ESEM-oriented radon solid track image counting method and system

Abstract

The invention belongs to the technical field of nuclear radiation detection, and particularly relates to an ESEM-oriented radon solid track image counting method and system, and the method and system are composed of a solid track detector, an environment scanning electron microscope, a high-resolution camera, an image analysis processing system, a fixed frame and a communication module, and achieve the automatic image preprocessing and image edge detection. The method comprises the steps of image binarization threshold segmentation, morphological processing, connected component analysis, feature extraction, body track automatic counting, result output and the like, automatic collection and processing of a solid track image of a radon environment are completed, a marked image is obtained, the marked image comprises the marked solid track position and the serial number of a connected component, the marked image can be directly used for observing a sample, and the detection accuracy is improved. The requirements on chemical fixed trace and vacuum observation environment are reduced, the influence of charging effect and high vacuum environment on the sample is avoided, and real-time detection can be carried out.

Description

A method and system for counting radon solid track images for ESEM Technical Field

[0001] This invention belongs to the field of nuclear radiation detection technology, specifically relating to a method and system for counting radon solid track images for environmental scanning electron microscopy (ESEM). Background Technology

[0002] In radon concentration measurement techniques, solid-state nuclear track detectors (SSNDs) are a widely used method. The basic principle of SSNTDs for radon concentration measurement is based on the alpha particles emitted during the decay of radon and its decay products, such as polonium-218 and polonium-214. These high-energy alpha particles form tiny tracks in the detector material, which can be revealed and counted through methods such as chemical etching. The number of tracks is proportional to the radon concentration and exposure time; therefore, the radon concentration in the environment can be estimated by analyzing the tracks. Types of solid-state track detectors include the CR-39 detector, a common polymer track detector widely used for radon measurement due to its sensitivity to alpha particles and low background noise; and the LR-115 detector, a nitrocellulose-based track detector with high sensitivity, but whose processing and analysis are more complex than CR-39. Polycarbonate detectors, also used for radon measurement, offer good mechanical stability but have slightly lower sensitivity compared to CR-39. During measurement, the solid track detector is placed in the environment to be measured, typically in a sealed box or a specially designed detector container, exposing it to radon gas. The exposure time usually ranges from several days to several months, depending on the expected radon concentration level. After exposure, the detector is removed and chemically etched to reveal the tracks formed by alpha particles. The etched detector is then analyzed using an optical microscope or an automated track counter to calculate the track density, which is proportional to the radon concentration. Based on the track density, detector exposure time, and detector sensitivity coefficient, the radon concentration in the environment is calculated, typically expressed in becquerels per cubic meter (Bq / m³).

[0003] Existing solid track detector systems and methods have the following limitations: 1. Low temporal resolution: Due to the long exposure time required, this method cannot provide real-time radon concentration data. 2. Cumbersome corrosion and counting steps: The detector requires chemical treatment and microscopic counting, making the entire process relatively complex and time-consuming. 3. Influence of environmental conditions: Environmental factors such as temperature and humidity may affect the detector's sensitivity and the corrosion process, requiring appropriate calibration.

[0004] To achieve real-time monitoring and automated measurement of radon, it is necessary to combine solid track detectors with other real-time radon measurement technologies to reduce human error and improve measurement efficiency, thereby automating and intelligently counting tracks. ESEM, a working mode of scanning electron microscopy (SEM), can be directly used for observing natural samples, reducing the requirements for chemical immobilization and vacuum observation environments, and avoiding the influence of charging effects and high vacuum environments on the sample. Therefore, a method and system for counting radon solid track images for ESEM is needed. Summary of the Invention

[0005] To address the problems mentioned in the background art, this invention provides a radon solid track image counting method and system for ESEM. It can directly acquire solid track image information of the sample surface of the solid track detector through ESEM, and then complete the recognition and counting of the counting image by extracting image features from the solid track image to obtain radon concentration calculation parameters.

[0006] To achieve the above objectives, a first aspect of the present invention provides a method for counting radon solid track images for ESEM, comprising:

[0007] Step S1, Image preprocessing: Preprocess the sample image to generate a preprocessed image, enhancing the detectability of solid tracks;

[0008] Step S2, image edge detection: the solid track edges in the preprocessed image are identified by an edge detection algorithm, and an edge grayscale image is generated;

[0009] Step S3, image binarization thresholding segmentation, converting the edge grayscale image into a black and white binary image through global or local thresholding segmentation;

[0010] Step S4, morphological processing: Apply morphological operations to smooth the edges of solid tracks in the black and white binary image, fill in holes or broken solid tracks, and form a continuous solid track structure map.

