Traditional image segmentation algorithms have disadvantages such as single description of image feature information and poor segmentation effect. Therefore, a dual feature Markov random field (MRF) image segmentation method is proposed. First, the spatial information between pixels is used to constrain the prior and posterior probabilities of the Gaussian mixture model (GMM) to establish a grayscale random field. Second, on the basis of non-linearly preserving the edge contours and texture details of the image by the fractional differential operator, a grayscale co-occurrence matrix is used to describe the texture feature information of the image and establish a random field of texture features. Finally, a dual feature Markov random field for image segmentation is designed, and the conditional iterative algorithm is used to optimize the maximum posterior probability of the labeled field to achieve image segmentation. Experiments verify the effectiveness of the segmentation algorithm and the segmentation accuracy is 93.9%. The proposed dual feature random field can improve the robustness and accuracy of the image segmentation algorithm.