A New Approach for Biomedical Image Segmentation: Combined Complex-Valued Artificial Neural Network Case Study: Lung Segmentation on Chest CT Images

Basit Öğe Kaydı
dc.contributor.authorCeylan, Murat
dc.contributor.authorÖzbay, Yüksel
dc.contributor.authorYıldırım, Erkan
dc.date.accessioned2020-03-26T17:46:42Z
dc.date.available2020-03-26T17:46:42Z
dc.date.issued2010
dc.departmentSelçuk Üniversitesien_US
dc.description5th Cairo International Biomedical Engineering Conference (CIBEC) -- DEC 16-18, 2010 -- Cairo, EGYPTen_US
dc.description.abstractThe principal goal of the segmentation process is to partition an image into classes or subsets that are homogeneous with respect to one or more characteristics or features. In medical imaging, segmentation is important for feature extraction, image measurements, and image display. This study presents a new version of complex-valued artificial neural networks (CVANN) for the biomedical image segmentation. Proposed new method is called as combined complex-valued artificial neural network (CCVANN) which is a combination of two complex-valued artificial neural networks. To check the validation of proposed method, lung segmentation is realized. For this purpose, we used 32 chest CT images of 6 female and 26 male patients. These images were recorded from Baskent University Radiology Department in Turkey. The accuracy of the CCVANN model is more satisfactory as compared to the single CVANN model.en_US
dc.description.sponsorshipIEEE, EMB, ITIDA, CASEEC, EGYPTAIR, Fresenius Kabien_US
dc.description.sponsorshipCoordinatorship of Selcuk University's Scientific Research ProjectsSelcuk Universityen_US
dc.description.sponsorshipThis work is supported by the Coordinatorship of Selcuk University's Scientific Research Projects.en_US
dc.identifier.citationCeylan, M., Özbay, Y., Yıldırım, E., (2010). A New Approach for Biomedical Image Segmentation: Combined Complex-Valued Artificial Neural Network Case Study: Lung Segmentation on Chest CT Images. 2010 5th Cairo International Biomedical Engineering Conference (Cibec 2010), 33-36. Doi: 10.1109/CIBEC.2010.5716083
dc.identifier.doi10.1109/CIBEC.2010.5716083en_US
dc.identifier.endpage36en_US
dc.identifier.isbn978-1-4244-7170-6
dc.identifier.issn2156-6097en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage33en_US
dc.identifier.urihttps://dx.doi.org/10.1109/CIBEC.2010.5716083
dc.identifier.urihttps://hdl.handle.net/20.500.12395/24524
dc.identifier.wosWOS:000395157100009en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorCeylan, Murat
dc.institutionauthorÖzbay, Yüksel
dc.language.isoenen_US
dc.publisherIeeeen_US
dc.relation.ispartof2010 5th Cairo International Biomedical Engineering Conference (Cibec 2010)en_US
dc.relation.ispartofseriesCairo International Biomedical Engineering Conference
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.selcuk20240510_oaigen_US
dc.titleA New Approach for Biomedical Image Segmentation: Combined Complex-Valued Artificial Neural Network Case Study: Lung Segmentation on Chest CT Imagesen_US
dc.typeConference Objecten_US

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim
İsim:
4524.pdf
Boyut:
208.7 KB
Biçim:
Adobe Portable Document Format
Açıklama:
Makale Dosyası
İndir

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Elektirik ve Elektronik Mühendisliği/Makale Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu