Line Artefact Quantification in Lung Ultrasound Images of COVID-19 Patients via Non-Convex Regularisation

Oktay Karakuş, Nantheera Anantrasirichai, Amazigh Aguersif, Stein Silva, Adrian Basarab, Alin Achim

Research output: Contribution to journalArticle (Academic Journal)

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In this paper, we present a novel method for line artefacts quantification in lung ultrasound (LUS) images of COVID-19 patients. We formulate this as a non-convex regularisation 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 artefacts. Despite being non-convex, the proposed method has guaranteed convergence via a proximal splitting algorithm and accurately identifies both horizontal and vertical line artefacts in LUS images. In order 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. In addition, owing to its fast convergence, which takes around 12 seconds for a given frame, our proposed method is readily applicable for processing LUS image sequences.
Original languageEnglish
Number of pages16
Publication statusUnpublished - 6 May 2020

Bibliographical note

15 pages, 9 figures

Structured keywords

  • Covid19


  • eess.IV
  • cs.CV
  • eess.SP

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