Wang Yuliang, Lu Tongda, Li Xiaolai, Ren Shuai, Bi Shusheng
School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, P. R. China.
Beilstein J Nanotechnol. 2017 Dec 1;8:2572-2582. doi: 10.3762/bjnano.8.257. eCollection 2017.
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.
界面纳米气泡（NBs）和纳米液滴（NDs）因其在众多应用中的潜力而受到越来越多的关注。因此，人们急切期待在原子力显微镜（AFM）图像中对NBs和NDs进行自动分割和形态表征。由于AFM扫描仪的热漂移和滞后效应，当前的分割方法在AFM图像中存在背景不均匀的问题。在本研究中，提出了一种两步法以自动分割AFM图像中的NBs和NDs。将球形霍夫变换（SHT）和边界优化操作相结合以实现稳健分割。首先使用SHT初步检测NBs和NDs。之后，应用所谓的轮廓扩展操作来获得优化边界。介绍了所提方法的原理和详细步骤，随后展示了自动分割和形态表征。结果表明，与阈值法和圆形霍夫变换法相比，所提方法给出了更好的分割结果。此外，所提方法在背景不均匀的AFM图像中表现出很强的分割稳健性。
