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This thesis covers various facets of brain image computing methods and illustrates the scientific understanding of neurodegenerative disorders based on four general aspects of multimodal neuroimaging computing: neuroimaging data pre-processing, brain feature modeling, pathological pattern analysis, and translational model development. It demonstrates how multimodal neuroimaging computing techniques can be integrated and applied to neurodegenerative disease research and management, highlighting relevant examples and case studies. Readers will also discover a number of interesting extension topics in longitudinal neuroimaging studies, subject-centered analysis, and the brain connectome. As such, the book will benefit all health informatics postgraduates, neuroscience researchers, neurology and psychiatry practitioners, and policymakers who are interested in medical image computing and computer-assisted interventions.
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List of contents
Introduction.- Background.- Datasets and Pre-processing.- Neurodegenerative Feature Modeling and Learning.- Neurodegenerative Pattern Analysis.- Alzheimer's Disease Staging and Prediction.- Neuroimaging Content-Based Retrieval.- Conclusions and Future Directions.
About the author
Sidong Liu received his Bachelor Degree in Bioinformatics from Harbin Institute of Technology (HIT) in 2007. He then obtained a Master of Applied Science with a major in Bioinformatics in 2009, and a Master of IT with a major in Computer Science at the University of Sydney. He conducted his PhD study with a focus on medical image computing in the Biomedical and Multimedia Information Technology(BMIT) Research Group at the School of Information Technologies, the University of Sydney.
During his PhD study, supported by an Australian Postgraduate Award (APA),
Australia Alzheimer's Disease Research Foundation (AADRF) Top-up Scholarship
and Australia Sydney University Graduates Union North America (SUGUNA) Travel
Grant, he spent one year at the Surgical Planning Laboratory (SPL), Harvard Medical School, as a visiting scholar in 2014. He was awarded a PhD Degree in Dec 2015, and his PhD thesis has received the Springer Thesis Award. He is currently a postdoctoral researchfellow with School of Information Technologies, the University of Sydney. His research interests include neuroimage computing, computational neuroscience, biomedical and health informatics, machine learning and big data analytics and its applications in biomedicine.
Summary
This thesis covers various facets of brain image computing methods and illustrates the scientific understanding of neurodegenerative disorders based on four general aspects of multimodal neuroimaging computing: neuroimaging data pre-processing, brain feature modeling, pathological pattern analysis, and translational model development. It demonstrates how multimodal neuroimaging computing techniques can be integrated and applied to neurodegenerative disease research and management, highlighting relevant examples and case studies. Readers will also discover a number of interesting extension topics in longitudinal neuroimaging studies, subject-centered analysis, and the brain connectome. As such, the book will benefit all health informatics postgraduates, neuroscience researchers, neurology and psychiatry practitioners, and policymakers who are interested in medical image computing and computer-assisted interventions.
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