To overcome the target boundary prone to be misclassification for an original image when the user-selected seed pixels become less in the graph cut algorithm. An interactive K-means and graph cut algorithm (KMGC) is proposed in the combination of the K-means with graph cut(GC) algorithm and the interactive segmentation with brain magnetic resonance image (MRI). The MRI intensity inhomogeneity is processed by K-means clustering algorithm. On this basis, the graph cut algorithm will further refine the MRI, so as to obtain effective segmentation of white matter and gray matter. We implement extensive segmentation experiments using both synthetic and real brain MRIs. Quantitative and qualitative analyses are carried out about the experimental results, and the results are compared with other segmentation algorithms. The experimental results show that the KMGC algorithm can effectively divide the brain MRI, and outperform others on the segmentation effect.