Robust nanobubble and nanodroplet segmentation in atomic force microscope images using the spherical Hough transform
Interfacial nanobubbles (NBs) and nanodroplets (NDs) have been attracting increasing attention due to their potential for numerous applications. As a result, the automated segmentation and morphological characterization of NBs and NDs in atomic force microscope (AFM) images is highly awaited. The cu...
Main Authors: | , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Beilstein-Institut
2017-12-01
|
Series: | Beilstein Journal of Nanotechnology |
Subjects: | |
Online Access: | https://doi.org/10.3762/bjnano.8.257 |
id |
doaj-6dab7428492d4ffaaeb11d888c215097 |
---|---|
record_format |
Article |
spelling |
doaj-6dab7428492d4ffaaeb11d888c2150972020-11-25T01:46:54ZengBeilstein-InstitutBeilstein Journal of Nanotechnology2190-42862017-12-01812572258210.3762/bjnano.8.2572190-4286-8-257Robust nanobubble and nanodroplet segmentation in atomic force microscope images using the spherical Hough transformYuliang Wang0Tongda Lu1Xiaolai Li2Shuai Ren3Shusheng Bi4School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, P. R. ChinaSchool of Mechanical Engineering and Automation, Beihang University, Beijing 100191, P. R. ChinaSchool of Mechanical Engineering and Automation, Beihang University, Beijing 100191, P. R. ChinaSchool of Mechanical Engineering and Automation, Beihang University, Beijing 100191, P. R. ChinaSchool of Mechanical Engineering and Automation, Beihang University, Beijing 100191, P. R. ChinaInterfacial nanobubbles (NBs) and nanodroplets (NDs) have been attracting increasing attention due to their potential for numerous applications. As a result, the automated segmentation and morphological characterization of NBs and NDs in atomic force microscope (AFM) images is highly awaited. The current segmentation methods suffer from the uneven background in AFM images due to thermal drift and hysteresis of AFM scanners. In this study, a two-step approach was proposed to segment NBs and NDs in AFM images in an automated manner. The spherical Hough transform (SHT) and a boundary optimization operation were combined to achieve robust segmentation. The SHT was first used to preliminarily detect NBs and NDs. After that, the so-called contour expansion operation was applied to achieve optimized boundaries. The principle and the detailed procedure of the proposed method were presented, followed by the demonstration of the automated segmentation and morphological characterization. The result shows that the proposed method gives an improved segmentation result compared with the thresholding and circle Hough transform method. Moreover, the proposed method shows strong robustness of segmentation in AFM images with an uneven background.https://doi.org/10.3762/bjnano.8.257atomic force microscopyHough transformmorphological characterizationnanobubblesnanodropletssegmentation |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yuliang Wang Tongda Lu Xiaolai Li Shuai Ren Shusheng Bi |
spellingShingle |
Yuliang Wang Tongda Lu Xiaolai Li Shuai Ren Shusheng Bi Robust nanobubble and nanodroplet segmentation in atomic force microscope images using the spherical Hough transform Beilstein Journal of Nanotechnology atomic force microscopy Hough transform morphological characterization nanobubbles nanodroplets segmentation |
author_facet |
Yuliang Wang Tongda Lu Xiaolai Li Shuai Ren Shusheng Bi |
author_sort |
Yuliang Wang |
title |
Robust nanobubble and nanodroplet segmentation in atomic force microscope images using the spherical Hough transform |
title_short |
Robust nanobubble and nanodroplet segmentation in atomic force microscope images using the spherical Hough transform |
title_full |
Robust nanobubble and nanodroplet segmentation in atomic force microscope images using the spherical Hough transform |
title_fullStr |
Robust nanobubble and nanodroplet segmentation in atomic force microscope images using the spherical Hough transform |
title_full_unstemmed |
Robust nanobubble and nanodroplet segmentation in atomic force microscope images using the spherical Hough transform |
title_sort |
robust nanobubble and nanodroplet segmentation in atomic force microscope images using the spherical hough transform |
publisher |
Beilstein-Institut |
series |
Beilstein Journal of Nanotechnology |
issn |
2190-4286 |
publishDate |
2017-12-01 |
description |
Interfacial nanobubbles (NBs) and nanodroplets (NDs) have been attracting increasing attention due to their potential for numerous applications. As a result, the automated segmentation and morphological characterization of NBs and NDs in atomic force microscope (AFM) images is highly awaited. The current segmentation methods suffer from the uneven background in AFM images due to thermal drift and hysteresis of AFM scanners. In this study, a two-step approach was proposed to segment NBs and NDs in AFM images in an automated manner. The spherical Hough transform (SHT) and a boundary optimization operation were combined to achieve robust segmentation. The SHT was first used to preliminarily detect NBs and NDs. After that, the so-called contour expansion operation was applied to achieve optimized boundaries. The principle and the detailed procedure of the proposed method were presented, followed by the demonstration of the automated segmentation and morphological characterization. The result shows that the proposed method gives an improved segmentation result compared with the thresholding and circle Hough transform method. Moreover, the proposed method shows strong robustness of segmentation in AFM images with an uneven background. |
topic |
atomic force microscopy Hough transform morphological characterization nanobubbles nanodroplets segmentation |
url |
https://doi.org/10.3762/bjnano.8.257 |
work_keys_str_mv |
AT yuliangwang robustnanobubbleandnanodropletsegmentationinatomicforcemicroscopeimagesusingthesphericalhoughtransform AT tongdalu robustnanobubbleandnanodropletsegmentationinatomicforcemicroscopeimagesusingthesphericalhoughtransform AT xiaolaili robustnanobubbleandnanodropletsegmentationinatomicforcemicroscopeimagesusingthesphericalhoughtransform AT shuairen robustnanobubbleandnanodropletsegmentationinatomicforcemicroscopeimagesusingthesphericalhoughtransform AT shushengbi robustnanobubbleandnanodropletsegmentationinatomicforcemicroscopeimagesusingthesphericalhoughtransform |
_version_ |
1725017311963774976 |