• DocumentCode
    3129751
  • Title

    Counting in Intracellular Images Using Partial Least Squares Regression and Correlation between Features

  • Author

    Kumagai, Shinya ; Hotta, Kazuhiro

  • Author_Institution
    Meijo Univ., Nagoya, Japan
  • fYear
    2013
  • fDate
    4-6 Dec. 2013
  • Firstpage
    275
  • Lastpage
    280
  • Abstract
    Light spot counting in intracellular images is important for investigating the cause diseases. However, light spots are manually counted by human observers now. This paper proposes an automatic light spot counting method by computer. To count light spots, we use partial least squares regression and correlation between 2 different types of features using higher-order local autocorrelation. The proposed method gives higher accuracy than counting by support vector regression and ImageJ. The effectiveness of our method is shown by experiments.
  • Keywords
    biology computing; feature extraction; least squares approximations; regression analysis; ImageJ; automatic light spot counting method; higher-order local autocorrelation; intracellular images; partial least squares regression; support vector regression; Correlation; Feature extraction; Image edge detection; Information filters; Kernel; Lipidomics; Co-occurrence; Higher-order Local Auto Correlation; Intracellular Images; Light Spot Counting; Partial Least Squares Regression;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computing and Networking (CANDAR), 2013 First International Symposium on
  • Conference_Location
    Matsuyama
  • Print_ISBN
    978-1-4799-2795-1
  • Type

    conf

  • DOI
    10.1109/CANDAR.2013.48
  • Filename
    6726910