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, such
as medical images and computer vision. It is considered as a challenging problem, so we need to
develop an efficient, fast technique for medical image segmentation. In this paper, the proposed
framework is based on two segmentation methods: Fractional-order Darwinian Particle Swarm
Optimization (FODPSO) and Mean Shift segmentation (MS). FODPSO is a favorable method for
specifying a predefined number of clusters and it can find the optimal set of thresholds with a higher
between-class variance in less computational time. In the pre-processing phase,the MRI image is
filtered and the skull is removed. In the segmentation phase, the result of FODPSO is used as the input
to MS. Finally, we make a validation to thesegmented image. We compared our proposed system with
some state of the art segmentation techniques using brain benchmark data set. The experimental results
show that the proposed system enhances the accuracy of the MRI brain image segmentation.