Image segmentation is an important research direction in computer vision. Fuzzy clustering methods have been widely applied in image segmentation due to their unsupervised nature. However, traditional fuzzy clustering methods often fail to segment images with high-intensity noise and complex shapes. To solve this problem, a weighted factor is proposed based on saliency detection to construct a weighted filter and a pixel correlation model, which improves the noise resistance of the algorithm. The proposed weighted filter outperforms the optimal results of the traditional filter in terms of structural similarity by 0.1. Moreover, a kernel metric is introduced to accommodate the segmentation needs of complex images. Extensive experimental results on synthetic, natural, remote sensing and medical images demonstrate that the proposed algorithm outperforms the traditional methods in visual effects and improves the segmentation accuracy by 2% compared with the optimal results of traditional methods.