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
Link To Document :
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