Abstract
There are several image segmentation algorithms; each one has its advantages and its limits. In this work, we aim to use the advantages of two algorithms, in a massive multi-agents environment. We use the FCM (Fuzzy C-Mean) algorithm, to manage uncertainty and imprecision and the Region Growing algorithm, to act locally on the image. The massive multi-agents paradigm is then introduced into the region growing process in order to improve the segmentation quality. However in some cases some defaults appear in the segmented image, we propose then the use of a double predicate for the Region Growing algorithm, through a massive cooperative process, in order to improve the quality of the segmented image. Massiveness of the system allows for a better quality analysis.
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Ahmed, M.N., Yamany, S.M., Farag, A.A., Moriarty, T.: A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data. IEEE Transactions on medical imaging 21(3), 193–199 (2002)
Chaib-draa, B.: Systèmes multi-agents principes généraux et applications, cours notes, computer department, Laval University, Canada (2001), http://www.damas.ift.ulaval.ca/~coursMAS/
Coquerez, J.-P., Philipp, S.: Analyse d’images: filtrage et segmentation, Masson (1995)
Dimitriadou, E., Barth, M., Windischberger, C., Hornik, K., Moser, E.: A quantitative comparison of functional MRI cluster analysis. Artificial Intelligence in Medicine 20 (2003)
Duchesnay, E.: Agents situés dans l’image et organisés en pyramide irrégulière: contribution à la segmentation par une approche d’agrégation coopérative et adaptative, PHD thesis of the Rennes-1 University (2001)
Efford, N.: Digital image processing a practical introduction using Java. Addison Wesley, Reading (2000)
Géraud, T.: Segmentation des structures internes du cerveau en imagerie par résonance magnétique, PHD thesis of the ENST Paris (1998)
Germond, L., Dojat, M., Taylor, C., Garbay, C.: A cooperative framework for segmentation of MRI brain scans. Artificial Intelligence in Medicine 20, 77–94 (2000)
Grekovs, R.: Methods of fuzzy pattern recognition. In: Scientific proceeding of RIGA technical university, Computer Science, Information Technology and Management Science (2002)
Gonzalez, R.C., Woods, R.E.: Digital image processing, p. 613. Addison-Wesley, Reading (2002)
Haroun, R., Hamami, L., Boumghar, F.: Segmentation d’images médicales IRM par un système hybride flou - croissance de régions, dans un système multi agents. Journées d’ETudes algéro-françaises en Imagerie Médicale, 21–30 (2004)
Jaggi, C.: Segmentation par méthode markovienne de l’encéphale humain par résonance magnétique: théorie, mise en oeuvre et évaluation. PHD thesis of the Caen University (1998)
Jennings, N.R., Wooldridge, M.: Applications of Agent Technology. In: Agent Technology: Foundation Applications and Markets. Springer, Heidelberg (1998)
Kastele, B., Vetter, D., Patay, Z., Germain, P.: Comprendre l’IRM, Masson (2001)
Liu, J., Tang, Y.Y., Cao, Y.C.: An evolutionary autonomous agents approach to image feature extraction. IEEE Trans. on Evolutionary Computation 1(2), 141–158 (1997)
Porquet, C., Settache, H., Ruan, S., Revenu, M.: Une plate-forme multi-agent pour la segmentation d’images. In: Etude des stratégies de coopération contour-région, ORASIS Géradmer, pp. 413–422 (2003)
Richard, N., Dojat, M., Garbay, C.: Multi-Agent approach for image processing for MRI human brain scans interpretation. In: 9th Conference on Artificial Intelligence in Medicine Europe (2003)
Rick, A.: Représentation de la variabilité dans le traitement d’images flou application à la mammographie numérique, PHD thesis of the Paris 6 University (1999)
Stao, K., Sugawara, K., Narita, Y., Namura, I.: Consideration of the method of image diagnosis with respect to frontal lobe atrophy. IEEE Transactions on nuclear science 43(6), 3230–3239 (1996)
Wooldrige, M.: Agent-based computing. Interoperable Communication Networks 1, 71–97 (1998)
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Haroun, R., Boumghar, F., Hassas, S., Hamami, L. (2005). A Massive Multi-agent System for Brain MRI Segmentation. In: Ishida, T., Gasser, L., Nakashima, H. (eds) Massively Multi-Agent Systems I. MMAS 2004. Lecture Notes in Computer Science(), vol 3446. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11512073_13
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DOI: https://doi.org/10.1007/11512073_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26974-8
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