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

Razmig Kéchichian 1 Sébastien Valette 1 Michel Desvignes 2 Rémy Prost 1
1 CREATIS - Centre de recherche et d'applications en traitement de l'image et du signal
2 GIPSA-AGPIG - AGPIG
GIPSA-DIS - Département Images et Signal
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.
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Conference papers
18th IEEE International Conference on Image Processing (ICIP 2011), Sep 2011, Bruxelles, Belgium. pp.2149-2152, 2011, <10.1109/ICIP.2011.6116036>
https://hal.archives-ouvertes.fr/hal-00658028
Contributor : Sébastien Valette <>
Submitted on : Monday, January 9, 2012 - 5:04:58 PM
Last modification on : Tuesday, November 17, 2015 - 5:31:26 PM
Document(s) archivé(s) le : Tuesday, April 10, 2012 - 2:37:35 AM

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Razmig Kéchichian, Sébastien Valette, Michel Desvignes, Rémy Prost. Efficient multi-object segmentation of 3D medical images using clustering and graph cuts. 18th IEEE International Conference on Image Processing (ICIP 2011), Sep 2011, Bruxelles, Belgium. pp.2149-2152, 2011, <10.1109/ICIP.2011.6116036>. <hal-00658028>

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