18 January 2018 Segmenting overlapping nano-objects in atomic force microscopy image
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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.
© 2018 Society of Photo-Optical Instrumentation Engineers (SPIE)
Qian Wang, Qian Wang, Yuexing Han, Yuexing Han, Qing Li, Qing Li, Bing Wang, Bing Wang, Akihiko Konagaya, Akihiko Konagaya, } "Segmenting overlapping nano-objects in atomic force microscopy image," Journal of Nanophotonics 12(1), 016003 (18 January 2018). https://doi.org/10.1117/1.JNP.12.016003 . Submission: Received: 20 July 2017; Accepted: 15 December 2017
Received: 20 July 2017; Accepted: 15 December 2017; Published: 18 January 2018
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