Mathematical Problems in Engineering
Volume 2012 (2012), Article ID 369472, 16 pages
Research Article

Adaptive Regularized Level Set Method for Weak Boundary Object Segmentation

1College of Mathematics and Statistics, Chongqing University, Chongqing 400044, China
2School of Mathematics and Finances, Chongqing University of Arts and Sciences, Yongchuan, Chongqing 402160, China
3College of Mathematics and Statistics, Chongqing Technology and Business University, Chongqing 400067, China

Received 24 August 2011; Revised 31 January 2012; Accepted 3 February 2012

Academic Editor: J. Jiang

Copyright © 2012 Meng Li 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.


An adaptive regularized level set method for image segmentation is proposed. A weighted 𝑝 ( 𝑥 ) -Dirichlet integral is presented as a geometric regularization on zero level curve, which is used to diminish the influence of image noise on level set evolution while ensuring the active contours not to pass through weak object boundaries. The idea behind the new energy integral is that the amount of regularization on the zero level curve can be adjusted automatically by the variable exponent 𝑝 ( 𝑥 ) to fit the image data. This energy is then incorporated into a level set formulation with an external energy term that drives the motion of the zero level set toward the desired objects boundaries, and a level set function regularization term that is necessary for maintaining stable level set evolution. The proposed model has been applied to a wide range of both real and synthetic images with promising results.