[0011] Step S5, Connectivity component analysis: Use the connectivity component analysis algorithm to mark and identify independent solid tracks in the continuous solid track structure map, with each connected region corresponding to one solid track;

[0012] Step S6, Feature extraction: Extract features of each connected component in the continuous solid track structure map, including one or more of area, perimeter, and shape factor, filter out noise or pseudo solid tracks that do not meet the feature criteria, and generate a filtered map of the continuous solid track structure.

[0013] Step S7: Automatic counting of solid tracks; counting the number of connected components in the filter map of continuous solid track structures; the result of the counting is the total number of solid tracks in the sample.

[0014] Step S8, Output the results. Output the total number of solid tracks and the marked image. The total number of solid tracks includes the average area and shape information of the solid tracks. The marked image includes the location of the marked solid tracks and the number of the connected components.

[0015] As a further description of the above technical solution: the preprocessing in step S1 includes one or more of grayscale conversion, noise removal, and contrast enhancement, including:

[0016] Step S101, grayscale conversion, converting the sample image acquired by ESEM to grayscale. Convert to grayscale image ,satisfy:

[0017] (1)

[0018] in, The image after preprocessing. for The red component, for The green component, for The blue component;

[0019] Step S102, noise removal, extracting sample images acquired by ESEM. Noise removal can be achieved through mean filtering or median filtering; mean filtering removes noise from the image. satisfy:

[0020] (2)

[0021] in, , This refers to the filter window size, used for median filtering to remove noise from the image. satisfy:

[0022] (3)

[0023] in, It is the median value of the pixels within the window;

[0024] Step S103, contrast enhancement, using the sample image acquired by ESEM. Solid track features in images are enhanced by contrast stretching. satisfy:

[0025] (4)

[0026] in, These are the maximum and minimum grayscale values ​​of the image. It refers to the grayscale level.

[0027] As a further description of the above technical solution: the edge detection algorithm in step S2 is one of the Roberts operator, Sobel operator, Prewitt operator, Laplacian operator, and Canny operator, which generates an edge grayscale image. .

[0028] As a further description of the above technical solution: In step S3, the edge grayscale image is converted into a black and white binary image. The solid track portion is white against a black background, or the solid track portion is black against a white background.

[0029] As a further description of the above technical solution: the morphological processing in step S4 includes one of dilation, erosion, opening operation, and closing operation, including:

[0030] Step S401, Dilation, increases the size of the object by expanding the foreground region in the black-and-white binary image, satisfying:

[0031] (5)

[0032] in, It is a black and white binary image. This is a diagram of the structure of a continuous solid track after morphological processing. For expansion operation, It is a structuring element, a small binary matrix used to define the manner in which dilation and erosion operations are performed, and can be one of a rectangle, circle, or cross shape. It is a structural element The translation, the center is at point;

[0033] Step S402, erosion, reduces the size of the object by shrinking the foreground region in the black-and-white binary image, satisfying:

[0034] (6)

[0035] in, This is a corrosion operation;

[0036] Step S403, Opening operation. The opening operation is used to remove small objects or small protrusions in a black and white binary image. It is an operation that first performs erosion and then dilation, satisfying the following conditions:

[0037] (7)

[0038] in, This is an opening operation;

[0039] Step S404, closing operation, is used to fill small holes or broken parts in the black and white binary image. It is an operation that first performs dilation and then erosion, satisfying the following:

[0040] (8)

[0041] in, This is a closing operation.

[0042] As a further description of the above technical solution: Step S5 uses a connected component analysis algorithm to label and identify independent solid tracks in a continuous solid track structure map, including the following steps:

[0043] Step S501: Identify connected regions by using 4-connected regions, which are connected to a pixel and its horizontally and vertically adjacent pixels, or 8-connected regions, which are connected to a pixel and its horizontally, vertically, and diagonally adjacent pixels.

[0044] Step S502, Initialization: Create a labeled image to store the component label for each pixel, and initialize the label counter. ;

[0045] Step S503: Scan the image, scan sequentially. For each pixel, if it is a foreground pixel and has not yet been labeled, perform region growing using a breadth-first search or depth-first search algorithm, and let... Otherwise, repeat step S503 until each connected region is assigned a unique label.

