To improve the segmentation precision of brain Magnetic Resonance(MR) imaging, a novel brain tissue automated segmentation method was proposed. Firstly, the merits and demerits of Gaussian mixture model and active contour model used for MR image segmentation were analyzed, and a new energy function was constructed through combining the probability density function of the Gaussian mixture model with the energy function of the active contour model. Then, the genetic algorithm and expectation maximization algorithm were used to get the parameter values of the probability density function. Finally, segmentation results were achieved through minimizing the novel energy function by using the level set method and the gradient descent algorithm. The experiment results clearly indicate that the segmentation accuracies of white matter and gray matter in brain tissue by the proposed method are increased by 6.73% and 14.07%, respectively as compared with that of the traditional methods. By using the area information and probability values of pixel points to drive the active contour curve, the proposed method automatically segments the brain MR image with high enough accuracy and improves the segmentation accuracy of brain MR images.