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dc.contributor.authorGunduz Demir, C.en_US
dc.contributor.authorKandemir, M.en_US
dc.contributor.authorTosun, A. B.en_US
dc.contributor.authorSokmensuer, C.en_US
dc.date.accessioned2016-02-08T10:00:07Z
dc.date.available2016-02-08T10:00:07Z
dc.date.issued2010en_US
dc.identifier.issn1361-8415
dc.identifier.urihttp://hdl.handle.net/11693/22441
dc.description.abstractGland segmentation is an important step to automate the analysis of biopsies that contain glandular structures. However, this remains a challenging problem as the variation in staining, fixation, and sectioning procedures lead to a considerable amount of artifacts and variances in tissue sections, which may result in huge variances in gland appearances. In this work, we report a new approach for gland segmentation. This approach decomposes the tissue image into a set of primitive objects and segments glands making use of the organizational properties of these objects, which are quantified with the definition of object-graphs. As opposed to the previous literature, the proposed approach employs the object-based information for the gland segmentation problem, instead of using the pixel-based information alone. Working with the images of colon tissues, our experiments demonstrate that the proposed object-graph approach yields high segmentation accuracies for the training and test sets and significantly improves the segmentation performance of its pixel-based counterparts. The experiments also show that the object-based structure of the proposed approach provides more tolerance to artifacts and variances in tissues. © 2009 Elsevier B.V. All rights reserved.en_US
dc.language.isoEnglishen_US
dc.source.titleMedical Image Analysisen_US
dc.relation.isversionofhttp://dx.doi.org/10.1016/j.media.2009.09.001en_US
dc.subjectAttributed graphsen_US
dc.subjectColon adenocarcinomaen_US
dc.subjectGland segmentationen_US
dc.subjectHistopathological image analysisen_US
dc.subjectImage segmentationen_US
dc.subjectObject - graphsen_US
dc.titleAutomatic segmentation of colon glands using object-graphsen_US
dc.typeArticleen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.citation.spage1en_US
dc.citation.epage12en_US
dc.citation.volumeNumber14en_US
dc.citation.issueNumber1en_US
dc.identifier.doi10.1016/j.media.2009.09.001en_US
dc.publisherElsevier BVen_US


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