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Journal of Flow Visualization and Image Processing
SJR: 0.161 SNIP: 0.312 CiteScore™: 0.1

ISSN Imprimir: 1065-3090
ISSN En Línea: 1940-4336

Journal of Flow Visualization and Image Processing

DOI: 10.1615/JFlowVisImageProc.2019030652
pages 239-252


Dong Xiang
School of Computer Science and Technology, Wuhan University of Technology, Wuhan, 430063, China
Di Cai
School of Computer Science and Technology, Wuhan University of Technology, Wuhan, 430063, China
Xiangneng Hu
School of Computer Science and Technology, Wuhan University of Technology, Wuhan, 430063, China
Hanbing Yao
School of Computer Science and Technology, Wuhan University of Technology, Wuhan, 430063, China
Ting Liu
School of Environmental Science & Engineering, Hubei Polytechnic University, Hubei Key Laboratory of Mine Environmental Pollution Control and Remediation, Huangshi, 435003, China
Lung-Wen Antony Chen
Department of Environmental and Occupational Health, School of Community Health Sciences, University of Nevada Las Vegas, Las Vegas, NV, 89154, USA
Mi Zhang
Department of Environmental and Occupational Health, School of Community Health Sciences, University of Nevada Las Vegas, Las Vegas, NV, 89154, USA


This paper addresses the task of particle segmentation in high-resolution aerosol images. A particle-boundary model is introduced to predict particles and their boundaries, simultaneously using a fully convolutional neural network. Given a RGB color image, the model directly outputs a predicted particle map and a boundary map. A simple and parameter-free watershed post-processing procedure is performed on the predicted particle and boundary map to produce the final segmented particles. An overlapping 256 × 256 patch extraction method is also designed for seamless prediction of the particle in large images. During the training phase, a combined loss function is used to alleviate the problem of unbalanced classes. We compare two weight initialization schemes: He's normal initializer and one pretrained on the 2018 Data Science Bowl. Experimental results show that the pretrained network is able to converge much faster to its steady value in comparison with the nonpretrained network and achieve higher intersection over union scores. Our method also outperforms the traditional Otsu and triangle methods and gets better segmentation results on raw and contrast adjusted test images.


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