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Published September 4, 2014 | Version v1
Journal article Restricted

Variational segmentation of vector-valued images with gradient vector flow.

  • 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.

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Additional details

Funding

INMIND – Imaging of Neuroinflammation in Neurodegenerative Diseases 278850
European Commission