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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Beilstein-Institut
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5905267/ https://www.ncbi.nlm.nih.gov/pubmed/29719750 http://dx.doi.org/10.3762/bjnano.9.91 |
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author | Wang, Yuliang Lu, Tongda Li, Xiaolai Wang, Huimin |
author_facet | Wang, Yuliang Lu, Tongda Li, Xiaolai Wang, Huimin |
author_sort | Wang, Yuliang |
collection | PubMed |
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. |
format | Online Article Text |
id | pubmed-5905267 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Beilstein-Institut |
record_format | MEDLINE/PubMed |
spelling | pubmed-59052672018-05-01 Automated image segmentation-assisted flattening of atomic force microscopy images Wang, Yuliang Lu, Tongda Li, Xiaolai Wang, Huimin Beilstein J Nanotechnol Full Research Paper 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. Beilstein-Institut 2018-03-26 /pmc/articles/PMC5905267/ /pubmed/29719750 http://dx.doi.org/10.3762/bjnano.9.91 Text en Copyright © 2018, Wang et al. https://creativecommons.org/licenses/by/4.0https://www.beilstein-journals.org/bjnano/termsThis is an Open Access article under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The license is subject to the Beilstein Journal of Nanotechnology terms and conditions: (https://www.beilstein-journals.org/bjnano/terms) |
spellingShingle | Full Research Paper Wang, Yuliang Lu, Tongda Li, Xiaolai Wang, Huimin Automated image segmentation-assisted flattening of atomic force microscopy images |
title | 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_short | Automated image segmentation-assisted flattening of atomic force microscopy images |
title_sort | automated image segmentation-assisted flattening of atomic force microscopy images |
topic | Full Research Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5905267/ https://www.ncbi.nlm.nih.gov/pubmed/29719750 http://dx.doi.org/10.3762/bjnano.9.91 |
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