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Updated: Oct 3, 2025

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COVID-19 CT image denoising algorithm based on adaptive threshold and optimized weighted median filter.

Shuli Guo1, Guowei Wang1, Lina Han2

  • 1State Key Laboratory of Intelligent Control and Decision of Complex Systems, School of Automation, Beijing Institute of Technology, Beijing, China.

Biomedical Signal Processing and Control
|February 21, 2022
PubMed
Summary

This study introduces advanced algorithms to denoise computed tomography (CT) scans for COVID-19 detection. These methods improve the clarity of early lung lesions and differentiate them from noise, enhancing diagnostic accuracy.

Keywords:
Adaptive thresholdCOVID-19 CT imageHybrid genetic algorithmMedian filterWeighting parameters

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Area of Science:

  • Medical Imaging
  • Computer-Aided Diagnosis
  • Signal Processing

Background:

  • Computed tomography (CT) imaging is crucial for diagnosing COVID-19, but impulse noise can obscure early, low-density lesions.
  • Advanced COVID-19 lesions, like ground-glass opacities, present challenges due to uneven density and unclear boundaries, mimicking other pneumonias.
  • Traditional filters struggle with low contrast and fuzzy boundaries in noisy CT images, impacting diagnostic precision.

Purpose of the Study:

  • To develop and evaluate novel image denoising algorithms for COVID-19 CT scans.
  • To enhance the detection accuracy of subtle, early-stage COVID-19 lung lesions.
  • To improve the differentiation between COVID-19 related lung changes and noise or other pneumonias.

Main Methods:

  • Proposed a median filtering algorithm with an adaptive two-stage threshold for improved noise detection.
  • Developed an adaptive weighted median filter using a hybrid genetic algorithm for denoising.
  • Optimized denoising parameters based on lung lobe and lesion characteristics, adapting genetic algorithm parameters to regional image data.

Main Results:

  • The proposed adaptive two-stage threshold median filter demonstrated enhanced accuracy in noise detection.
  • The hybrid genetic algorithm-based weighted median filter achieved superior denoising performance compared to traditional methods.
  • The improved algorithms showed significant advantages in peak signal-to-noise ratio (PSNR), image enhancement factor (IEF), and mean absolute error (MSE) across various noise densities.
  • The methods effectively preserved lesion details while enhancing overall image denoising capabilities.

Conclusions:

  • The developed adaptive filtering techniques offer a significant improvement in denoising COVID-19 CT images.
  • These algorithms enhance the visibility of early and advanced COVID-19 lesions, aiding in more accurate diagnosis.
  • The proposed methods provide a robust solution for noise reduction in medical imaging, particularly for differentiating COVID-19 from other lung conditions.