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Summary: MEDIAL-BASED BAYESIAN TRACKING FOR VASCULAR SEGMENTATION:
APPLICATION TO CORONARY ARTERIES IN 3D CT ANGIOGRAPHY
David Lesage
, Elsa D. Angelini
, Isabelle Bloch
and Gareth Funka-Lea
Siemens Corporate Research, Imaging and Visualization dept., Princeton NJ, USA.
Institut Telecom, Telecom ParisTech, CNRS LTCI, Paris, France.
ABSTRACT
We propose a new Bayesian, stochastic tracking algorithm for the
segmentation of blood vessels from 3D medical image data. Inspired
by the recent developments in particle filtering, it relies on a con-
strained, medial-based geometric model and on an original sampling
scheme for the selection of tracking hypotheses. A key property of
this new sampling scheme is the ability to take into account a distri-
bution of hypotheses broader than similar methods such as classical
particle filters, while remaining computationally efficient. The pro-
posed method was applied to the challenging and medically critical
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