In most approaches, tissue and subcortical structure segmentations of MR brain scans are handled globally over the entire brain volume through two relatively independent sequential steps. We propose a fully Bayesian joint model that integrates within a multi-agent framework local tissue and structure segmentations and local intensity distribution modeling. It is based on the specification of three conditional Markov Random Field (MRF) models. The first two encode cooperations between tissue and structure segmentations and integrate
anatomical knowledge. The third model specifies a Markovian spatial prior over the model parameters that enables local estimations while ensuring their consistency, handling this way nonuniformity of intensity without any bias field modeling. The complete joint model provides then a sound theoretical framework for carrying out tissue and structure segmentations by distributing a set of local agents that estimate cooperatively local MRF models. The evaluation, using a previously affine-registered atlas of 17 structures, was performed using both phantoms and real 3T brain scans. It shows good results and in particular robustness to nonuniformity and noise with a low computational cost. The innovative coupling of agent-based and Markov-centered designs appears as a robust, fast and promising approach to MR brain scan segmentation.