Usefulness of digital images segmentation in pulmonary transplantation
dc.contributor.author | Gelvez-Almeida, E | |
dc.contributor.author | Huérfano, Y | |
dc.contributor.author | Vera, M | |
dc.contributor.author | Vera, M I | |
dc.contributor.author | Valbuena, O | |
dc.contributor.author | Salazar-Torres, J | |
dc.date.accessioned | 2020-04-15T04:08:48Z | |
dc.date.available | 2020-04-15T04:08:48Z | |
dc.date.issued | 2019 | |
dc.description.abstract | In the presence of pulmonary pathologies such as chronic obstructive pulmonary disease, diffuse pulmonary disease and cystic fibrosis, among others, it is common to require the removal or replacement of a portion of lungs. There are several requirements for both donors and organ receivers (recipients) established in the literature. May be the main one is the volume that the donor's lungs occupy in the thoracic cavity. This parameter is vital because if the volume of the lungs exceeds the thoracic cavity of the recipients the transplant, logically, is unfeasible for physical reasons such as the incompatibility between the receiver lung volume and the donor lung volume. In this sense, the present paper proposes the creation of a hybrid technique, based on digital image processing techniques application to raise the quality of the information related to lungs captured in three-dimensional sequences of computed tomography and for generating the morphology and the volumes of the lungs, belonging to a patient. During the filtering stage median, saturated and gradient magnitude filters are applied with the purpose of addressing the noise and artefacts images problems; whereas during the segmentation stage, methods based on clustering processes are used to extract the lungs from the images. The values obtained for the metric that assesses the quality of the hybrid computational technique reflect its good performance. Additionally, these results are very important in clinical processes where both the shapes and volumes of lungs are vital for monitoring some lung diseases that can affect the normal lung physiology. | eng |
dc.format.mimetype | eng | |
dc.identifier.issn | 17426596 | |
dc.identifier.uri | https://hdl.handle.net/20.500.12442/5111 | |
dc.language.iso | eng | eng |
dc.publisher | IOP Publishing | eng |
dc.rights.accessrights | info:eu-repo/semantics/openAccess | eng |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | |
dc.source | Journal of Physics: Conference Series | eng |
dc.source | Vol. 1386 (2019) | eng |
dc.source.uri | https://iopscience.iop.org/article/10.1088/1742-6596/1386/1/012134 | eng |
dc.title | Usefulness of digital images segmentation in pulmonary transplantation | eng |
dc.type | article | eng |
dc.type.driver | article | eng |
dcterms.references | Hardy J, Webb W, Dalton M and Walker G 1963 Lung homotransplantations in man JAMA 186 1065 | eng |
dcterms.references | Hiroshi D 2017 Living-related lung transplantation J. Thorac. Dis. 9 3362 | eng |
dcterms.references | Park C, Kim T, Lee S, Paik H and Haam S 2015 New predictive equation for lung volume using chest computed tomography for size matching in lung transplantation Transplant. Proc. 47 498 | eng |
dcterms.references | Konheim J, Kon Z, Pasrija C, Luo Q, Sanchez P, Garcia J, Griffith B and Jeudy J 2016 Predictive equations for lung volumes from computed tomography for size matching in pulmonary transplantation J. Thorac. Cardiovasc. Surg. 151 1163 | eng |
dcterms.references | Wang J, Li F and Li Q 2009 Automated segmentation of lungs with severe interstitial lung disease in ct American Association of Physicists in Medicine 36 4592 | eng |
dcterms.references | Rebouças P, Cortez P, Da Silva A, De Albuquerque V and Tavares J 2017 Novel and powerful 3d adaptive crisp active contour method applied in the segmentation of ct lung images Med. Image. Anal 35 503 | eng |
dcterms.references | Mingjie X, Shouliang Q, Yong Y, Yueyang T, Lisheng X, Yudong Y and Wei Q 2019 Segmentation of lung parenchyma in ct images using cnn trained with the clustering algorithm generated dataset Biomed. Eng. 18 2 | eng |
dcterms.references | González R and Woods R 2001 Digital image processing (New Jersey: Prentice Hall) | eng |
dcterms.references | Huérfano Y, Vera M, Gelvez E, Salazar J, Del Mar A, Valbuena O and Molina V 2019 A computational strategy for the identification of pulmonary squamous cell carcinoma in computerized tomography images J. Phys.: Conf. Ser. 1160 012004 | eng |
dcterms.references | Vera M, Medina R, Del Mar A, Arellano J, Huérfano Y and Bravo A 2019 An automatic technique for left ventricle segmentation from msct cardiac volumes J. Phys.: Conf. Ser. 1160 012001 | eng |
dcterms.references | Ibañez L 2004 The ITK software guide (USA: Kitware Inc) | eng |
dcterms.references | Dice L 1945 Measures of the amount of ecologic association between species Ecology 26 297 | eng |
oaire.version | info:eu-repo/semantics/publishedVersion | eng |