Automatic diagnostic system for segmentation of 3D/2D brain MRI images based on a hardware architecture

Abstract

Magnetic resonance imaging is among the advanced diagnostic testing tools for brain health issues. This method captures a series of detailed head images. These images are then printed and diagnosed by a specialist doctor to demonstrate differences in the brain tissue. Accordingly, additional diagnostic information can be given to determine the extent of the damage and the appropriate treatment methods. In this paper, and in order to facilitate the work of the specialist doctor and help him, we propose an automated hardware architecture for 3D/2D segmentation on MRI images to diagnose differences in brain tissue. For this, we used the metaheuristic technique based on Particle Swarm Optimization (PSO); for which we proposed improvements both for the velocity and position equations and for the fitness function. The goal of the work is to develop a real time automatic system for MRI images segmentation with improved metrics such as accuracy, sensitivity, specificity, dice metrics, execution time and resources utilization. The proposed hardware architecture was synthetized and then co-simulated using Matlab-Vivado System (VSM) for Field Programmable Gate Array (FPGA). Results show that our 3D segmentation method benefited from 2D segmentation with 95.39% accuracy rate and 87.97% DSC similarity (for 5-level segmentation) with 4.57 ms execution time for the case of BraTS 2013 dataset of brain MRI Images.