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 the monitoring of health status. Non-contrast chest computed tomography (CT) has been used for this purpose, mainly in China, with significant success. However, this approach cannot be massively used, mainly for both high risk and cost, also in some countries, 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 (Pipeline for Advanced Contrast Enhancement), combining properly Fast and Adaptive Bidimensional Empirical Mode Decomposition (FABEMD) 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, who evaluated the images separately, and confirmed by CT-scans. The results show this method is a flexible and an effective approach for medical image enhancement and can be used as a post-processing tool for medical image understanding and analysis.
Highlights
Non-contrast chest computed tomography (CT) has many advantages over the planar chest X-ray (CXR) such as the better spatial and densitometric resolution and the possibility of a more clear identification of morphologic features of lesions [1]
Those images are often of low quality for the environment difficulties [8,9,10] as well as for non-collaborative and severely ill patients in most cases [11,12] causing many different artifacts originating inhomogeneities in luminance distribution of radiograms [13]. To overcome these limitations that can impact on diagnostic effectiveness, here we develop a nonlinear post-processing tool, which we named Pipeline for Advanced Contrast Enhancement (PACE) which is aimed at improving the image quality of the CXR as evaluated in terms of contrast improvement index (CII), image entropy (ENT), and the measurement of enhancement (EME)
The values are plotted in descending order considering the results of PACE; the same order of images enhanced with Contrast Limited Adaptive Histogram Equalization (CLAHE) is used and those curves are nonmonotonic
Summary
Non-contrast chest computed tomography (CT) has many advantages over the planar chest X-ray (CXR) such as the better spatial and densitometric resolution and the possibility of a more clear identification of morphologic features of lesions [1]. As already discussed in literature, chest CT can be used for the study of COVID-19 progression in patients providing quantitative information [3] This approach needs several scans in a short time window giving rise to additional risks such as an increased possibility of cancer induction, because CT scan can expose a patient to as much radiations as 20–70 chest X-rays (CXR) [4]. An anteroposterior grey scale CXR in supine position has been acquired for each patient at bed with portable X-ray equipment Those images are often of low quality for the environment difficulties [8,9,10] as well as for non-collaborative and severely ill patients in most cases [11,12] causing many different artifacts originating inhomogeneities in luminance distribution of radiograms [13]. To overcome these limitations that can impact on diagnostic effectiveness, here we develop a nonlinear post-processing tool, which we named Pipeline for Advanced Contrast Enhancement (PACE) which is aimed at improving the image quality of the CXR as evaluated in terms of contrast improvement index (CII), image entropy (ENT), and the measurement of enhancement (EME)
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