CN114264576A - Particle identification method for improving particle analysis speed based on electron microscope - Google Patents
Particle identification method for improving particle analysis speed based on electron microscope Download PDFInfo
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- CN114264576A CN114264576A CN202111594992.1A CN202111594992A CN114264576A CN 114264576 A CN114264576 A CN 114264576A CN 202111594992 A CN202111594992 A CN 202111594992A CN 114264576 A CN114264576 A CN 114264576A
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- 229910052742 iron Inorganic materials 0.000 claims description 3
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Abstract
The invention belongs to the technical field of electron microscope substance identification analysis, and particularly relates to a particle identification method for improving particle analysis speed based on an electron microscope. Compared with the prior art, the invention has the beneficial effects that: 1) the invention improves the traditional particle identification method, has no great influence on the overall measurement accuracy, and has obvious effects on the two aspects of improving the measurement speed and reducing the calculation amount. 2) In the analysis of the engine wear debris, the analyzed substances are mainly various metal particles, and the gray scale and the substances have relatively stable corresponding relation, so that the particles with the same gray scale can be considered to be the same substance, and the final particle analysis speed is greatly improved.
Description
Technical Field
The invention belongs to the technical field of electron microscope substance identification and analysis, and particularly relates to a particle identification method for improving particle analysis speed based on an electron microscope.
Background
An automatic material identification and analysis system of an electron microscope generally collects BSE image frames of a sample through an electron microscope API, then manually sets gray values for distinguishing the foreground and background of the image, removes background pixels through pixel-by-pixel processing, and finally realizes particle identification through a communication algorithm. The elemental composition of the particles is collected by spectral X-ray after the location of the particles is identified. And finally, the identification of the particulate matter is realized.
Application No. as201410413256.5The Chinese patent of the invention discloses a particle morphology recognition method based on a scanning electron microscope, which is suitable for recognizing particle substances suspended in air. The method comprises the steps of obtaining a suspended particle image which is amplified by 5000 times in the air to be detected by utilizing a scanning electron microscope, binarizing the particle image, finding out wedge-shaped pixels in the binarized image as initial end wedge-shaped pixels of a separation point according to the adhesive particle pattern in the binarized image, finding out terminal end wedge-shaped pixels corresponding to the initial end wedge-shaped pixels, drawing separation lines by adopting a Bresenham algorithm, completing segmentation of the adhesive particle image, and then identifying morphological characteristics of each particle pattern after the segmentation of the adhesive particles is completed by an equivalent diameter and shape factor method.
After the background is removed, if the number of the foreground particles is small, the method can well identify the foreground particles, but the identification speed is very low when the number of the foreground particles is large, and accurate identification effect can be realized only when the time of 522ms to 1s is needed for subsequent X-ray analysis of single particles. If the number of particles is very large and reaches the level of thousands of particles, the analysis time of a single frame of image is very long, and the identification efficiency of the electron microscope on the particles is influenced.
Disclosure of Invention
The invention aims to provide a particle identification method for improving particle analysis speed based on an electron microscope, which overcomes the defects of the prior art, and groups all particles to be identified after background removal according to the average gray level thereof, and selects a small part of large particles for X-ray analysis in each group, thereby avoiding X-ray analysis of a large number of particles.
In order to solve the technical problems, the technical scheme of the invention is as follows:
a particle identification method for improving particle analysis speed based on an electron microscope is characterized in that all particles to be identified after background removal are grouped according to average gray level, and a small part of large particles are selected for X-ray analysis in each group, so that metal particles are rapidly identified, and the method comprises the following steps:
1) obtaining the positions of all frame images to be measured, and sequencing the frame images to be measured;
2) controlling an electron microscope to move a sample stage to a certain specified position;
3) shooting a BSE image of a frame image to be measured, carrying out coordinate positioning on the working position of an electron microscope, and shooting a BMP image;
4) setting or calculating a background gray scale range of the image frame, and removing background pixels of the image frame according to the background gray scale range;
5) extracting all particle coordinates after background removal;
6) grouping according to the average gray value of the particles;
7) selecting large particles in each group for X-ray analysis;
8) applying the analysis results to the whole group;
9) repeat step 2) -step 8) until the analysis of the other groups is completed.
