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
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