Shape and Texture Based Novel Features for Automated Juxtapleural Nodule Detection in Lung CTs

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dc.contributor.authorTasci, Erdal
dc.contributor.authorUgur, Aybars
dc.date.accessioned2019-10-27T22:27:07Z
dc.date.available2019-10-27T22:27:07Z
dc.date.issued2015
dc.departmentEge Üniversitesien_US
dc.description.abstractLung cancer is one of the types of cancer with highest mortality rate in the world. In case of early detection and diagnosis, the survival rate of patients significantly increases. In this study, a novel method and system that provides automatic detection of juxtapleural nodule pattern have been developed from cross-sectional images of lung CT (Computerized Tomography). Shape-based and both shape and texture based 7 features are contributed to the literature for lung nodules. System that we developed consists of six main stages called preprocessing, lung segmentation, detection of nodule candidate regions, feature extraction, feature selection (with five feature ranking criteria) and classification. LIDC dataset containing cross-sectional images of lung CT has been utilized, 1410 nodule candidate regions and 40 features have been extracted from 138 cross-sectional images for 24 patients. Experimental results for 10 classifiers are obtained and presented. Adding our derived features to known 33 features has increased nodule recognition performance from 0.9639 to 0.9679 AUC value on generalized linear model regression (GLMR) for 22 selected features and being reached one of the most successful results in the literature.en_US
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TUBITAK) 2211 National Graduate Scholarship ProgramTurkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK)en_US
dc.description.sponsorshipThis study has been supported by Scientific and Technological Research Council of Turkey (TUBITAK) 2211 National Graduate Scholarship Program.en_US
dc.identifier.doi10.1007/s10916-015-0231-5en_US
dc.identifier.issn0148-5598
dc.identifier.issn1573-689X
dc.identifier.issue5en_US
dc.identifier.pmid25732079en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1007/s10916-015-0231-5
dc.identifier.urihttps://hdl.handle.net/11454/50573
dc.identifier.volume39en_US
dc.identifier.wosWOS:000352821900005en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofJournal of Medical Systemsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectFeature extractionen_US
dc.subjectImage processingen_US
dc.subjectLung canceren_US
dc.subjectMachine learningen_US
dc.subjectPattern recognitionen_US
dc.titleShape and Texture Based Novel Features for Automated Juxtapleural Nodule Detection in Lung CTsen_US
dc.typeArticleen_US

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