[1]标本,梁恺彬,管一弘. 高斯马尔可夫随机场的人脑MR图像分割方法[J].计算机技术与发展,2017,27(07):180-184.
　BIAO Ben,LIANG Kai-bin,GUAN Yi-hong. An Image Segmentation Method of Brain MR Based on Gaussian Markov Random Field[J].,2017,27(07):180-184.
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 高斯马尔可夫随机场的人脑MR图像分割方法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
27
期数:
2017年07期
页码:
180-184
栏目:
应用开发研究
出版日期:
2017-07-10

文章信息/Info

Title:
 An Image Segmentation Method of Brain MR Based on Gaussian Markov Random Field
文章编号:
1673-629X（2017)07-0180-05
作者:
 标本; 梁恺彬; 管一弘
 昆明理工大学 理学院
Author(s):
 BIAO Ben; LIANG Kai-bin; GUAN Yi-hong
关键词:
 ; 人脑MRI ; 空间信息; 模糊C均值; 高斯马尔可夫随机场; 图像分割
Keywords:
 ; human brain MRI; spatial information; fuzzy C means; Gaussian Markov random field; image segmentation
分类号:
TP391
文献标志码:
A
摘要:
 传统的聚类分割方法通常是基于图像灰度值的相似程度进行像素划分,对强噪声或边缘模糊的人脑MRI进行分割的效果并不理想.为此,提出了高斯马尔可夫随机场的人脑MR图像分割方法.该方法将空间信息的模糊C均值与高斯马尔可夫随机场相结合,利用空间信息模糊C均值的良好抗噪性能对人脑MRI进行初始分割,降低噪声的影响.由于马尔可夫随机场拥有优良的空间相关性,所以基于马尔可夫随机场的分割方法能够很好地对人脑MRI纹理和边缘进行有效划分.但是它对噪声较为敏感,同时分割往往会因为噪声的影响导致噪点扩大或边缘外扩.鉴于人脑MRI的灰度分布拥有高斯特征,采用高斯函数建立的马尔可夫随机场模型能很好地反映人脑MRI的分布特点.为验证所提算法的有效性,以人脑MRI作为实验数据进行了大量实验,结果表明:所提出的分割方法对人脑MRI具有较好的分割结果,同时鲁棒性与抗噪性能大大增强.
Abstract:
 The traditional clustering segmentation method is usually used to divide pixels based on the degree of similarity of the image gray value,but its effect is not ideal for the strong noise or edge blur brain MRI.So an image segmentation method of brain MR based on Gaussian Markov random field is proposed.It combines fuzzy C mean with spatial information and Gaussian Markov random field and utilizes the well anti-noise performance of fuzzy C mean of spatial information for the initial segmentation,which can reduce the influence of noise.Due to the excellent spatial correlation of Markov random field,the method based on Markov random field can effectively divide the texture and edge of human brain MRI,but it is more sensitive to noises and results in the expansion of noise points or edges.Since the gray distribution of human brain MRI has the Gaussian characteristic,the Markov random field model created by Gaussian function can well reflect the distribution characteristics of brain MRIs.To verify the effectiveness of the proposed method,the numerous experiments have be conducted and their results have shown that the method has good segmentation effects for human brain MRI.At the same time,the robustness and the anti-noise ability are greatly enhanced.

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更新日期/Last Update: 2017-08-24