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      Object-oriented texture analysis for the unsupervised segmentation of biopsy images for cancer detection

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      Author(s)
      Tosun, A. B.
      Kandemir, M.
      Sokmensuer, C.
      Gunduz Demir, C.
      Date
      2009-06
      Source Title
      Pattern Recognition
      Print ISSN
      0031-3203
      Publisher
      Elsevier BV
      Volume
      42
      Issue
      6
      Pages
      1104 - 1112
      Language
      English
      Type
      Article
      Item Usage Stats
      128
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      144
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      Abstract
      Staining methods routinely used in pathology lead to similar color distributions in the biologically different regions of histopathological images. This causes problems in image segmentation for the quantitative analysis and detection of cancer. To overcome this problem, unlike previous methods that use pixel distributions, we propose a new homogeneity measure based on the distribution of the objects that we define to represent tissue components. Using this measure, we demonstrate a new object-oriented segmentation algorithm. Working with colon biopsy images, we show that this algorithm segments the cancerous and normal regions with 94.89 percent accuracy on the average and significantly improves the segmentation accuracy compared to its pixel-based counterpart. © 2008 Elsevier Ltd. All rights reserved.
      Keywords
      Cancer detection
      Colon adenocarcinoma
      Histopathological image analysis
      Image segmentation
      Biopsy
      Digital image storage
      Image analysis
      Image processing
      Pixels
      Textures
      Color distributions
      Object-oriented segmentations
      Pixel distributions
      Quantitative analysis
      Segmentation accuracies
      Staining methods
      Tissue components
      Unsupervised segmentations
      Permalink
      http://hdl.handle.net/11693/22733
      Published Version (Please cite this version)
      http://dx.doi.org/10.1016/j.patcog.2008.07.007
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      • Department of Computer Engineering 1435
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