MRI BRAIN IMAGE SEGMENTATION BASED ON CASCADED FRACTIONAL-ORDER DARWINIAN PARTICLE SWARM OPTIMIZATION AND MEAN SHIFT

Image segmentation is an initiative with massive interest in many imaging applications, suchas medical images and computer vision. It is considered as a challenging problem, so we need todevelop an efficient, fast technique for medical image segmentation. In this paper, the proposedframework is based on two segmentation methods: Fractional-order Darwinian Particle SwarmOptimization (FODPSO) and Mean Shift segmentation (MS). FODPSO is a favorable method forspecifying a predefined number of clusters and it can find the optimal set of thresholds with a higherbetween-class variance in less computational time. In the pre-processing phase,the MRI image isfiltered and the skull is removed. In the segmentation phase, the result of FODPSO is used as the inputto MS. Finally, we make a validation to thesegmented image. We compared our proposed system withsome state of the art segmentation techniques using brain benchmark data set. The experimental resultsshow that the proposed system enhances the accuracy of the MRI brain image segmentation.