Intuitionistic Center-Free FCM Clustering for MR Brain Image Segmentation
Intuitionistic Center-Free FCM Clustering for MR Brain Image Segmentation
复制标题
用于 MR 脑图像分割的直观无中心 FCM 聚类
DOI：
10.1109/jbhi.2018.2884208
复制
发表时间：
2019-09-01
影响因子：
7.7
通讯作者：
Wang, Yingfan
中科院分区：
文献类型：
--
作者：
Bai, Xiangzhi;Zhang, Yuxuan;Wang, Yingfan
提出了一种直觉无中心模糊c均值聚类方法（ICFFCM）用于磁共振（MR）脑图像分割。首先，为了抑制MR脑图像中噪声的影响，定义了具有空间信息的像素到像素的相似性。然后，针对MR脑图像的模糊性和聚类过程中的不确定性，采用直觉模糊隶属度函数定义了一种像素到聚类的相似性度量。利用这两个相似性对无中心FCM算法进行改进，提高了该算法对MR脑图像的分割能力。其次，在改进的无中心FCM方法的基础上，在目标函数中加入了一个直观的无中心局部信息项。这就产生了最后提议的FFCM。局部信息的考虑进一步增强了ICFFCM对MR脑图像中噪声的鲁棒性。在模拟和真实的MR脑图像数据集上的实验结果表明，ICFFCM是有效和鲁棒的。此外，ICFFCM可以优于几种基于模糊聚类的方法，并可以实现与标准公布的方法，如统计参数映射和FMRIB自动分割工具相当的结果。
In this paper, an intuitionistic center-free fuzzy c-means clustering method (ICFFCM) is proposed for magnetic resonance (MR) brain image segmentation. First, in order to suppress the effect of noise in MR brain images, a pixel-to-pixel similarity with spatial information is defined. Then, for the purpose of handling the vagueness in MR brain images as well as the uncertainty in clustering process, a pixel-to-cluster similarity measure is defined by employing the intuitionistic fuzzy membership function. These two similarities are used to modify the center-free FCM so that the ability of the method for MR brain image segmentation could be improved. Second, on the basis of the improved center-free FCM method, a local information term, which is also intuitionistic and center-free, is appended to the objective function. This generates the final proposed ICFFCM. The consideration of local information further enhances the robustness of ICFFCM to the noise in MR brain images. Experimental results on the simulated and real MR brain image datasets show that ICFFCM is effective and robust. Moreover, ICFFCM could outperform several fuzzy-clustering-based methods and could achieve comparable results to the standard published methods like statistical parametric mapping and FMRIB automated segmentation tool.