Robust nanobubble and nanodroplet segmentation in atomic force microscope images using the spherical Hough transform

Area of Science:

• Surface science and nanotechnology
• Microscopy and imaging techniques
• Materials characterization

Background:

• Interfacial nanobubbles (NBs) and nanodroplets (NDs) show promise for various applications.
• Automated segmentation and characterization of NBs and NDs in atomic force microscope (AFM) images are crucial.
• Existing AFM image segmentation methods struggle with uneven backgrounds caused by thermal drift and scanner hysteresis.

Purpose of the Study:

• To develop an automated, robust method for segmenting NBs and NDs in AFM images.
• To improve the morphological characterization of NBs and NDs.
• To overcome limitations of current segmentation techniques in handling uneven AFM image backgrounds.

Main Methods:

• A two-step automated segmentation approach was proposed.
• The method combines the spherical Hough transform (SHT) for preliminary detection with a contour expansion operation for boundary optimization.
• Detailed procedures for segmentation and characterization were presented and demonstrated.

Main Results:

• The proposed method achieved improved segmentation results compared to thresholding and circle Hough transform methods.
• Demonstrated robust segmentation performance even in AFM images with uneven backgrounds.
• Enabled accurate morphological characterization of NBs and NDs.

Conclusions:

• The developed two-step method offers a robust and automated solution for segmenting NBs and NDs in AFM images.
• This technique effectively addresses challenges posed by uneven backgrounds in AFM imaging.
• The improved segmentation facilitates more reliable morphological characterization for advanced applications.