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      • Faculty of Engineering
      • Department of Electrical and Electronics Engineering
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      2-D adaptive prediction based Gaussianity tests in microcalcification detection

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      Author(s)
      Gürcan, M. Nafi
      Yardımcı, Yasemin
      Çetin, A. Enis
      Date
      1998-01
      Source Title
      Proceedings - Visual Communications and Image Processing '98 - Photonics West '98 Electronic Imaging
      Print ISSN
      0277-786X
      Publisher
      SPIE
      Pages
      625 - 633
      Language
      English
      Type
      Conference Paper
      Item Usage Stats
      194
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      Abstract
      With increasing use of Picture Archiving and Communication Systems (PACS), Computer-aided Diagnosis (CAD) methods will be more widely utilized. In this paper, we develop a CAD method for the detection of microcalcification clusters in mammograms, which are an early sign of breast cancer. The method we propose makes use of two-dimensional (2-D) adaptive filtering and a Gaussianity test recently developed by Ojeda et al. for causal invertible time series. The first step of this test is adaptive linear prediction. It is assumed that the prediction error sequence has a Gaussian distribution as the mammogram images do not contain sharp edges. Since microcalcifications appear as isolated bright spots, the prediction error sequence contains large outliers around microcalcification locations. The second step of the algorithm is the computation of a test statistic from the prediction error values to determine whether the samples are from a Gaussian distribution. The Gaussianity test is applied over small, overlapping square regions. The regions, in which the Gaussianity test fails, are marked as suspicious regions. Experimental results obtained from a mammogram database are presented.
      Keywords
      Adaptive filtering
      Algorithms
      Computer aided diagnosis
      Database systems
      Mammography
      Gaussianity tests
      Mammograms
      Image processing
      Permalink
      http://hdl.handle.net/11693/27664
      Published Version (Please cite this version)
      https://doi.org/10.1117/12.298376
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      • Department of Electrical and Electronics Engineering 3702
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