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Medical image segmentation is a complex and challenging task due to the intrinsic nature of the images. Magnetic resonance imaging (MRI) segmentation is of particular importance for further image analysis. Fuzzy c-mean (FCM) is a common clustering algorithm which is used for segmentation of MR images. However in the case of noisy MR images, efficiency of this algorithm considerably reduces. Recently, researchers have introduced two new parameters in order to improve the performance of traditional FCM in the case of noisy images. New parameters are computed using artificial neural networks and through a complex and time consuming optimization problem. In this paper, we present a new method for computation of these two parameters, efficiently. We use a particle swarm optimization (PSO) method and show the capability of PSO to find optimal values of these parameters. The advantage of the new proposed method is its simplified computations. Our simulation results on a set of noisy MR images, demonstrate the effectiveness of proposed optimization method compared with some related recent algorithms.