title: Variational segmentation of vector-valued images with gradient vector flow.
abstract: 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.
authors.forename: Vincent
authors.initials: V
authors.lastname: Jaouen
authors.forename: Paulo
authors.initials: P
authors.lastname: González
authors.forename: Simon
authors.initials: S
authors.lastname: Stute
authors.forename: Denis
authors.initials: D
authors.lastname: Guilloteau
authors.forename: Sylvie
authors.initials: S
authors.lastname: Chalon
authors.forename: Irène
authors.initials: I
authors.lastname: Buvat
authors.forename: Clovis
authors.initials: C
authors.lastname: Tauber
mesh_terms: D000465:Algorithms; D001921:Brain / Q000000981:diagnostic imaging*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D049268:Positron-Emission Tomography / Q000379:methods*; D015203:Reproducibility of Results; D012212:Rheology / Q000379:methods*; D012680:Sensitivity and Specificity