A novel multi-agent image interpretation system has been developed which is markedly di erent from previous approaches in especially its elaborate high-level knowledge-based control over low-level image segmentation algorithms. Agents dynamically adapt segmentation algorithms based on knowledge about global constraints, contextual knowledge, local image information and personal beliefs. Generally agent control allows the underlying segmentation algorithms to be simpler and be applied to a wider range of problems with a higher reliability.
The agent knowledge model is general and modular to support easy construction and addition of agents to any image processing task. Each agent in the system is further responsible for one type of high-level object and cooperates with other agents to come to a consistent overall image interpretation. Cooperation involves communicating hypotheses and resolving con icts between the interpretations of individual agents.
The system has been applied to IntraVascular UltraSound (IVUS) images which are segmented by ΓΏve agents, specialized in lumen, vessel, calciΓΏed-plaque, shadow and sidebranch detection. IVUS image sequences from 7 patients were processed and vessel and lumen contours were detected fully automatically. These were compared with expert-corrected semiautomatically detected contours. Results show good correlations between agents and expert with r = 0:84 for the lumen and r = 0:92 for the vessel cross-sectional areas, respectively.