An advanced fuzzy C-Means algorithm for the tissue segmentation from brain magnetic resonance images in the presence of noise and intensity inhomogeneity

Abstract

ABSTRACT Segmentation of brain Magnetic Resonance Images (MRIs) into various brain tissues such as white matter (WM), grey matter (GM), and cerebrospinal fluid (CSF) is very important to detect and diagnose different brain-related disorders at the primitive level. Accurate segmentation of brain MRIs is very difficult because of the intricate anatomical structure of the tissues, the existence of Intensity Inhomogeneity (IIH), noise, and Partial Volume Effects (PVE). Clustering-based methods are generally used to segment brain images. This work proposes a Chaotic based Enhanced Firefly Algorithm Integrated with Fuzzy C-Means (CEFAFCM) for the segmentation of brain tissues WM, GM, and CSF from brain MRIs. The proposed method can handle IIH, PVE, and noise. CEFAFCM is a spatially modified FCM algorithm combined with the Firefly Algorithm (FA) along with a chaotic map for the initialization of the population of fireflies. The algorithm is tested with brain MRIs acquired from the BrainWeb database. The experimental results demonstrate that the proposed technique is producing better results in comparison with some existing brain MRI segmentation methods such as FCM, BCFCM, FAFCM, and En-FAFCM.