Statistical and Computational Methods in Brain Image Analysis
Author:   Moo K. Chung
Publisher:   Taylor & Francis Inc
Volume:   1
ISBN:  

9781439836354


Pages:   432
Publication Date:   23 July 2013
Format:   Hardback
Availability:   In Print   Availability explained
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Statistical and Computational Methods in Brain Image Analysis


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Full Product Details

Author:   Moo K. Chung
Publisher:   Taylor & Francis Inc
Imprint:   CRC Press Inc
Volume:   1
Dimensions:   Width: 15.60cm , Height: 1.80cm , Length: 23.40cm
Weight:   0.930kg
ISBN:  

9781439836354


ISBN 10:   1439836353
Pages:   432
Publication Date:   23 July 2013
Audience:   Professional and scholarly ,  General/trade ,  Professional & Vocational ,  General
Format:   Hardback
Publisher's Status:   Active
Availability:   In Print   Availability explained
This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us.

Table of Contents

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.

Reviews

...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


Author Information

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.

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