Multi-Objective Image Threshold Segmentation Based on Statistical Curve Difference Method

doi:10.16182/j.issn1004731x.joss.201711044

Abstract

Abstract: In order to accurately segment the multi-objective image whose objective area is too small, and the background gray value is similar to the objective, a threshold segmentation algorithm based on statistical curve difference method is proposed. The mountain model of multi-objective gray image is established and the gray image is normalized. The number of connected area is counted by threshold division. The statistical curve is plotted with the horizontal coordinate being the gray level with equal interval, and the vertical coordinate being the counting result. The threshold point is the point where the difference value of the statistical curve is close to zero. Experiment results show that the proposed method is more accurate than the Otsu and the maximum entropy threshold segmentation method.

Key words: objective image, statistical curve, threshold segmentation, gray normalization

CLC Number:

TP311

Zhu Yuanfei, Zhang Sixiang, Zhou Wei, Wang Xiaochen, Li Zhidong. Multi-Objective Image Threshold Segmentation Based on Statistical Curve Difference Method[J]. Journal of System Simulation, 2017, 29(11): 2927-2933.

References

[1] Albuquerque M P, Esquef I A, Mello A R G. Image thresholding using Tsallis entropy. Pattern Recognition Let-ters (S0167-8655), 2004, 25(10): 1059-1065.
[2] OTSU N.A threshold selection method from gray-level histogram[J]. IEEE Trans.on Systems, Man and Cybernetic (S2168-2216), 1979, 9: 62-66.
[3] Jostein Dahle, Manish Kakar, etc. Automated counting of mammalian cell colonies by means of a flat bed scanner and image processing[J]. Journal of the Internation Society for Ad vancement of Cytometry (S1552-4930), 2004, 6: 182-188.
[4] Chen, W B, Zhang C. An automated bacterial co-lony counting and classification system[J]. Information Sys-tems Frontiers (S1572-9419), 2009, 11(4): 349-368.
[5] Qian R J,and Huang T S.Optimal edge detection in two-dimendional images[J]. IEEE Trans.Image Processing (S1057-7149), 1996, 5(7): 1215-1220.
[6] Rajapakse J C, Giedd J N, Rapoport J L.Statistical approach to segmentation of single-channel cerebral MR images[J]. IEEE Trans-actions on Medical Imaging (S0278-0062), 1997, 16(2): 176-186.
[7] 景晓军, 蔡安妮, 孙景鳌. 一种基于二维最大类间方差的图像分割算法[J]. 通信学报, 2001, 22(4): 71-76.
Jing Yi, Cai Anni, Sun Jingao.An image segmentation algorithm based on two dimensional maximum inter class variance[J]. Journal of Communications, 2001, 22(4): 71-76.
[8] 吴成茂. 一种新信息熵定义及其在图像分割中的应用[J]. 西安邮电学院学报, 2009, 14(1): 72-79.
Wu Chengmao.A new definition of information entropy and its application in image segmentation[J]. Journal of Xi'an University of Posts and Telecommunications, 2009, 14(1): 72-79.
[9] 房华, 程国建, 吴文海. 一种基于最大熵的改进型 PCNN图像分割新方法[J]. 软件导刊, 2010, 9(5): 189-191.
Fang Hua, Cheng Guojian, Wu Wenhai.A new segmentation method of improved PCNN image based on maximum entropy[J]. Software Guide, 2010, 9(5): 189-191.
[10] Doyle W.Operation. Useful for Similarity-Invariant Pattern Recognition[J]. Assoc. Computer. Mach (S0004-5411), 1962, 9: 259-267.
[11] LAN J H, ZENG Y L Multi-threshold image segmentation using maximum fuzzy entropy based on a new 2D histogram[J]. Optik-Int. J. Light Electron Opt.(S0030-4026), 2013, 124(18): 3756-3760.
[12] C Yi, Y L Tian.Text string detection from natural scene by structure-based partion and grouping[J]. IEEE transation on image processing (S1057-7149), 2011, 20(9): 2594-2605.
[13] 宋斌. 一种新的图像连通区域快速标号算法[J]. 电子测量技术, 2009, 32(9): 67-68.
Song Bin.A new fast labeling algorithm for image connected regions[J]. electronic measurement technology, 2009, 32(9): 67-68.
[14] Zhang B G, Zhou Y.Oscillation of Difference Equations with Several Delays[J]. Computers and Mathematics with Applications (S0898-1221), 2002, 44(5): 817-821.
[15] Wang X P.Oscillation of Delay Difference Equations with Several Delays[J]. J. Math. Anal. Appl (S0022-247X), 2003, 286(2): 664-674.
[16] Gong X H, Zhong X Z, Jia J Q, et al.Oscillation of First-order Neutral Difference Equation[J]. Modem Applied Science (S1913-1844), 2009, 3(8): 90-94.