辽宁石油化工大学学报 ›› 2023, Vol. 43 ›› Issue (1): 89-96.DOI: 10.12422/j.issn.1672-6952.2023.01.015

• 信息与控制工程 • 上一篇    

一种基于马尔科夫随机场的脑MR图像分割改进算法

王国良(), 任允帅, 王阳   

  1. 辽宁石油化工大学 信息与控制工程学院,辽宁 抚顺 113001
  • 收稿日期:2021-01-26 修回日期:2021-02-19 出版日期:2023-02-25 发布日期:2023-03-13
  • 作者简介:王国良(1981⁃),男,博士,教授,从事Markov随机过程的建模、控制及应用等研究;E⁃mail:glwang@lnpu.edu.cn
  • 基金资助:
    国家自然科学基金项目(62073158);辽宁省“兴辽人才”支持计划项目(XLYC1807030);辽宁省“高校创新人才”计划项目(LR2017029)

An Improved Brain MR Image Segmentation Algorithm Based on Markov Random Field

Guoliang Wang(), Yunshuai Ren, Yang Wang   

  1. School of Information and Control Engineering,Liaoning Petrochemical University,Funshun Liaoning 113001,China
  • Received:2021-01-26 Revised:2021-02-19 Published:2023-02-25 Online:2023-03-13

摘要:

高斯混合模型(GMM)易受噪声影响,马尔科夫随机场(MRF)模型能够很好地刻画空间特性。两者结合适用于对含有噪声的图片进行分割,但MRF模型用于图像分割时,容易出现过分割现象。针对这个问题,提出一种自适应权值系数的图像分割改进算法,从核磁共振成像(MRI)中较好地分割出脑脊液、灰质和白质组织。首先,使用K?means算法得到初始分割结果,通过期望最大化算法(EM)估计GMM参数,进而得到图像像素灰度的联合概率能量函数。然后,利用MRF邻域系统中心像素与邻域像素的灰度值、后验概率和欧式距离得到自适应的权值系数,使用MRF模型得到先验概率能量函数。最后,借助贝叶斯准则得到最终图像分割结果。实验结果表明,该算法具有较强的自适应性,能够较好地克服噪声对图像分割的影响织。与同类算法相比,该算法对含有噪声的脑部MRI图像具有较高的分割精度,可得到较好的图像分割结果。

关键词: 脑磁共振图像分割, 高斯混合模型, 马尔科夫随机场模型, 贝叶斯准则, 邻域信息

Abstract:

Gaussian mixture model (GMM) is easily affected by noise, and Markov random field (MRF) model can well describe the spatial characteristics. The combination of the two is suitable for image segmentation with noise, but MRF model is prone to over segmentation. To solve this problem, an improved image segmentation algorithm based on adaptive weight coefficient was proposed, which can segment cerebrospinal fluid, gray matter and white matter from magnetic resonance imaging (MRI). Firstly, the K?means algorithm was used to obtain the initial segmentation results, and the Expectation?Maximization (EM) algorithm was used to estimate the parameters of GMM, and then the joint probability energy function of the pixel gray level of the image was obtained. Then, the adaptive weight coefficient was obtained by using the gray value, posterior probability and Euclidean distance of the center pixel and the neighboring pixels of the MRF neighborhood system, and the prior probability energy function was obtained by MRF. Finally, the final image segmentation results were obtained by Bayesian criterion. Experimental results show that the algorithm has strong adaptability, can better overcome the impact of noise on image segmentation. Compared with similar algorithms, the proposed algorithm has higher segmentation accuracy for brain MR images with noise, and obtains better segmentation results.

Key words: Brain MRI image segmentation, Gaussian mixture model, Markov random field model, Bayesian criterion, Neighborhood information

中图分类号: 

引用本文

王国良, 任允帅, 王阳. 一种基于马尔科夫随机场的脑MR图像分割改进算法[J]. 辽宁石油化工大学学报, 2023, 43(1): 89-96.

Guoliang Wang, Yunshuai Ren, Yang Wang. An Improved Brain MR Image Segmentation Algorithm Based on Markov Random Field[J]. Journal of Liaoning Petrochemical University, 2023, 43(1): 89-96.

使用本文

0
    /   /   推荐

导出引用管理器 EndNote|Ris|BibTeX

链接本文: https://journal.lnpu.edu.cn/CN/10.12422/j.issn.1672-6952.2023.01.015

               https://journal.lnpu.edu.cn/CN/Y2023/V43/I1/89