Covid-19 related Lung Consolidation’s Digital signature in CT scans
DOI:
https://doi.org/10.62438/tunismed.v104i06.6023Keywords:
Covid-19, Computed tomography, Lung consolidation, Statistics, Image segmentationAbstract
Introduction: Research analyzing COVID-19 CT imagery categorizes visual findings into three specific groups, the third of which is lung consolidation (LC).
Objective: This study evaluates CT scans from RT-PCR-confirmed COVID-19 cases to define the digital signature of lung consolidation based on grayscale pixel intensity.
Methods: By leveraging statistical analysis and modelization techniques, we implemented a dual-threshold filter in Python to achieve efficient CT image segmentation.
Results:
1- The pixel’s grayscale values of Covid-19 related lung consolidation are normally distributed
2- The proposed filter successfully isolates the lung consolidation spots
Conclusion: This study presents a novel statistical framework for identifying COVID-19 lung consolidation patterns in CT imagery. Given the observed
variability across different imaging hardware and geographic cohorts, further investigation is required to improve the model's generalizability.
Downloads
References
Domenico Cucinotta MV. WHO Declares COVID-19 a Pandemic. National Center of Biotechnlogy Information [Internet]. 2020.
Available from: https://pubmed.ncbi.nlm.nih.gov/32191675/
Tison GH, M M. Worldwide Effect of COVID-19 on Physical Activity: A Descriptive Study. Annals of internal medecine [Internet]. 2020;
Available from: https://www.acpjournals.org/doi/full/10.7326/ M20-2665
Le Quéré C, Jackson RB, Jones MW, Smith AJP, Abernethy S, Andrew RM, et al. Temporary reduction in daily global CO2 emissions
during the COVID-19 forced confinement. Nat Clim Chang. 2020 Jul;10(7):647–53.
Johns Hopkins Coronavirus Resource Center [Internet]. [cited 2025 Apr 26]. COVID-19 Map. Available from: https://coronavirus.jhu.
edu/map.html
Buddhisha Udugama PK. Diagnosing COVID-19: The Disease and Tools for Detection [Internet]. American Chemical Society;
Available from: https://pubs.acs.org/doi/10.1021/ acsnano.0c02624
Surkova E, Nikolayevskyy V, Drobniewski F. False-positive COVID-19 results: hidden problems and costs. The Lancet Respiratory
Medicine. 2020 Dec 1;8(12):1167–8.
The Role of Emergency Radiology in COVID-19: From Preparedness to Diagnosis - Muhammad Umer Nasir, James Roberts, Nestor L.
Muller, Francesco Macri, Mohammed F. Mohammed, Shahram Akhlaghpoor, William Parker, Arash Eftekhari, Susan Rezaei,
John Mayo, Savvas Nicolaou, 2020 [Internet]. [cited 2025 Apr 26]. Available from: https://journals.sagepub.com/doi/
full/10.1177/0846537120916419
Delrue L, Gosselin R, Ilsen B, Van Landeghem A, de Mey J, Duyck P. Difficulties in the Interpretation of Chest Radiography. In: Coche
EE, Ghaye B, de Mey J, Duyck P, editors. Comparative Interpretation of CT and Standard Radiography of the Chest [Internet]. Berlin,
Heidelberg: Springer; 2011 [cited 2025 Apr 26]. p. 27–49. Available from: https://doi.org/10.1007/978-3-540-79942-9_2
Gadde SS. Artificial Intelligence - The Future of Radiology. International Journal for Research in Applied Science &
Engineering Technology [Internet]. 2020 Jan 1 [cited 2025 Apr 26]; Available from: https://www.academia.edu/43389861/Artificial_
Intelligence_The_Future_of_Radiology
Radiologic diagnosis of patients with COVID-19 [Internet]. [cited 2025 Apr 26]. Available from: http://www.elsevier.es/en-revista-
radiologia-english-edition--419-avance-resumen-radiologic- diagnosis-patients-with-covid-19-S2173510721000033
Chang KW. Large Linear Classification When Data Cannot Fit in Memory. dblp computer science bibliography [Internet].
Available from: https://www.researchgate.net/ publication/220345144_Large_Linear_Classification_When_Data_
Cannot_Fit_in_Memory
Mirzaei Shahnam ES. Noise reduction and image smoothing using gaussian blur. California State University, Northridge.
Department of Electrical and Computer Engineering [Internet]. 2015. Available from: https://scholarworks.csun.edu/
handle/10211.3/142402?show=full
Additional Files
Published
Issue
Section
Categories
License
Copyright (c) 2026 La Tunisie Médicale
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
