Abstract：Based on traditional Chan-Vese (CV) model, combining image clustering information, an effective active contour model for image segmentation is proposed in this paper.Firstly, the energy functional of CV model is improved, the gradient information of image is considered, and the accuracy of image segmentation is improved. Then, the coefficient K based on image clustering information is added in energy functional. And the image clustering information is used to initialize the level set curves automatically. In color image segmentation processing, weighting process on the RGB channel is proposed to improve the efficiency of segmentation. Finally, regularization term is added in energy functional to avoid re-initialization of the level set. The gray images and color images are segmented quickly and accurately. Experimental results shows the effectiveness of the proposed method.
李敏，梁久祯，廖翠萃. 基于聚类信息的活动轮廓图像分割模型*[J]. 模式识别与人工智能, 2015, 28(7): 665-672.
LI Min, LIANG Jiu-Zhen, LIAO Cui-Cui. Active Contour Model for Image Segmentation Based on Clustering Information. , 2015, 28(7): 665-672.
[1] Kass M, Witkin A, Terzopoulos D. Snakes: Active Contour Models. International Journal of Computer Vision, 1988, 1(4): 321-331 [2] Mansouri A R, Konrad J. Multiple Motion Segmentation with Level Sets. IEEE Trans on Image Processing, 2003, 12(2): 201-220 [3] Caselles V, Kimmel R, Sapiro G. Geodesic Active Contours. International Journal of Computer Vision, 1997, 22(1): 61-79 [4] Mumford D, Shah J. Optimal Approximations by Piecewise Smooth Functions and Associated Variational Problems. Communications on Pure and Applied Mathematics, 1989, 42(5): 577-685 [5] Chan T F, Vese L A. Active Contours without Edges. IEEE Trans on Image Processing, 2001, 10(2): 266-277 [6] Vese L A, Chan T F. A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model. International Journal of Computer Vision, 2002, 50(3): 271-293 [7] Kimmel R. Fast Edge Integration // Osher S, Paragios N, eds. Geometric Level Set Methods in Imaging, Vision and Graphics. New York, USA: Springer, 2003: 59-77 [8] Li C M, Xu C Y, Gui C F, et al. Distance Regularized Level Set Evolution and Its Application to Image Segmentation. IEEE Trans on Image Processing, 2010, 19(12): 3243-3254 [9] Yuan Y, He C J. Variational Level Set Methods for Image Segmentation Based on both L2 and Sobolev Gradients. Nonlinear Analysis: Real World Applications, 2012, 13(2): 959-966 [10] Chan T F, Sandberg B Y, Vese L A. Active Contours without Edges for Vector-Valued Images. Journal of Visual Communication and Image Representation, 2000, 11(2): 130-141 [11] Li C M, Xu C Y, Gui C F, et al. Level Set Evolution without Rei-nitialization: A New Variational Formulation // Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Re-cognition. San Diego, USA, 2005: 430-436 [12] Li C M, Xu C Y, Gui C F, et al. Distance Regularized Level Set Evolution and Its Application to Image Segmentation. IEEE Trans on Image Processing, 2010, 19(12): 3243-3254 [13] Yu C Y, Zhang W S, Yu Y Y, et al. A Novel Active Contour Model for Image Segmentation Using Distance Regularization Term. Computers & Mathematics with Applications, 2013, 65(11): 1746-1759 [14] Xu J, Wu J, Ye F, et al. A Level Set Method for Color Image Segmentation Based on Bayesian Classifier // Proc of the International Conference on Computer Science and Software Engineering. Wuhan, China, 2008, II: 886-890 [15] Pi L, Shen C M, Li F, et al. A Variational Formulation for Segmenting Desired Objects in Color Images. Image and Vision Computing, 2007, 25(9): 1414-1421 [16] Wu J Y. Research on Partial Differential Equation-Based Methods for Image Processing. Ph.D Dissertation. Beijing, China: Beijing Jiaotong University, 2009 (in Chinese) (仵冀颖.偏微分方程在图像处理中应用的研究.博士学位论文.北京:北京交通大学, 2009) [17] Hao C J. Image Segmentation Based on Partial Differential Equation. Master Dissertation. Xi′an, China: Xidian University, 2010 (in Chinese) (郝朝军.基于偏微分方程的图像分割.硕士学位论文.西安:西安电子科技大学, 2010)
2015, Vol. 28 
