Mathematical Problems in Engineering
Volume 2012 (2012), Article ID 490879, 20 pages
http://dx.doi.org/10.1155/2012/490879
Research Article

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

1College of Mathematics and Statistics, Chongqing University, Chongqing 400044, China
2School of Mathematics and Finances, Chongqing University of Arts and Sciences, Chongqing 402160, China
3Key Laboratory of Data Analyzing and Image Processing, Chongqing University of Arts and Sciences, Chongqing 402160, China
4College of Mathematics and Statistics, Chongqing Technology and Business University, Chongqing 400067, China

Received 7 March 2012; Revised 8 June 2012; Accepted 11 July 2012

Academic Editor: Kui Fu Chen

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.