• DocumentCode
    2955308
  • Title

    Multiphase level set with multi dynamic shape models on kidney segmentation of CT image

  • Author

    Huang, Yao-Pin ; Chung, Pau-Choo ; Huang, Chieh-Ling ; Huang, Chun-Rong

  • Author_Institution
    Dept. of Electr. Eng., Nat. Cheng Kung Univ., Tainan, Taiwan
  • fYear
    2009
  • fDate
    26-28 Nov. 2009
  • Firstpage
    141
  • Lastpage
    144
  • Abstract
    In this paper, a multiphase level set method with multi dynamic shape models is proposed to segment the kidneys on the abdominal computed tomography (CT) images. Comparing with the original Chan-Vese model three changes are made to improve the segmentation result. The first is using shape model to help the segmentation. The second is using dynamic shape model to deal with the variation of the kidneys. The third is using multi level set to simultaneously segment multi objects. We also develop an algorithm to automatically get the initial level set curves and initial shape models which are essential to apply the proposed method. In the experiments, the proposed method is compared with the Chan-Vese model and the single level set method with shape prior to prove that the proposed method can work better on the kidneys segmentation.
  • Keywords
    computerised tomography; image segmentation; kidney; medical image processing; Chan-Vese model; abdominal computed tomography images; initial level set curves; initial shape models; kidney CT image segmentation; multidynamic shape models; multiphase level set CT image; Abdomen; Active contours; Computed tomography; Image segmentation; Information science; Level set; Minimization methods; Piecewise linear approximation; Piecewise linear techniques; Shape;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Biomedical Circuits and Systems Conference, 2009. BioCAS 2009. IEEE
  • Conference_Location
    Beijing
  • Print_ISBN
    978-1-4244-4917-0
  • Electronic_ISBN
    978-1-4244-4918-7
  • Type

    conf

  • DOI
    10.1109/BIOCAS.2009.5372065
  • Filename
    5372065