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10.1109/TUFFC.2020.3016092 http://scihub22266oqcxt.onion/10.1109/TUFFC.2020.3016092 32784133!8544933!32784133     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       IEEE+Trans+Ultrason+Ferroelectr+Freq+Control 2020 ; 67 (11): 2218-2229 Nephropedia Template TP {{tp|p=32784133|t=2020. Detection of Line Artifacts in Lung Ultrasound Images of COVID-19 Patients Via Nonconvex Regularization |pdf=|usr=}}{{32784133}} gab.com Text Detection of Line Artifacts in Lung Ultrasound Images of COVID-19 Patients Via Nonconvex Regularization JO: IEEE Trans Ultrason Ferroelectr Freq Control http://www.kidney.de/pget.php?&tw=1&pmid=32784133 #FOAvip In this article, we present a novel method for line artifacts quantification in lung ultrasound (LUS) images of COVID-19 patients. We formulate this as a nonconvex regularization problem involving a sparsity-enforcing, Cauchy-based penalty function, and the inverse Radon transform. We employ a simple local maxima detection technique in the Radon transform domain, associated with known clinical definitions of line artifacts. Despite being nonconvex, the proposed technique is guaranteed to convergence through our proposed Cauchy proximal splitting (CPS) method, and accurately identifies both horizontal and vertical line artifacts in LUS images. To reduce the number of false and missed detection, our method includes a two-stage validation mechanism, which is performed in both Radon and image domains. We evaluate the performance of the proposed method in comparison to the current state-of-the-art B-line identification method, and show a considerable performance gain with 87% correctly detected B-lines in LUS images of nine COVID-19 patients. Twit Text FOAVip Detection of Line Artifacts in Lung Ultrasound Images of COVID-19 Patients Via Nonconvex Regularization ????IEEE Trans Ultrason Ferroelectr Freq Control http://www.kidney.de/pget.php?&tw=1&pmid=32784133 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32784133 #FOAvip English Wikipedia <ref>{{Cite pmid|32784133}}</ref> Detection of Line Artifacts in Lung Ultrasound Images of COVID-19 Patients Via Nonconvex Regularization #MMPMID32784133 Karakus O; Anantrasirichai N; Aguersif A; Silva S; Basarab A; Achim A IEEE Trans Ultrason Ferroelectr Freq Control 2020[Nov]; 67 (11): 2218-2229 PMID32784133show ga In this article, we present a novel method for line artifacts quantification in lung ultrasound (LUS) images of COVID-19 patients. We formulate this as a nonconvex regularization problem involving a sparsity-enforcing, Cauchy-based penalty function, and the inverse Radon transform. We employ a simple local maxima detection technique in the Radon transform domain, associated with known clinical definitions of line artifacts. Despite being nonconvex, the proposed technique is guaranteed to convergence through our proposed Cauchy proximal splitting (CPS) method, and accurately identifies both horizontal and vertical line artifacts in LUS images. To reduce the number of false and missed detection, our method includes a two-stage validation mechanism, which is performed in both Radon and image domains. We evaluate the performance of the proposed method in comparison to the current state-of-the-art B-line identification method, and show a considerable performance gain with 87% correctly detected B-lines in LUS images of nine COVID-19 patients. |Aged[MESH] |Algorithms[MESH] |Artifacts[MESH] |Betacoronavirus[MESH] |COVID-19[MESH] |Coronavirus Infections/*diagnostic imaging[MESH] |Female[MESH] |Humans[MESH] |Image Interpretation, Computer-Assisted/*methods[MESH] |Lung/*diagnostic imaging[MESH] |Male[MESH] |Middle Aged[MESH] |Pandemics[MESH] |Pleura/diagnostic imaging[MESH] |Pneumonia, Viral/*diagnostic imaging[MESH] |ROC Curve[MESH] |SARS-CoV-2[MESH]Detection of Line Artifacts in Lung Ultrasound Images of COVID-19 Pati(epmc).pdf DeepDyve Pubget Overpricing