2.765

2022影响因子

(CJCR)

  • 中文核心
  • EI
  • 中国科技核心
  • Scopus
  • CSCD
  • 英国科学文摘

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于图像片马尔科夫随机场的脑MR图像分割算法

宋艳涛 纪则轩 孙权森

宋艳涛, 纪则轩, 孙权森. 基于图像片马尔科夫随机场的脑MR图像分割算法. 自动化学报, 2014, 40(8): 1754-1763. doi: 10.3724/SP.J.1004.2014.01754
引用本文: 宋艳涛, 纪则轩, 孙权森. 基于图像片马尔科夫随机场的脑MR图像分割算法. 自动化学报, 2014, 40(8): 1754-1763. doi: 10.3724/SP.J.1004.2014.01754
SONG Yan-Tao, JI Ze-Xuan, SUN Quan-Sen. Brain MR Image Segmentation Algorithm Based on Markov Random Field with Image Patch. ACTA AUTOMATICA SINICA, 2014, 40(8): 1754-1763. doi: 10.3724/SP.J.1004.2014.01754
Citation: SONG Yan-Tao, JI Ze-Xuan, SUN Quan-Sen. Brain MR Image Segmentation Algorithm Based on Markov Random Field with Image Patch. ACTA AUTOMATICA SINICA, 2014, 40(8): 1754-1763. doi: 10.3724/SP.J.1004.2014.01754

基于图像片马尔科夫随机场的脑MR图像分割算法

doi: 10.3724/SP.J.1004.2014.01754
基金项目: 

国家自然科学基金(61273251)资助

详细信息
    作者简介:

    宋艳涛 南京理工大学计算机学院博士研究生. 主要研究方向为医学图像处理,模式识别.E-mail:yantaosong@hotmail.com

    通讯作者:

    孙权森 南京理工大学计算机学院教授.主要研究方向为图像处理与模式识别.本文通信作者.E-mail:sunquansen@njust.edu.cn

Brain MR Image Segmentation Algorithm Based on Markov Random Field with Image Patch

Funds: 

Supported by National Natural Science Foundation of China (61273251)

  • 摘要: 传统的高斯混合模型(Gaussian mixture model,GMM)算法在图像分割中未考虑像素的空间信息,导致其对于噪声十分敏感.马尔科 夫随机场(Markov random field,MRF)模型通过像素类别标记的Gibbs分布先验概率引入了图像的空间信息,能较好地分割含有噪声的图 像,然而MRF模型的分割结果容易出现过平滑现象.为了解决上述缺陷,提出了一种新的基于图像片权重方法的马 尔科夫随机场图像分割模型,对邻域内的不同图像片根据相似度赋予不同的权重,使其在克服噪声影响的同时能 保持图像细节信息.同时,采用KL距离引入先验概率与后验概率关于熵的惩罚项,并对该惩罚项进行平滑,得到 最终的分割结果.实验结果表明,算法具有较强的自适应性,能够有效克服噪声对于分割结果的影响,并获得较高的分割精度.
  • [1] Verbeek J J, Vlassis N, Krse B. Efficient greedy learning of Gaussian mixture models. Neural Computation, 2003, 15(2): 469-485
    [2] [2] Redner R A, Walker H F. Mixture densities, maximum likelihood, and the EM algorithm. Society for Industrial and Applied Mathematics Review, 1984, 26(2): 195-239
    [3] [3] Nguyen T M, Wu Q M J, Ahuja S. An extension of the standard mixture model for image segmentation. IEEE Transactions on Neural Networks, 2010, 21(8): 1326-1338
    [4] [4] Balarf M A, Ramli A R, Saripan M I, Mashohor S. Review of brain MRI image segmentation methods. Artificial Intelligence Review, 2010, 33(3): 261-274
    [5] [5] Skibbe H, Reisert M, Burkhardt H. Gaussian neighborhood descriptors for brain segmentation. In: Proceedings of the 2011 Machine Vision Applications. Nara, Japan: Nara Centennial Hall, 2011. 35-38
    [6] [6] Geman S, Geman D. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1984, 6(6): 721-741
    [7] [7] Besag J. On the statistical analysis of dirty pictures. Journal of the Royal Statistical Society, 1986, 48(3): 259-302
    [8] [8] Diplaros A, Vlassis N, Gevers T. A spatially constrained generative model and an EM algorithm for image segmentation. Neural Networks, 2007, 18(3): 798-808
    [9] [9] Qian W, Titterington D M. Estimation of parameters in hidden Markov models. Philosophical Transactions of the Royal Society A: Mathematical, Physical And Engineering Sciences, 1991, 337(1647): 407-428
    [10] Zhang Y, Brady M, Smith S. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Transactions on Medical Imaging, 2001, 20(1): 45-57
    [11] Sanjay-Gopal S, Hebert T J. Bayesian pixel classification using spatially variant finite mixtures and the generalized EM algorithm. IEEE Transactions on Image Processing, 1998, 7(7): 1014-1028
    [12] Yousefi S, Azmi R, Zahedi M. Brain tissue segmentation in MR images based on a hybrid of MRF and social algorithms. Medical Image Analysis, 2012, 16(4): 840-848
    [13] Wang Q. HMRF-EM-image: implementation of the hidden Markov random field model and its expectation-maximization algorithm. Computer Vision and Pattern Recognition, DOI: arXiv: 1207.3510, 2012
    [14] Roche A, Ribes D, Bach-Cuadra M, Kr
    [15] ger G. On the convergence of EM-like algorithms for image segmentation using Markov random fields. Medical Image Analysis, 2011, 16(6): 830-839
    [16] Bishop C M. Pattern Recognition and Machine Learning. Berlin: Springer-Verlag, 2006
    [17] Celeux G, Forbes F, Peyrard N. EM procedures using mean field-like approximations for Markov model-based image segmentation. Pattern Recognition, 2003, 36(1): 131-144
    [18] Besag J. Statistical analysis of non-lattice data. The Statistician, 1975, 24(3): 179-195
    [19] Efros A A, Leung T K. Texture synthesis by non-parametric sampling. In: Proceedings of the 7th IEEE International Conference on Computer Vision. Kerkyra, Greece, 1999, 2: 1033-1038
    [20] Buades A, Coll B, Morel J M. A non-local algorithm for image denoising. In: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego, CA: IEEE, 2005. 60-65
    [21] Wang Huan-Liang, Han Ji-Qing, Zheng Tie-Ran. Approximation of Kullback-leibler divergence between two Gaussian mixture distributions. Acta Automatica Sinica, 2008, 34(5): 529-534 (王欢良, 韩纪庆, 郑铁然. 高斯混合分布之间K-L散度的近似计算. 自动化学报, 2008, 34(5): 529-534)
    [22] Roweis S T, Saul L K, Hinton G E. Global coordination of local linear models. Advances in Neural Information Processing Systems, 2002, 14: 889-896
    [23] Verbeek J J, Vlassis N, Krse B J A. Self-organizing mixture models. Neurocomputing, 2005, 63: 99-123
    [24] Vovk U, Pernug F, Likar B. A review of methods for correction of intensity inhomogeneity in MRI. IEEE Transactions on Medical Imaging, 2007, 26(3): 405-421
  • 加载中
计量
  • 文章访问数:  2525
  • HTML全文浏览量:  123
  • PDF下载量:  1498
  • 被引次数: 0
出版历程
  • 收稿日期:  2012-07-05
  • 修回日期:  2013-01-06
  • 刊出日期:  2014-08-20

目录

    /

    返回文章
    返回