Objective Magnetic resonance imaging (MRI) provides high resolution for brain tissue, yet the presence of noise, bias field, and partial volume effect (PVE), make automatic segmentation of MRI image a challenge task. Fuzzy C-means (FCM) clustering algorithm is widely studied these years. This paper investigates different variants of FCM methods for brain tissue segmentation and explores its improvement, especially in the presence of noise and bias field in MRI images. Methods Nine variants of FCM methods are analyzed theoretically first. Then brain tissue segmentation experiments are done to evaluate these algorithms’ performance. Results We compare the merits of different algorithms and give the qualitative and quantitative results. Conclusions Bias field and noise degrade the classification quality apparently. Though certain methods have the abilities to decrease the influence of noise and bias field, the difficulty of choosing the optimum parameters hinders their performance. Reasonable utilization of spatial information has research value in the future.