Active Contours with Adaptively Normal Biased Gradient Vector Flow External Force

Abstract

Gradient vector flow (GVF) is an effective external force for active contours, but its isotropic nature handicaps its performance. The recently proposed NGVF model is an isotropic since it only keeps the diffusion along the normal direction of the isophotes, however, it is sensitive to noise and could erase weak boundaries. In this paper, we propose a novel external force called adaptively normal biased gradient vector flow (ANBGVF) for active contours, which adaptively generates the diffusion along the tangential direction of the isophotes and biases that along the normal direction. Consequently, the ANBGVF snake can preserve weak edges and smooth out noise while maintaining other desirable properties of GVF and NBGVF, such as enlarged capture range, initialization insensitivity and good convergence at concavities. We demonstrate the advantages on synthetic and real images.