Year: 2011
Type: dissertation
Abstract: Neurodegenerative diseases afflict a significant portion of the world's aging population, with Alzheimer's disease alone affecting close to 500,000 Canadians.Non-invasive magnetic resonance imaging has shown potential to detect structural changes at an earlier stage than neuropsychological or cognitive evaluations, however, the high degree of anatomical variability among our brains has proven to be a challenge in developing general computational methods.Neuroanatomical registration and segmentation are fundamental components of many structural and functional analysis techniques, thus advances here can lead to the development of biomarkers for early disease progression.This dissertation focuses on methods for improved accuracy and robustness using contextspecific anatomical guidance and locally-adaptive methods in registration, segmentation and brain morphometry.We first describe a subcortical brain mapping algorithm using I would like to first thank my senior supervisor, Dr. Faisal Beg, for introducing me to the world of brain imaging and for the immeasurable encouragement and support throughout my undergraduate and graduate years.I am also very grateful to Dr. Lei Wang for the constant mentorship and providing the many opportunities to apply my research.I am very thankful for my examining committee, Dr. Tony Herdman, Dr. Stella Atkins, and Dr. James Gee for taking the time and effort to provide valuable insight and a critical assessment of my work.I also thank the numerous collaborators around the world for providing the indispensable data and manual tracings which I am indebted to.My fellow students have been a great support throughout, engaging in stimulating discussions and collaborating on various projects.I am especially thankful for the mentorship of Dr. Aaron Ward and the discussions that
Source: Summit (Simon Fraser University)
Author: Ali R. Khan
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W1186595242