• DocumentCode
    697911
  • Title

    Chan-Vese based method to segment mouse brain MRI images: Application to cerebral malformation analysis in Trisomy 21

  • Author

    Almhdie, Ahmad ; Lopes-Pereira, Patricia ; Meme, Sandra ; Colombier, Caroline ; Brault, Veronique ; Szeremeta, Frederic ; Doan, Bich-Thuy ; Ledee, Roger ; Harba, Rachid ; Herault, Yann ; Beloeil, Jean-Claude ; Leger, Christophe

  • Author_Institution
    PRISME, Univ. d´Orleans, Orléans, France
  • fYear
    2009
  • fDate
    24-28 Aug. 2009
  • Firstpage
    1883
  • Lastpage
    1887
  • Abstract
    A semi automatic active contour method based on Chan-Vese model is proposed for the segmentation of mouse brain MR images. First, a 2 ½ D strategy is applied on the axial images to segment the 3D volume of interest. The method takes into account the special shape of the object to segment. Moreover, the user defines the limits where to search these contours and also provides an initial contour. This semi automatic method makes that human intervention is limited and the tedious manual handling is greatly reduced. Results have shown that the brain volumes estimated by the method are identical to expert manually estimated volumes. Last but not least, the new method was used in the analysis of the cerebral malformations linked to Trisomy 21: no significant difference of the brain volumes between Trisomy 21 mice and the control ones were found.
  • Keywords
    biomedical MRI; brain; image segmentation; medical disorders; medical image processing; 2 ½ D strategy; 3D volume of interest; Chan-Vese based method; Trisomy 21 mice; axial images; brain volumes; cerebral malformation analysis; human intervention; initial contour; mouse brain MRI image segmentation; semiautomatic active contour method; Biological cells; Brain modeling; Image segmentation; Magnetic resonance imaging; Mice; Three-dimensional displays;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Signal Processing Conference, 2009 17th European
  • Conference_Location
    Glasgow
  • Print_ISBN
    978-161-7388-76-7
  • Type

    conf

  • Filename
    7077483