve particle system obtained by different preparation methods can be obtained, such as the particle size, particle size distribution and other geometric information of the catalyst and catalytic materials; the method specifically comprises the following steps:
1) collecting images of each particle electron microscope under the same condition, and sequentially calculating and obtaining the number of particles, the equivalent radius of the particles and the NND of the particles of each particle system in each image;
2) the operations mentioned in 1) are repeated for a series of particle electron microscope images. Statistical information is obtained about the equivalent radii of the series of particle electron microscope images and the frequency distribution of the NND of the particles.
FIG. 8 is a schematic illustration of the center of gravity of each particle for an embodiment of the present invention; FIG. 9 is a schematic illustration of the equivalent radius distribution of particles in an example of the invention; FIG. 10 is a schematic representation of the NDD of the particles in an example of the invention.
In summary, in the embodiment of the present invention, a corresponding binary image matrix is generated by graying and binarizing a particle image, and then, the pixel position and the pixel number information of each particle in the grayscale image are obtained by clustering the separated points in the binary image; then, the geometric property data of each particle is obtained by calculating the gravity center position, the area, the equivalent radius and the nearest neighbor close range of the particle system of each particle; and finally, under the condition of considering the scale, performing frequency distribution calculation on the equivalent radius and the NND of the obtained particle system to obtain the overall geometric information of the particle system observed by the material electron microscope.
Therefore, by using the technical scheme in the embodiment of the invention, manual observation and screening can be replaced, so that a large amount of manual workload is effectively reduced, and the instability of a judgment result caused by subjective judgment during manual screening is eliminated through a unified algorithm, thereby improving the measurement accuracy and stability of the characteristics such as the granularity, the dispersion form and the like of the catalytic material.
Furthermore, in the embodiment of the invention, in the process of clustering the separated points in the binary image to obtain the pixel position and the pixel quantity information of each particle in the gray level image, the separation of all the particles in the binary image is realized, so that when the geometric properties of the particles are calculated subsequently, a more accurate result can be obtained, and the measurement accuracy of the characteristics such as the particle size, the dispersion form and the like of the catalytic material is further improved.
In order to demonstrate the effectiveness and stability of the algorithm of the present invention with respect to the overall image processing process, the processing results of the embodiments of the present invention are compared with manual processing by several different operators.
The original image, binary cluster image and particle size and nearest close range distribution image involved in the whole processing are shown in fig. 10.
Table 1: the manual retrieval method is compared with the method provided by the invention in effect:
Figure BDA0002243580890000201
table 2: comparing the effect of the manual searching method with that of the method mentioned in the present invention
Manual search analysis The method of the invention
Number of particles included in the system 870 870
Average particle diameter/um 0.052 0.049
Nearest neighbor near distance/um 0.1528 0.1534
Completion time About 3 hours 5 seconds
The analysis of table 2 shows that the technical solution in the embodiment of the present invention has a significant advantage of fast processing speed compared with the manual method.
Example two
In another aspect of the embodiment of the present invention, there is further provided an apparatus for measuring geometric properties of electron microscope particles, and fig. 2 shows a schematic structural diagram of the apparatus for measuring geometric properties of electron microscope particles according to the embodiment of the present invention, where the apparatus for measuring geometric properties of electron microscope particles is a device corresponding to the method for measuring geometric properties of electron microscope particles in the first embodiment corresponding to fig. 1, that is, the method for measuring geometric properties of electron microscope particles in the embodiment corresponding to fig. 1 is implemented by using a virtual device, and each virtual module constituting the apparatus for measuring geometric properties of electron microscope particles may be executed by an electronic device, such as a network device, a terminal device, or a server. Specifically, the electron microscope particle geometry measuring apparatus according to the embodiment of the present invention includes:
a gradation information generating unit 01 for generating corresponding gradation image information including a gradation image described by the gradation value matrix data, respectively, from each particle image acquired from the electron microscope;
a corresponding unit 02, configured to establish a corresponding relationship between image pixels and an image observation scale according to the scale and the gray matrix scale of the particle image; the gray matrix scale is the size of two dimensions of the gray matrix; the scale is the ratio relation of the microscopic scale of the particle image and the indication length;
the matrix generation unit 03 is configured to convert the grayscale image into a binary image according to a preset grayscale image threshold, and generate a corresponding binary image matrix;
a clustering unit 04, configured to cluster the separated points in the binary image to obtain pixel position and pixel number information of each particle in the grayscale image;
a property calculation unit 05, configured to obtain geometric properties of particles in the grayscale image, including: respectively calculating the gravity center position of each particle according to the pixel position of each particle; calculating the area of each particle according to the pixel position and the pixel quantity information of each particle; calculating the equivalent radius of each particle according to the area of each particle and the corresponding relation between the image pixel and the image observation scale; calculating the nearest neighbor near distance NND of the particle system according to the gravity center position of each particle;
and the information statistical unit 06 is used for performing frequency distribution calculation on the obtained equivalent radius and NND of the particle system to obtain the overall geometric information of the particle system observed by the electron microscope.
