Machine vision-driven automatic recognition of particle size and morphology in SEM images
Abstract
Scanning Electron Microscopy (SEM) images provide a variety of structural and morphological information of the nanomaterials. In the material informatics domain, automatic recognition and quantitative analysis of SEM images in a high-throughput manner are critical, but challenges still remain due to the complexity and the diversity of the image configurations in both shape and size. In this paper, we present a generally applicable approach using computer vision and machine learning techniques to quantitatively extract particle size, size distribution and morphology information in SEM images. The proposed pipeline offers automatic, high-throughput measurements even when overlapping nanoparticles, rod-shapes, and core-shell nanostructures are present. We demonstrate effectiveness of the proposed approach by performing experiments on SEM images of nanoscale materials and structures with different shapes and sizes. The proposed approach shows promising results (Spearman coefficients of 0.91 and 0.99 using the fully automated and semi-automated processes, respectively) when compared with manually measured sizes.
- Authors:
-
[1];
[2];
[2]
- Center for Applied Scientific Computing, Lawrence Livermore National Laboratory, USA
- Materials Science Division, Lawrence Livermore National Laboratory, USA
- Publication Date:
- Research Org.:
- Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
- Sponsoring Org.:
- USDOE National Nuclear Security Administration (NNSA); USDOE Laboratory Directed Research and Development (LDRD) Program
- OSTI Identifier:
- 1664530
- Alternate Identifier(s):
- OSTI ID: 1651192
- Report Number(s):
- LLNL-JRNL-809488
Journal ID: ISSN 2040-3364; NANOHL
- Grant/Contract Number:
- 16-ERD-019; 19-SI-001; AC52-07NA27344
- Resource Type:
- Published Article
- Journal Name:
- Nanoscale
- Additional Journal Information:
- Journal Name: Nanoscale Journal Volume: 12 Journal Issue: 37; Journal ID: ISSN 2040-3364
- Publisher:
- Royal Society of Chemistry
- Country of Publication:
- United Kingdom
- Language:
- English
- Subject:
- 77 NANOSCIENCE AND NANOTECHNOLOGY; High-throughput pipeline; Nanoparticle structure recognition; Size measurement; Shape recognition; Morphology recognition; SEM; Machine learning; Computer vision; Machine vision
Citation Formats
Kim, Hyojin, Han, Jinkyu, and Han, T. Yong-Jin. Machine vision-driven automatic recognition of particle size and morphology in SEM images. United Kingdom: N. p., 2020.
Web. doi:10.1039/D0NR04140H.
Kim, Hyojin, Han, Jinkyu, & Han, T. Yong-Jin. Machine vision-driven automatic recognition of particle size and morphology in SEM images. United Kingdom. https://doi.org/10.1039/D0NR04140H
Kim, Hyojin, Han, Jinkyu, and Han, T. Yong-Jin. Thu .
"Machine vision-driven automatic recognition of particle size and morphology in SEM images". United Kingdom. https://doi.org/10.1039/D0NR04140H.
@article{osti_1664530,
title = {Machine vision-driven automatic recognition of particle size and morphology in SEM images},
author = {Kim, Hyojin and Han, Jinkyu and Han, T. Yong-Jin},
abstractNote = {Scanning Electron Microscopy (SEM) images provide a variety of structural and morphological information of the nanomaterials. In the material informatics domain, automatic recognition and quantitative analysis of SEM images in a high-throughput manner are critical, but challenges still remain due to the complexity and the diversity of the image configurations in both shape and size. In this paper, we present a generally applicable approach using computer vision and machine learning techniques to quantitatively extract particle size, size distribution and morphology information in SEM images. The proposed pipeline offers automatic, high-throughput measurements even when overlapping nanoparticles, rod-shapes, and core-shell nanostructures are present. We demonstrate effectiveness of the proposed approach by performing experiments on SEM images of nanoscale materials and structures with different shapes and sizes. The proposed approach shows promising results (Spearman coefficients of 0.91 and 0.99 using the fully automated and semi-automated processes, respectively) when compared with manually measured sizes.},
doi = {10.1039/D0NR04140H},
journal = {Nanoscale},
number = 37,
volume = 12,
place = {United Kingdom},
year = {Thu Oct 01 00:00:00 EDT 2020},
month = {Thu Oct 01 00:00:00 EDT 2020}
}
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