• DocumentCode
    2459249
  • Title

    2D-PCA Based Statistical Shape Model from few Medical Samples

  • Author

    Tateyama, Tomoko ; Foruzan, Hossein ; Chen, Yen-wei

  • Author_Institution
    Coll. of Inf. Sci. & Eng., Ritsumeikan Univ., Kusatsu, Japan
  • fYear
    2009
  • fDate
    12-14 Sept. 2009
  • Firstpage
    1266
  • Lastpage
    1269
  • Abstract
    Statistical shape model (SSM) is to model the shape variation of an object. In this paper, we propose an efficient shape representation method and a new 2D-PCA based statistical shape modeling. In our proposed method, we used the radii of these surface points as shape feature instead of their coordinates, and the shape is represented by a 2D matrices. We then apply 2D-PCA to construct a statistical shape model with generalization even from fewer samples.
  • Keywords
    matrix algebra; medical image processing; principal component analysis; 2D PCA; 2D matrices; generalization; object shape variation; principal component analysis; shape feature; shape representation method; statistical shape modeling; Biomedical engineering; Computed tomography; Educational institutions; Feature extraction; Image segmentation; Information analysis; Information science; Liver; Shape; Signal processing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Information Hiding and Multimedia Signal Processing, 2009. IIH-MSP '09. Fifth International Conference on
  • Conference_Location
    Kyoto
  • Print_ISBN
    978-1-4244-4717-6
  • Electronic_ISBN
    978-0-7695-3762-7
  • Type

    conf

  • DOI
    10.1109/IIH-MSP.2009.246
  • Filename
    5337215