Interpretable graph methods for determining nanoparticles ordering in electron microscopy image
An important step in determining the properties of carbon materials is the analysis of images from a scanning electron microscope (SEM). These images show the material surface after the application of metal nanoparticles. The order of these nanoparticles is a key characteristic that affects the mate...
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| Format: | Article |
| Language: | English |
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Samara National Research University
2025-06-01
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| Series: | Компьютерная оптика |
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| Online Access: | https://computeroptics.ru/KO/Annot/KO49-3/490313.html |
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| author | M.Y. Kurbakov V.V. Sulimova O.S. Seredin A.V. Kopylov |
| author_facet | M.Y. Kurbakov V.V. Sulimova O.S. Seredin A.V. Kopylov |
| author_sort | M.Y. Kurbakov |
| collection | DOAJ |
| description | An important step in determining the properties of carbon materials is the analysis of images from a scanning electron microscope (SEM). These images show the material surface after the application of metal nanoparticles. The order of these nanoparticles is a key characteristic that affects the material properties. We have previously proposed an approach to formalize the order features based on the identification of lines by nanoparticles in the SEM image. This paper proposes a novel approach to line allocation that is based on the concept of constructing a minimum spanning forest. Additionally, it introduces a set of novel ordering functions that are derived from this approach. The experimental study demonstrates that the combination of these new and previously extracted features improves the recognition quality of SEM images with ordered and disordered nanoparticles arrangements. This approach allows us to gain a better understanding of the nanoparticles arrangement and their effect on the material properties. |
| format | Article |
| id | doaj-art-0b4a2aa81ed34c4983327f83f4e8c636 |
| institution | Kabale University |
| issn | 0134-2452 2412-6179 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Samara National Research University |
| record_format | Article |
| series | Компьютерная оптика |
| spelling | doaj-art-0b4a2aa81ed34c4983327f83f4e8c6362025-08-21T07:01:40ZengSamara National Research UniversityКомпьютерная оптика0134-24522412-61792025-06-0149347047910.18287/2412-6179-CO-1568Interpretable graph methods for determining nanoparticles ordering in electron microscopy imageM.Y. Kurbakov0V.V. Sulimova1O.S. Seredin2A.V. Kopylov3Tula State UniversityTula State UniversityTula State UniversityTula State UniversityAn important step in determining the properties of carbon materials is the analysis of images from a scanning electron microscope (SEM). These images show the material surface after the application of metal nanoparticles. The order of these nanoparticles is a key characteristic that affects the material properties. We have previously proposed an approach to formalize the order features based on the identification of lines by nanoparticles in the SEM image. This paper proposes a novel approach to line allocation that is based on the concept of constructing a minimum spanning forest. Additionally, it introduces a set of novel ordering functions that are derived from this approach. The experimental study demonstrates that the combination of these new and previously extracted features improves the recognition quality of SEM images with ordered and disordered nanoparticles arrangements. This approach allows us to gain a better understanding of the nanoparticles arrangement and their effect on the material properties.https://computeroptics.ru/KO/Annot/KO49-3/490313.htmlexplainable machine learningimage analysisnanoparticle detectionnanoparticles ordering features |
| spellingShingle | M.Y. Kurbakov V.V. Sulimova O.S. Seredin A.V. Kopylov Interpretable graph methods for determining nanoparticles ordering in electron microscopy image Компьютерная оптика explainable machine learning image analysis nanoparticle detection nanoparticles ordering features |
| title | Interpretable graph methods for determining nanoparticles ordering in electron microscopy image |
| title_full | Interpretable graph methods for determining nanoparticles ordering in electron microscopy image |
| title_fullStr | Interpretable graph methods for determining nanoparticles ordering in electron microscopy image |
| title_full_unstemmed | Interpretable graph methods for determining nanoparticles ordering in electron microscopy image |
| title_short | Interpretable graph methods for determining nanoparticles ordering in electron microscopy image |
| title_sort | interpretable graph methods for determining nanoparticles ordering in electron microscopy image |
| topic | explainable machine learning image analysis nanoparticle detection nanoparticles ordering features |
| url | https://computeroptics.ru/KO/Annot/KO49-3/490313.html |
| work_keys_str_mv | AT mykurbakov interpretablegraphmethodsfordeterminingnanoparticlesorderinginelectronmicroscopyimage AT vvsulimova interpretablegraphmethodsfordeterminingnanoparticlesorderinginelectronmicroscopyimage AT osseredin interpretablegraphmethodsfordeterminingnanoparticlesorderinginelectronmicroscopyimage AT avkopylov interpretablegraphmethodsfordeterminingnanoparticlesorderinginelectronmicroscopyimage |