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- W2038061950 abstract "In this paper, a segmentation technique of multi-spectral magnetic resonance image of the brain using a new differential evolution based crisp clustering is proposed. Real-coded encoding of the cluster centres is used for this purpose. Here assignments of points to different clusters are made based on the Euclidean distance. The proposed method is applied on several simulated T1-weighted, T2-weighted and proton density for normal and MS lesion magnetic resonance brain images. Superiority of the proposed method over genetic algorithm based crisp clustering, simulated annealing based crisp clustering, K-means and average linkage are demonstrated quantitatively. Segmentation obtained by differential evolution based crisp clustering technique is also compared with the available ground truth information. Also statistical analysis has been conducted to judge the effectiveness. Matlab version of the software is available at http://bio.icm.edu.pl/~darman/MRI." @default.
- W2038061950 created "2016-06-24" @default.
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- W2038061950 date "2010-07-01" @default.
- W2038061950 modified "2023-10-18" @default.
- W2038061950 title "Real-coded differential crisp clustering for MRI brain image segmentation" @default.
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- W2038061950 doi "https://doi.org/10.1109/cec.2010.5586282" @default.
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