Graph run-length matrices for histopathological image segmentation

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dc.citation.epage732en_US
dc.citation.issueNumber3en_US
dc.citation.spage721en_US
dc.citation.volumeNumber30en_US
dc.contributor.authorTosun, A. B.en_US
dc.contributor.authorGunduz Demir, C.en_US
dc.date.accessioned2016-02-08T09:54:06Z
dc.date.available2016-02-08T09:54:06Z
dc.date.issued2011-03en_US
dc.departmentDepartment of Computer Engineeringen_US
dc.description.abstractThe histopathological examination of tissue specimens is essential for cancer diagnosis and grading. However, this examination is subject to a considerable amount of observer variability as it mainly relies on visual interpretation of pathologists. To alleviate this problem, it is very important to develop computational quantitative tools, for which image segmentation constitutes the core step. In this paper, we introduce an effective and robust algorithm for the segmentation of histopathological tissue images. This algorithm incorporates the background knowledge of the tissue organization into segmentation. For this purpose, it quantifies spatial relations of cytological tissue components by constructing a graph and uses this graph to define new texture features for image segmentation. This new texture definition makes use of the idea of gray-level run-length matrices. However, it considers the runs of cytological components on a graph to form a matrix, instead of considering the runs of pixel intensities. Working with colon tissue images, our experiments demonstrate that the texture features extracted from graph run-length matrices lead to high segmentation accuracies, also providing a reasonable number of segmented regions. Compared with four other segmentation algorithms, the results show that the proposed algorithm is more effective in histopathological image segmentation. © 2006 IEEE.en_US
dc.description.provenanceMade available in DSpace on 2016-02-08T09:54:06Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 70227 bytes, checksum: 26e812c6f5156f83f0e77b261a471b5a (MD5) Previous issue date: 2011en
dc.identifier.doi10.1109/TMI.2010.2094200en_US
dc.identifier.issn0278-0062
dc.identifier.urihttp://hdl.handle.net/11693/22001
dc.language.isoEnglishen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/TMI.2010.2094200en_US
dc.source.titleIEEE Transactions on Medical Imagingen_US
dc.subjectCanceren_US
dc.subjectGraphsen_US
dc.subjectHistopathological image analysisen_US
dc.subjectImage segmentationen_US
dc.subjectImage texture analysisen_US
dc.subjectPerceptual image segmentationen_US
dc.titleGraph run-length matrices for histopathological image segmentationen_US
dc.typeArticleen_US

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