Magnetic resonance image segmentation based on two-dimensional exponential entropy and a parameter free PSO

Authors:
Amir Nakib;Yann Cooren;Hamouche Oulhadj;Patrick Siarry
Affiliations:
Université de Paris XII, Laboratoire Images, Signaux et Systèmes Intelligents, Créteil, France;Université de Paris XII, Laboratoire Images, Signaux et Systèmes Intelligents, Créteil, France;Université de Paris XII, Laboratoire Images, Signaux et Systèmes Intelligents, Créteil, France;Université de Paris XII, Laboratoire Images, Signaux et Systèmes Intelligents, Créteil, France
Venue:
EA'07 Proceedings of the Evolution artificielle, 8th international conference on Artificial evolution
Year:
2007

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Abstract

In this paper, a magnetic resonance image (MRI) segmentationmethod based on two-dimensional exponential entropy (2DEE) and parameterfree particle swarm optimization (PSO) is proposed. The 2DEE technique doesnot consider only the distribution of the gray level information but also takesadvantage of the spatial information using the 2D-histogram. The problem withthis method is its time-consuming computation that is an obstacle in real timeapplications for instance. We propose to use a parameter free PSO algorithmcalled TRIBES, that was proved efficient for combinatorial and non convexoptimization. The experiments on segmentation of MRI images proved that theproposed method can achieve a satisfactory segmentation with a lowcomputation cost.