Since the working principle and the beneficial effects of the device for measuring the geometric properties of the electron microscope particles in the embodiment of the present invention have been described and illustrated in the method for measuring the geometric properties of the electron microscope particles corresponding to fig. 1, they can be referred to each other and are not described herein again.
EXAMPLE III
In an embodiment of the present invention, there is further provided a memory, wherein the memory includes a software program, and the software program is adapted to be executed by the processor to perform the steps of the method for determining geometry of particles of an electron microscope corresponding to fig. 1.
The embodiment of the present invention can be implemented by a software program, that is, by writing a software program (and an instruction set) for implementing each step in the method for measuring geometric properties of electron microscope particles corresponding to fig. 1, the software program is stored in a storage device, and the storage device is provided in a computer device, so that the software program can be called by a processor of the computer device to implement the purpose of the embodiment of the present invention.
Example four
In an embodiment of the present invention, there is also provided an electron microscope particle geometry determining apparatus, where the memory included in the electron microscope particle geometry determining apparatus includes a corresponding computer program product, and program instructions included in the computer program product, when executed by a computer, can make the computer execute the electron microscope particle geometry determining method according to the above aspects, and achieve the same technical effects.
Fig. 3 is a schematic diagram of a hardware structure of an electron microscope particle geometry measuring apparatus as an electronic apparatus according to an embodiment of the present invention, and as shown in fig. 3, the apparatus includes one or more processors 610, a bus 630, and a memory 620. Taking one processor 610 as an example, the apparatus may further include: input device 640, output device 650.
The processor 610, the memory 620, the input device 640, and the output device 650 may be connected by a bus or other means, such as the bus connection in fig. 3.
The memory 620, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and modules. The processor 610 executes various functional applications and data processing of the electronic device, i.e., the processing method of the above-described method embodiment, by executing the non-transitory software programs, instructions and modules stored in the memory 620.
The memory 620 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data and the like. Further, the memory 620 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory 620 optionally includes memory located remotely from the processor 610, which may be connected to the processing device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input device 640 may receive input numeric or character information and generate a signal input. The output device 650 may include a display device such as a display screen.
The one or more modules are stored in the memory 620 and, when executed by the one or more processors 610, perform:
s11, respectively generating corresponding gray level image information including a gray level image described by the gray level matrix data according to each particle image obtained from the electron microscope;
s12, establishing a corresponding relation between image pixels and image observation scales according to scales of the particle images and the gray matrix scales; the gray matrix scale is the size of two dimensions of the gray matrix; the scale is the ratio relation of the microscopic scale of the particle image and the indication length;
s13, converting the gray level image into a binary image according to a preset gray level image threshold value, and generating a corresponding binary image matrix;
s14, clustering the separated points in the binary image to obtain the pixel position and the pixel quantity information of each particle in the gray level image;
s15, acquiring the geometric properties of the particles in the gray-scale image, including: respectively calculating the gravity center position of each particle according to the pixel position of each particle; calculating the area of each particle according to the pixel position and the pixel quantity information of each particle; calculating the equivalent radius of each particle according to the area of each particle and the corresponding relation between the image pixel and the image observation scale; calculating the nearest neighbor near distance NND of the particle system according to the gravity center position of each particle;
and S16, performing frequency distribution calculation on the equivalent radius and the NND of the obtained particle system to obtain the overall geometric information of the particle system observed by the electron microscope.
