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

• -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

• . Additionally, the role of image flattening on the morphological characterization and segmentation of AFM images were verified with the proposed method. Keywords: atomic force microscopy; contour expansion; image flattening; polynomial fitting; sliding window; Introduction Since its invention, the atomic

• required prior to analysis. In AFM image flattening, individual scan lines are fitted as polynomial curves with the least-square method [29]. The obtained polynomial curves are then subtracted from AFM scan lines to get flattened images. The direct polynomial fitting can cause stripe-type artifacts

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

• the masks. This sequence determines the unique 1-D offset for each line. The third block subtracts these 1-D offsets from the raw data and performs a single polynomial fitting on the data within the mask. This background is subtracted from the entire image (raw-offsets). Finally, the height of the