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Related Concept Videos

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Related Experiment Video

Updated: Aug 6, 2025

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
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COVID-19 chest X-ray image analysis by threshold-based segmentation.

Walid Al-Zyoud1, Dana Erekat1, Rama Saraiji1

  • 1Department of Biomedical Engineering, School of Applied Medical Sciences, German Jordanian University, 11180 Amman Jordan.

Heliyon
|March 15, 2023
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Summary

This study developed an automatic method using chest X-ray segmentation to diagnose COVID-19. Results show COVID-19 primarily affects the lower lung lobes, aiding in early detection and understanding disease progression.

Keywords:
Binary segmentationCOVID-19Ground-glass opacitiesX-ray

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Area of Science:

  • Medical Imaging
  • Radiology
  • Infectious Diseases

Background:

  • COVID-19, a severe acute respiratory syndrome, has caused a global pandemic.
  • Chest X-ray (CXR) and CT imaging are crucial for assessing COVID-19 complications.
  • Accurate diagnostic methods are needed to understand the disease's impact on the respiratory system.

Purpose of the Study:

  • To develop and evaluate an automatic method for COVID-19 diagnosis using binary segmentation of chest X-ray images.
  • To analyze the distribution of lung abnormalities in COVID-19 patients using image segmentation.
  • To correlate imaging findings with known COVID-19 pathology.

Main Methods:

  • Utilized frontal chest X-ray images from the Kaggle COVID-19 Radiography Database.
  • Applied binary segmentation and quartering techniques in MATLAB for image analysis.
  • Quantified white pixel ratios in four lung quadrants (upper/lower, left/right) to assess lung attenuation.

Main Results:

  • COVID-19 patients exhibited significantly higher lung attenuation in lower lobes compared to healthy individuals (p < 0.00001).
  • Ground-glass opacities and consolidations are likely causes of observed lung attenuation.
  • The left lower lung quarter showed the highest white pixel count, suggesting potential viral accumulation, though not statistically significant (p = 0.102792).

Conclusions:

  • COVID-19 predominantly affects the lower and lateral lung fields.
  • The findings support the hypothesis of viral accumulation in the lower left lung quadrant.
  • This automated segmentation method can aid in accurate COVID-19 diagnosis and understanding disease patterns.