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Article

Pipeline for Advanced Contrast Enhancement (PACE) of Chest X-ray in Evaluating COVID-19 Patients by Combining Bidimensional Empirical Mode Decomposition and Contrast Limited Adaptive Histogram Equalization (CLAHE)

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Department of Electric, Electronic and Computer Engineering, University of Catania, Viale Andrea Doria 6, 95125 Catania, Italy
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Department of Biomedical Sciences, Dental and of Morphological and Functional Images, University of Messina, Via Consolare Valeria 1, 98125 Messina, Italy
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Istituto Nazionale di Geofisica e Vulcanologia (INGV), Via di Vigna Murata 605, I-00143 Roma, Italy
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Maris Scarl, via Vigna Murata 606, 00143 Roma, Italy
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Department of Mathematical and Computer Sciences, Physical Sciences and Earth Sciences, V.le F. Stagno D’Alcontres 31, University of Messina, 98166 Messina, Italy
*
Authors to whom correspondence should be addressed.
Sustainability 2020, 12(20), 8573; https://doi.org/10.3390/su12208573
Received: 12 September 2020 / Revised: 5 October 2020 / Accepted: 6 October 2020 / Published: 16 October 2020
(This article belongs to the Special Issue COVID-19: Defense Strategies and Technologies)
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. View Full-Text
Keywords: hedging; transaction costs; dynamic programming; risk management; post-decision state variable hedging; transaction costs; dynamic programming; risk management; post-decision state variable
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MDPI and ACS Style

Siracusano, G.; La Corte, A.; Gaeta, M.; Cicero, G.; Chiappini, M.; Finocchio, G. Pipeline for Advanced Contrast Enhancement (PACE) of Chest X-ray in Evaluating COVID-19 Patients by Combining Bidimensional Empirical Mode Decomposition and Contrast Limited Adaptive Histogram Equalization (CLAHE). Sustainability 2020, 12, 8573. https://doi.org/10.3390/su12208573

AMA Style

Siracusano G, La Corte A, Gaeta M, Cicero G, Chiappini M, Finocchio G. Pipeline for Advanced Contrast Enhancement (PACE) of Chest X-ray in Evaluating COVID-19 Patients by Combining Bidimensional Empirical Mode Decomposition and Contrast Limited Adaptive Histogram Equalization (CLAHE). Sustainability. 2020; 12(20):8573. https://doi.org/10.3390/su12208573

Chicago/Turabian Style

Siracusano, Giulio, Aurelio La Corte, Michele Gaeta, Giuseppe Cicero, Massimo Chiappini, and Giovanni Finocchio. 2020. "Pipeline for Advanced Contrast Enhancement (PACE) of Chest X-ray in Evaluating COVID-19 Patients by Combining Bidimensional Empirical Mode Decomposition and Contrast Limited Adaptive Histogram Equalization (CLAHE)" Sustainability 12, no. 20: 8573. https://doi.org/10.3390/su12208573

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