Fast brain MRI segmentation based on two-dimensional survival exponential entropy and particle swarm optimization.
In this paper, an MRI image segmentation method based on two-dimensional survival exponential entropy (2DSEE) and particle swarm optimization (PSO) is proposed. The 2DSEE technique does not consider only the cumulative distribution of the gray level information but also takes advantage of the spatial information using the 2D-histogram. The problem with this method is its time-consuming computation that is an obstacle in real time applications for instance. We propose to use PSO algorithm, that was proved very efficient for non convex and combinatorial optimization. The experiments on segmentation of MRI images proved that the proposed method can achieve a satisfactory segmentation with a low computation cost.
Nakib
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A
Laboratoire Images, Signaux et Systèmes Intelligents, Université de Paris XII, 61 avenue du Général De Gaulle 94010 Créteil France. nakib@univ-paris12.fr
Roman
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S
Oulhadj
H
H
Siarry
P
P
D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000033:anatomy & histology*; D019277:Entropy; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity