Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder that affects a large part of the aging population around the world. The disease progression is characterized by intermediate stages of early and late Mild Cognitive Impairment (MCI) followed by mild to severe AD. The morphological alterations of Lateral Ventricle (LV) and Corpus Callosum (CC) of the brain are considered as significant biomarkers for AD diagnosis. Magnetic Resonance Imaging (MRI) is a useful technique to identify the morphological alterations of the brain, for its high resolution and soft tissue contrast. Accurate segmentation of brain regions plays a vital role in identifying the morphological variations associated with the disease progression. Shape descriptors are essential in quantifying the alterations of brain structures associated with the disease progression. The aim of this research work is to study the shape variations of the LV and CC from T1 weighted brain MR images for the differentiation of control, MCI and AD conditions. In this, the desired regions of interest are segmented from the whole brain images and are validated. Further, imaging based shape features are employed to characterize the morphometry of these sub-anatomic brain regions. Machine learning algorithms are also used to differentiate the disease conditions. Results of the investigations carried out on the morphological alterations of LV and CC will be presented in this viva-voce seminar.