Published September 4, 2014
| Version v1
Journal article
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Variational segmentation of vector-valued images with gradient vector flow.
Creators
- 1. Université François-Rabelais de Tours, INSERM U930 Imaging and Brain, Tours 37032, France
- 2. Université François-Rabelais de Tours, INSERM U930 Imaging and Brain, Tours 37032, France and Universidad Católica del Maule, Talca, Chile
- 3. Commissariat à l’énergie Atomique, Institut d’Imagerie Biomédicale, Service Hospitalier Frédéric Joliot, Orsay 91405, France
Description
In this paper, we extend the gradient vector flow
field for robust variational segmentation of vector-valued images.
Rather than using scalar edge information, we define a vectorial
edge map derived from a weighted local structure tensor of
the image that enables the diffusion of the gradient vectors in
accurate directions through the 4D gradient vector flow equation.
To reduce the contribution of noise in the structure tensor,
image channels are weighted according to a blind estimator of
contrast. The method is applied to biological volume delineation
in dynamic PET imaging, and validated on realistic Monte Carlo
simulations of numerical phantoms as well as on real images.