Publicação
Region-based clustering for lung segmentation in low-dose CT images
| Resumo: | Lung segmentation in thoracic computed tomography scans is essential for the development of computer-aided diagnostic methods for identifying the lung diseases. Low-dose CT scans are increasingly utilized in lung studies, but segmenting them with traditional threshold segmentation algorithms often yields less than satisfying results. In this paper we present a hybrid framework to lung segmentation which joints region-based information based on watershed transform with clustering techniques. The proposed method eliminates the task of finding an optimal threshold and the over-segmentation produced by watershed. We have applied our approach on several pulmonary low-dose CT images and the results reveal the robustness and accuracy of this method. |
|---|---|
| Autores principais: | Monteiro, Fernando C. |
| Assunto: | Lung segmentation Graph clustering Watershed transform Pulmonary CT image |
| Ano: | 2010 |
| País: | Portugal |
| Tipo de documento: | comunicação em conferência |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Instituto Politécnico de Bragança |
| Idioma: | inglês |
| Origem: | Biblioteca Digital do IPB |
| _version_ | 1867172778128441344 |
|---|---|
| author | Monteiro, Fernando C. |
| author_facet | Monteiro, Fernando C. |
| author_role | author |
| contributor_name_str_mv | Biblioteca Digital do IPB |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Monteiro, Fernando C.\",\"Person.identifier.orcid\":\"0000-0002-1421-8006\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | Biblioteca Digital do IPB |
| datacite.creators.creator.creatorName.fl_str_mv | Monteiro, Fernando C. |
| datacite.date.Accepted.fl_str_mv | 2010-01-01T00:00:00Z |
| datacite.date.available.fl_str_mv | 2010-10-06T13:17:14Z |
| datacite.date.embargoed.fl_str_mv | 2010-10-06T13:17:14Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| datacite.subjects.subject.fl_str_mv | Lung segmentation Graph clustering Watershed transform Pulmonary CT image |
| datacite.titles.title.fl_str_mv | Region-based clustering for lung segmentation in low-dose CT images |
| dc.contributor.none.fl_str_mv | Biblioteca Digital do IPB |
| dc.creator.none.fl_str_mv | Monteiro, Fernando C. |
| dc.date.Accepted.fl_str_mv | 2010-01-01T00:00:00Z |
| dc.date.available.fl_str_mv | 2010-10-06T13:17:14Z |
| dc.date.embargoed.fl_str_mv | 2010-10-06T13:17:14Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://hdl.handle.net/10198/2631 |
| dc.language.none.fl_str_mv | eng |
| dc.publisher.none.fl_str_mv | Theodore E. Simos, George Psihoyios, Ch. Tsitouras |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| dc.subject.none.fl_str_mv | Lung segmentation Graph clustering Watershed transform Pulmonary CT image |
| dc.title.fl_str_mv | Region-based clustering for lung segmentation in low-dose CT images |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_5794 |
| description | Lung segmentation in thoracic computed tomography scans is essential for the development of computer-aided diagnostic methods for identifying the lung diseases. Low-dose CT scans are increasingly utilized in lung studies, but segmenting them with traditional threshold segmentation algorithms often yields less than satisfying results. In this paper we present a hybrid framework to lung segmentation which joints region-based information based on watershed transform with clustering techniques. The proposed method eliminates the task of finding an optimal threshold and the over-segmentation produced by watershed. We have applied our approach on several pulmonary low-dose CT images and the results reveal the robustness and accuracy of this method. |
| dirty | 0 |
| eu_rights_str_mv | openAccess |
| format | conferencePaper |
| fulltext.url.fl_str_mv | https://bibliotecadigital.ipb.pt/bitstreams/4726c46a-6abb-42e1-b77e-bb380cffec15/download |
