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      Graph run-length matrices for histopathological image segmentation

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      Author
      Tosun, A. B.
      Gunduz Demir, C.
      Date
      2011-03
      Source Title
      IEEE Transactions on Medical Imaging
      Print ISSN
      0278-0062
      Publisher
      Institute of Electrical and Electronics Engineers
      Volume
      30
      Issue
      3
      Pages
      721 - 732
      Language
      English
      Type
      Article
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      Abstract
      The 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.
      Keywords
      Cancer
      Graphs
      Histopathological image analysis
      Image segmentation
      Image texture analysis
      Perceptual image segmentation
      Permalink
      http://hdl.handle.net/11693/22001
      Published Version (Please cite this version)
      http://dx.doi.org/10.1109/TMI.2010.2094200
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