Covid-19 related Lung Consolidation’s Digital signature in CT scans

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Published Jun 21, 2026
https://doi.org/10.62438/tunismed.v104i06.6023
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Vol. 104 No. 06 (2026): Tunis Med Jun 2026

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Adnane Laghrissi
Moulay Ismail University, Meknes, Morocco
Mohammed Douimi
Moulay Ismail University, Meknes, Morocco
El Moukhtar Zemmouri
Moulay Ismail University, Meknes, Morocco
https://orcid.org/0000-0001-9165-0858
Wajih Rhalim
Mohamed 5 University, Rabat, Morocco
Najwa Benslima
Mohammed VI International University Hospital, Bouskoura, Morocco
Najib Alidrissi
Mohammed VI University of Health Sciences, Casablanca, Morocco
https://orcid.org/0000-0001-6872-7060
Khaoula Echabbi
Moulay Ismail University, Meknes, Morocco
Hassan Ghazal
National Centre for Scientific and Technical Research, Rabat, Morocco
Salsabil Hamdi
Pasteur Institute of Morocco, Casablanca, Morocco

Abstract

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.

Keywords:

Covid-19, Computed tomography, Lung consolidation, Statistics, Image segmentation

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