دورية أكاديمية
Detection of Line Artefacts in Lung Ultrasound Images of COVID-19 Patients via Non-Convex Regularization
| العنوان: | Detection of Line Artefacts in Lung Ultrasound Images of COVID-19 Patients via Non-Convex Regularization |
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| المؤلفون: | Karakuş, Oktay, Anantrasirichai, Nantheera, Aguersif, Amazigh, Silva, Stein, Basarab, Adrian, Achim, Alin |
| المصدر: | Karakuş , O , Anantrasirichai , N , Aguersif , A , Silva , S , Basarab , A & Achim , A 2020 , ' Detection of Line Artefacts in Lung Ultrasound Images of COVID-19 Patients via Non-Convex Regularization ' , IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control , vol. 67 , no. 11 , pp. 2218 - 2229 . https://doi.org/10.1109/TUFFC.2020.3016092Test |
| سنة النشر: | 2020 |
| المجموعة: | University of Bristol: Bristol Reserach |
| مصطلحات موضوعية: | /dk/atira/pure/core/keywords/uob_covid19, name=Covid19, Lung Ultrasound, COVID-19, Line Artefacts, Radon Transform, Cauchy-based penalty |
| الوصف: | 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. |
| نوع الوثيقة: | article in journal/newspaper |
| وصف الملف: | application/pdf |
| اللغة: | English |
| العلاقة: | https://research-information.bris.ac.uk/en/publications/682881ba-c8cb-420a-a1ec-c78ce3755a5bTest |
| DOI: | 10.1109/TUFFC.2020.3016092 |
| الإتاحة: | https://doi.org/10.1109/TUFFC.2020.3016092Test https://hdl.handle.net/1983/682881ba-c8cb-420a-a1ec-c78ce3755a5bTest https://research-information.bris.ac.uk/en/publications/682881ba-c8cb-420a-a1ec-c78ce3755a5bTest https://research-information.bris.ac.uk/ws/files/235099365/2005.03080v1.pdfTest |
| حقوق: | info:eu-repo/semantics/openAccess |
| رقم الانضمام: | edsbas.3022ACD5 |
| قاعدة البيانات: | BASE |
| DOI: | 10.1109/TUFFC.2020.3016092 |
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