EFFICIENT MULTI-OBJECT SEGMENTATION OF 3D MEDICAL IMAGES USING CLUSTERING AND GRAPH CUTS

被引：0

作者：

Kechichian, Razmig ^{[1
]}

Valette, Sebastien ^{[1
]}

Desvignes, Michel ^{[2
]}

Prost, Remy ^{[1
]}

机构：

[1] Univ Lyon, CREATIS, CNRS, INSERM,U630,UMR5220, Lyon, France

[2] INPG, GIPSA LAB, Grenoble, France

来源：

2011 18TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) | 2011年

关键词：

Medical image segmentation; clustering; graph-cuts; ENERGY MINIMIZATION;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页数：4

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