Graph theory based algorithm for magnetic resonance brain images segmentation

Image segmentation is often required as a preliminary and indispensable stage in the computer aided medical image process, particularly during the clinical analysis of magnetic resonance (MR) brain images. The segmentation of magnetic resonance image (MRI) is a challenging problem that has received an enormous amount of attention lately. In this paper, we propose a simple and effective segmentation method combining watershed algorithm and normalized cuts (CWNC) for MR brain images. An initial partitioning of the MRI into primitive regions is set by applying the watershed transform. The latter process uses a region similarity graph representation of the image regions. And then the graph is segmented by normalized cuts algorithm. The efficacy of the proposed algorithm is demonstrated by extensive segmentation experiments using both simulated and real MR images and by comparison with other published algorithms.