Automated image segmentation-assisted flattening of atomic force microscopy images

• Yuliang Wang,
• Tongda Lu,
• Xiaolai Li and
• Huimin Wang

Beilstein J. Nanotechnol. 2018, 9, 975–985, doi:10.3762/bjnano.9.91

• Engineering, Ohio State University, 2041 College Rd., Columbus, OH 43210, USA 10.3762/bjnano.9.91 Abstract Atomic force microscopy (AFM) images normally exhibit various artifacts. As a result, image flattening is required prior to image analysis. To obtain optimized flattening results, foreground features

• are generally manually excluded using rectangular masks in image flattening, which is time consuming and inaccurate. In this study, a two-step scheme was proposed to achieve optimized image flattening in an automated manner. In the first step, the convex and concave features in the foreground were

• -based polynomial fitting was proposed to process images with complex background trends. The working principle of the two-step image flattening scheme were presented, followed by the investigation of the influence of a sliding-window size and polynomial fitting direction on the flattened images

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

• Yuliang Wang,
• Tongda Lu,
• Xiaolai Li,
• Shuai Ren and
• Shusheng Bi

Beilstein J. Nanotechnol. 2017, 8, 2572–2582, doi:10.3762/bjnano.8.257

• . By applying image flattening, an AFM image with improved contrast can be obtained, as shown in Figure 1b. Today, the most widely applied segmentation method is the thresholding method [19][26] in which the choice of threshold value is a matter of great concern due to the uneven background of AFM

Large-scale analysis of high-speed atomic force microscopy data sets using adaptive image processing

• Blake W. Erickson,
• Séverine Coquoz,
• Jonathan D. Adams,
• Daniel J. Burns and
• Georg E. Fantner

Beilstein J. Nanotechnol. 2012, 3, 747–758, doi:10.3762/bjnano.3.84

• an array of pixels in the image along the fast scan axis. Standard deviation as a metric for image flatness In order to show that the standard deviation of an image is a suitable metric for monitoring the progress of image flattening we can use uncertainty propagation. We can start by describing the