The fuzzy local information C-means clustering algorithm (FLICM) is an important robust fuzzy clustering segmentation method, which has attracted considerable attention over the years. However, it lacks certain robustness to high noise or severe outliers. To improve the accuracy and robustness of the FLICM algorithm for images corrupted by high noise, a novel fuzzy local information c-means clustering utilizing total Bregman divergence (TFLICM) is proposed in this paper. The total Bregman divergence is modified by the local neighborhood information of sample to further enhance the ability to suppress noise, and then modified total Bregman divergence is introduced into the FLICM to construct a new objective function of robust fuzzy clustering, and the iterative clustering algorithm with high robustness is obtained through optimization theory. The convergence of the TFLICM algorithm is proved by the Zangwill theorem. In addition, the validity of the TFLICM algorithm applied in noise image segmentation is explained by means of sample weighting fuzzy clustering. Meanwhile, the generalized total Bregman divergence unifies the Bregman divergence with the total Bregman divergence and enhances the universality of the TFLICM algorithm applied in segmenting complex medical and remote sensing images. Some experimental results show that the TFLICM algorithm can obtain better segmentation quality and stronger anti-noise robustness than the existing FLICM algorithm.

模糊局部信息C均值聚类算法（FLICM）是一种重要的鲁棒模糊聚类分割方法，多年来引起了人们的广泛关注。但是，它缺乏对高噪声或严重异常值的鲁棒性。为了提高FLICM算法对高噪声图像的准确性和鲁棒性，提出了一种基于总布雷格曼散度（TFLICM）的模糊局部信息c均值聚类算法。通过样本的局部邻域信息修改总的Bregman发散度，以进一步增强抑制噪声的能力，然后将修改后的总Bregman发散度引入FLICM中，以构造新的鲁棒模糊聚类目标函数，并采用迭代聚类算法通过优化理论可以获得很高的鲁棒性。Zangwill定理证明了TFLICM算法的收敛性。另外，通过样本加权模糊聚类来解释TFLICM算法在噪声图像分割中的有效性。同时，广义总Bregman发散使Bregman发散与总Bregman发散相统一，并增强了TFLICM算法在分割复杂医学图像和遥感图像时的通用性。实验结果表明，与现有的FLICM算法相比，TFLICM算法具有更好的分割质量和较强的抗噪鲁棒性。广义总Bregman发散使Bregman发散与总Bregman发散相统一，并增强了用于分割复杂医学和遥感图像的TFLICM算法的通用性。实验结果表明，与现有的FLICM算法相比，TFLICM算法具有更好的分割质量和较强的抗噪鲁棒性。广义总Bregman发散使Bregman发散与总Bregman发散相统一，并增强了用于分割复杂医学和遥感图像的TFLICM算法的通用性。实验结果表明，与现有的FLICM算法相比，TFLICM算法具有更好的分割质量和较强的抗噪鲁棒性。