Novel Imaging Biomarker Extraction Method for Prognostic Applications in Neuroimaging

Biomarkers derived from brain magnetic resonance imaging have promise in being able to assist in the clinical diagnosis of brain pathologies. Imaging biomarkers are a compact representation of knowledge extracted from the medical images. They can be derived from the shape of a particular brain organ, or from the deformation of a region of interest or based on the clinical understanding of a disease. In this thesis, we present novel imaging biomarkers that demonstrate potential for prognostic applications in neuroimaging and dementia. One imaging biomarker is based on the graph-theoretic analysis of inter-regional co-variation in cortical thickness, while the other is based on the Laplacian Eigen decomposition of the segmentation of a brain organ. We test these features on three distinct, but related, classification problems i.e. early detection of Alzheimer disease (AD), differential diagnosis of AD and Frontotemporal disease, and earlier detection of AD via the sub-classification of multiple domain amnestic mild cognitive impairment (MCI). We also present a novel cross- validation method that can handle class imbalance, and present comprehensive analysis into their classification performance.