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Automatic clustering method to segment COVID-19 CT images

Author

Listed:
  • Mohamed Abd Elaziz
  • Mohammed A. A. Al-qaness
  • Esraa Osama Abo Zaid
  • Songfeng Lu
  • Rehab Ali Ibrahim
  • Ahmed A. Ewees

Abstract

Coronavirus pandemic (COVID-19) has infected more than ten million persons worldwide. Therefore, researchers are trying to address various aspects that may help in diagnosis this pneumonia. Image segmentation is a necessary pr-processing step that implemented in image analysis and classification applications. Therefore, in this study, our goal is to present an efficient image segmentation method for COVID-19 Computed Tomography (CT) images. The proposed image segmentation method depends on improving the density peaks clustering (DPC) using generalized extreme value (GEV) distribution. The DPC is faster than other clustering methods, and it provides more stable results. However, it is difficult to determine the optimal number of clustering centers automatically without visualization. So, GEV is used to determine the suitable threshold value to find the optimal number of clustering centers that lead to improving the segmentation process. The proposed model is applied for a set of twelve COVID-19 CT images. Also, it was compared with traditional k-means and DPC algorithms, and it has better performance using several measures, such as PSNR, SSIM, and Entropy.

Suggested Citation

  • Mohamed Abd Elaziz & Mohammed A. A. Al-qaness & Esraa Osama Abo Zaid & Songfeng Lu & Rehab Ali Ibrahim & Ahmed A. Ewees, 2021. "Automatic clustering method to segment COVID-19 CT images," PLOS ONE, Public Library of Science, vol. 16(1), pages 1-13, January.
    • Handle: RePEc:plo:pone00:0244416
    DOI: 10.1371/journal.pone.0244416
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    Cited by:

    1. Mohamed Abd Elaziz & Esraa Osama Abo Zaid & Mohammed A. A. Al-qaness & Rehab Ali Ibrahim, 2021. "Automatic Superpixel-Based Clustering for Color Image Segmentation Using q-Generalized Pareto Distribution under Linear Normalization and Hunger Games Search," Mathematics, MDPI, vol. 9(19), pages 1-18, September.

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