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dc.contributor.authorTosun, A. B.en_US
dc.contributor.authorKandemir, M.en_US
dc.contributor.authorSokmensuer, C.en_US
dc.contributor.authorGunduz Demir, C.en_US
dc.date.accessioned2016-02-08T10:04:05Z
dc.date.available2016-02-08T10:04:05Z
dc.date.issued2009-06en_US
dc.identifier.issn0031-3203
dc.identifier.urihttp://hdl.handle.net/11693/22733
dc.description.abstractStaining methods routinely used in pathology lead to similar color distributions in the biologically different regions of histopathological images. This causes problems in image segmentation for the quantitative analysis and detection of cancer. To overcome this problem, unlike previous methods that use pixel distributions, we propose a new homogeneity measure based on the distribution of the objects that we define to represent tissue components. Using this measure, we demonstrate a new object-oriented segmentation algorithm. Working with colon biopsy images, we show that this algorithm segments the cancerous and normal regions with 94.89 percent accuracy on the average and significantly improves the segmentation accuracy compared to its pixel-based counterpart. © 2008 Elsevier Ltd. All rights reserved.en_US
dc.language.isoEnglishen_US
dc.source.titlePattern Recognitionen_US
dc.relation.isversionofhttp://dx.doi.org/10.1016/j.patcog.2008.07.007en_US
dc.subjectCancer detectionen_US
dc.subjectColon adenocarcinomaen_US
dc.subjectHistopathological image analysisen_US
dc.subjectImage segmentationen_US
dc.subjectBiopsyen_US
dc.subjectDigital image storageen_US
dc.subjectImage analysisen_US
dc.subjectImage processingen_US
dc.subjectPixelsen_US
dc.subjectTexturesen_US
dc.subjectColor distributionsen_US
dc.subjectObject-oriented segmentationsen_US
dc.subjectPixel distributionsen_US
dc.subjectQuantitative analysisen_US
dc.subjectSegmentation accuraciesen_US
dc.subjectStaining methodsen_US
dc.subjectTissue componentsen_US
dc.subjectUnsupervised segmentationsen_US
dc.titleObject-oriented texture analysis for the unsupervised segmentation of biopsy images for cancer detectionen_US
dc.typeArticleen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.citation.spage1104en_US
dc.citation.epage1112en_US
dc.citation.volumeNumber42en_US
dc.citation.issueNumber6en_US
dc.identifier.doi10.1016/j.patcog.2008.07.007en_US
dc.publisherElsevier BVen_US


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