OBJECTIVE: Markov random field (MRF) models have been traditionally applied to the task of robust-to-noise image segmentation. Most approaches estimate MRF parameters on the whole image via a global expectation-maximization (EM) procedure. The resulting estimated parameters are likely to be uncharacteristic of local image features. Instead, we propose to distribute a set of local MRF models within a multiagent framework. MATERIALS AND METHODS: Local segmentation agents estimate local MRF models via local EM procedures and cooperate to ensure a global consistency of local models. We demonstrate different types of cooperations between agents that lead to additional levels of regularization compared to the standard label regularization provided by MRF. Embedding Markovian EM procedures into a multiagent paradigm shows interesting properties that are illustrated on magnetic resonance (MR) brain scan segmentation. RESULTS: A cooperative tissue and subcortical structure segmentation approach is designed with such a framework, where both models mutually improve. Several experiments are reported and illustrate the working of Markovian EM agents. The evaluation of MR brain scan segmentation was performed using both phantoms and real 3T brain scans. It showed a robustness to intensity non-uniformity and noise, together with a low computational time. CONCLUSION: Based on these experiments MRF agent-based approach appears to be a very promising new tool for complex image segmentation.