• DocumentCode
    2795667
  • Title

    Local maximum detection for fully automatic classification of EM algorithm

  • Author

    Lerddararadsamee, Thararin ; Jiraraksopakun, Yuttapong

  • Author_Institution
    Electron. & Telecommun. Eng. Dept., King Mongkut´´s Univ. of Technol. Thonburi, Bangkok, Thailand
  • fYear
    2012
  • fDate
    16-18 May 2012
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    In this paper, we proposed a method for fully-automatic EM segmentation on brain MR images without a priori knowledge. Instead of manually predetermination on number of tissue classes, the proposed method automatically find mean intensities of distinct tissues from the histogram. The brain MR images were chosen to test our proposed method, but our method can, in fact, be general for other MR segmentations using EM with which the Gaussian mixture distribution of an image histogram holds. The results from our method suggested that a fully automatic segmentation using EM can be achieved with no significant difference in segmentation accuracy compared to the conventional EM.
  • Keywords
    Gaussian distribution; biological tissues; biomedical MRI; brain; expectation-maximisation algorithm; image classification; image segmentation; medical image processing; Gaussian mixture distribution; automatic expectation maximization segmentation; brain MRI segmentation; expectation maximization algorithm; fully automatic classification; image histogram; local maximum detection; tissue classes; Accuracy; Brain models; Classification algorithms; Histograms; Image segmentation; Automatic segmentation; Expectation Maximization (EM); Magnetic Resonance Image (MRI); local maximum detection;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), 2012 9th International Conference on
  • Conference_Location
    Phetchaburi
  • Print_ISBN
    978-1-4673-2026-9
  • Type

    conf

  • DOI
    10.1109/ECTICon.2012.6254193
  • Filename
    6254193