Automated analysis of grain morphology in TEM images using convolutional neural network with CHAC algorithm

Xu, Xinyuan; Yu, Zefeng; Chen, Wei-Ying; Chen, Aiping; Motta, Arthur; Wang, Xing

doi:10.1016/j.jnucmat.2023.154813

Journal of Nuclear Materials

2024

DOI: 10.1016/j.jnucmat.2023.154813

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Automated analysis of grain morphology in TEM images using convolutional neural network with CHAC algorithm

Xinyuan Xu,

Zefeng Yu,

Wei-Ying Chen

et al.

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2024

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Cited by 3 publications

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Advances in Experimental Studies of Grain Growth in Thin Films

Barmak,

Rickman,

Patrick

2024

JOM

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Advances in Experimental Studies of Grain Growth in Thin Films

Barmak,

Rickman,

Patrick

2024

JOM

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Performance of Machine Learning Models for Detecting Grain Boundaries in Transmission Electron Microscopy Images

Ochoa,

Xu,

Wang

2024

Microscopy and Microanalysis

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Unsupervised Learning for the Automatic Counting of Grains in Nanocrystals and Image Segmentation at the Atomic Resolution

Sohn,

Kim,

Moon

et al. 2024

Nanomaterials

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Identifying the grain distribution and grain boundaries of nanoparticles is important for predicting their properties. Experimental methods for identifying the crystallographic distribution, such as precession electron diffraction, are limited by their probe size. In this study, we developed an unsupervised learning method by applying a Gabor filter to HAADF-STEM images at the atomic level for image segmentation and automatic counting of grains in polycrystalline nanoparticles. The methodology comprises a Gabor filter for feature extraction, non-negative matrix factorization for dimension reduction, and K-means clustering. We set the threshold distance and angle between the clusters required for the number of clusters to converge so as to automatically determine the optimal number of grains. This approach can shed new light on the nature of polycrystalline nanoparticles and their structure–property relationships.

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Automated analysis of grain morphology in TEM images using convolutional neural network with CHAC algorithm

Cited by 3 publications

References 47 publications

Advances in Experimental Studies of Grain Growth in Thin Films

Advances in Experimental Studies of Grain Growth in Thin Films

Performance of Machine Learning Models for Detecting Grain Boundaries in Transmission Electron Microscopy Images

Unsupervised Learning for the Automatic Counting of Grains in Nanocrystals and Image Segmentation at the Atomic Resolution

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