A Bayesian approach for segmentation of three-dimensional (3-D) magnetic resonance imaging (MRI) data of the human brain is presented. Connectivity and smoothness constraints are imposed on the segmentation in 3 dimensions. The resulting segmentation is suitable for 3-D display and for volumetric analysis of structures. The algorithm is based on the maximum a posteriori probability (MAP) criterion, where a 3-D Gibbs random field (GRF) is used to model the a priori probability distribution of the segmentation. The proposed method can be applied to a spatial sequence of 2-D images (cross-sections through a volume), as well as 3-D sampled data. We discuss the optimization methods for obtaining the MAP estimate. Experimental results obtained using clinical data are included.