Airways Segmentation using Fast Marching

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dc.contributor.authorBustacara-Medina, César
dc.contributor.authorFlórez-Valencia, Leonardo
dc.contributor.authorHurtado, José Hernando
dc.contributor.editorSkala, Václav
dc.date.accessioned2020-07-27T12:21:32Z
dc.date.available2020-07-27T12:21:32Z
dc.date.issued2020
dc.description.abstract-translatedDirect measurements of airway tree and wall areas are potentially useful as a diagnostic tool and as an aid to understanding pathophysiology underlying of the airway diseases. Direct measurements can be made from images obtained using computer tomography (CT) by applying computer-based algorithms to segment airway, however, current validation techniques cannot establish adequately the accuracy of these algorithms. Additional, the majority of the studies only include the airway from trachea to bronchi’s tree avoiding the upper respiratory system, because the main problems appears in the lower respiratory system, for example, asthma and chronic obstructive pulmonary (airflow obstruction or limitation, including chronic bronchitis, emphysema and bronchiectasis). Airway tree segmentation can be performed manually by an image analyst, but the complexity of the tree makes manual segmentation tedious and extremely time-consuming (require several hours of analysis), only including trachea and lower airway system. Airway segmentation in CT images is a challenging problem for two reasons, it is a complex anatomy and exists limitations in image quality inherent to CT image acquisition. This paper describes a semi-automatic technique to segment the airway tree (upper airway system and trachea), using CT images of head-neck and applying a fast marching algorithm. Additionally, a heuristic is proposed to determine the algorithm parameters without have to review manually all structures to segmentation.en
dc.format7 s.cs
dc.format.mimetypeapplication/pdf
dc.identifier.citationWSCG 2020: full papers proceedings: 28th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 179-185.en
dc.identifier.doihttps://doi.org/10.24132/CSRN.2020.3001.21
dc.identifier.isbn978-80-86943-35-0
dc.identifier.issn2464–4617 (print)
dc.identifier.issn2464–4625 (CD-ROM)
dc.identifier.urihttp://wscg.zcu.cz/WSCG2020/2020-CSRN-3001.pdf
dc.identifier.urihttp://hdl.handle.net/11025/38465
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencycs
dc.relation.ispartofseriesWSCG 2020: full papers proceedingsen
dc.rights© Václav Skala - UNION Agencycs
dc.rights.accessopenAccessen
dc.subjectsegmentace obrazucs
dc.subjectstrom dýchacích cestcs
dc.subjectrychlé pochodovánícs
dc.subjectpočítačová tomografiecs
dc.subject.translatedimage segmentationen
dc.subject.translatedairway treeen
dc.subject.translatedfast marchingen
dc.subject.translatedcomputed tomographyen
dc.titleAirways Segmentation using Fast Marchingen
dc.typeconferenceObjecten
dc.typekonferenční příspěvekcs
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen

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