Karakus, Oktay  ORCID: https://orcid.org/0000-0001-8009-9319, 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
|
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: | Schools > Computer Science & Informatics |
| Publisher: | Acoustical Society of America |
| ISSN: | 0001-4966 |
| Last Modified: | 19 May 2023 02:06 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/145178 |
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