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

Jaouen, Vincent; González, Paulo; Stute, Simon; Guilloteau, Denis; Chalon, Sylvie; Buvat, Irène; Tauber, Clovis

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<oai_dc:dc xmlns:dc="" xmlns:oai_dc="" xmlns:xsi="" xsi:schemaLocation="">
  <dc:creator>Jaouen, Vincent</dc:creator>
  <dc:creator>González, Paulo</dc:creator>
  <dc:creator>Stute, Simon</dc:creator>
  <dc:creator>Guilloteau, Denis</dc:creator>
  <dc:creator>Chalon, Sylvie</dc:creator>
  <dc:creator>Buvat, Irène</dc:creator>
  <dc:creator>Tauber, Clovis</dc:creator>
  <dc: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.</dc:description>
  <dc:source>IEEE Trans Image Process 23(11) 4773-85 (2014)</dc:source>
  <dc:subject>deformable models</dc:subject>
  <dc:subject>dynamic PET</dc:subject>
  <dc:subject>gradient vector flow</dc:subject>
  <dc:subject>structure tensor</dc:subject>
  <dc:title>Variational segmentation of vector-valued images with gradient vector flow.</dc:title>
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