• DocumentCode
    575351
  • Title

    Optimization of parameter for extracting brain regions using deformable contour models

  • Author

    Odagiri, Seiya ; Sato, Kazuhito ; Madokoro, Hirokazu

  • Author_Institution
    Grad. Sch., Fac. of Syst. Sci. & Technol., Akita Prefectural Univ., Yurihonjo, Japan
  • fYear
    2012
  • fDate
    20-23 Aug. 2012
  • Firstpage
    608
  • Lastpage
    613
  • Abstract
    This paper presents a method that considers sharing of parameters of Level Set Methods and optimizes method using Genetic Algorithm. We optimized parameters of LSMs that is deformable model using GA of evolutional learning. However, the proposal method needs Ground Truth for each clinical image. Therefore, application with clinical images without GT was challenging task. In this paper, we focus on shaering of parameters and try application of the proposal method for clinical images without GT for an individualization trade-off problem. Moreover, the extraction accuracy performed the optimal value search for iterations of updating to the low case using Active Appearance Models after sharing.
  • Keywords
    biomedical MRI; computational geometry; feature extraction; genetic algorithms; learning (artificial intelligence); medical image processing; parameter estimation; active appearance models; brain region extraction; clinical images; deformable contour models; deformable model; evolutionary learning; genetic algorithm; ground truth; individualization trade off problem; level set methods; optimized LSM parameters; parameter optimization; parameter sharing; Accuracy; Atrophy; Deformable models; Genetic algorithms; Head; Optimization; Shape; Active Appearance Models; Genetic Algorithm; Level Set Methods;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    SICE Annual Conference (SICE), 2012 Proceedings of
  • Conference_Location
    Akita
  • ISSN
    pending
  • Print_ISBN
    978-1-4673-2259-1
  • Type

    conf

  • Filename
    6318510