• DocumentCode
    2823406
  • Title

    Efficient multi-object segmentation of 3D medical images using clustering and graph cuts

  • Author

    Kéchichian, Razmig ; Valette, Sébastien ; Desvignes, Michel ; Prost, Rémy

  • Author_Institution
    CREATIS, Univ. de Lyon, Lyon, France
  • fYear
    2011
  • fDate
    11-14 Sept. 2011
  • Firstpage
    2149
  • Lastpage
    2152
  • Abstract
    We propose an application of multi-label “Graph Cut” optimization algorithms to the simultaneous segmentation of multiple anatomical structures, initialized via an over-segmentation of the image computed by a fast centroidal Voronoi diagram (CVD) clustering algorithm. With respect to comparable segmentations computed directly on the voxels of image volumes, we demonstrate performance improvements on both execution speed and memory footprint by, at least, an order of magnitude, making it possible to process large volumes on commodity hardware which could not be processed pixel-wise.
  • Keywords
    computational geometry; graph theory; image segmentation; medical image processing; optimisation; pattern clustering; performance evaluation; 3D medical images; CVD clustering algorithm; centroidal Voronoi diagram clustering algorithm; commodity hardware; execution speed; graph cuts; image over-segmentation; image volumes; memory footprint; multilabel graph cut optimization algorithms; multiobject segmentation; multiple anatomical structures; performance improvements; simultaneous segmentation; Biomedical imaging; Bones; Clustering algorithms; Computed tomography; Conferences; Image segmentation; Three dimensional displays; Medical image segmentation; clustering; graph-cuts;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Image Processing (ICIP), 2011 18th IEEE International Conference on
  • Conference_Location
    Brussels
  • ISSN
    1522-4880
  • Print_ISBN
    978-1-4577-1304-0
  • Electronic_ISBN
    1522-4880
  • Type

    conf

  • DOI
    10.1109/ICIP.2011.6116036
  • Filename
    6116036