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

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Document pages: 17 pages

Abstract: COVID-19 is a new pulmonary disease which is driving stress to the hospitalsdue to the large number of cases worldwide. Imaging of lungs can play a keyrole in monitoring of the healthy status. Non-contrast chest computedtomography (CT) has been used for this purpose, mainly in China, with asignificant success. However, this approach cannot be used massively mainly forboth high risk and cost and in some countries also because this tool is notextensively available. Alternatively, chest X-ray, although less sensitive thanCT-scan, can provide important information about the evolution of pulmonaryinvolvement during the disease, this aspect is very important to verify theresponse of a patient to treatments. Here, we show how to improve thesensitivity of chest X-ray via a nonlinear post processing tool, named PACE,combining properly fast and adaptive bidimensional empirical mode decompositionand contrast limited adaptive histogram equalization (CLAHE). The results showan 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 asidentified by two radiologists, which evaluate the images separately, andconfirmed by CT-scans. Based on our findings this method is proved as aflexible and effective way for medical image enhancement and can be used as apost-processing step for medical image understanding and analysis.

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