Author(s)

Hanane Barrah, Abdeljabbar Cherkaoui, Driss Sarsri

Laboratory of Innovative Technologies, National School of Applied Sciences, Abdelmalek Essadi University, Tangier, Morocco.

In the last decade, the MRI (Magnetic Resonance Imaging) image segmentation has become one of the most active research fields in the medical imaging domain. Because of the fuzzy nature of the MRI images, many researchers have adopted the fuzzy clustering approach to segment them. In this work, a fast and robust multi-agent system (MAS) for MRI segmentation of the brain is proposed. This system gets its robustness from a robust c-means algorithm (RFCM) and obtains its fastness from the beneficial properties of agents, such as autonomy, social ability and reactivity. To show the efficiency of the proposed method, we test it on a normal brain brought from the BrainWeb Simulated Brain Database. The experimental results are valuable in both robustness to noise and running times standpoints.

Agents and MAS, MR Images, Fuzzy Clustering, C-Means Algorithm, Image Segmentation

Barrah, H. , Cherkaoui, A. and Sarsri, D. (2016) Agent Based Segmentation of the MRI Brain Using a Robust C-Means Algorithm. Journal of Computer and Communications, 4, 13-21. doi: 10.4236/jcc.2016.410002.