  Minimal paths are regarded as a powerful and efficient tool for boundary
detection and image segmentation due to its global optimality and the
well-established numerical solutions such as fast marching method. In this
paper, we introduce a flexible interactive image segmentation model based on
the Eikonal partial differential equation (PDE) framework in conjunction with
region-based homogeneity enhancement. A key ingredient in the introduced model
is the construction of local geodesic metrics, which are capable of integrating
anisotropic and asymmetric edge features, implicit region-based homogeneity
features and/or curvature regularization. The incorporation of the region-based
homogeneity features into the metrics considered relies on an implicit
representation of these features, which is one of the contributions of this
work. Moreover, we also introduce a way to build simple closed contours as the
concatenation of two disjoint open curves. Experimental results prove that the
proposed model indeed outperforms state-of-the-art minimal paths-based image
segmentation approaches.

Computer Vision and Pattern Recognition;Computational Geometry