Fast Iterative Algorithm for Two-Dimensional Otsu Thresholding Method
WU Cheng-Mao1,2, TIAN Xiao-Ping1,2, TAN Tie-Niu2
1.Department of Electronics and Information Engineering, Xi'an Institute of Post and Telecommunications, Xi'an 7101212. National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190
Abstract A fast iterative algorithm for two-dimensional Otsu thresholding method is proposed. Considering the disadvantages of the classical two-dimensional Otsu thresholding method and its recursive algorithm, it is supposed that the two-dimensional histogram which is composed of original segmented image and its local neighborhood average image is a two-variable continuous probability distribution function. The method for seeking extreme value of multivariate function is employed and the fast iterated algorithm of two-dimensional Otsu thresholding method is obtained. The experimental results show that the proposed fast iterative algorithm is feasible and has better segmentation performance.
[1] Sahoo P K, Soltani S, Wong A K C, et al. A Survey of Thresholding Techniques. Computer Vision, Graphics and Image Processing, 1988, 41(2): 233-260 [2] Pal N R, Pal S K. A Review on Image Segmentation Techniques. Pattern Recognition, 1993, 26(9): 1277-1294 [3] Glasbey C A. An Analysis of Histogram Based Thresholding Algorithm. CVGIP: Graphical Models and Image Processing, 1993, 55(6): 532-537 [4] Sezgin M, Sankur B. Survey over Image Thresholding Techniques and Quantitative Performance Evaluation. Journal of Electronic Image, 2004, 13(1): 146-165 [5] Otsu N. A Threshold Selection Method from Gray-Level Histogram. IEEE Trans on Systems, Man and Cybernetics, 1979, 9(1): 62-66 [6] Reddi S S, Rudin S F, Keshavan H R. An Optimal Multiple Threshold Scheme for Image Segmentation. IEEE Trans on Systems, Man and Cybernetics, 1984, 14(4): 661-665 [7] Morii F. An Image Thresholding Method Using a Minimum Weighted Squared-Distortion Criterion. Pattern Recognition, 1995, 28(7): 1063-1071 [8] Wang Haojun, Mao Baixin, Bi Duyan, et al. A Study of Image Segmentation for Silver Grains in In-Situ Hybridization. Chinese Journal of Image and Graphics, 1999, 4(6): 454-457 (in Chinese) (王浩军,毛柏鑫,毕笃彦,等.原位分子杂交图像中银粒的分割方法研究.中国图象图形学报, 1999, 4(6): 454-457) [9] Liu Jianzhuang, Li Wenqin. The Automatic Thresholding of Gray-Level Pictures via Two-Dimensional Otsu Method. Acta Automatica Sinica, 1993, 19(1):101-105 (in Chinese) (刘健庄,栗文青.灰度图像的二维Otsu自动阈值分割法.自动化学报, 1993, 19(1): 101-105) [10] Gong Jian, Li Liyuan, Chen Weinan. Fast Recursive Algorithm for Two-Dimensional Thresholding. Pattern Recognition, 1998, 31(3): 295-300 [11] Hao Yingmin, Zhu Feng. Fast Algorithm for Two-Dimensional Otsu Adaptive Threshold Algorithm. Chinese Journal of Image and Graphics, 2005, 10(4): 484-488 (in Chinese) (郝颖明,朱 枫.2维Otsu自适应阈值的快速算法.中国图象图形学报, 2005, 10(4): 484-488) [12] Du Xiaocheng, Liu Jianpin. Infrared Image Segmentation Method Based on 2D Otsu Method and Genetic Algorithm. Infrared Technology, 2005, 27(1): 66-69 (in Chinese) (杜晓晨,刘建平.基于二维Otsu和遗传算法的红外图像分割方法.红外技术, 2005, 27(1): 66-69) [13] Tang Yinggan, Liu Dong, Guang Xinping. Fast Image Segmentation Based on Particle Swarm Optimization and Two-Dimension Otsu Method. Control and Decision, 2007, 22(2): 202-205 (in Chinese) (唐英干,刘 冬,关新平.基于粒子群和二维Otsu方法的快速图像分割.控制与决策, 2007, 22(2): 202-205) [14] Wang Haiyang, Pan Delu, Xia Deshen. A Fast Algorithm for Two-Dimensional Otsu Adaptive Threshold Algorithm. Acta Automatica Sinica, 2007, 33(9): 968-971 (in Chinese) (汪海洋,潘德炉,夏德深.二维Otsu自适应阈值选取算法的快速实现.自动化学报, 2007, 33(9): 968-971) [15] Fan Jiulun, Zhao Feng. Two-Dimensional Otsu's Curve Thresholding Segmentation Method for Gray-Level Images. Acta Electronica Sinica, 2007, 35(4): 751-755 (in Chinese) (范九伦,赵 凤.灰度图像的二维Otsu曲线阈值分割法.电子学报, 2007, 35(4): 751-755) [16] Wu Yiquan, Wu Wenyi, Pan Zhe. A Fast Iterative Algorithm for Image Segmentation Based on 2D Maximum between-Cluster Variance. Chinese Journal of Stereology and Image Analysis, 2007, 12(3): 216-220 (in Chinese) (吴一全,吴文怡,潘 喆.二维最大类间方差阈值分割的快速迭代算法.中国体视学与图像分析, 2007, 12(3): 216-220)
2008, Vol. 21 
