Multimodal Neuroimaging Computing for the Characterization of Neurodegenerative Disorders

  • Sidong Liu

Part of the Springer Theses book series (Springer Theses)

Table of contents

  1. Front Matter
    Pages i-xxv
  2. Sidong Liu
    Pages 1-24
  3. Sidong Liu
    Pages 25-39
  4. Sidong Liu
    Pages 109-124
  5. Sidong Liu
    Pages 125-129
  6. Back Matter
    Pages 131-136

About this book

Introduction

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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Keywords

Brain Informatics Medical Image Computing Representation Learning Biomedical Pattern Analysis Computer-Aided Diagnosis ADNI Datasets Neurodegenerative Patterns Neuroimaging Content-Based Retrieval Brain Image Analysis Brain Connectome Brain Function Mapping

Authors and affiliations

  • Sidong Liu
    • 1
  1. 1.School of Information TechnologiesUniversity of Sydney School of Information TechnologiesSydneyAustralia

Bibliographic information

  • DOI https://doi.org/10.1007/978-981-10-3533-3
  • Copyright Information Springer Nature Singapore Pte Ltd. 2017
  • Publisher Name Springer, Singapore
  • eBook Packages Computer Science
  • Print ISBN 978-981-10-3532-6
  • Online ISBN 978-981-10-3533-3
  • Series Print ISSN 2190-5053
  • Series Online ISSN 2190-5061
  • About this book