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https://doi.org/10.3938/NPSM.67.64
Component Tree and Multi-Layer Perceptron Techniques for Nanoparticle Image Segmentation and Classification
New Physics: Sae Mulli 2017; 67: 64~69
Published online January 31, 2017;  https://doi.org/10.3938/NPSM.67.64
© 2017 New Physics: Sae Mulli.

Sung-Hyon KIM*1, Il-Seok OH†2

1 Department of Nano Science Technology Graduate School, Chonbuk National University, Jeonju 54896, Korea
2 Department of Computer Science and Engineering Graduate School, Chonbuk National University, Jeonju 54896, Korea
Correspondence to: *starwise@jbnu.ac.kr, †isoh@jbnu.ac.kr
Received September 22, 2016; Revised September 29, 2016; Accepted September 30, 2016.
cc This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
With the development of the microscope, microscopic observations and experiments became possible; thus, fast and effective analysis of the images of cells or nanoparticles taken with high-performance microscopes has become more important than ever. The problems of particle segmentation for counting and classification by the type of particles are essential research issues that have been researched steadily so far. In this paper, we identify particle candidates for images, and we use a classifier in an attempt to classify the candidates by type. First, we build a component tree of input images in quasi-linear time and extract areas with a higher possibility of particles with their morphological features for making data set. Then, we use the data set to train multi-layer perceptron classifiers and attempt to classify the particle candidates. Experimental results showed that the particle clusters were correctly classified with high accuracy.
PACS numbers: 06.90.+v
Keywords: Nanoparticles, Segmentation, Classification, Component tree, Machine learning


March 2017, 67 (3)