| id | ipb_ecb9588bcc480ece2c026e4ac20de5a3 |
| identifier.url.fl_str_mv | http://hdl.handle.net/10198/2631 |
| instacron_str | ipb |
| institution | Instituto Politécnico de Bragança |
| instname_str | Instituto Politécnico de Bragança |
| language | eng |
| network_acronym_str | ipb |
| network_name_str | Biblioteca Digital do IPB |
| oai_identifier_str | oai:bibliotecadigital.ipb.pt:10198/2631 |
| organization_str_mv | urn:organizationAcronym:ipb |
| person_str_mv | Monteiro, Fernando C. Monteiro, Fernando C. https://www.ciencia-id.pt/2019-BDBF-10E2 2019-BDBF-10E2 http://orcid.org/0000-0002-1421-8006 0000-0002-1421-8006 |
| publishDate | 2010 |
| publisher.none.fl_str_mv | Theodore E. Simos, George Psihoyios, Ch. Tsitouras |
| reponame_str | Biblioteca Digital do IPB |
| repository_id_str | urn:repositoryAcronym:ipb |
| service_str_mv | urn:repositoryAcronym:ipb |
| spelling | engTheodore E. Simos, George Psihoyios, Ch. TsitourasporLung segmentation in thoracic computed tomography scans is essential for the development of computer-aided diagnostic methods for identifying the lung diseases. Low-dose CT scans are increasingly utilized in lung studies, but segmenting them with traditional threshold segmentation algorithms often yields less than satisfying results. In this paper we present a hybrid framework to lung segmentation which joints region-based information based on watershed transform with clustering techniques. The proposed method eliminates the task of finding an optimal threshold and the over-segmentation produced by watershed. We have applied our approach on several pulmonary low-dose CT images and the results reveal the robustness and accuracy of this method.application/pdfporRegion-based clustering for lung segmentation in low-dose CT imagesPersonalMonteiro, Fernando C.DSpacehttp://dspace.org/items/363b6c37-282c-4cd6-bb54-3c97cc700d78DSpacehttp://dspace.org/items/363b6c37-282c-4cd6-bb54-3c97cc700d78MonteiroFernando C.Ciência IDhttps://www.ciencia-id.pt2019-BDBF-10E2ORCIDhttp://orcid.org0000-0002-1421-8006Researcher IDhttps://www.researcherid.comH-9213-2016Scopus Author IDhttps://www.scopus.com8986162600HostingInstitutionOrganizationalBiblioteca Digital do IPBe-mailmailto:dspace@ipb.ptdspace@ipb.ptISBNIsPartOf978-0-7354-0834-0DOIIsPartOf10.1063/1.34984132010-10-06T13:17:14Z20102010-01-01T00:00:00ZHandlehttp://hdl.handle.net/10198/2631http://purl.org/coar/access_right/c_abf2open accessLung segmentationGraph clusteringWatershed transformPulmonary CT image329655 bytesother research producthttp://purl.org/coar/resource_type/c_5794conference paperhttp://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://bibliotecadigital.ipb.pt/bitstreams/4726c46a-6abb-42e1-b77e-bb380cffec15/downloadICNAAM 2010: International Conference of Numerical Analysis and Applied Mathematics 201020612064Rhodes, Greece |
| spellingShingle | Region-based clustering for lung segmentation in low-dose CT images Monteiro, Fernando C. Lung segmentation Graph clustering Watershed transform Pulmonary CT image |
| status | SINGLETON |
| subject.fl_str_mv | Lung segmentation Graph clustering Watershed transform Pulmonary CT image |
| title | Region-based clustering for lung segmentation in low-dose CT images |
| title_full | Region-based clustering for lung segmentation in low-dose CT images |
| title_fullStr | Region-based clustering for lung segmentation in low-dose CT images |
| title_full_unstemmed | Region-based clustering for lung segmentation in low-dose CT images |
| title_short | Region-based clustering for lung segmentation in low-dose CT images |
| title_sort | Region-based clustering for lung segmentation in low-dose CT images |
| topic | Lung segmentation Graph clustering Watershed transform Pulmonary CT image |
| topic_facet | Lung segmentation Graph clustering Watershed transform Pulmonary CT image |
| url | http://hdl.handle.net/10198/2631 |
| visible | 1 |