A soft-computing based hybrid tool to extract the tumour section from brain MRI

Abstract

In recent days, examination of medical images had been carried out using a number of image processing tools, specifically implemented for such purposes. This proposed work is based on a hybrid image processing technique focuses on extracting the tumour section from the brain Magnetic-Resonance-Image (MRI) recorded with various MR sequences. The proposed technique aims to identify the best possible image processing methodology for brain MRI investigation and subsequently to extract the tumour section for clinical setting. For exploring the proposed technique, most popular Radiopedia database, BraTS 2015 dataset is primarily considered for the assessment and later, real time clinical brain MRI slices are investigated. The proposed work implements Shannon Entropy (SE) objective function assisted with Social Group Optimization (SGO) algorithm to enhance the image. The results produced by SGO are compared with the other heuristic approaches like the Firefly-Algorithm (FA), Bat-Algorithm (BA) and Differential-Evolution (DE). Then Distance-Regularized-Level-Set (DRLS) segmentation technique is performed for extracting the tumour part from the enhanced slices. Further, the segmentation comparison of DRLS against traditional Active-Contour (AC) is also adopted for the evaluation. This integrated approach offers better picture-similarity-measures (PSM) compared with the alternatives.