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AuthorAkbari Y.
AuthorHassen H.
AuthorAl-Maadeed, Somaya
AuthorZughaier S.M.
Available date2022-05-19T10:23:07Z
Publication Date2021
Publication NameApplied Sciences (Switzerland)
ResourceScopus
Identifierhttp://dx.doi.org/10.3390/app11178039
URIhttp://hdl.handle.net/10576/31086
AbstractPneumonia is a lung infection that threatens all age groups. In this paper, we use CT scans to investigate the effectiveness of active contour models (ACMs) for segmentation of pneumonia caused by the Coronavirus disease (COVID-19) as one of the successful methods for image segmentation. A comparison has been made between the performances of the state-of-the-art methods performed based on a database of lung CT scan images. This review helps the reader to identify starting points for research in the field of active contour models on COVID-19, which is a high priority for researchers and practitioners. Finally, the experimental results indicate that active contour methods achieve promising results when there are not enough images to use deep learning-based methods as one of the powerful tools for image segmentation.
SponsorFunding: This research was funded by Qatar University Emergency Response Grant (QUERG-CENG-2020-1) from Qatar University.
Languageen
PublisherMDPI
SubjectActive contour models
Chest CT scans
COVID-19 infection
Edge-based models
Level set methods
Parametric methods
Pneumonia
Region-based models
TitleCOVID-19 lesion segmentation using lung CT scan images: Comparative study based on active contour models
TypeArticle
Issue Number17
Volume Number11
dc.accessType Abstract Only


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