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 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.