Dynamic Adaptation of Cooperative Agents for MRI Brain Scans Segmentation
To cope with the difficulty of MRI brain scans automatic segmentation, we need to constrain and control the selection and the adjustment of processing tools depending on the local image characteristics. To extract domain and control knowledge from the image, we propose to use situated cooperative agents whose dedicated behavior, i.e. segmentation of one type of tissue, is dynamically adapted with respect to their position in the image. Qualitative maps are used as a common framework to represent knowledge. Constraints that drive the agents behavior, based on topographic relationships and radiometric information, are gradually gained and refined during the segmentation progress. Incremental refinement of the segmentation is obtained through the combination, distribution and opposition of solutions concurrently proposed by the agents, via respectively three types of cooperation: integrative, augmentative and confrontational. We report in detail our multi-agent approach and results obtained on MRI brain scans.
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