Automated Brain Tumor Segmentation and Detection in MRI Using Enhanced Darwinian Particle Swarm Optimization(EDPSO)

Abstract

Medical Image segmentation is the most challenging problems in the research field of MRI scan analysis. Automated brain tumor segmentation and detection are eminently important in medical diagnostics because it provides information related to functional structures as well as potential abnormal tissue necessary to demarcate surgical plan. But automatic tumor segmentation is still challenging because of low contrast and ill-defined boundaries and accuracy problem. Therefore Enhanced Darwinian Particle Swarm Optimization (EDPSO) is proposed for automated tumor segmentation which overcomes the drawback of existing Particle Swarm Optimization(PSO). This innovative method consists of four steps. First step is pre-processing, film artifacts and unwanted portions of MRI images are removed using tracking algorithm. Second step involves the process of removing the noises and high frequency component using Gaussian filter. Third step, segmentation is done using Darwinian Particle Swarm Optimization and Fourth step is classification, which is done by Adaptive Neuro Fuzzy Inference System. The performance of the proposed method is systematically evaluated using the MRI brain images.