Variational segmentation of vector-valued images with gradient vector flow

Vincent Jaouen (1) , Paulo Gonzalez (1) , Simon Stute (2) , Denis Guilloteau (1) , Sylvie Chalon (1) , Irène Buvat (2) , Clovis Tauber (1)

1 iBraiN - Imaging, Brain & Neuropsychiatry
2 SHFJ - Service Hospitalier Frédéric Joliot

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 4DGVF 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.

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

Vincent Jaouen, Paulo Gonzalez, Simon Stute, Denis Guilloteau, Sylvie Chalon, et al.. Variational segmentation of vector-valued images with gradient vector flow. IEEE Transactions on Image Processing, 2014, pp.1. ⟨hal-01064937⟩