To deal with the difficulties caused by noise disturbance, intensity inhomogeneity and edge blurring in Magnetic Resonance Imaging (MRI) image segmentation, a new multi-threshold MRI image segmentation algorithm based on mixed entropy using Curvelet transformation and Multi-Objective Particle Swarm Optimization (MOPSO) was proposed. First, the high-frequency and the low-frequency subbands were obtained using Curvelet decomposition, which were used to construct the profile-detail gray level matrix model that could represent edge details accurately. Then, with the consideration of both inter-class similarity and intra-class difference of background and object region, two-dimensional reciprocal entropy and reciprocal gray entropy were proposed and combined to define the mixed entropy, which was used as the objective function of MOPSO. The optimal multi-threshold was searched cooperatively to get an accurate segmentation. Finally, in order to speed up the segmentation process, gradient-based multi-threshold estimation algorithms for two-dimensional reciprocal entropy and reciprocal gray entropy were proposed. The experimental results show that the proposed method is more adaptive and accurate when applied to gray uneven and noisy MRI image segmentation in comparison with two-dimensional tsallis entropy, Adaptive Bacterial Foraging (ABF) and improved Otsu multi-threshold segmentation algorithms.