The fuzzy C-means (FCM) clustering procedure is an unsupervised form of grouping the homogenous pixels of an image in the feature space into clusters. A brain magnetic resonance (MR) image is affected by noise and intensity inhomogeneity (IIH) during the acquisition process. FCM has been used in MR brain tissue segmentation. However, it does not consider the neighboring pixels for computing the membership values, thereby misclassifying the noisy pixels. The inaccurate cluster centers obtained in FCM do not address the problem of IIH. A fixed value of the fuzzifier ( m) used in FCM brings uncertainty in controlling the fuzziness of the extracted clusters. To resolve these issues, we suggest a novel type-2 adaptive weighted spatial FCM (AWSFCM) clustering algorithm for MR brain tissue segmentation. The idea of type-2 FCM applied to the problem on hand is new and is reported in this article. The application of the proposed technique to the problem of MR brain tissue segmentation replaces the fixed fuzzifier value with a fuzzy linguistic fuzzifier value ( M). The introduction of the spatial information in the membership function reduces the misclassification of noisy pixels. Furthermore, the incorporation of adaptive weights into the cluster center update function improves the accuracy of the final cluster centers, thereby reducing the effect of IIH. The suggested algorithm is evaluated using T1-w, T2-w, and proton density (PD) brain MR image slices. The performance is justified in terms of qualitative and quantitative measures followed by statistical analysis. The outcomes demonstrate the superiority and robustness of the algorithm in comparison to the state-of-the-art methods. This article is useful for the cybernetics application.

中文翻译：

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用于大脑 MR 图像分割的新型 2 型模糊 C 均值聚类


模糊 C 均值 (FCM) 聚类过程是将特征空间中图像的同质像素分组为聚类的无监督形式。脑磁共振 (MR) 图像在采集过程中会受到噪声和强度不均匀性 (IIH) 的影响。 FCM 已用于 MR 脑组织分割。然而，它没有考虑相邻像素来计算隶属值，从而对噪声像素进行错误分类。 FCM中获得的不准确的聚类中心并不能解决IIH问题。 FCM 中使用的模糊器 ( m) 的固定值给控制提取的簇的模糊性带来了不确定性。为了解决这些问题，我们提出了一种用于 MR 脑组织分割的新型 2 型自适应加权空间 FCM (AWSFCM) 聚类算法。将 2 型 FCM 应用于当前问题的想法是新的，本文对此进行了报道。所提出的技术应用于MR脑组织分割问题，用模糊语言模糊器值（M）代替固定模糊器值。在隶属函数中引入空间信息减少了噪声像素的错误分类。此外，将自适应权重纳入聚类中心更新函数中，提高了最终聚类中心的准确性，从而降低了IIH的影响。使用 T1-w、T2-w 和质子密度 (PD) 脑 MR 图像切片评估建议的算法。绩效通过定性和定量测量以及随后的统计分析来证明是合理的。结果证明了该算法与最先进的方法相比的优越性和鲁棒性。本文对于控制论应用很有用。