Fast brain MRI segmentation based on two-dimensional survival exponential entropy and particle swarm optimization

• Published in: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society Vol. 2007; pp. 5563 - 5566
• Main Authors: Nakib, A., Roman, S., Oulhadj, H., Siarry, P.
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 01.01.2007
• Subjects: Algorithms; Artificial Intelligence; Brain - anatomy & histology; Computational efficiency; Entropy; Histograms; Humans; Image analysis; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Image segmentation; Lesions; Magnetic analysis; Magnetic resonance; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Particle swarm optimization; Pattern Recognition, Automated - methods; Reproducibility of Results; Sensitivity and Specificity
• ISBN: 9781424407873; 1424407877
• ISSN: 1094-687X; 1557-170X
• DOI: 10.1109/IEMBS.2007.4353607

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.