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Brain Extraction and Fuzzy Tissue Segmentation in Cerebral 2D T1-Weigthed Magnetic Resonance Images

Author(s): Bouchaib Cherradi | Omar Bouattane | Mohamed Youssfi | Abdelhadi Raihani

Journal: International Journal of Computer Science Issues
ISSN 1694-0784

Volume: 8;
Issue: 3;
Start page: 215;
Date: 2011;
Original page

Keywords: Clustering | Fuzzy c-means | histogram analysis | Brain Extraction | Image segmentation | IJCSI

In medical imaging, accurate segmentation of brain MR images is of interest for many brain manipulations. In this paper, we present a method for brain Extraction and tissues classification. An application of this method to the segmentation of simulated MRI cerebral images in three clusters will be made. The studied method is composed with different stages, first Brain Extraction from T1-weighted 2D MRI slices (TMBE) is performed as pre-processing procedure, then Histogram based centroids initialization is done, and finally the fuzzy c-means clustering algorithm is applied on the results to segment the image in three clusters. The introduction of this pre-processing procedure has been made in the goal to have a targeted segmentation method. The convergence speed for tissues classification has been considerably improved by avoiding a random initialization of the cluster centres and reduction of the volume of data processing.
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