Pipeline for Advanced Contrast Enhancement (PACE) of chest X-ray in evaluating COVID-19 patients by combining bidimensional empirical mode decomposition and CLAHE

TL;DR

This paper introduces PACE, a nonlinear post-processing method combining bidimensional empirical mode decomposition and CLAHE, to enhance chest X-ray images for better detection of COVID-19 lung lesions, aiding diagnosis and monitoring.

Contribution

The study presents a novel image enhancement pipeline that improves chest X-ray contrast and lesion detectability, validated against CT scans and radiologist evaluations.

Findings

Enhanced contrast confirmed by multiple metrics

Increased detectability of lung lesions by radiologists

Method proves effective for medical image analysis

Abstract

COVID-19 is a new pulmonary disease which is driving stress to the hospitals due to the large number of cases worldwide. Imaging of lungs can play a key role in monitoring of the healthy status. Non-contrast chest computed tomography (CT) has been used for this purpose, mainly in China, with a significant success. However, this approach cannot be used massively mainly for both high risk and cost and in some countries also because this tool is not extensively available. Alternatively, chest X-ray, although less sensitive than CT-scan, can provide important information about the evolution of pulmonary involvement during the disease, this aspect is very important to verify the response of a patient to treatments. Here, we show how to improve the sensitivity of chest X-ray via a nonlinear post processing tool, named PACE, combining properly fast and adaptive bidimensional empirical mode decomposition and contrast limited adaptive histogram equalization (CLAHE). The results show an enhancement of the image contrast as confirmed by three widely used metrics: (i) contrast improvement index, (ii) entropy, and (iii) measure of enhancement. This improvement gives rise to a detectability of more lung lesions as identified by two radiologists, which evaluate the images separately, and confirmed by CT-scans. Based on our findings this method is proved as a flexible and effective way for medical image enhancement and can be used as a post-processing step for medical image understanding and analysis.