Generalized fuzzy C-means algorithm is a faster convergence algorithm than fuzzy C-means algorithm. However, it is sensitive to noise when segmenting gray images. In order to improve its robustness, a generalized fuzzy C-means algorithm based on the weighting of pixel gray value in image patch is proposed. In this algorithm, instead of a single pixel, the image patch is used to construct the objective function. The weight of each pixel in the image patch is determined by the spatial relationship between neighboring pixels and central pixel and the gray relationship of each pixel in the image patch. The expressions of membership and cluster center, in the form of image patch, are derived by using Lagrange multiplier method based on the new objective function. In this way, the neighborhood information is integrated into the clustering process, and then improves the robustness of the algorithm. Segmentation experiments are carried out with a synthetic image and several real images, and the segmentation results show that the proposed algorithm has strong robustness and good segmentation performance.