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Journal of Nanophotonics • new

Segmenting overlapping nano-objects in atomic force microscopy image
Author(s): Qian Wang; Yuexing Han; Qing Li; Bing Wang; Akihiko Konagaya
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Paper Abstract

Recently, techniques for nanoparticles have rapidly been developed for various fields, such as material science, medical, and biology. In particular, methods of image processing have widely been used to automatically analyze nanoparticles. A technique to automatically segment overlapping nanoparticles with image processing and machine learning is proposed. Here, two tasks are necessary: elimination of image noises and action of the overlapping shapes. For the first task, mean square error and the seed fill algorithm are adopted to remove noises and improve the quality of the original image. For the second task, four steps are needed to segment the overlapping nanoparticles. First, possibility split lines are obtained by connecting the high curvature pixels on the contours. Second, the candidate split lines are classified with a machine learning algorithm. Third, the overlapping regions are detected with the method of density-based spatial clustering of applications with noise (DBSCAN). Finally, the best split lines are selected with a constrained minimum value. We give some experimental examples and compare our technique with two other methods. The results can show the effectiveness of the proposed technique.

Paper Details

Date Published: 18 January 2018
PDF: 15 pages
J. Nanophoton. 12(1) 016003 doi: 10.1117/1.JNP.12.016003
Published in: Journal of Nanophotonics Volume 12, Issue 1
Show Author Affiliations
Qian Wang, Shanghai Univ. (China)
Yuexing Han, Shanghai Univ. (China)
Tokyo Institute of Technology (Japan)
Qing Li, Shanghai Univ. (China)
Bing Wang, Shanghai Univ. (China)
Akihiko Konagaya, Tokyo Institute of Technology (Japan)
National Institute of Informatics (Japan)


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