  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
technique is guaranteed to convergence through our proposed Cauchy proximal
splitting (CPS) method 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, our proposed method is readily
applicable for processing LUS image sequences.
