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山东大学学报(工学版) ›› 2017, Vol. 47 ›› Issue (3): 49-55.doi: 10.6040/j.issn.1672-3961.0.2016.310

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基于Markov随机场的Student's t混合模型的脑MR图像分割

李璐,范文涛,杜吉祥*   

  1. 华侨大学计算机科学与技术学院, 福建 厦门 361021
  • 收稿日期:2016-07-22 出版日期:2017-06-20 发布日期:2016-07-22
  • 通讯作者: 杜吉祥(1977— ),男,山东高唐人,教授,主要研究方向为模式识别、图像处理.E-mail:jxdu@hqu.edu.cn E-mail:lilu920815@126.com
  • 作者简介:李璐(1992— ),女,江苏南通人,硕士研究生,主要研究方向为模式识别与图像处理.E-mail:lilu920815@126.com
  • 基金资助:
    国家自然科学基金资助项目(61673186,61502183);福建省自然科学资金资助项目(2013J06014);华侨大学中青年教师科研提升资助计划项目(ZQN-YX108);华侨大学高层次人次引进科研启动资助项目(600005-Z15Y0016);国家自然科学基金青年资助项目(61502183);华侨大学研究生科研创新能力培养资助项目(1400214003,1400214009)

Brain MR image segmentation based on student's t mixture model with Markov random field

LI Lu, FAN Wentao, DU Jixiang*   

  1. College of Computer Science, Huaqiao University, Xiamen 361021, Fujian, China
  • Received:2016-07-22 Online:2017-06-20 Published:2016-07-22

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摘要： 为解决在使用期望最大化(EM)算法求解混合模型前需要额外的计算问题,提出一种新的基于Markov随机场的Student's t混合模型,该模型能直接利用简单有效的EM算法求解。试验结果表明,该方法能有效克服噪声对图像分割的影响,获得较好的分割结果。

关键词: 脑MR图像分割, 期望最大化算法, Student's t混合模型, Markov随机场

Abstract: Extra computation was always needed when using Expectation Maximization(EM)algorithm for solving mixture models. To overcome this drawback, a novel Student's t-mixture model based on Markov random field was proposed. EM algorithm was used directly in the proposed model, which was convenient and efficient. According to the experimental results, the proposed method could overcome the impact of noise on the segmentation results efficiently, and got better segmentation results.

Key words: brain MR image segmentation, Markov random field, expectation maximization algorithm, student's t-mixture model

中图分类号: 

  • TP391
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