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      • Department of Electrical and Electronics Engineering
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      Microscopic image classification via WT-based covariance descriptors using Kullback-Leibler distance

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      Author
      Keskin, Furkan
      Çetin, A. Enis
      Erşahin, Tülin
      Çetin-Atalay, Rengül
      Date
      2012
      Source Title
      ISCAS 2012 - 2012 IEEE International Symposium on Circuits and Systems
      Publisher
      IEEE
      Pages
      2079 - 2082
      Language
      English
      Type
      Conference Paper
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      Abstract
      In this paper, we present a novel method for classification of cancer cell line images using complex wavelet-based region covariance matrix descriptors. Microscopic images containing irregular carcinoma cell patterns are represented by randomly selected subwindows which possibly correspond to foreground pixels. For each subwindow, a new region descriptor utilizing the dual-tree complex wavelet transform coefficients as pixel features is computed. WT as a feature extraction tool is preferred primarily because of its ability to characterize singularities at multiple orientations, which often arise in carcinoma cell lines, and approximate shift invariance property. We propose new dissimilarity measures between covariance matrices based on Kullback-Leibler (KL) divergence and L 2-norm, which turn out to be as successful as the classical KL divergence, but with much less computational complexity. Experimental results demonstrate the effectiveness of the proposed image classification framework. The proposed algorithm outperforms the recently published eigenvalue-based Bayesian classification method. © 2012 IEEE.
      Keywords
      Bayesian classification
      Cancer cell lines
      Carcinoma cell lines
      Carcinoma cells
      Classification framework
      Covariance matrices
      Descriptors
      Dissimilarity measures
      Dual-tree complex wavelet transform
      KL-divergence
      Kullback-Leibler distance
      Kullback-Leibler divergence
      Microscopic image
      Multiple orientations
      Region covariance matrixes
      Shift invariance
      Cell culture
      Covariance matrix
      Eigenvalues and eigenfunctions
      Feature extraction
      Image classification
      Pixels
      Permalink
      http://hdl.handle.net/11693/28162
      Published Version (Please cite this version)
      http://dx.doi.org/10.1109/ISCAS.2012.6271692
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