The massive amount of nonstandard high-dimensional brain imaging data being generated is often difficult to analyze using current techniques. This challenge in brain image analysis requires new computational approaches and solutions. But none of the research papers or books in the field describe the quantitative techniques with detailed illustrations of actual imaging data and computer codes. Using MATLAB (R) and case study data sets, Statistical and Computational Methods in Brain Image Analysis is the first book to explicitly explain how to perform statistical analysis on brain imaging data.
The book focuses on methodological issues in analyzing structural brain imaging modalities such as MRI and DTI. Real imaging applications and examples elucidate the concepts and methods. In addition, most of the brain imaging data sets and MATLAB codes are available on the author's website.
By supplying the data and codes, this book enables researchers to start their statistical analyses immediately. Also suitable for graduate students, it provides an understanding of the various statistical and computational methodologies used in the field as well as important and technically challenging topics.
Rezensionen / Stimmen
"The writing style is pleasing and the book has the important virtue of using a consistent mathematical notation and terminology throughout the book, unlike collections of chapters from various authors that are usually published on this kind of topic. One important and interesting aspect of this book is the use of MATLAB code to illustrate the theory that the author is developing. In addition, the data mentioned in the text are provided so that the reader can experiment and learn using the same examples as the ones described in the book. This provides an excellent supplement and will appeal to students starting in the field as well as researchers wanting to refresh their knowledge or learn more about some aspects of brain analysis. ... a very good book to have in a lab, and it is a pleasure to recommend it."
-Australian & New Zealand Journal of Statistics, 56(4), 2014
"... a great new reference text to the field of structural brain imaging. The presence of MATLAB code will make it easy for people to play around with the various data formats and more easily get involved in this exciting field. As a researcher already involved in neuroimaging data analysis, I have a feeling that this is a book I will return to often as a reference source, and I am happy to have it as part of my library."
-Martin A. Lindquist, Journal of the American Statistical Association, September 2014, Vol. 109
Reihe
Sprache
Verlagsort
Verlagsgruppe
Zielgruppe
Für Beruf und Forschung
Academic
Produkt-Hinweis
Broschur/Paperback
Klebebindung
Illustrationen
167 s/w Abbildungen
167 Illustrations, black and white
Maße
Höhe: 234 mm
Breite: 156 mm
Gewicht
ISBN-13
978-1-032-91995-9 (9781032919959)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
Moo K. Chung, Ph.D. is an associate professor in the Department of Biostatistics and Medical Informatics at the University of Wisconsin-Madison. He is also affiliated with the Waisman Laboratory for Brain Imaging and Behavior. He has won the Vilas Associate Award for his applied topological research (persistent homology) to medical imaging and the Editor's Award for best paper published in Journal of Speech, Language, and Hearing Research. Dr. Chung received a Ph.D. in statistics from McGill University. His main research area is computational neuroanatomy, concentrating on the methodological development required for quantifying and contrasting anatomical shape variations in both normal and clinical populations at the macroscopic level using various mathematical, statistical, and computational techniques.
Introduction to Brain and Medical Images. Bernoulli Models for Binary Images. General Linear Models. Gaussian Kernel Smoothing. Random Fields Theory. Anisotropic Kernel Smoothing. Multivariate General Linear Models. Cortical Surface Analysis. Heat Kernel Smoothing on Surfaces. Cosine Series Representation of 3D Curves. Weighted Spherical Harmonic Representation. Multivariate Surface Shape Analysis. Laplace-Beltrami Eigenfunctions for Surface Data. Persistent Homology. Sparse Networks. Sparse Shape Models. Modeling Structural Brain Networks. Mixed Effects Models. Bibliography. Index.
