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      • Faculty of Engineering
      • Department of Electrical and Electronics Engineering
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      Microscopic image classification using sparsity in a transform domain and Bayesian learning

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      Author
      Suhre, Alexander
      Erşahin, Tülin
      Çetin-Atalay, Rengül
      Çetin, A. Enis
      Date
      2011
      Source Title
      2011 19th European Signal Processing Conference
      Print ISSN
      2076-1465
      Publisher
      IEEE
      Pages
      1005 - 1009
      Language
      English
      Type
      Conference Paper
      Item Usage Stats
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      Abstract
      Some biomedical images show a large quantity of different junctions and sharp corners. It is possible to classify several types of biomedical images in a region covariance approach. Cancer cell line images are divided into small blocks and covariance matrices of image blocks are computed. Eigen-values of the covariance matrices are used as classification parameters in a Bayesian framework using the sparsity of the parameters in a transform domain. The efficiency of the proposed method over classification using standard Support Vector Machines (SVM) is demonstrated on biomedical image data. © 2011 EURASIP.
      Keywords
      A-transform
      Bayesian frameworks
      Bayesian learning
      Biomedical image data
      Biomedical images
      Cancer cell lines
      Classification parameters
      Covariance matrices
      Image blocks
      Region covariance
      Sharp corners
      Cell culture
      Covariance matrix
      Signal processing
      Support vector machines
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      http://hdl.handle.net/11693/28294
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      • Department of Electrical and Electronics Engineering 3524
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