[0046] As a further description of the above technical solution: In step S6, the area of ​​each connected component is extracted to satisfy: ,in, For the first Area of ​​each connected region yes The Middle Connected regions For each pixel, extract the perimeter of each connected component that satisfies: ,in, For the first Perimeter of a connected region This is a function for determining boundary pixels.

[0047] As a further description of the above technical solution: after outputting the total number of solid tracks and the marked image, step S8 performs verification and correction. When the track breaks, the size of the structural element in the expansion operation is increased; when a false noise point is detected, the number of steps S402 is increased.

[0048] A radon solid track image counting system for ESEM includes: a solid track detector for exposure to a radon environment to record tracks;

[0049] An environmental scanning electron microscope is positioned above the solid track detector to scan and image the surface of the solid track detector.

[0050] An image acquisition module, connected to the ESEM, is used to acquire sample images generated by ESEM scanning;

[0051] An image analysis and processing system, connected to the image acquisition module, is configured to perform the steps of image preprocessing, edge detection, binarization segmentation, morphological processing, connected component analysis, feature extraction, automatic counting, and result output.

[0052] And a fixing frame for fixing the solid track detector and the ESEM.

[0053] Compared with the prior art, the method of this application has the following beneficial technical effects:

[0054] 1. This invention directly utilizes ESEM to perform in-situ imaging of samples, avoiding the damage to detector materials and errors introduced by chemical treatment, and realizing non-destructive testing of samples. This not only ensures the reliability of measurement results, but also allows samples to be preserved or used for subsequent analysis.

[0055] 2. This invention achieves full automation of the process from image preprocessing and track recognition to counting output through integrated image processing algorithms, replacing subjective manual microscope counting, greatly reducing the analysis time from several hours to minutes, and ensuring the high objectivity, consistency and repeatability of the counting results.

[0056] 3. This invention utilizes the advantages of ESEM's large field of view, large depth of field, and high-resolution imaging to acquire a large-scale, high-definition image of the detector surface in a single scan. This increases the sample size and spatial representativeness of track statistics and provides a reliable foundation for accurate segmentation and feature extraction in subsequent algorithms, thereby improving detection coverage and counting accuracy. Attached Figure Description

[0057] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0058] Figure 1 is a flowchart of a radon solid track image counting method for ESEM proposed in this invention. Detailed Implementation

[0059] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0060] Example 1

[0061] A first aspect of the present invention provides a method for counting radon solid track images for ESEM, comprising:

[0062] Step S1, image preprocessing: enhancing the detectability of solid tracks by grayscale conversion, and processing the sample images acquired by ESEM. Convert to grayscale image ,satisfy:

[0063] (9)

[0064] in, The image after preprocessing. for The red component, for The green component, for The blue component;

[0065] Step S2, image edge detection: The Sobel operator is used as the edge detection algorithm to identify solid track edges in the image and generate an edge grayscale image. ,satisfy:

[0066] (10)

[0067] (11)

[0068] (12)

[0069] in, , ;

[0070] Step S3: Image binarization and thresholding segmentation. The grayscale image is converted into a black-and-white binary image through global or local thresholding segmentation. The solid track area is white, and the background is black. A threshold is set. Determine the relationship with the threshold to generate a black-and-white binary image. ;

[0071] Step S4, morphological processing: Apply morphological operations to smooth the edges of solid tracks in the black-and-white binary image, fill in holes or breaks in the solid tracks, and form a continuous solid track structure map. The morphological processing is dilation, which increases the size of the object by expanding the foreground region in the black-and-white binary image, satisfying the following:

[0072] (13)

[0073] in, It is a black and white binary image. This is a diagram of the structure of a continuous solid track after morphological processing. For expansion operation, It is a structuring element, a small binary matrix used to define the manner in which dilation and erosion operations are performed, and can be one of a rectangle, circle, or cross shape. It is a structural element The translation, the center is at point;

[0074] Step S5, Connectivity Component Analysis: This step uses a connectivity component analysis algorithm to label and identify independent solid tracks within a continuous solid track structure map. It includes the following steps:

[0075] Step S501: Identify connected regions by using 4-connected regions, which are connected to a pixel and its horizontally and vertically adjacent pixels, or 8-connected regions, which are connected to a pixel and its horizontally, vertically, and diagonally adjacent pixels.

