Universal image segmentation for optical identification of 2D materials

Abstract Machine learning methods are changing the way data is analyzed. One of the most powerful and widespread applications of these techniques is in image segmentation wherein disparate objects of a digital image are partitioned and classified. Here we present an image segmentation program incorp...

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Autores principales:	Randy M. Sterbentz, Kristine L. Haley, Joshua O. Island
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/bc46e0476ced4b9ca84a9176522342e8
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spelling	oai:doaj.org-article:bc46e0476ced4b9ca84a9176522342e82021-12-02T15:54:14ZUniversal image segmentation for optical identification of 2D materials10.1038/s41598-021-85159-92045-2322https://doaj.org/article/bc46e0476ced4b9ca84a9176522342e82021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-85159-9https://doaj.org/toc/2045-2322Abstract Machine learning methods are changing the way data is analyzed. One of the most powerful and widespread applications of these techniques is in image segmentation wherein disparate objects of a digital image are partitioned and classified. Here we present an image segmentation program incorporating a series of unsupervised clustering algorithms for the automatic thickness identification of two-dimensional materials from digital optical microscopy images. The program identifies mono- and few-layer flakes of a variety of materials on both opaque and transparent substrates with a pixel accuracy of roughly 95%. Contrasting with previous attempts, application generality is achieved through preservation and analysis of all three digital color channels and Gaussian mixture model fits to arbitrarily shaped data clusters. Our results provide a facile implementation of data clustering for the universal, automatic identification of two-dimensional materials exfoliated onto any substrate.Randy M. SterbentzKristine L. HaleyJoshua O. IslandNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Randy M. Sterbentz Kristine L. Haley Joshua O. Island Universal image segmentation for optical identification of 2D materials
description	Abstract Machine learning methods are changing the way data is analyzed. One of the most powerful and widespread applications of these techniques is in image segmentation wherein disparate objects of a digital image are partitioned and classified. Here we present an image segmentation program incorporating a series of unsupervised clustering algorithms for the automatic thickness identification of two-dimensional materials from digital optical microscopy images. The program identifies mono- and few-layer flakes of a variety of materials on both opaque and transparent substrates with a pixel accuracy of roughly 95%. Contrasting with previous attempts, application generality is achieved through preservation and analysis of all three digital color channels and Gaussian mixture model fits to arbitrarily shaped data clusters. Our results provide a facile implementation of data clustering for the universal, automatic identification of two-dimensional materials exfoliated onto any substrate.
format	article
author	Randy M. Sterbentz Kristine L. Haley Joshua O. Island
author_facet	Randy M. Sterbentz Kristine L. Haley Joshua O. Island
author_sort	Randy M. Sterbentz
title	Universal image segmentation for optical identification of 2D materials
title_short	Universal image segmentation for optical identification of 2D materials
title_full	Universal image segmentation for optical identification of 2D materials
title_fullStr	Universal image segmentation for optical identification of 2D materials
title_full_unstemmed	Universal image segmentation for optical identification of 2D materials
title_sort	universal image segmentation for optical identification of 2d materials
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/bc46e0476ced4b9ca84a9176522342e8
work_keys_str_mv	AT randymsterbentz universalimagesegmentationforopticalidentificationof2dmaterials AT kristinelhaley universalimagesegmentationforopticalidentificationof2dmaterials AT joshuaoisland universalimagesegmentationforopticalidentificationof2dmaterials
_version_	1718385472801079296

Universal image segmentation for optical identification of 2D materials

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