  Alzheimer's Disease (AD) is an irreversible neurodegenerative disorder
affecting millions of individuals today. The prognosis of the disease solely
depends on treating symptoms as they arise and proper caregiving, as there are
no current medical preventative treatments apart from newly developing drugs
which can, at most, slow the progression. Thus, early detection of the disease
at its most premature state is of paramount importance. This work aims to
survey imaging biomarkers corresponding to the progression of AD and also
reviews some of the existing feature extraction methods. A longitudinal study
of structural MR images was performed for given temporal test subjects with AD
selected randomly from the Alzheimer's Disease Neuroimaging Initiative (ADNI)
database. A pipeline was implemented to study the data, including modern
pre-processing techniques such as spatial image registration, skull stripping,
inhomogeneity correction and tissue segmentation using an unsupervised learning
approach using intensity histogram information. The temporal data across
multiple visits is used to study the structural change in volumes of these
tissue classes, namely, cerebrospinal fluid (CSF), grey matter (GM), and white
matter (WM) as the patients progressed further into the disease. To detect
changes in volume trends, we also analyse the data with a modified Mann-Kendall
statistic. The segmented features thus extracted and the subsequent trend
analysis provide insights such as atrophy, increase or intolerable shifting of
GM, WM and CSF and should help in future research for automated analysis of
Alzheimer's detection with clinical domain explainability.
