Copyright © 2012 Wenying Wen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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.