全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

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, PP. 1754-1763

Keywords: 脑MR图像,图像分割,图像片,高斯混合模型,马尔科夫随机场

Full-Text   Cite this paper   Add to My Lib

Abstract:

?传统的高斯混合模型(Gaussianmixturemodel,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(Markovrandomfield,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.

References

[1]  Verbeek J J, Vlassis N, Kr?se B. Efficient greedy learning of Gaussian mixture models. Neural Computation, 2003, 15(2): 469-485
[2]  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
[3]  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
[4]  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
[5]  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
[6]  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
[7]  Roche A, Ribes D, Bach-Cuadra M, Krü
[8]  Bishop C M. Pattern Recognition and Machine Learning. Berlin: Springer-Verlag, 2006
[9]  Besag J. Statistical analysis of non-lattice data. The Statistician, 1975, 24(3): 179-195
[10]  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
[11]  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)
[12]  Verbeek J J, Vlassis N, Kr?se B J A. Self-organizing mixture models. Neurocomputing, 2005, 63: 99-123
[13]  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
[14]  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
[15]  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
[16]  Besag J. On the statistical analysis of dirty pictures. Journal of the Royal Statistical Society, 1986, 48(3): 259-302
[17]  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
[18]  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
[19]  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
[20]  ger G. On the convergence of EM-like algorithms for image segmentation using Markov random fields. Medical Image Analysis, 2011, 16(6): 830-839
[21]  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
[22]  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
[23]  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
[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

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133