The product can execute the method provided by the embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method. For technical details that are not described in detail in this embodiment, reference may be made to the method provided by the embodiment of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method can be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage device and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage device includes: a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a ReRAM, an MRAM, a PCM, a NAND Flash, a NOR Flash, a Memory, a magnetic disk, an optical disk, or other various media that can store program codes.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (11)

1. A method for measuring particle geometry in an electron microscope, comprising the steps of:
s11, respectively generating corresponding gray level image information including a gray level image described by the gray level matrix data according to each particle image obtained from the electron microscope;
s12, establishing a corresponding relation between image pixels and image observation scales according to scales of the particle images and the gray matrix scales;
s13, converting the gray level image into a binary image according to a preset gray level image threshold value, and generating a corresponding binary image matrix;
s14, clustering the separated points in the binary image to obtain the pixel position and the pixel quantity information of each particle in the gray level image;
s15, acquiring the geometric properties of the particles in the gray-scale image, including: calculating the gravity center position, area and equivalent radius of each particle; calculating the nearest neighbor near distance NND of the particle system according to the gravity center position of each particle;
and S16, performing frequency distribution calculation on the equivalent radius and the NND of the obtained particle system to obtain the overall geometric information of the particle system observed by the electron microscope.
2. The electron microscope particle geometry determination method of claim 1, wherein the format of the particle image comprises:
one of BMP format, HDF format, PCX format, JPEG format and TIFF format and any mixture thereof.
3. The electron microscopy particle geometry determination method of claim 1 wherein the grayscale image described by grayscale value matrix data comprises:
the data structure is a fluid 8 type two-dimensional matrix, and the data range is [0,255 ].
4. The electron microscope particle geometry determination method of claim 3, wherein the grayscale image information includes:
the size of two dimensions of the fluid 8 type two-dimensional matrix and all the gray data values of the fluid 8 type two-dimensional matrix.
5. The method of claim 4, wherein the establishing a correspondence between image pixels and image observation dimensions based on the scale and gray matrix dimensions of the particle image comprises:
and S21, measuring the length and width dimensions a and b of the gray-scale image.
S22, calculating the corresponding relation p between the image pixels of the gray-scale image and the image observation scale, wherein the corresponding relation p represents the observation scale represented by each pixel in the particle image; the calculation formula includes formula (1) or formula (2):
p ═ R × a/m formula (1);
p ═ R × b/n formula (2);
wherein R is the number of scales, and m and n are the number scales of two scales of the gray value matrix.
6. The method for determining geometrical properties of electron microscope particles according to claim 5, wherein the converting the gray scale image into a binary image according to a preset gray scale image threshold and generating a corresponding binary image matrix comprises:
s31, presetting a fluid 8 type data with the gray level image threshold value between 0 and 255;
s32, converting the gray image into a binary image, comprising: and when the gray value of the gray value matrix of the gray image at a certain point is smaller than the threshold value of the gray image, setting the matrix element at the point to be 0, otherwise setting the matrix element at the point to be 1.
