Abstract
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.
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References
Ashburner, J., Friston, K.: Multimodal image coregistration and partitioning - a unified framework. NeuroImage 6, 209–217 (1997)
Germond, L., Dojat, M., Taylor, C., Garbay, C.: A cooperative framework for segmentation of MRI brain scans. Artif. Intell. in Med. 20, 277–294 (2000)
Joshi, M., Cui, J., Doolittle, K., Joshi, S., Van Essen, D., Wang, L., Miller, M.I.: Brain segmentation and the generation of cortical surfaces. NeuroImage 9, 461–476 (1999)
Richard, N., Dojat, M., Garbay, C.: Situated Cooperative Agents: a Powerful Paradigm for MRI Brain Scans Segmentation. In: Van Harmelen, F. (ed.) ECAI 2002 Proceedings of the European Conference on Artificial Intelligence, Lyon, Fr, 21-26 July, pp. 33–37. IOS Press, Amsterdam (2002)
Shattuck, D.W., Sandor-Leahy, S.R., Schaper, K.A., Rottenberg, D.A., Leahy, R.M.: Magnetic resonance image tissue classification using a partial volume model. NeuroImage 13, 856–876 (2001)
Teo, P.C., Sapiro, G., Wandell, B.A.: Creating connected representations of cortical gray matter for functional MRI visualization. IEEE Trans. Med. Imag. 16, 852–863 (1997)
Van Leemput, K., Maes, F., Vandermeulen, D., Suetens, P.: Automated model-based tissue classification of MR images of the brain. IEEE Trans. Med. Imag. 18, 897–908 (1999)
Zhang, Y., Brady, M., Smith, S.: Segmentation of Brain MR images through a hidden Markov random field model and the expectation-maximisation algorithm. IEEE Trans. Med. Imag. 20, 45–57 (2001)
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© 2003 Springer-Verlag Berlin Heidelberg
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Richard, N., Dojat, M., Garbay, C. (2003). Multi-agent Approach for Image Processing: A Case Study for MRI Human Brain Scans Interpretation. In: Dojat, M., Keravnou, E.T., Barahona, P. (eds) Artificial Intelligence in Medicine. AIME 2003. Lecture Notes in Computer Science(), vol 2780. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39907-0_14
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DOI: https://doi.org/10.1007/978-3-540-39907-0_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20129-8
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