Next Article in Journal
A Functional Data Analysis for Assessing the Impact of a Retrofitting in the Energy Performance of a Building
Previous Article in Journal
Hybridization of Multi-Objective Deterministic Particle Swarm with Derivative-Free Local Searches
Open AccessArticle

Lung X-ray Segmentation using Deep Convolutional Neural Networks on Contrast-Enhanced Binarized Images

1
Department of Electronics and Computer Engineering, National Taiwan University of Science and Technology, Taipei 10607, Taiwan
2
Department of Computer Science, National Chengchi University, Taipei 11605, Taiwan
*
Author to whom correspondence should be addressed.
Mathematics 2020, 8(4), 545; https://doi.org/10.3390/math8040545
Received: 2 March 2020 / Revised: 29 March 2020 / Accepted: 1 April 2020 / Published: 7 April 2020
(This article belongs to the Special Issue Artificial Intelligence and Big Data Computing)
Automatically locating the lung regions effectively and efficiently in digital chest X-ray (CXR) images is important in computer-aided diagnosis. In this paper, we propose an adaptive pre-processing approach for segmenting the lung regions from CXR images using convolutional neural networks-based (CNN-based) architectures. It is comprised of three steps. First, a contrast enhancement method specifically designed for CXR images is adopted. Second, adaptive image binarization is applied to CXR images to separate the image foreground and background. Third, CNN-based architectures are trained on the binarized images for image segmentation. The experimental results show that the proposed pre-processing approach is applicable and effective to various CNN-based architectures and can achieve comparable segmentation accuracy to that of state-of-the-art methods while greatly expediting the model training by up to 20.74 % and reducing storage space for CRX image datasets by down to 94.6 % on average. View Full-Text
Keywords: lung X-ray segmentation; deep convolutional neural networks; image binarization; histogram equalization lung X-ray segmentation; deep convolutional neural networks; image binarization; histogram equalization
Show Figures

Figure 1

MDPI and ACS Style

Chen, H.-J.; Ruan, S.-J.; Huang, S.-W.; Peng, Y.-T. Lung X-ray Segmentation using Deep Convolutional Neural Networks on Contrast-Enhanced Binarized Images. Mathematics 2020, 8, 545.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop