Image Segmentation Based on Geometric Active Contour Model
CHEN Bo1,DAI Qiu-Ping2
1.College of Mathematics and Computational Science,Shenzhen University,Shenzhen 518060 2.Institute of Hydroelectric Power,Hebei University of Engineering,Handan 056021
Abstract：A geometric active contour model is proposed to reduce the influence of noise on image segmentation. The corresponding partial differential equations evolved by the level set curve are got through variational principle. Prior information of the regions and boundaries of the image is considered in this model and the statistical information of the image is considered as well. Moreover, a penalized term is used as an internal energy term to avoid the time-consuming re-initialization process. To verify the efficiency of the proposed model, a segmentation instance based on simple Gauss probability density function is given, and the additive operator splitting (AOS) scheme which is efficient and unconditionally stable is employed. Experimental results show that the proposed model has high accuracy, efficiency and noisy resistance.
[1] Zhu Songchun, Yuille A. Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation. IEEE Trans on Pattern Analysis and Machine Intelligence, 1996, 18(9): 884-900 [2] Kass M, Witkin A, Terzopoulos D. Snakes: Active Contour Models. International Journal of Computer Vision, 1988, 1(4): 321-331 [3] Osher S, Sethian J. Fronts Propagating with Curvature-Dependent Speed: Algorithms Based on the Hamilton-Jacobi Formulation. Journal of Computational Physics, 1988, 79(1): 12-49 [4] Casselles V, Kimmel R, Sapiro G. Geodesic Active Contours // Proc of the 5th International Conference on Computer Vision. Boston, USA, 1995: 694-699 [5] Paragios N, Deriche R. Geodesic Active Contours and Level Sets for the Detection and Tracking of Moving Objects. IEEE Trans on Pattern Analysis and Machine Intelligence, 2000, 22(3): 266-280 [6] Rousson M, Deriche R. A Variational Framework for Active and Adaptive Segmentation of Vector Valued Images // Proc of the IEEE Workshop on Motion and Video Computing. Orlando, USA, 2002: 56-61 [7] Chan T F, Osher S, Shen J H. The Digital TV Filter and Nonlinear Denoising. IEEE Trans on Image Processing, 2001, 10(2): 231-241 [8] Mumford D, Shah J. Optimal Approximation by Piece-Wise Smooth Functions and Associated Variational Problems. Communications on Pure and Applied Mathematics, 1989, 42(5): 577-585 [9] Li Jun, Yang Xin, Shi Pengfei. A Fast Level Set Approach to Image Segmentation Based on Mumford-Shah Model. Chinese Journal of Computers, 2002, 25(11): 1175-1183 (in Chinese) (李 俊,杨 新,施鹏飞.基于Mumford-Shah模型的快速水平集图像分割方法.计算机学报, 2002, 25(11): 1175-1183) [10] Kimmel R. Fast Edge Integration // Osher S, Paragios N, eds. Geometric Level Set Methods in Imaging, Vision, and Graphics. Berlin, Germany: Springer Verlag, 2003: 59-77 [11] Chen Bo, Lai Jianhuang, Ma Jianhua. A Coupled Active Contour Model and Its Application in Image Segmentation. Journal of Image and Graphics, 2007, 12(3): 444-449 (in Chinese) (陈 波,赖剑煌,马建华.一种耦合的活动轮廓模型及其在图像分割中的应用.中国图象图形学报, 2007, 12(3): 444-449) [12] Li Chunming, Xu Chenyang, Gui Changfeng, et al. Level Set Evolution without Re-Initialization: A New Variational Formulation // Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego, USA, 2005, Ⅰ: 430-436
2010, Vol. 23 
