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

Hsin-Jui Chen,Sha-Wo Huang,Yan-Tsung Peng,Shanq-Jang Ruan

doi:10.3390/math8040545

Hsin-Jui Chen, Sha-Wo Huang + Show 2 more

Open Access

https://doi.org/10.3390/math8040545

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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.

Highlights

Detecting the lung boundary in chest X-ray (CXR) images has been extensively utilized in the diagnosis of lung health [1]
An ENT radiologist is trained to instinctively recognize any pulmonary disease based on particular differences that occur within the lung regions [2]
Our work aims to propose a pre-processing approach to achieve low-cost lung X-ray segmentation based on CNN-based architectures, which semantically segments the regions of the lung boundary in CXR images

Summary

Introduction

Detecting the lung boundary in chest X-ray (CXR) images has been extensively utilized in the diagnosis of lung health [1]. Shape irregularity, size measurement, and total lung volume provide clues for serious diseases such as cardiomegaly, pneumothorax, pneumoconiosis, or emphysema. This subjective approach relies on the condition and the experience of a radiologist. The impact of air pollution on human health is well-documented. The probability of a person to suffer from a pulmonary disease shall increase when the air pollution level increases. More patients will need to have an X-ray checkup, which adds more workloads to ENT radiologists and may increase the possibility of error diagnosis

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