[0076] Step S502, Initialization: Create a labeled image to store the component label for each pixel, and initialize the label counter. ;

[0077] Step S503: Scan the image, scan sequentially. For each pixel, if it is a foreground pixel and has not yet been labeled, perform region growing using a breadth-first search or depth-first search algorithm, and let... Otherwise, repeat step S503 until each connected region is assigned a unique label.

[0078] Step S6: Feature extraction. Extract the area features of each connected component in the continuous solid track structure map, filter out noise or pseudo solid tracks that do not meet the area feature criteria, and generate a filtered map of the continuous solid track structure. Extract the area of ​​each connected component to satisfy: ,in, For the first Area of ​​each connected region yes The Middle Connected regions The pixels;

[0079] Step S7: Automatic counting of solid tracks; counting the number of connected components in the filter map of continuous solid track structures; the result of the counting is the total number of solid tracks in the sample.

[0080] Step S8, Output the results. Output the total number of solid tracks and the marked image. The total number of solid tracks includes the average area and shape information of the solid tracks. The marked image includes the location of the marked solid tracks and the number of the connected components.

[0081] Example 2

[0082] A first aspect of the present invention provides a radon solid track image counting system for an ESEM, comprising: a solid track detector for exposure to a radon environment to record tracks; an environmental scanning electron microscope disposed above the solid track detector for scanning and imaging the surface of the solid track detector; an image acquisition module connected to the ESEM for acquiring sample images generated by the ESEM scan; an image analysis and processing system connected to the image acquisition module and configured to perform the steps of image preprocessing, edge detection, binarization segmentation, morphological processing, connected component analysis, feature extraction, automatic counting, and result output; and a fixing frame for fixing the solid track detector and the ESEM.

[0083] A solid track detector exposed to radon is mounted and fixed on a fixed frame, ensuring that the sampling window of the solid track detector is perpendicularly aligned with the electron beam path of the environmental scanning electron microscope. The fixed frame is adjusted so that the field of view of the environmental scanning electron microscope completely covers the sampling of the solid track detector. A low vacuum mode of 20-50 Pa is used to avoid sample charging. The accelerating voltage is 5-10 kV and the beam current is 1-5 nA to balance resolution and signal-to-noise ratio. A high-resolution camera acquires secondary electron signal images at a resolution of 2048×2048 pixels. The images are transmitted in real time to the image analysis and processing system via a communication module. The system performs image preprocessing, image edge detection, image binarization thresholding, morphological processing, connected component analysis, feature extraction, automatic volume track counting, and result output. This completes the automatic acquisition and processing of solid track images in the radon environment, resulting in marked images, including the marked solid track locations and connected component numbers.

[0084] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

[0085] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above-described wireless terminal can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0086] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0087] In the embodiments provided by this invention, it should be understood that the disclosed systems / terminal devices and methods can be implemented in other ways. For example, the system / terminal device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between systems or units may be electrical, mechanical, or other forms.

[0088] The units described as separate components may or may not be physically separate. Components displayed as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0089] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for counting radon solid track images for ESEM, characterized in that, include: Step S1, Image preprocessing: Preprocess the sample image to generate a preprocessed image, enhancing the detectability of solid tracks; Step S2, Image Edge Detection: The edge detection algorithm identifies the solid track edges in the preprocessed image, generating an edge grayscale image. Step S3, Image Binarization Thresholding: The edge grayscale image is converted into a black-and-white binary image through global or local thresholding. Step S4, Morphological Processing: Morphological operations are applied to smooth the solid track edges in the black-and-white binary image, fill in holes or breaks in the solid tracks, forming a continuous solid track structure map. Step S5, Connected Component Analysis: A connected component analysis algorithm is used to label and identify independent solid tracks in the continuous solid track structure map; each connected region corresponds to one solid track. Step S6... Feature extraction: Extract features of each connected component in the continuous solid track structure map, including one or more of area, perimeter, and shape factor. Filter out noise or pseudo solid tracks that do not meet the feature criteria to generate a filtered map of the continuous solid track structure. Step S7: Automatic solid track counting: Count the number of connected components in the filtered map of the continuous solid track structure. The count result is the total number of solid tracks in the sample. Step S8: Result output: Output the total number of solid tracks and a labeled image. The total number of solid tracks includes the average area and shape information of the solid tracks. The labeled image includes the location of the labeled solid tracks and the number of the connected components.

2. The radon solid track image counting method for ESEM according to claim 1, characterized in that, The preprocessing in step S1 includes at least one of grayscale conversion, noise removal, and contrast enhancement.

