Effective segmentation of white matter lesion based on roughset theory and Fuzzy clustering

Magnetic Resonance Imaging (MRI) is the state of art medical imaging technology that captures cross sectional view of the brain. Fluid Attenuation Inversion Recovery-FLAIR MRI is a pulse sequence used in MRI and together with T1, T2& PD images. The volume of White Matter Lesion (WML) is measured using Partial Volume Averaging (PVA) artifact. Change in white matter is related to carotid disease and chance of occurrence of stroke and related diseases can be predicted with subvoxel precision by volumetric analysis. To improve the quality of Segmentation of WML Multiple Kernel Fuzzy-C Means clustering (MKFCM) with rough set theory is used in the proposed work. Rough set theory is introduced with fuzzy classifier for obtaining better solution. Automatic segmentation is efficient and reliable than manual segmentation. The major advantage is the exact identification and diagnosis the related disease at the initial state.