Automated line flattening of atomic force microscopy images

S. A. Tsaftaris; J. Zujovic; Aggelos K Katsaggelos

doi:10.1109/ICIP.2008.4712418

Automated line flattening of atomic force microscopy images

S. A. Tsaftaris, J. Zujovic, Aggelos K Katsaggelos

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Scopus citations

Abstract

In this paper, an automated algorithm to flatten lines from Atomic Force Microscopy (AFM) images is presented. Due to the mechanics of the AFM, there is a curvature distortion (bowing effect) present in the acquired images. At present, flattening such images requires human intervention to manually segment object data from the background, which is time consuming and highly inaccurate. The proposed method classifies the data into objects and background, and fits convex lines in an iterative fashion. Results on real images from DNA wrapped carbon nanotubes (DNA-CNTs) and synthetic experiments are presented, demonstrating the effectiveness of the proposed algorithm in increasing the resolution of the surface topography.

Original language	English (US)
Title of host publication	2008 IEEE International Conference on Image Processing, ICIP 2008 Proceedings
Pages	2968-2971
Number of pages	4
DOIs	https://doi.org/10.1109/ICIP.2008.4712418
State	Published - Dec 1 2008
Event	2008 IEEE International Conference on Image Processing, ICIP 2008 - San Diego, CA, United States Duration: Oct 12 2008 → Oct 15 2008

Publication series

Name	Proceedings - International Conference on Image Processing, ICIP
ISSN (Print)	1522-4880

Other

Other	2008 IEEE International Conference on Image Processing, ICIP 2008
Country	United States
City	San Diego, CA
Period	10/12/08 → 10/15/08

Keywords

Curve fitting
Nanotechnology
Object detection
Polynomial approximation

ASJC Scopus subject areas

Software
Computer Vision and Pattern Recognition
Signal Processing

Access to Document

10.1109/ICIP.2008.4712418

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title = "Automated line flattening of atomic force microscopy images",

abstract = "In this paper, an automated algorithm to flatten lines from Atomic Force Microscopy (AFM) images is presented. Due to the mechanics of the AFM, there is a curvature distortion (bowing effect) present in the acquired images. At present, flattening such images requires human intervention to manually segment object data from the background, which is time consuming and highly inaccurate. The proposed method classifies the data into objects and background, and fits convex lines in an iterative fashion. Results on real images from DNA wrapped carbon nanotubes (DNA-CNTs) and synthetic experiments are presented, demonstrating the effectiveness of the proposed algorithm in increasing the resolution of the surface topography.",

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author = "Tsaftaris, {S. A.} and J. Zujovic and Katsaggelos, {Aggelos K}",

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Tsaftaris, SA, Zujovic, J & Katsaggelos, AK 2008, Automated line flattening of atomic force microscopy images. in 2008 IEEE International Conference on Image Processing, ICIP 2008 Proceedings., 4712418, Proceedings - International Conference on Image Processing, ICIP, pp. 2968-2971, 2008 IEEE International Conference on Image Processing, ICIP 2008, San Diego, CA, United States, 10/12/08. https://doi.org/10.1109/ICIP.2008.4712418

Automated line flattening of atomic force microscopy images. / Tsaftaris, S. A.; Zujovic, J.; Katsaggelos, Aggelos K.

2008 IEEE International Conference on Image Processing, ICIP 2008 Proceedings. 2008. p. 2968-2971 4712418 (Proceedings - International Conference on Image Processing, ICIP).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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N2 - In this paper, an automated algorithm to flatten lines from Atomic Force Microscopy (AFM) images is presented. Due to the mechanics of the AFM, there is a curvature distortion (bowing effect) present in the acquired images. At present, flattening such images requires human intervention to manually segment object data from the background, which is time consuming and highly inaccurate. The proposed method classifies the data into objects and background, and fits convex lines in an iterative fashion. Results on real images from DNA wrapped carbon nanotubes (DNA-CNTs) and synthetic experiments are presented, demonstrating the effectiveness of the proposed algorithm in increasing the resolution of the surface topography.

AB - In this paper, an automated algorithm to flatten lines from Atomic Force Microscopy (AFM) images is presented. Due to the mechanics of the AFM, there is a curvature distortion (bowing effect) present in the acquired images. At present, flattening such images requires human intervention to manually segment object data from the background, which is time consuming and highly inaccurate. The proposed method classifies the data into objects and background, and fits convex lines in an iterative fashion. Results on real images from DNA wrapped carbon nanotubes (DNA-CNTs) and synthetic experiments are presented, demonstrating the effectiveness of the proposed algorithm in increasing the resolution of the surface topography.

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KW - Nanotechnology

KW - Object detection

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