Publication

Abstract : Alzheimer's Disease (AD) is an irreversible, progressive neurodegenerative disorder affecting a large population worldwide. Automated diagnosis of AD using Magnetic Resonance (MR) imaging-based biomarkers plays a crucial role in disease management. Compositional changes in cerebrospinal fluid due to AD might induce textural variations in Lateral Ventricles (LV) of the brain. In this work, an attempt has been made to differentiate Alzheimer's condition by quantifying the textural changes in LV using Kernel Density Estimation (KDE) technique. Reaction-Diffusion level set method is used to segment the LV from T1-weighted trans-axial brain MR images obtained from a publically available database. Spatial KDE is used to analyze the local intensity variations within the segmented LV. The optimal kernel function and bandwidth are selected for KDE. The statistical features such as mean, median, standard deviation, variance, kurtosis, skewness and entropy, representing the distribution of KDE values within LV, are evaluated. The extracted KDE-based statistical features show significant discrimination between normal and AD subjects (p< 0.01). An accuracy of 86.20% and sensitivity of 96% are obtained using SVM classifier. The results indicate that KDE seems to be a potential tool for analyzing the textural changes in brain, and thus can be clinically relevant for diagnosis of AD.