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A non-convex regularization based line artefact quantification method in lung ultrasound imagery for pulmonary disease evaluation

Karakus, Oktay, Anantrasirichai, Nantheera, Basarab, Adrian and Achim, Alin 2020. A non-convex regularization based line artefact quantification method in lung ultrasound imagery for pulmonary disease evaluation. The Journal of the Acoustical Society of America 148 (4) , p. 2735. 10.1121/1.5147598

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Abstract

Lung (Pulmonary) diseases are among the most severe health problems which cause multiple deaths (more than 100 thousand people in the UK) every year. Moreover, following the COVID-19 pandemic, analysis and diagnosis of pulmonary disease became even more crucial. The common feature in all clinical conditions, both local to the lungs [e.g., pneumonia, chronic obstructive pulmonary disease (COPD)] and those manifesting themselves in the lungs (e.g., kidney disease, COVID-19) is the presence in lung ultrasound (LUS) images of a variety of artefacts. This work presents a novel method for line artefacts quantification in LUS images of pulmonary disease patients by using a non-convex regularization method. We employ a simple local maxima detection technique in the Radon transform domain, associated with known clinical definitions of line artefacts. Notwithstanding its non-convex characteristics, the proposed technique is guaranteed to converge through our proposed Cauchy proximal splitting (CPS) method and accurately identifies both horizontal (pleural, sub-pleural, A-) and vertical (B- and Z-) line artefacts in LUS images. The proposed method includes a two-stage validation mechanism, which is performed in both Radon and image domains to reduce the number of false and missed detections.

Item Type: Article
Date Type: Published Online
Status: Published
Schools: Computer Science & Informatics
Publisher: Acoustical Society of America
ISSN: 0001-4966
Last Modified: 15 Nov 2021 13:45
URI: https://orca.cardiff.ac.uk/id/eprint/145178

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