Robust nanobubble and nanodroplet segmentation in atomic force microscope images using the spherical Hough transform
| Autor: | Xiaolai Li, Tongda Lu, Yuliang Wang, Shuai Ren, Shusheng Bi |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
Materials science
Automated segmentation General Physics and Astronomy Boundary (topology) 02 engineering and technology 010402 general chemistry lcsh:Chemical technology 01 natural sciences lcsh:Technology Full Research Paper Hough transform law.invention law Robustness (computer science) Nanotechnology morphological characterization General Materials Science Segmentation Computer vision lcsh:TP1-1185 Electrical and Electronic Engineering lcsh:Science atomic force microscopy business.industry Atomic force microscopy lcsh:T segmentation nanodroplets 021001 nanoscience & nanotechnology Thresholding lcsh:QC1-999 0104 chemical sciences Characterization (materials science) Nanoscience lcsh:Q Artificial intelligence 0210 nano-technology business lcsh:Physics nanobubbles |
| Zdroj: | Beilstein Journal of Nanotechnology, Vol 8, Iss 1, Pp 2572-2582 (2017) Beilstein Journal of Nanotechnology |
| ISSN: | 2190-4286 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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