Edge Detection Using Multi-Agent System: Evaluation on Synthetic and Medical MR Images
Recent developments on multi-agent system have brought a new research field on image processing. Several algorithms are used simultaneously and improved in deferent applications while new methods are investigated. This paper presents a new automatic method for edge detection using several agents and many different actions. The proposed multi-agent system is based on parallel agents that locally perceive their environment, that is to say, pixels and additional environmental information. This environment is built using Vector Field Convolution that attract free agent to the edges. Problems of partial, hidden or edges linking are solved with the cooperation between agents. The presented method was implemented and evaluated using several examples on different synthetic and medical images. The obtained experimental results suggest that this approach confirm the efficiency and accuracy of detected edge.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1124201Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1503
 S. J. Ros, N. Andarawis-Puri, E. L. Flatow, ‘‘Tendon extracellular matrix damage detection and quantiﬁcation using automated edge detection analysis’’ Journal of Biomechanics 46, pp: 2844–2847, 2013
 M. Kass, A. Witkin, D. Terzopoulos, Snakes: active contour models. Computer Vision (1), pp: 21–31, 1988.
 P. P. R. Filho, P. C. Cortez, A. C. S. Barros, V. H. Albuquerque ‘‘Novel Adaptive Balloon Active Contour Method based on internal force for image segmentation – A systematic evaluation on synthetic and real images’’ Expert Systems with Applications, pp: 7707—7721, 2014.
 A. K. Mohanty, M. R. Senapati, S. K. Lenka, A novel image mining technique for classiﬁcation of mammograms using hybrid feature selection. Neural Computing and Applications, 22(6), pp: 1151–1161, 2013.
 A. Nachour, L. Ouzizi, Y. Aoura, Multi-Agent 3D Reconstruction of Human Femur from MR Images. 15th International Conference on Intelligent Systems Design and Applications, 2015.
 T. Arai, H. Ogata, T. Suzuki, Collision avoidance among multiple robots using virtual impedance. In Proceedings of the IEEE/RSJ international conference on intelligent robots and systems. pp: 479–485, 1989.
 Z. Li, J. Liu. A multi-agent genetic algorithm for community detection in complex networks. Physica A: Statistical Mechanics and its Applications, 449. Pp: 336-347, 2016.
 A. Kasaiezadeh, A. Khajepour, Multi-agent stochastic level set method in image segmentation. Computer Vision and Image Understanding 117 pp: 1147–1162, 2013.
 A. Guillaud et al, ‘‘A Multiagent Systemfor Edge Detection and Continuity Perception on Fish Otolith Images’’ Journal on Applied Signal Processing, 7, pp: 746–753, 2002.
 B. Li, ‘‘Active Contour External Force Using Vector Field Convolution for Image Segmentation’’ IEEE transaction on image processing, 16, pp: 8, 2007.
 V. Rodin, F. Harrouet, P. Ballet, J. Tisseau, oRis: Multiagents Approach for Image Processing, in: H. Shi, P.C. CoJeld (Eds.), SPIE Conference on Parallel and Distributed Methods for Image Processing II, Vol. 3452, SPIE, San Diego, CA, pp. 57–68, 1998.
 J. Liu, Y.Y. Tang, Adaptative image segmentation with distributed behavior based agents, IEEE Trans. Pattern Anal. Mach. Intell. 6, pp: 544–551,1999.
 E.G.P. Bovenkamp, J. Dijkstra, J.G. Bosch, J.H.C. Reiber, Multi-agent segmentation of IVUS images. Pattern Recognition 37, pp: 647 – 663, 2004.
 H. Settache, C. Porquet, S. Ruan. Une plate-forme multi agents pour lasegmentation d’images: application dans le domaine des IRM cérébrales 2D. Technical report, Université de Caen, 2002.
 J. Fleureau, M. Garreau, D. Boulmier, C. Leclercq and A. Hernandez, Segmentation 3D multi-objets d’images scanner cardiaques: une approche multi-agent. In IRBM 30 pp:104-11, 2009.
 F. Bellet, Une approche incrémentale, coopérative et adaptative pour la segmentation des images en niveau de gris. Institut National Polytechnique de Grenoble, France, 1998.
 K. Yanai. An image understanding system for various images based on multi-agent architecture, December 1999.
 F. Bellifemine, A. Poggi, G. Rimassa, JADE A FIPA compliant agent framework, CSELT internal technical report. Proceedings of PAAM'99, London, pp: 97—108, 1999.
 FIPA: Foundation for Intelligent Physical Agents, Agent Communication Language, FIPA 99 Specification Draft, 1999.
 N. Paragios, O. Gotardo, V. Ramesh, Gradient vector flow fast geodesic active contours. pp 67–75.
 L. Je. Prince, C. Xu. Gradient Vector Flow: A New External Force Model for Snakes. In IEEE Image and Multidimensional Signal Processing Workshop, pp 30–31, 1996.