IEEE Trans Image Process - Variational segmentation of vector-valued images with gradient vector flow.

Tópicos

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Resumo

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.

Resumo Limpo

paper extend gradient vector flow field robust variat segment vectorvalu imag rather use scalar edg inform defin vectori edg map deriv weight local structur tensor imag enabl diffus gradient vector accur direct d gradient vector flow equat reduc contribut nois structur tensor imag channel weight accord blind estim contrast method appli biolog volum delin dynam pet imag valid realist mont carlo simul numer phantom well real imag

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