Multi-agent Approach for Image Processing: A Case Study for MRI Human Brain Scans Interpretation

Image interpretation consists in finding a correspondence between radiometric information and symbolic labelling with respect to specific spatial constraints. To cope with the difficulty of image interpretation, several information processing steps are required to gradually extract information from the image grey levels and to introduce symbolic information. In this paper, we evaluate the use of situated cooperative agents as a framework for managing such steps. Dedicated agent behaviours are dynamically adapted function of their position in the image, topographic relationships and radiometric information available. Acquired knowledge is diffused to acquaintance and incremental refinement of interpretation is obtained through focalisation and coordination of agents tasks. Based on several experiments on real images we demonstrate the potential interest of multi-agents for MRI brain scans interpretation. 1 Modelling and Interpretation Processes Automatic interpretation of Magnetic Resonance Imaging (MRI) brain scans could greatly help clinicians and neuroscientists in decision making. Due to various image artefacts and in spite of several research efforts, this presently remains a challenging application. Based on several experiments, we demonstrate in this paper the potential interest of situated cooperative agents as a framework to manage the information processing steps, essentially modelling and interpretation via fusion mechanisms, required in this context.