IEEE J Biomed Health Inform. 2019 Sep;23(5):2039-2051. doi: 10.1109/JBHI.2018.2884208. Epub 2018 Nov 30.
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.
本文提出了一种直觉中心无模糊 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 脑图像中的噪声影响，定义了具有空间信息的像素到像素相似度。然后，为了处理 MR 脑图像中的模糊性以及聚类过程中的不确定性，通过使用直觉模糊隶属函数定义了像素到聚类的相似度度量。这两个相似度用于修改无中心 FCM，以提高该方法对 MR 脑图像分割的能力。其次，在改进的无中心 FCM 方法的基础上，将局部信息项（也是直觉和无中心的）附加到目标函数中。这产生了最终提出的 ICFFCM。对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，并可以达到与标准发布的方法（如统计参数映射和 FMRIB 自动分割工具）相当的结果。
