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10.1371/journal.pone.0244416 http://scihub22266oqcxt.onion/10.1371/journal.pone.0244416 33417610!7793265!33417610     free     free     free Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       PLoS+One 2021 ; 16 (1): e0244416 Nephropedia Template TP {{tp|p=33417610|t=2021. Automatic clustering method to segment COVID-19 CT images |pdf=|usr=}}{{33417610}} gab.com Text Automatic clustering method to segment COVID-19 CT images JO: PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=33417610 #FOAvip 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. Twit Text FOAVip Automatic clustering method to segment COVID-19 CT images ????PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=33417610 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33417610 #FOAvip English Wikipedia <ref>{{Cite pmid|33417610}}</ref> Automatic clustering method to segment COVID-19 CT images #MMPMID33417610 Abd Elaziz M; A A Al-Qaness M; Abo Zaid EO; Lu S; Ali Ibrahim R; A Ewees A PLoS One 2021[]; 16 (1): e0244416 PMID33417610show ga 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. |*Cluster Analysis[MESH] |COVID-19/*diagnostic imaging[MESH] |Humans[MESH] |Image Processing, Computer-Assisted/*methods[MESH] |Lung/*diagnostic imaging[MESH]Automatic clustering method to segment COVID-19 CT images (epmc).pdf DeepDyve Pubget Overpricing