Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2015, Article ID 343159, 11 pages
http://dx.doi.org/10.1155/2015/343159
Research Article

Efficient and Enhanced Diffusion of Vector Field for Active Contour Model

Guoqi Liu,1,2,3 Lin Sun,1,2,3 and Shangwang Liu1,2,3

1School of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China
2Engineering Lab of Intelligence Business and Internet of Things, Henan 453007, China
3Engineering Technology Research Center for Computing Intelligence and Data Mining, Henan 453007, China

Received 21 March 2015; Revised 21 June 2015; Accepted 25 June 2015

Academic Editor: Chih-Cheng Hung

Copyright © 2015 Guoqi Liu 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

Gradient vector flow (GVF) is an important external force field for active contour models. Various vector fields based on GVF have been proposed. However, these vector fields are obtained with many iterations and have difficulty in capturing the whole image area. On the other hand, the ability to converge to deep and complex concavity with these vector fields is also needed to improve. In this paper, by analyzing the diffusion equation of GVF, a normalized set is defined and a dynamically normalized constraint of vector fields is used for efficient diffusion, which makes the edge vector diffusing rapidly to the entire image region. In order to improve the ability to converge to concavity, an enhanced diffusion term is integrated into the original energy functional. With the dynamically normalized constraint and enhanced diffusion term, new vector fields of EDGVF (efficient and enhanced diffusion for GVF) and EDNGVF (efficient and enhanced diffusion of NGVF) are obtained. Experimental results demonstrate that vector fields with proposed method capture the entire image and are obtained with less iterations and computational times. In particular, EDNGVF greatly improves the ability to converge to concavity.