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

We propose an L p ( | ∇ 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 L p ( | ∇ I | ) ( p ( | ∇ I | ) > 2 ) norm instead of the L 2 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 L p ( | ∇ 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.