Aiming at the problems of slow convergence speed and pixel misjudgment when a suppressed fuzzy C-means clustering algorithm is applied to gray image segmentation, the suppressed fuzzy C-means clustering image segmentation algorithm based on combined iteration with double centers is proposed via excavating the correlation between pixels in the homogeneous region of an image and analyzing the effect of pixel position on the category judgement. Firstly, by the three steps, i.e., selecting, expanding and extracting, the initial clustering centers are chosen from the pixels in an image. Then, for every initial clustering center, the pixels whose gray values are equal to that of the clustering center are searched in the image, and the hidden centers can be captured by filtering. Then, position features are calculated by the normalization of the Euclidean distance between the pixel positions and hidden centers by using a negative exponential function. Moreover, the fuzzy partition matrix is directly modified after the position features are weighted. Finally, in order to reduce the number of iteration further, the idea of the suppressed fuzzy C-means clustering algorithm is added. Experimental results show that the proposed algorithm can obtain dense and well-separated clustering in comparison with several existing algorithms, which improves accuracy and effectiveness in image segmentation.