• DocumentCode
    3355880
  • Title

    Segmentation of vessel images using a localized hybrid level-set method

  • Author

    Qingqi Hong ; Beizhan Wang

  • Author_Institution
    Software Sch., Xiamen Univ., Xiamen, China
  • Volume
    2
  • fYear
    2013
  • fDate
    16-18 Dec. 2013
  • Firstpage
    631
  • Lastpage
    635
  • Abstract
    In this paper, a localized hybrid level-set method for vessel image segmentation is proposed. The proposed method integrates both local region information and boundary information for vessel segmentation, which is essential for the accurate extraction of tiny vessel structures. In our proposed technique, the local intensity information is firstly embedded into a region-based contour model, and then incorporated into the level-set formulation of the geodesic active contour model. Compared with the global threshold based method, the use of locally specified dynamic thresholds enables the extraction of the local image information, which is essential for the segmentation of vessel images. Experimental results on vessel images are presented to demonstrate the strengths of our localized hybrid level-set method.
  • Keywords
    differential geometry; feature extraction; image segmentation; medical image processing; boundary information; geodesic active contour model; local intensity information; local region information; localized hybrid level-set method; region-based contour model; vessel image segmentation; Active contours; Biomedical imaging; Computational modeling; Data mining; Deformable models; Geometry; Image segmentation; level-set; segmentation; vessel images;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Image and Signal Processing (CISP), 2013 6th International Congress on
  • Conference_Location
    Hangzhou
  • Print_ISBN
    978-1-4799-2763-0
  • Type

    conf

  • DOI
    10.1109/CISP.2013.6745243
  • Filename
    6745243