Statistical and Computational Methods in Brain Image Analysis

by Moo K. Chung

Overview

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 illustratio

Product Details

ISBN-13:	9781040196199
Publisher:	CRC Press
Publication date:	07/23/2013
Series:	Chapman & Hall/CRC Mathematical and Computational Imaging Sciences Series
Sold by:	Barnes & Noble
Format:	eBook
Pages:	416
File size:	23 MB
Note:	This product may take a few minutes to download.

About the Author

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.

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.