Automatic clustering method to segment COVID-19 CT images

Mohamed Abd Elaziz; Mohammed A A Al-Qaness; Esraa Osama Abo Zaid; Songfeng Lu; Rehab Ali Ibrahim; Ahmed A Ewees

doi:10.1371/journal.pone.0244416

Automatic clustering method to segment COVID-19 CT images

PLoS One. 2021 Jan 8;16(1):e0244416. doi: 10.1371/journal.pone.0244416. eCollection 2021.

Authors

Mohamed Abd Elaziz^{1

2}, Mohammed A A Al-Qaness³, Esraa Osama Abo Zaid⁴, Songfeng Lu¹, Rehab Ali Ibrahim², Ahmed A Ewees⁵

Affiliations

¹ Hubei Engineering Research Center on Big Data Security, School of Cyber Science and Engineering, Huazhong University of Science and Technology, Wuhan, China.
² Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, Egypt.
³ State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China.
⁴ Department of Mathematics, Faculty of Science, Seuz University, Suez, Egypt.
⁵ Department of Computer, Damietta University, Damietta, Egypt.

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.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

COVID-19 / diagnostic imaging*
Cluster Analysis*
Humans
Image Processing, Computer-Assisted / methods*
Lung / diagnostic imaging*
Tomography, X-Ray Computed / methods*

Grants and funding

The paper has fund from China Postdoctoral Science Foundation Grant No. 2019M652647, also, the Hubei Provincinal Science and Technology Major Project of China under Grant No. 2020AEA011 and the Key Research & Developement Plan of Hubei Province of China under Grant No. 2020BAB100. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.