吉林大学学报(工学版) ›› 2017, Vol. 47 ›› Issue (1): 255-261.doi: 10.13229/j.cnki.jdxbgxb201701037

• 论文 • 上一篇    下一篇

基于高斯分解的多尺度3D Otsu阈值分割算法

肖明尧1, 2, 李雄飞2   

  1. 1.长春师范大学 计算机科学与技术学院,长春 130032;
    2.吉林大学 计算机科学与技术学院, 长春 130012
  • 收稿日期:2016-02-22 出版日期:2017-01-20 发布日期:2017-01-20
  • 通讯作者: 李雄飞(1963-),男,教授,博士生导师.研究方向:信息融合与数据挖掘.E-mail:lxf@jlu.edu.cn
  • 作者简介:肖明尧(1980-),男,博士研究生.研究方向:数据挖掘与图像处理.E-mail:fengyuanqing@tom.com
  • 基金资助:
    国家自然科学基金项目(61272209); 国家科技支撑计划项目(2012BAH48F02).

Multi-scale 3D Otsu thresholding algorithm based on Gaussian decomposition

XIAO Ming-yao1, 2, LI Xiong-fei2   

  1. 1.College of Computer Science and Technology, Changchun Normal University, Changchun 130032, China;
    2.College of Computer Science and Technology, Jilin University, Changchun 130012, China
  • Received:2016-02-22 Online:2017-01-20 Published:2017-01-20

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摘要： 针对阈值图像分割算法对噪音敏感的问题,提出了一种新的基于分解的Otsu阈值分割算法。整个分割算法为一个迭代过程,在每次迭代中,该图像首先用3D Otsu算法进行分割,然后利用高斯核函数对原图像进行滤波,得到一个平滑的图像,然后被输入到下一个迭代中。最后,合并每次迭代过程中产生的分割结果,获得最终的分割结果。该算法的优点在于分割结果稳定,且具有较强的抗噪性。本文在MR大脑图像上进行实验,结果表明,该算法优于其他同类阈值分割算法。

关键词: 计算机应用, 图像分割, Otsu算法, 噪音, 高斯分解

Abstract: Current thresholding algorithms for image segmentation are sensitive to noise. To overcome this problem, a new Otsu thresholding algorithm is proposed based on image decomposition. The whole segmentation algorithm is designed as an iteration procedure. In each iteration the image is segmented by the 3D Ostu, and then it is filtered by Gaussian kernel filtering to get a smoothed image, which is taken as the input of the next iteration. Finally, segmentation results obtained in the iterations and are pooled to get final segmentation. The advantages of the proposed algorithm are that its segmentation results are stable and it is robust to noise. Experiments on medical MR brain images are conducted to demonstrate the effectiveness of the proposed method. Results indicate that the proposed algorithm is superior to other thresholding algorithms.

Key words: computer application, image segmentation, Otsu algorithm, noises, Gaussian decomposition

中图分类号: 

