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Interpretable graph methods for determining nanoparticles ordering in electron microscopy image
M.Y. Kurbakov 1, V.V. Sulimova 1, O.S. Seredin 1, A.V. Kopylov 1

1 Tula State University,
300012, Russia, Tula, Lenina Av. 92

 PDF, 2458 kB

DOI: 10.18287/2412-6179-CO-1568

Pages: 470-479.

Full text of article: English language.

Abstract:
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.

Keywords:
explainable machine learning, image analysis, nanoparticle detection, nanoparticles ordering features.

Citation:
Kurbakov MY, Sulimova VV, Seredin OS, Kopylov AV. Interpretable graph methods for determining nanoparticles ordering in electron microscopy images. Computer Optics 2025; 49(3): 470-479. DOI: 10.18287/2412-6179-CO-1568.

Acknowledgements:
This work was supported by the Ministry of Science and Higher Education of the Russian Federation within the framework of the state task FEWG-2024-0001.
     Experiments were partially made using the equipment of the shared research facilities of HPC computing resources at the Lomonosov Moscow State University.
The authors thank the Scientific School of Academic V.P. Ananikov for the research topic, useful discussions and provided experimental data.

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