Adaptively active contours based on variable exponent L p(|∇I|) norm for image segmentation

Abstract

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. © 2012 Wenying Wen et al.