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 
