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

Razmig Kéchichian; Sébastien Valette; Michel Desvignes; Rémy Prost

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

Razmig Kéchichian ¹ Sébastien Valette ² Michel Desvignes ³ Rémy Prost ²

1 Images et Modèles

CREATIS - Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé

2 CREATIS - Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé

3 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.

Document type :

Conference papers

Domain :

Computer Science [cs] / Signal and Image Processing

Engineering Sciences [physics] / Signal and Image processing

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https://hal.archives-ouvertes.fr/hal-00658028
Contributor : Sébastien Valette <>
Submitted on : Monday, January 9, 2012 - 5:04:58 PM
Last modification on : Wednesday, March 6, 2019 - 1:34:02 PM
Long-term archiving on : Tuesday, April 10, 2012 - 2:37:35 AM

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

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

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