Adaptively Active Contours Based on Variable Exponent Lp(|∇I|) Norm for Image Segmentation

We propose an Lp(|∇I|)-based adaptively active contours model for image segmentation which is derived from the well-known Chan-Vese (C-V) model. Unlike the C-V model, the proposed model uses the Lp(|∇I|) (p(|∇I|)>2) norm instead of the L2 norm to define the external energy and incorporates an extra internal energy into the overall energy. Due to the variable exponent p(|∇I|)  which could fit the image gradient information adaptively, the proposed Lp(|∇I|)-based model has the hope of segmenting those images with low contrast and blurred boundaries. Experimental results show that the proposed model with p(|∇I|)>2 really can effectively and quickly segment those images with low contrast and blurred boundaries.