Color graphs for automated cancer diagnosis and grading

dc.citation.epage674en_US
dc.citation.issueNumber3en_US
dc.citation.spage665en_US
dc.citation.volumeNumber57en_US
dc.contributor.authorAltunbay, D.en_US
dc.contributor.authorCigir, C.en_US
dc.contributor.authorSokmensuer, C.en_US
dc.contributor.authorGunduz Demir, C.en_US
dc.date.accessioned2016-02-08T09:59:36Z
dc.date.available2016-02-08T09:59:36Z
dc.date.issued2010-03en_US
dc.departmentDepartment of Computer Engineeringen_US
dc.description.abstractThis paper reports a new structural method to mathematically represent and quantify a tissue for the purpose of automated and objective cancer diagnosis and grading. Unlike the previous structural methods, which quantify a tissue considering the spatial distributions of its cell nuclei, the proposed method relies on the use of distributions of multiple tissue components for the representation. To this end, it constructs a graph on multiple tissue components and colors its edges depending on the component types of their endpoints. Subsequently, it extracts a new set of structural features from these color graphs and uses these features in the classification of tissues. Working with the images of colon tissues, our experiments demonstrate that the color-graph approach leads to 82.65% test accuracy and that it significantly improves the performance of its counterparts. © 2006 IEEE.en_US
dc.description.provenanceMade available in DSpace on 2016-02-08T09:59:36Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 70227 bytes, checksum: 26e812c6f5156f83f0e77b261a471b5a (MD5) Previous issue date: 2010en
dc.identifier.doi10.1109/TBME.2009.2033804en_US
dc.identifier.eissn1558-2531en_US
dc.identifier.issn0018-9294en_US
dc.identifier.urihttp://hdl.handle.net/11693/22400en_US
dc.language.isoEnglishen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/TBME.2009.2033804en_US
dc.source.titleIEEE Transactions on Biomedical Engineeringen_US
dc.subjectBiomedical image processingen_US
dc.subjectCanceren_US
dc.subjectGraph theoryen_US
dc.subjectHistopathological image analysisen_US
dc.subjectImage representationsen_US
dc.subjectMedical diagnosisen_US
dc.subjectAutomated cancer diagnosisen_US
dc.subjectCancer diagnosisen_US
dc.subjectCell nucleusen_US
dc.subjectColon tissuesen_US
dc.subjectColor graphsen_US
dc.titleColor graphs for automated cancer diagnosis and gradingen_US
dc.typeArticleen_US

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