7. The method of claim 6, wherein the obtaining pixel position and pixel number information for each particle in the grayscale image by clustering separated points in the binary image comprises:
s41, setting the binary image matrixIs Mm,n。
S42, traversing the binary matrix Mm,nIf its element is equal to 1, the position coordinate [ i, j ] of the matrix element is recorded]Generating a position matrix Np,2(ii) a Wherein p is the binary matrix Mm,nThe number of elements in the middle matrix element is equal to 1;
s43, according to the binary matrix Mm,nIs based on the position matrix Np,2Each row vector [ i, j ] of]Sequentially recording the coordinates of adjacent elements of the matrix element and recording; the determination rule of the adjacent element coordinate comprises the following steps:
1. when i is 1 and j is 1, the adjacent element coordinates are { [1,2], [2,1], [2,2] };
2. when i ═ m and j ═ 1, adjacent element coordinates are { [ m-1,1], [ m,2], [ m-1,2] };
3. when i is 1 and j is n, the adjacent element coordinates are { [1, n-1], [2, n ], [2, n-1] };
4. when i ═ m and j ═ n, the adjacent element coordinates are { [ m-1, n ], [ m, n-1], [ m-1, n-1] };
5. when i is 1 and j is not equal to n, the adjacent element coordinates are { [2, j-1], [2, j ], [2, j +1], [1, j-1], [1, j +1] };
6. when i ═ m and j ≠ 1 and j ≠ n, the adjacent element coordinates are { [ m-1, j-1], [ m-1, j ], [ m-1, j +1], [ m, j-1], [ m, j +1] };
7. when j is 1 and i is not equal to m, the adjacent element coordinates are { [ i-1,2], [ i,2], [ i +1,2], [ i-1,1], [ i +1,1] };
8. when j is equal to n, i is equal to 1, and i is equal to m, the adjacent element coordinates are { [ i-1, n-1], [ i, n-1], [ i +1, n-1], [ i-1, n ], [ i +1, n ] };
9. when i, j belong to other cases, the adjacent element coordinates are { [ i-1, j-1], [ i-1, j ], [ i-1, j +1], [ i, j-1], [ i, j +1], [ i +1, j-1], [ i +1, j ] };
s44, sequentially importing the position matrix Np,2N (i,1) and N (i,2), find and record [ N (i,1), N (i,2)]Adjacent element coordinates of (2); two sets are generated simultaneously, the first set being a set PP of position coordinates of the pointiOf the form { { N (i,1), N (i,2) }; the second set is the position of the adjacent elements of the pointSet of criteria QQi；
S45, sequentially importing the position matrix Np,2And N (i,1) and N (i,2) and performing this step until the position matrix N is completed for each pairp,2The introduction of all the line vectors and the updating of the system are carried out, and the position and pixel quantity information of all the particles in the gray level image is obtained;
when the set { { N (i,1), N (i,2) } } is associated with the existing set QQiWhen all the intersections are empty sets, generating corresponding sets PP in turn according to the method of step S44iAnd set QQi；
When the set { { N (i,1), N (i,2) } } is associated with the existing set QQiIf there is a non-empty set for all intersections, all k sets QQ that are not empty with the set { { N (i,1), N (i,2) } } intersection will bejAnd the PP aggregatejSequentially extracting; all the QQQ setsjAnd PPjCalculating to generate a new position coordinate set NPP and a new adjacent element position coordinate set NQQ; adding a new position coordinate set PP and a position coordinate set QQ of a new adjacent element to the particle position and pixel information respectively by using the generated set NPP and the set NQQ to realize the QQ of the original setjAnd the PP aggregatejUpdating of (1); the operation rules of the set NPP and the set NQQ are shown in formula (3) and formula (4):
Figure FDA0002243580880000041
Figure FDA0002243580880000042
8. the method of claim 7, further comprising, prior to said obtaining the geometric properties of the particles in the intensity image:
extracting position coordinates of all points of each particle contained in the set PP to obtain a corresponding abscissa { xn } and ordinate { yn }, and extracting an enclosure for each particle, including:
s51, sequencing all the points in the particles, wherein the sequencing principle is as follows: firstly, sorting all points from small to large according to an abscissa x; secondly, sorting all the points according to the ordinate y from small to large under the same abscissa condition; finally, forming an ordered sequence of points { p1, p2, …, pn } for all points, and defining a point O; the horizontal and vertical coordinates of the O point are defined as shown in formula (5) and formula (6), and include:
Figure FDA0002243580880000043
Figure FDA0002243580880000044
wherein n is the number of particles contained in each particle;
s52, forming initial set pi of upper enclosureup_set＝{p1,p2}；
S53, selecting a point p3 from the set { p3, p4, …, pn } to define two vectors; the definition is shown in formula (7) and formula (8), and includes:
Figure FDA0002243580880000051
Figure FDA0002243580880000052
