MRI brain segmentation based on improved global best-guided artificial bee colony

Abstract

Abstract Brain Magnetic Resonance Imaging (MRI) plays a critical role in medical research and clinical applications, ranging from quantifying tissue volume, facilitating surgical simulations, assisting in treatment planning, enabling brain mapping, aiding in disease diagnosis, and evaluating therapeutic efficacy. This study introduces a novel method for MRI brain segmentation, which harnesses the power of a hybrid approach combining Artificial Bee Colony (ABC) algorithm with Fuzzy C-Means (FCM) clustering. Our approach leverages the exploration capability of the ABC algorithm, with an improved global best guidance (IABC), to optimally initialize the cluster centroid values of the FCM, thus enhancing the segmentation outputs. Comparative evaluation of the proposed method, denoted as IABC-FCM, conducted on a diverse set of MRI brain images, reveals its superior performance. The results indicate the potential of this hybrid approach as a robust tool for improved MRI brain segmentation tasks.