Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Detection of line artifacts in lung ultrasound images of COVID-19 patients via nonconvex regularization

Karakus, Oktay, Anantrasirichai, Nantheera, Aguersif, Amazigh, Silva, Stein, Basarab, Adrian and Achim, Alin 2020. Detection of line artifacts in lung ultrasound images of COVID-19 patients via nonconvex regularization. IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control 67 (11) , pp. 2218-2229. 10.1109/tuffc.2020.3016092

Full text not available from this repository.


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.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Publisher: Institute of Electrical and Electronics Engineers
ISSN: 1525-8955
Last Modified: 15 Nov 2021 15:00

Citation Data

Cited 16 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item Edit Item