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      • Department of Computer Engineering
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      Cell-graph mining for breast tissue modeling and classification

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      Author
      Bilgin, C.
      Demir, Çiğdem
      Nagi, C.
      Yener, B.
      Date
      2007-08
      Source Title
      29th Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
      Publisher
      IEEE
      Pages
      5311 - 5314
      Language
      English
      Type
      Conference Paper
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      Abstract
      We consider the problem of automated cancer diagnosis in the context of breast tissues. We present graph theoretical techniques that identify and compute quantitative metrics for tissue characterization and classification. We segment digital images of histopatological tissue samples using k-means algorithm. For each segmented image we generate different cell-graphs using positional coordinates of cells and surrounding matrix components. These cell-graphs have 500-2000 cells(nodes) with 1000-10000 links depending on the tissue and the type of cell-graph being used. We calculate a set of global metrics from cell-graphs and use them as the feature set for learning. We compare our technique, hierarchical cell graphs, with other techniques based on intensity values of images, Delaunay triangulation of the cells, the previous technique we proposed for brain tissue images and with the hybrid approach that we introduce in this paper. Among the compared techniques, hierarchical-graph approach gives 81.8% accuracy whereas we obtain 61.0%, 54.1% and 75.9% accuracy with intensity-based features, Delaunay triangulation and our previous technique, respectively. © 2007 IEEE.
      Keywords
      Biological organs
      Characterization
      Data mining
      Graph theory
      Learning systems
      Tissue
      Cancer diagnosis
      Delaunay triangulation
      Global metrics
      K-means algorithms
      Oncology
      Permalink
      http://hdl.handle.net/11693/26965
      Published Version (Please cite this version)
      http://dx.doi.org/10.1109/IEMBS.2007.4353540
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