Automatic morphological characterization of nanobubbles with a novel image segmentation method and its application in the study of nanobubble coalescence

Nanobubbles (NBs) on hydrophobic surfaces in aqueous solvents have shown great potential in numerous applications. In this study, the morphological characterization of NBs in AFM images was carried out with the assistance of a novel image segmentation method. The method combines the classical thresh...

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Main Authors:	Wang, Yuliang, Wang, Huimin, Bi, Shusheng, Guo, Bin
Format:	Online
Language:	English
Published:	Beilstein-Institut 2015
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4419579/

id	pubmed-4419579
recordtype	oai_dc
spelling	pubmed-44195792015-05-14 Automatic morphological characterization of nanobubbles with a novel image segmentation method and its application in the study of nanobubble coalescence Wang, Yuliang Wang, Huimin Bi, Shusheng Guo, Bin Full Research Paper Nanobubbles (NBs) on hydrophobic surfaces in aqueous solvents have shown great potential in numerous applications. In this study, the morphological characterization of NBs in AFM images was carried out with the assistance of a novel image segmentation method. The method combines the classical threshold method and a modified, active contour method to achieve optimized image segmentation. The image segmentation results obtained with the classical threshold method and the proposed, modified method were compared. With the modified method, the diameter, contact angle, and radius of curvature were automatically measured for all NBs in AFM images. The influence of the selection of the threshold value on the segmentation result was discussed. Moreover, the morphological change in the NBs was studied in terms of density, covered area, and volume occurring during coalescence under external disturbance. Beilstein-Institut 2015-04-14 /pmc/articles/PMC4419579/ /pubmed/25977866 http://dx.doi.org/10.3762/bjnano.6.98 Text en Copyright © 2015, Wang et al; licensee Beilstein-Institut. http://www.beilstein-journals.org/bjnano This is an Open Access article under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The license is subject to the Beilstein Journal of Nanotechnology terms and conditions: (http://www.beilstein-journals.org/bjnano)
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Wang, Yuliang Wang, Huimin Bi, Shusheng Guo, Bin
spellingShingle	Wang, Yuliang Wang, Huimin Bi, Shusheng Guo, Bin Automatic morphological characterization of nanobubbles with a novel image segmentation method and its application in the study of nanobubble coalescence
author_facet	Wang, Yuliang Wang, Huimin Bi, Shusheng Guo, Bin
author_sort	Wang, Yuliang
title	Automatic morphological characterization of nanobubbles with a novel image segmentation method and its application in the study of nanobubble coalescence
title_short	Automatic morphological characterization of nanobubbles with a novel image segmentation method and its application in the study of nanobubble coalescence
title_full	Automatic morphological characterization of nanobubbles with a novel image segmentation method and its application in the study of nanobubble coalescence
title_fullStr	Automatic morphological characterization of nanobubbles with a novel image segmentation method and its application in the study of nanobubble coalescence
title_full_unstemmed	Automatic morphological characterization of nanobubbles with a novel image segmentation method and its application in the study of nanobubble coalescence
title_sort	automatic morphological characterization of nanobubbles with a novel image segmentation method and its application in the study of nanobubble coalescence
description	Nanobubbles (NBs) on hydrophobic surfaces in aqueous solvents have shown great potential in numerous applications. In this study, the morphological characterization of NBs in AFM images was carried out with the assistance of a novel image segmentation method. The method combines the classical threshold method and a modified, active contour method to achieve optimized image segmentation. The image segmentation results obtained with the classical threshold method and the proposed, modified method were compared. With the modified method, the diameter, contact angle, and radius of curvature were automatically measured for all NBs in AFM images. The influence of the selection of the threshold value on the segmentation result was discussed. Moreover, the morphological change in the NBs was studied in terms of density, covered area, and volume occurring during coalescence under external disturbance.
publisher	Beilstein-Institut
publishDate	2015
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4419579/
_version_	1613219107311714304

Automatic morphological characterization of nanobubbles with a novel image segmentation method and its application in the study of nanobubble coalescence

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