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© 2018

Pathological Brain Detection

  • Illustrates the complete process of developing a pathological brain detection system

  • Provides Matlab codes and Functions for most functions

  • Summarizes and reviews the state-of-the-art in the field of pathological brain detection

Book

Part of the Brain Informatics and Health book series (BIH)

Table of contents

  1. Front Matter
    Pages i-xxvi
  2. Shui-Hua Wang, Yu-Dong Zhang, Zhengchao Dong, Preetha Phillips
    Pages 1-11
  3. Shui-Hua Wang, Yu-Dong Zhang, Zhengchao Dong, Preetha Phillips
    Pages 13-28
  4. Shui-Hua Wang, Yu-Dong Zhang, Zhengchao Dong, Preetha Phillips
    Pages 29-44
  5. Shui-Hua Wang, Yu-Dong Zhang, Zhengchao Dong, Preetha Phillips
    Pages 45-70
  6. Shui-Hua Wang, Yu-Dong Zhang, Zhengchao Dong, Preetha Phillips
    Pages 71-84
  7. Shui-Hua Wang, Yu-Dong Zhang, Zhengchao Dong, Preetha Phillips
    Pages 85-104
  8. Shui-Hua Wang, Yu-Dong Zhang, Zhengchao Dong, Preetha Phillips
    Pages 105-118
  9. Shui-Hua Wang, Yu-Dong Zhang, Zhengchao Dong, Preetha Phillips
    Pages 119-147
  10. Shui-Hua Wang, Yu-Dong Zhang, Zhengchao Dong, Preetha Phillips
    Pages 149-178
  11. Shui-Hua Wang, Yu-Dong Zhang, Zhengchao Dong, Preetha Phillips
    Pages 179-190
  12. Shui-Hua Wang, Yu-Dong Zhang, Zhengchao Dong, Preetha Phillips
    Pages 191-210
  13. Back Matter
    Pages 211-214

About this book

Introduction

This book provides detailed practical guidelines on how to develop an efficient pathological brain detection system, reflecting the latest advances in the computer-aided diagnosis of structural magnetic resonance brain images. Matlab codes are provided for most of the functions described. In addition, the book equips readers to easily develop the pathological brain detection system further on their own and apply the technologies to other research fields, such as Alzheimer’s detection, multiple sclerosis detection, etc. 

Keywords

Pathological Brain Detection Machine Learning Artificial Intelligence Pattern Recognition Magnetic Resonance Imaging Image Normalization Wavelet Classifier Bioinspired Algorithm Deep Learning

Authors and affiliations

  1. 1.School of Electronics Science and EngineeringNanjing UniversityNanjingChina
  2. 2.Department of InformaticsUniversity of LeicesterLeicesterUnited Kingdom
  3. 3.Translational Imaging Division & MRI Unit, New York State Psychiatric InstituteColumbia University Medical CenterNew YorkUSA
  4. 4.Shepherd UniversityShepherdstownUSA

About the authors

Dr. Shui-Hua Wang received her B.S. degree from Southeast University in 2008 and an M.S. from the City University of New York in 2012. She received her Ph.D. from Nanjing University in 2016. She is currently an assistant professor at Nanjing Normal University, and a visiting researcher in University of Leicester, UK. She serves as the editor of Journal of Alzheimer’s disease. In the past, she served as the guest lead editor of International Journal of Biomedical Imaging and Multimedia Tools and Application.

 

Prof. Dr. Yu-Dong Zhang received his B.S. and M.S. degrees from Nanjing University of Aeronautics and Astronautics in 2004 and 2007. He received his Ph.D. degree in Signal and Information Processing from Southeast University in 2010. From 2010 to 2012, he worked at Columbia University as a postdoc. From 2012 to 2013, he worked as a research scientist at Columbia University and New York State Psychiatric Institute. From 2013 to 2017, he worked as a full professor and doctoral advisor at School of computer science and technology at Nanjing Normal University. From 2018, he is a full Professor in Department of Informatics, University of Leicester, UK. His research interests include biomedical image processing, pattern recognition, and artificial intelligence.

 

Prof. Dr. Zhengchao Dong is a former Associate Professor of Translational Imaging at Columbia University, USA. Having also served at New York State Psychiatry Institute, USA, he has published over 20 papers on JAMA Psychiatry, Progress in Nuclear Magnetic Resonance Spectroscopy, Neuropsychopharmacology, Neuroimages, Human Brain Mapping, etc.

 

Preetha Phillips is currently pursuing her Doctor of Osteopathic Medicine degree at West Virginia School of Osteopathic Medicine. She received her B.S. degree from Shepherd University in May 2016. She has conducted research on using magnetic resonance spectroscopy (MRS) to diagnose panic disorder, and on the analysis of pain mechanisms in rats.

Bibliographic information

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