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Related Experiment Video

Updated: Nov 22, 2025

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
08:05

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

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Automatic clustering method to segment COVID-19 CT images.

Mohamed Abd Elaziz1,2, Mohammed A A Al-Qaness3, Esraa Osama Abo Zaid4

  • 1Hubei Engineering Research Center on Big Data Security, School of Cyber Science and Engineering, Huazhong University of Science and Technology, Wuhan, China.

Plos One
|January 8, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces an improved Density Peaks Clustering (DPC) method using Generalized Extreme Value (GEV) distribution for segmenting COVID-19 Computed Tomography (CT) images, enhancing diagnostic accuracy.

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Data Science

Background:

  • Accurate segmentation of COVID-19 Computed Tomography (CT) images is crucial for diagnosis.
  • Traditional image segmentation methods face challenges in automated optimal parameter selection.
  • Density Peaks Clustering (DPC) offers speed and stability but requires careful parameter tuning.

Purpose of the Study:

  • To develop an efficient image segmentation method for COVID-19 CT images.
  • To enhance the Density Peaks Clustering (DPC) algorithm for improved segmentation accuracy.
  • To automate the selection of optimal clustering centers using Generalized Extreme Value (GEV) distribution.

Main Methods:

  • The study proposes an enhanced Density Peaks Clustering (DPC) algorithm.
  • Generalized Extreme Value (GEV) distribution is integrated to determine an optimal threshold for clustering centers.
  • The method was applied to segment twelve COVID-19 CT images.

Main Results:

  • The proposed method demonstrated superior performance compared to traditional k-means and standard DPC algorithms.
  • Quantitative evaluation using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) showed significant improvements.
  • The integration of GEV distribution effectively determined the optimal number of clustering centers, enhancing segmentation quality.

Conclusions:

  • The proposed GEV-enhanced DPC method provides an efficient and accurate approach for segmenting COVID-19 CT images.
  • This technique can aid in the automated diagnosis and analysis of pneumonia.
  • The findings suggest a promising direction for improving medical image analysis in the context of infectious diseases.