Furthermore, the frame images to be measured are grouped in a matrix, the number of rows x columns in each group must be an odd number n, and n is a natural number of more than 3.
Further, the frame images to be measured are ordered in a spiral or serpentine shape.
Further, the particles are alloy particles consisting of iron, copper and aluminum.
Furthermore, the distance range of the adjacent frame images in the frame image matrix is 0.2-1 mm.
Further, the particles are engine wear debris filtered by a filter membrane in an automobile cleanliness analysis system.
Compared with the prior art, the invention has the beneficial effects that: 1) the invention improves the traditional particle recognition method, has no great influence on the overall measurement accuracy, but fills the frame image matrix with the sequence number before measurement, and can obtain the measurement sequence of all the frame images after sequencing without calculating the positions of the frame images in real time in the motion displacement of the sample stage, thereby obviously reducing the code processing amount, obviously reducing the computer calculation amount and having obvious effects on improving the measurement speed and reducing the calculation amount. 2) In the analysis of the engine wear debris, the analyzed substances are mainly various metal particles, and the gray scale and the substances have relatively stable corresponding relation, so that the particles with the same gray scale can be considered to be the same substance, and the final particle analysis speed is greatly improved.
Drawings
FIG. 1 is a measurement flow diagram of an embodiment of the present invention;
FIG. 2 is a schematic diagram of a spiral fill ordering in accordance with an embodiment of the present invention;
FIG. 3 is a schematic diagram of a serpentine fill ordering according to an embodiment of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to specific embodiments of the present invention, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the invention is applied to an automobile cleanliness analysis system, the particles are engine wear debris filtered by a filter membrane in the automobile cleanliness analysis system, and most of the particles are alloy particles consisting of iron, copper and aluminum. After the method is applied, the measuring speed is improved by 50%, the code amount of the partial algorithm program is reduced by 20%, the operation amount of a computer is reduced by more than 30%, the program is greatly simplified, and the maintenance is more convenient.
Referring to fig. 1, the particle recognition method for improving particle analysis speed based on electron microscope of the present invention mainly groups all particles to be recognized after background removal according to average gray scale, and selects a small part of large particles for X-ray analysis in each group to rapidly recognize metal particles, and the steps are as follows:
1) obtaining the positions of all frame images to be measured, and sequencing the frame images to be measured; the frame images to be measured are first grouped in a matrix, and the number of rows x columns of each group is set to be an odd number, such as 3 × 3, 5 × 5, 7 × 7, 9 × 9, 11 × 11, and the like. Sequencing the frame image to be measured in a spiral or snake shape; in case of spiral filling order, see fig. 2, the central frame map of the whole matrix is filled with the sequence number 1, and then the other frame maps are filled in spiral order with the sequence numbers 2, 3 … …; if serpentine fill order, see FIG. 3, then fill sequence number 1 from the top left-most corner frame map, and then fill other frame maps in serpentine order with sequence numbers 2, 3 … …;
2) controlling an electron microscope to move a sample platform to a certain specified position, wherein the sample platform is carried out in a spiral or snake-shaped moving path according to the Arabic number sequence; the distance range between adjacent frame images is 0.3-0.6 mm.
3) Shooting a BSE image of a frame image to be measured, carrying out coordinate positioning on the working position of an electron microscope, and shooting a BMP image; the BMP image file is a standard image file format in the Windows operating system, and is the simplest image format. The BMP image format is very simple, has only the most basic image data storage function, and can store 1-bit, 4-bit, 8-bit, and 24-bit bitmaps per pixel. Although the information provided by the BMP is too simple, the BMP image file format is popularized due to the characteristics of simple format, standard and transparency of the BMP, and the BMP image file format is generally applied to some simple image processing systems.
4) Setting a background gray scale range of an image frame, removing background pixels of the image frame according to the background gray scale range, and considering that particles with the same gray scale are the same material as the analyzed material is mainly various metal particles and the gray scale and the material have a relatively stable corresponding relation, a fixed gray scale value can be set, for example, 50, and background lower than 50 is removed;
5) extracting all particle coordinates after background removal, and determining a grouping range;
6) grouping according to the mean gray value of the particles, for example into 10 groups;
7) selecting the large particles in any group for X-ray analysis, and marking the number and the size of the large particles;
8) applying a small part of large particle analysis results to the whole group, namely measuring results of the whole group;
9) repeat step 2) -step 8) until the analysis of the other groups is completed.
