A computational model based on cooperative mobile agents for big data image segmentation

The aim of this paper is to present interesting results obtained by implementing big data classification algorithm using a cooperative mobile agents model. In this work we focused on the application of this classification for Magnetic Resonance Images (MRI) segmentation which is presented based on parallel fine grained c-means algorithm. This algorithm is converged to distributed classification c-means algorithm in order to be implemented on a parallel and distributed virtual machine based on mobile agents. The big data MRI image to be processed is split into small elementary images by the host agent. The obtained list of item images is distributed to mobile agents deployed on each node of the distributed system. Each agent is asked to perform the execution of the c-means algorithm using its assigned image item and return its elementary results to the host agent which computes the current global class centers and newly distributes them. This process is repeated until the convergence of the distributed algorithm. The experimental results show that the distributed classification method can improve the big data segmentation.