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      Tissue object patterns for segmentation in histopathological images

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      Author
      Gündüz-Demir, Çiğdem
      Date
      2011
      Source Title
      ISABEL '11 Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies
      Publisher
      ACM
      Language
      English
      Type
      Conference Paper
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      Abstract
      In the current practice of medicine, histopathological examination is the gold standard for routine clinical diagnosis and grading of cancer. However, as this examination involves the visual analysis of biopsies, it is subject to a considerable amount of observer variability. In order to decrease the variability, it has been proposed to develop systems that mathematically model the histopathological tissue images and automate the analysis. Segmentation constitutes the first step for most of these automated systems. Nevertheless, the segmentation in histopathological images remains a challenging task since these images typically show variances due to their complex nature and may include a large amount of noise and artifacts due to the tissue preparation procedures. In our research group, we recently developed different segmentation algorithms that rely on representing a tissue image with a set of tissue objects and using the structural pattern of these objects in segmentation. In this paper, we review these segmentation algorithms, discussing their clinical demonstrations on colon tissues. © 2011 ACM.
      Keywords
      Gland segmentation
      Automated systems
      Clinical diagnosis
      Colon tissues
      Complex nature
      Gold standards
      Histopathological examinations
      Histopathological images
      Object patterns
      Observer variability
      Research groups
      Segmentation algorithms
      Structural pattern
      Tissue images
      Tissue preparation
      Visual analysis
      Algorithms
      Automation
      Communication
      Image texture
      Textures
      Tissue
      Image segmentation
      Permalink
      http://hdl.handle.net/11693/28290
      Published Version (Please cite this version)
      http://dx.doi.org/10.1145/2093698.2093853
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      • Department of Computer Engineering 1368
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