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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...

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Detalles Bibliográficos
Autores principales: Wang, Yuliang, Lu, Tongda, Li, Xiaolai, Wang, Huimin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Beilstein-Institut 2018
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.
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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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