It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications therefrom are within the scope of the invention.
Claims (6)
1. A particle identification method for improving particle analysis speed based on an electron microscope is characterized in that all particles to be identified after background removal are grouped according to average gray level, and a small part of large particles are selected for X-ray analysis in each group, so that metal particles are rapidly identified, and the method comprises the following steps:
1) obtaining the positions of all frame images to be measured, and sequencing the frame images to be measured;
2) controlling an electron microscope to move a sample stage to a certain specified position;
3) shooting a BSE image of a frame image to be measured, carrying out coordinate positioning on the working position of an electron microscope, and shooting a BMP image;
4) setting or calculating a background gray scale range of the image frame, and removing background pixels of the image frame according to the background gray scale range;
5) extracting all particle coordinates after background removal;
6) grouping according to the average gray value of the particles;
7) selecting large particles in each group for X-ray analysis;
8) applying the analysis results to the whole group;
9) repeat step 2) -step 8) until the analysis of the other groups is completed.
2. The method for identifying particles based on electron microscope to improve particle analysis speed according to claim 1, wherein the frame images to be measured are grouped in a matrix, the number of rows and columns in each group must be an odd number n, and n is a natural number of more than 3.
3. The electron microscope-based particle identification method for increasing the particle analysis speed according to claim 2, wherein the frame images to be measured are ordered in a spiral or serpentine shape.
4. The method for identifying particles capable of improving particle analysis speed based on the electron microscope as claimed in claim 1, wherein the particles are alloy particles composed of iron, copper and aluminum.
5. The particle identification method for improving the particle analysis speed based on the electron microscope as claimed in claim 2, wherein the distance range of adjacent frame images in the frame image matrix is 0.2-1 mm.
6. The method for identifying particles capable of improving particle analysis speed based on the electron microscope as claimed in claim 1, wherein the particles are engine wear debris filtered by a filter membrane in an automobile cleanliness analysis system.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160153892A1 (en) * | 2013-06-18 | 2016-06-02 | Leica Microsystems Cms Gmbh | Method and optical device for microscopically examining a multiplicity of specimens |
| CN107505457A (en) * | 2016-06-14 | 2017-12-22 | 韩国化学研究院 | Very small items thing observation device |
| CN111368844A (en) * | 2020-03-10 | 2020-07-03 | 浙江中科锐晨智能科技有限公司 | Mineral particle automatic identification method based on BSE (sparse State image) diagram |
| US20210310943A1 (en) * | 2018-07-27 | 2021-10-07 | Industry-University Cooperation Foundation Hanyang University | Specimen inspection device and specimen inspection method |
-
2021
- 2021-12-24 CN CN202111594992.1A patent/CN114264576A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160153892A1 (en) * | 2013-06-18 | 2016-06-02 | Leica Microsystems Cms Gmbh | Method and optical device for microscopically examining a multiplicity of specimens |
| CN107505457A (en) * | 2016-06-14 | 2017-12-22 | 韩国化学研究院 | Very small items thing observation device |
| US20210310943A1 (en) * | 2018-07-27 | 2021-10-07 | Industry-University Cooperation Foundation Hanyang University | Specimen inspection device and specimen inspection method |
| CN111368844A (en) * | 2020-03-10 | 2020-07-03 | 浙江中科锐晨智能科技有限公司 | Mineral particle automatic identification method based on BSE (sparse State image) diagram |
Non-Patent Citations (2)
| Title |
|---|
| 余成龙 等: "基于SEM-EDS的清洁度检测技术研究", 《现代车用动力》, no. 2, 31 May 2019 (2019-05-31), pages 4 * |
| 郭爱春: "基于图像处理的液压油污染颗粒检测", 《中国优秀硕士学位论文全文数据库 信息科技辑》, no. 11, 15 November 2013 (2013-11-15), pages 4 * |
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