Magnetic Resonance Brain Imaging Segmentation Based on Cascaded Fractional-Order Darwinian Particle Swarm Optimization and Mean Shift Clustering

Abstract

A digital image can be partitioned into multiple segments, which is known as image segmentation. There are many challenging problems for making image segmentation. Therefore, medical image segmentation technique is required to develop an efficient, fast diagnosis system. In this paper, we proposed a segmentation framework that is based on Fractional-order Darwinian Particle Swarm Optimization (FODPSO) and Mean Shift (MS) techniques. In pre-processing phase, MRI image is filtered, and the skull stripping is removed. In segmentation phase, the output of FODPSO is used as input to MS. Finally, we make a validation to the segmented image. The proposed system is compared with some segmentation techniques by using three standard datasets of MRI brain. For the first dataset, proposed system was achieved 99.45 % accuracy, whereas the DPSO was achieved 97.08 % accuracy. For the second dataset, the accuracy of the proposed system is 99.67 %, whereas the accuracy of DPSO is 97.08 %. Proposed system improves the accuracy of image segmentation of brain MRI as shown in the experimental results.