the vector product of two vectors is calculated in the manner shown in equation (9), and includes:
Figure FDA0002243580880000053
if the product of scalar quantities
Figure FDA0002243580880000054
Defining the points p1, p2 and p3 as a right turn;
if p1, p2 and p3 make a right turn, p3 is added to IIup_setIn which a new set Π is formedup_set＝{p1，p2，p3}；
If p1, p2 and p3 do not form a right turn, p2 is moved from piup_setDeleting to form new set piup_set＝{p1，p3}；
S54, sequentially carrying out the operations of the steps S53 on the points P4 to Pn, and continuously updating the pi setup_setWhen all the point-aligning operations are completed, a set pi of the upper half enclosure is formedup_set；
S55, forming an initial set pi of lower enclosuredown_set＝{p(n),p(n-1)}；
S56, selecting a point p (n-2) from the set { p (n-2), p (n-3), …, p1} to define two vectors; the definition mode is shown as formula (10) and formula (11), and includes:
Figure FDA0002243580880000055
Figure FDA0002243580880000056
the vector product of two vectors is calculated in the manner shown in equation (12), and includes:
Figure FDA0002243580880000061
if the product of scalar quantities
Figure FDA0002243580880000062
Defining the points p (n), p (n-1) and p (n-2) to form a right turn;
if p (n), p (n-1) and p (n-2) form a right turn, then p (n-2) is addedPut into a set IIdown_setIn which a new set Π is formedup_set＝{p(n),p(n-1),p(n-2)}；
If p (n), p (n-1) and p (n-2) do not form a right turn, p (n-1) is taken from the set Πdown_setDeleting to form new set pidown_set＝{p(n),p(n-2)}；
S57, sequentially carrying out the operations of the steps S56 on the points P (n-3) to P1, and continuously updating the pi setdown_setWhen all the point-aligning operations are completed, a set pi of the lower half enclosure is formeddown_set；
S58, IIdown_setConnected to a set Πup_setAt the back, a new set pi is formedsetThe point corresponding to the set is the enclosure corresponding to the particle;
s59, repeating the following steps until the NPP set and NQQ set at the point are updated:
judging all points in NQQ set corresponding to each particle, if the point is in II set corresponding to the particlesetIf so, the point in the NQQ set is drawn into the NPP set of the point, and the point is deleted from the NQQ set; otherwise, the operation is not carried out on the set to which the point belongs.
9. An electron microscope particle geometry measuring apparatus, comprising:
a gradation information generating unit for generating corresponding gradation image information including a gradation image described by the gradation value matrix data, respectively, from each particle image acquired from the electron microscope;
the corresponding unit is used for establishing the corresponding relation between image pixels and the image observation scale according to the scale and the gray matrix scale of the particle image; the gray matrix scale is the size of two dimensions of the gray matrix; the scale is the ratio relation of the microscopic scale of the particle image and the indication length;
the matrix generation unit is used for converting the gray level image into a binary image according to a preset gray level image threshold value and generating a corresponding binary image matrix;
the clustering unit is used for clustering the points separated from the binary image to obtain the pixel position and the pixel quantity information of each particle in the gray level image;
a property calculation unit for obtaining geometric properties of particles in the grayscale image, comprising: respectively calculating the gravity center position of each particle according to the pixel position of each particle; calculating the area of each particle according to the pixel position and the pixel quantity information of each particle; calculating the equivalent radius of each particle according to the area of each particle and the corresponding relation between the image pixel and the image observation scale; calculating the nearest neighbor near distance NND of the particle system according to the gravity center position of each particle;
and the information statistical unit is used for carrying out frequency distribution calculation on the equivalent radius and the NND of the obtained particle system to obtain the whole geometric information of the particle system observed by the electron microscope.
10. A memory comprising a software program adapted to be executed by a processor for performing the steps of the method of determining geometry of particles of an electron microscope according to any one of claims 1 to 8.
11. An electron microscope particle geometry measuring apparatus comprising a bus, a processor and a memory as claimed in claim 10;
the bus is used for connecting the memory and the processor;
the processor is configured to execute a set of instructions in the memory.
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