Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/10587
Full metadata record
DC FieldValueLanguage
dc.contributor.authorÖnder, Devrim-
dc.contributor.authorSarıoğlu, Sülen-
dc.contributor.authorKaraçalı, Bilge-
dc.date.accessioned2021-01-24T18:45:18Z-
dc.date.available2021-01-24T18:45:18Z-
dc.date.issued2014-
dc.identifier.issn0884-6812-
dc.identifier.urihttps://hdl.handle.net/11147/10587-
dc.descriptionPubMed: 25803989en_US
dc.description.abstractOBJECTIVE: To evaluate the performance of a quasisupervised statistical learning algorithm, operating on datasets having normal and neoplastic tissues, to identify larynx squamous cell carcinomas. Furthermore, cancer texture separability measures against normal tissues are to be developed and compared either for colorectal or larynx tissues. STUDY DESIGN: Light microscopic digital images from histopathological sections were obtained from laryngectomy materials including squamous cell carcinoma and nonneoplastic regions. The texture features were calculated by using co-occurrence matrices and local histograms. The texture features were input to the quasisupervised learning algorithm. RESULTS: Larynx regions containing squamous cell carcinomas were accurately identified, having false and true positive rates up to 21% and 87%, respectively. CONCLUSION: Larynx squamous cell carcinoma versus normal tissue texture separability measures were higher than colorectal adenocarcinoma versus normal textures for the colorectal database. Furthermore, the resultant labeling performances for all larynx datasets are higher than or equal to that of colorectal datasets. The results in larynx datasets, in comparison with the former colorectal study, suggested that quasi-supervised texture classification is to be a helpful method in histopathological image classification and analysis.en_US
dc.description.sponsorshipThe computational infrastructure of the Biomedical Information Processing Laboratory (BIPLAB), which was supported by a grant from the European Commission (PIRG03-GA-2008-230903), was used in this study.en_US
dc.language.isoenen_US
dc.publisherScience Printers and Publishers Inc.en_US
dc.relation.ispartofAnalytical and Quantitative Cytopathology and Histopathologyen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectClassificationen_US
dc.subjectHistopathologyen_US
dc.subjectQuasi-supervised learningen_US
dc.subjectScatter matricesen_US
dc.subjectStatistical learningen_US
dc.subjectTexture classificationen_US
dc.titleAutomated labeling of cancer textures in larynx histopathology slides using quasi-supervised learningen_US
dc.typeArticleen_US
dc.institutionauthorÖnder, Devrim-
dc.institutionauthorKaraçalı, Bilge-
dc.departmentIzmir Institute of Technology. Electronics and Communication Engineeringen_US
dc.identifier.volume36en_US
dc.identifier.issue6en_US
dc.identifier.startpage314en_US
dc.identifier.wosWOS:000349428300002en_US
dc.identifier.scopus2-s2.0-84920272459en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.pmid25803989en_US
dc.identifier.scopusqualityQ3-
item.cerifentitytypePublications-
item.grantfulltextnone-
item.openairetypeArticle-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.languageiso639-1en-
item.fulltextNo Fulltext-
crisitem.author.dept03.05. Department of Electrical and Electronics Engineering-
Appears in Collections:Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği
PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Show simple item record

CORE Recommender

Page view(s)

7,026
checked on Aug 8, 2022

Google ScholarTM

Check


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.