B-Spline based globally optimal segmentation combining low-level and high-level information

Highlights：

• The low-level and high-level information are combined to build an energy functional using multi-scale. Rough contour achieved at coarsest scale by multiple Gaussian kernel gray equalization is used as the prior shape of object. This shape is updated and used as the constraint of evolving contour in following fine-scale.

• A new edge stopping function based on the TV regularization is proposed which is beneficial to both global-based method and fast decrease of energy minimization.

• We proposed a statistical based globally optimal segmentation model using cubic B-Spline basis functions. These functions are used to explicitly represent the relaxation characteristic function which contributes to fast convergence and intrinsic smoothing globally optimal segmentation results.