  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.
