• DocumentCode
    3315299
  • Title

    A variational approach for the segmentation of the left ventricle in MR cardiac images

  • Author

    Paragios, Nikos

  • Author_Institution
    Siemens Corp. Res. Inc., Princeton, NJ, USA
  • fYear
    2001
  • fDate
    2001
  • Firstpage
    153
  • Lastpage
    160
  • Abstract
    This paper proposes a new front propagation method to segment MR cardiac images. This framework is based on the geodesic active region model, refers to a coupled propagation of two curves (inner and outer cardiac contours) and integrates boundary and region-based segmentation modules. The boundary information is introduced to the objective function using the gradient vector flow framework while the region information using continuous probability density functions. The defined objective function is minimised using a gradient descent method and the obtained motion equations are implemented using a level set approach. A recently introduced numerical approximation scheme with fast convergence rate and stable behavior is used to implement the level set motion equations. Finally, according to the application the propagations of the level set contours are coupled using their relative distances. Encouraging experimental results are provided using real data
  • Keywords
    approximation theory; biomedical MRI; cardiology; differential geometry; edge detection; gradient methods; image segmentation; medical image processing; minimisation; numerical stability; probability; variational techniques; MR cardiac images; boundary-based segmentation; continuous probability density functions; convergence rate; coupled propagation; curve contours; front propagation method; geodesic active region model; gradient descent method; gradient vector flow framework; left ventricle; level set approach; motion equations; numerical approximation; objective function minimisation; region-based segmentation; relative distances; stable behavior; variational approach; Application software; Convergence of numerical methods; Educational institutions; Equations; Image segmentation; Level set; Noise shaping; Probability density function; Shape; Surface fitting;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Variational and Level Set Methods in Computer Vision, 2001. Proceedings. IEEE Workshop on
  • Conference_Location
    Vancouver, BC
  • Print_ISBN
    0-7695-1278-X
  • Type

    conf

  • DOI
    10.1109/VLSM.2001.938894
  • Filename
    938894