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