  • TP391
[1] Zhang L, Ji Q. A Bayesian network model for automatic and interactive image segmentation[J]. IEEE Transactions on Image Processing, 2011, 20(9): 2582-2593.
[2] Tlig L, Sayadi M, Fnaiech F. A new fuzzy segmentation approach based on S-FCM type 2 using LBP-GCO features[J]. Signal Processing: Image Communication, 2012, 27(6): 694-708.
[3] Sarkar S, Das S, Chaudhuri S S. A multilevel color image thresholding scheme based on minimum cross entropy and differential evolution[J]. Pattern Recognition Letters, 2015, 54: 27-35.
[4] Gómez D, Yáñez J, Guada C, et al. Fuzzy image segmentation based upon hierarchical clustering[J]. Knowledge-Based Systems, 2015,87(c): 26-37.
[5] 景晓军, 李剑峰, 刘郁林.一种基于三维最大类间方差的图像分割算法[J].自动化学报, 2003,31(9): 1281-1285.
Jing Xiao-jun, Li Jian-feng, Liu Yu-lin. Image segmentation based on 3-D maximum between-cluster variance[J]. Acta Electronica Sinica, 2013, 31(9) :1281-1285.
[6] Miao Q, Xu P, Liu T, et al. A novel fast image segmentation algorithm for large topographic maps[J]. Neurocomputing, 2015, 168: 808-822.
[7] Bhandari A K, Kumar A, Singh G K. Tsallis entropy based multilevel thresholding for colored satellite image segmentation using evolutionary algorithms[J]. Expert Systems with Applications, 2015, 42(22): 8707-8730.
[8] Bhandari A K, Kumar A, Singh G K. Modified artificial bee colony based computationally efficient multilevel thresholding for satellite image segmentation using Kapur's, Otsu and Tsallis functions[J]. Expert Systems with Applications, 2015, 42(3): 1573-1601.
[9] Li C, Wang X, Eberl S, et al. Supervised variational model with statistical inference and its application in medical image segmentation[J].IEEE Transactions on Biomedical Engineering,2015, 62(1):196-207.
[10] Smistad E, Falch T L, Bozorgi M, et al. Medical image segmentation on GPUs—a comprehensive review[J]. Medical Image Analysis, 2015, 20: 1-18.
[11] Aja-Fernández S, Curiale A H, Vegas-Sánchez-Ferrero G.A local fuzzy thresholding methodology for multiregion image segmentation[J]. Knowledge-Based Systems, 2015, 83:1-12.
[12] Manikandan S, Ramar K, Iruthayarajan M W, et al. Multilevel thresholding for segmentation of medical brain images using real coded genetic algorithm[J]. Measurement, 2014, 47:558-568.
[13] Sathya P D, Kayalvizhi R. Optimal segmentation of brain MRI based on adaptive bacterial foraging algorithm[J]. Neurocomputing, 2011, 74(14) : 2299-2313.
[14] Maitra M, Chatterjee A. A novel technique for multilevel optimal magnetic resonance brain image thresholding using bacterial foraging[J]. Measurement , 2008, 41(10): 1124-1134.
[15] 罗述谦, 唐宇. 基于有偏场的适配模糊聚类分割算法[J]. 中国图象图形学报, 2002, 7(2):111-114.
Luo Shu-qian, Tang Yu.A bias based adaptive fuzzy segmentation algorithm[J].Journal of Image and Graphics,2002, 7(2):111-114.
[16] Li C, Huang R, Ding Z, et al. A level set method for image segmentation in the presence of intensity in homogeneities with application to MRI[J]. IEEE Transactions on Image Processing, 2011, 20(7): 2007-2016.
[17] Dai S, Lu K, Dong J, et al. A novel approach of lung segmentation on chest CT images using graph cuts[J]. Neurocomputing, 2015, 168: 799-807.
[18] Otsu N. A threshold selection method from gray-level histograms[J]. IEEE Transactions on Systems Man & Cybernetics,2007,9(1):62-66.
[19] Kittler J, Illingworth J. Minimum error thresholding[J]. Pattern Recognition, 1986, 19(1): 41-47.
[20] Kapur J N, Sahoo P K, Wong A K C. A new method for gray-level picture thresholding using the entropy of the histogram[J]. Computer Vision, Graphics, and Image Processing, 1985, 29(3): 273-285.
[21] Lin Q, Ou C. Tsallis entropy and the long-range correlation in image thresholding[J]. Signal Processing, 2012, 92(12): 2931-2939.
[22] Cai H, Yang Z, Cao X, et al., A new iterative triclass thresholding technique in image segmentation[J]. IEEE Transactions on Image Processing, 2014, 23(3): 1038-1046.
[23] 龙建武,申铉京,陈海鹏. 自适应最小误差阈值分割算法[J].自动化学报,2012, 38(7) :1134-1144.
Long Jian-wu, Shen Xuan-jing, Chen Hai-peng. Adaptive minimum error thresholding algorithm[J]. Acta Automatica Sinica, 2012, 38(7) :1134-1144.
[24] Dey S, Saha I, Bhattacharyya S, et al. Multi-level thresholding using quantum inspired meta-heuristics[J]. Knowledge Based Systems, 2014, 67: 373-400.
[25] Hammouche K, Diaf M, Siarry P. A multilevel automatic thresholding method based on a genetic algorithm for a fast image segmentation[J]. Computer Vision & Image Understanding, 2008, 109(2): 163-175.
[26] Chen X, Udupa J K, Bagci U, et al. Medical image segmentation by combining graph cuts and oriented active appearance models[J]. IEEE Transactions on Image Processing, 2012, 21(4): 2035-2046.
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