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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.
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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

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