• DocumentCode
    2188583
  • Title

    A New Fast Chinese Visible Human Brain Skull Stripping Method

  • Author

    Chen Yunjie ; Zhang Jianwei ; Wang Shunfeng

  • Author_Institution
    Dept. of math, Nanjing Univ. of Inf. Sci. & Technol., Nanjing, China
  • fYear
    2009
  • fDate
    17-19 Oct. 2009
  • Firstpage
    1
  • Lastpage
    5
  • Abstract
    Image data of the entire cadaver from the chinese visible human is being used to produce three-dimensional images and software for human anatomy research. The anatomy of human brain is more complicated. With the effect of noise, bias field, and fake grey matters, it is a challenging task to build a digital three dimensional representation of a human brain. In order to obtain more accurate representation, the brain regions must be separated from highly variable background regions to obtain a suitable stack of segmentation images. We use adapted Gauss mixture model as a promising starting point for a sophisticated segmentation framework of color images within 3-dimensions. The model can classify images meanwhile estimate the bias field. For the effect of the fake grey matters, a proper image preprocessing strategy turned out to be necessary for accurate and robust segmentation results. We present a complete high resolution and accurate segmentation of the CVH brain. Based on these images, 3D representation is presented.
  • Keywords
    Gaussian processes; brain; image denoising; image representation; image segmentation; medical image processing; adapted Gauss mixture model; bias field effect; chinese visible human; digital three dimensional human brain representation; fake grey matter effect; human brain anatomy; image denoising; image segmentation; noise effect; skull stripping method; Anatomy; Brain; Cadaver; Color; Colored noise; Gaussian processes; Humans; Image reconstruction; Image segmentation; Skull;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Image and Signal Processing, 2009. CISP '09. 2nd International Congress on
  • Conference_Location
    Tianjin
  • Print_ISBN
    978-1-4244-4129-7
  • Electronic_ISBN
    978-1-4244-4131-0
  • Type

    conf

  • DOI
    10.1109/CISP.2009.5305306
  • Filename
    5305306