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

Hsin-Jui Chen, Shanq-Jang Ruan, Sha-Wo Huang and Yan-Tsung Peng
Additional contact information
Hsin-Jui Chen: Department of Electronics and Computer Engineering, National Taiwan University of Science and Technology, Taipei 10607, Taiwan
Shanq-Jang Ruan: Department of Electronics and Computer Engineering, National Taiwan University of Science and Technology, Taipei 10607, Taiwan
Sha-Wo Huang: Department of Computer Science, National Chengchi University, Taipei 11605, Taiwan
Yan-Tsung Peng: Department of Computer Science, National Chengchi University, Taipei 11605, Taiwan

Mathematics, 2020, vol. 8, issue 4, 1-12

Abstract: 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.

Keywords: lung X-ray segmentation; deep convolutional neural networks; image binarization; histogram equalization (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/8/4/545/pdf (application/pdf)
https://www.mdpi.com/2227-7390/8/4/545/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:8:y:2020:i:4:p:545-:d:342523

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().