TITLE:
Distributed C-Means Algorithm for Big Data Image Segmentation on a Massively Parallel and Distributed Virtual Machine Based on Cooperative Mobile Agents
AUTHORS:
Fatéma Zahra Benchara, Mohamed Youssfi, Omar Bouattane, Hassan Ouajji, Mohammed Ouadi Bensalah
KEYWORDS:
Multi-Agent System, Distributed Algorithm, Big Data Image Segmentation, MRI Image, C-Means Algorithm, Mobile Agent
JOURNAL NAME:
Journal of Software Engineering and Applications,
Vol.8 No.3,
March
3,
2015
ABSTRACT: The aim of this paper is to present a
distributed algorithm for big data classification, and its application for
Magnetic Resonance Images (MRI) segmentation. We choose the well-known
classification method which is the c-means method. The proposed method is
introduced in order to perform a cognitive program which is assigned to be
implemented on a parallel and distributed machine based on mobile agents. The
main idea of the proposed algorithm is to execute the c-means classification
procedure by the Mobile Classification Agents (Team Workers) on different nodes
on their data at the same time and provide the results to their Mobile Host
Agent (Team Leader) which computes the global results and orchestrates the
classification until the convergence condition is achieved and the output
segmented images will be provided from the Mobile Classification Agents. The
data in our case are the big data MRI image of size (m × n) which is splitted
into (m × n) elementary images one per mobile classification agent to perform
the classification procedure. The experimental results show that the use of the
distributed architecture improves significantly the big data segmentation
efficiency.