DocumentCode :
2084160
Title :
Vessel Crawlers: 3D Physically-based Deformable Organisms for Vasculature Segmentation and Analysis
Author :
McIntosh, Chris ; Hamarneh, Ghassan
Author_Institution :
Simon Fraser University, Canada
Volume :
1
fYear :
2006
fDate :
17-22 June 2006
Firstpage :
1084
Lastpage :
1091
Abstract :
We present a novel approach to the segmentation and analysis of vasculature from volumetric medical image data. Our method is an adoption and significant extension of deformable organisms, an artificial life framework for medical image analysis that complements classical deformable models with high-level, anatomically-driven control mechanisms. We extend deformable organisms to 3D, model their bodies as tubular spring-mass systems, and equip them with a new repertoire of sensory modules, behavioral routines, and decision making strategies. The result is a new breed of robust deformable organisms, vessel crawlers, that crawl along vasculature in 3D images, accurately segmenting vessel boundaries, detecting and exploring bifurcations, and providing sophisticated, clinically-relevant structural analysis. We validate our method through the segmentation and analysis of vascular structures in both noisy synthetic and real medical image data.
Keywords :
Bifurcation; Biomedical imaging; Crawlers; Decision making; Deformable models; Image analysis; Image segmentation; Medical control systems; Organisms; Robustness;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on
ISSN :
1063-6919
Print_ISBN :
0-7695-2597-0
Type :
conf
DOI :
10.1109/CVPR.2006.329
Filename :
1640871
Link To Document :
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