Intensity inhomogeneity is a common problem in medical images and poses many challenges for accurate segmentation of images. Image segmentation is a foundation step for computer vision and computing. A fuzzy C-means (FCM)-based energy minimization method that combines global clustering and local clustering was proposed for bias field estimation and segmentation in magnetic resonance (MR) brain images. The advantage of this method is that the MR image is decomposed into two components as global clustering items, taking full advantage of the real image that characterizes the physical properties of the tissue, bias fields that explain the intensity inhomogeneity, and their respective spatial properties. The decomposition of MR images describes the changes in bias field of the whole image,where the details of some deep changes of tissue boundaries may be lost. This method uses the local clustering terms of different bias fields in the local area of the image to better deal with the profound changes of strength between different tissues. As there is a lack of global control for distribution of bias field in local clustering method, we took advantage of global clustering and local clustering, and considered the combination of them. In the proposed method, bias field estimation and tissue segmentation were simultaneously achieved by an energy minimization process. The problem of energy minimization was iteratively optimized by FCM. Comparison experiments on some real and synthetic images with relevant models demonstrate the advantages of the proposed model in terms of bias correction and segmentation accuracy.