Publications

Abstract : Alzheimer’s Disease (AD) is a progressive fatal neurodegenerative disorder that causes cognitive decline in affected people. Image processing of brain MR images can aid in identifying significant imaging biomarkers for detection of AD and its prodromal stage Mild Cognitive Impairment (MCI). Bidimensional multiscale entropy-based texture analysis is a new approach to quantify the textural variations in images at multiple scales. This work is based on the application of bidimensional multiscale entropy for analyzing AD induced textural alterations in lateral ventricles of the brain MR images. For this T1 weighted MR brain images of normal, MCI and AD subjects are obtained from public database. Lateral ventricles (LV) are delineated using reaction–diffusion level set technique from transaxial image slice with high accuracy. Bidimensional multiscale entropy is then applied on segmented LV to extract entropy features at multiple image scales and complexity indices are evaluated for each scale to study textural variations. The parameters such as tolerance factor, window lengths and scales for computation of multiscale entropy for significant differentiation amongst the healthy and diseased subjects are experimentally evaluated. The obtained entropy values from healthy subjects are observed to be significantly lower from the pathological subjects across scales. Classification with extracted features using a linear discriminant classifier achieves an accuracy of 80.1% and 87.6% for Normal vs MCI and Normal vs AD classes, respectively. The proposed multiscale entropy-based approach captures the textural alterations in lateral ventricles of brain MR images and furthermore, can be used as automated tool for early diagnosis of AD.