3. The radon solid track image counting method for ESEM according to claim 1, characterized in that, The edge detection algorithm in step S2 is one of the Roberts operator, Sobel operator, Prewitt operator, Laplacian operator, and Canny operator, which generates an edge grayscale image.

4. The radon solid track image counting method for ESEM according to claim 1, characterized in that, In step S3, the edge grayscale image is converted into a black and white binary image. The solid track portion of the black and white binary image is white and the background is black, or the solid track portion is black and the background is white.

5. A method for counting radon solid track images for ESEM according to claim 1, characterized in that, The morphological processing in step S4 includes one of dilation, erosion, opening, or closing operations, specifically: Step S401, dilation, which increases the size of the object by expanding the foreground region in the black-and-white binary image, satisfying: in, It is a black and white binary image. This is a diagram of the structure of a continuous solid track after morphological processing. For expansion operation, It is a structuring element, a small binary matrix used to define the manner in which dilation and erosion operations are performed, and can be one of a rectangle, circle, or cross shape. yes The translation, the center is at Point; Step S402, erosion, reduces the size of the object by shrinking the foreground region in the black-and-white binary image, satisfying: in, For the erosion operation; step S403, opening operation, the opening operation is used to remove small objects or small protrusions in the black and white binary image. It is an operation that first performs erosion and then dilation, satisfying: in, For opening operation; step S404, closing operation, used to fill small holes or broken parts in the black and white binary image. It is an operation of first dilation and then erosion, satisfying: in, This is a closing operation.

6. A method for counting radon solid track images for ESEM according to claim 1, characterized in that, Step S5 uses a connected component analysis algorithm to label and identify independent solid tracks in a continuous solid track structure map, including the following steps: Step S501, identify connected regions by using 4-connected regions (connecting a pixel to its horizontally and vertically adjacent pixels) or 8-connected regions (connecting a pixel to its horizontally, vertically, and diagonally adjacent pixels); Step S502, initialize by creating a label image to store component labels for each pixel and initializing the label counter. Step S503: Scan the image, scan sequentially. For each pixel, if it is a foreground pixel and has not yet been labeled, perform region growing using a breadth-first search or depth-first search algorithm, and let... Otherwise, repeat step S503 until each connected region is assigned a unique label.

7. A method for counting radon solid track images for ESEM according to claim 1, characterized in that, In step S6, the area of ​​each connected component is extracted to satisfy: ,in, For the first Area of ​​each connected region yes The Middle Connected regions For each pixel, extract the perimeter of each connected component that satisfies: ,in, For the first Perimeter of a connected region This is a function for determining boundary pixels.

8. A method for counting radon solid track images for ESEM according to claim 2, characterized in that, The grayscale conversion refers to the process of converting the sample image acquired by ESEM into grayscale. Convert to grayscale image ,satisfy: in, The image after preprocessing. for The red component, for The green component, for The blue component; the noise removal is the removal of sample images acquired by ESEM. Noise removal can be achieved through mean filtering or median filtering; mean filtering removes noise from the image. satisfy: in, 、 This refers to the filter window size, used for median filtering to remove noise from the image. satisfy: in, It is the median of the pixel values ​​within the window; the contrast enhancement is to enhance the contrast of the pixels acquired by the ESEM. Solid track features in images are enhanced by contrast stretching. satisfy: in, These are the maximum and minimum grayscale values ​​of the image. It refers to the grayscale level.

9. A method for counting radon solid track images for ESEM according to claim 5, characterized in that, Step S8 verifies and corrects the total number of solid tracks and the marked image after outputting them. If the track breaks, the size of the structural element in the expansion operation is increased. If a false noise point is detected, the number of steps S402 is increased.

10. A radon solid track image counting system for ESEM, used in the radon solid track image counting method for ESEM as described in any one of claims 1 to 8, characterized in that, include: Solid track detectors are used to record tracks when exposed to radon environments. An environmental scanning electron microscope (ESEM) is positioned above the solid track detector and is used to scan and image the surface of the solid track detector. An image acquisition module is connected to the ESEM and is used to acquire sample images generated by the environmental scanning electron microscope. An image analysis and processing system is connected to the image acquisition module and is configured to perform the steps of image preprocessing, edge detection, binarization segmentation, morphological processing, connected component analysis, feature extraction, automatic counting, and result output. And a fixing frame for fixing the solid track detector and the ESEM.

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