Automated image segmentation-assisted flattening of atomic force microscopy images
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...
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doaj-6c4d2f16804e48efbc25396733a556df2020-11-25T00:59:40ZengBeilstein-InstitutBeilstein Journal of Nanotechnology2190-42862018-03-019197598510.3762/bjnano.9.912190-4286-9-91Automated image segmentation-assisted flattening of atomic force microscopy imagesYuliang Wang0Tongda Lu1Xiaolai Li2Huimin Wang3School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, P.R. ChinaSchool of Mechanical Engineering and Automation, Beihang University, Beijing 100191, P.R. ChinaSchool of Mechanical Engineering and Automation, Beihang University, Beijing 100191, P.R. ChinaDepartment of Materials Science and Engineering, Ohio State University, 2041 College Rd., Columbus, OH 43210, USAAtomic 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 automatically segmented with accurate boundary detection. The extracted foreground features were taken as exclusion masks. In the second step, data points in the background were fitted as polynomial curves/surfaces, which were then subtracted from raw images to get the flattened images. Moreover, sliding-window-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.https://doi.org/10.3762/bjnano.9.91atomic force microscopycontour expansionimage flatteningpolynomial fittingsliding window |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yuliang Wang Tongda Lu Xiaolai Li Huimin Wang |
spellingShingle |
Yuliang Wang Tongda Lu Xiaolai Li Huimin Wang Automated image segmentation-assisted flattening of atomic force microscopy images Beilstein Journal of Nanotechnology atomic force microscopy contour expansion image flattening polynomial fitting sliding window |
author_facet |
Yuliang Wang Tongda Lu Xiaolai Li Huimin Wang |
author_sort |
Yuliang Wang |
title |
Automated image segmentation-assisted flattening of atomic force microscopy images |
title_short |
Automated image segmentation-assisted flattening of atomic force microscopy images |
title_full |
Automated image segmentation-assisted flattening of atomic force microscopy images |
title_fullStr |
Automated image segmentation-assisted flattening of atomic force microscopy images |
title_full_unstemmed |
Automated image segmentation-assisted flattening of atomic force microscopy images |
title_sort |
automated image segmentation-assisted flattening of atomic force microscopy images |
publisher |
Beilstein-Institut |
series |
Beilstein Journal of Nanotechnology |
issn |
2190-4286 |
publishDate |
2018-03-01 |
description |
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 automatically segmented with accurate boundary detection. The extracted foreground features were taken as exclusion masks. In the second step, data points in the background were fitted as polynomial curves/surfaces, which were then subtracted from raw images to get the flattened images. Moreover, sliding-window-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. |
topic |
atomic force microscopy contour expansion image flattening polynomial fitting sliding window |
url |
https://doi.org/10.3762/bjnano.9.91 |
work_keys_str_mv |
AT yuliangwang automatedimagesegmentationassistedflatteningofatomicforcemicroscopyimages AT tongdalu automatedimagesegmentationassistedflatteningofatomicforcemicroscopyimages AT xiaolaili automatedimagesegmentationassistedflatteningofatomicforcemicroscopyimages AT huiminwang automatedimagesegmentationassistedflatteningofatomicforcemicroscopyimages |
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1725216860858744832 |