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[null,"1.","Record Nr.",null,"UNINA9910786825803321"]
[null,null,"Autore",null,"Chung Moo K."]
[null,null,"Titolo",null,"Statistical and computational methods in brain image analysis / / Moo K. Chung"]
[null,null,"Pubbl/distr/stampa",null,"Boca Raton : , : CRC Press, , 2014"]
[null,null,"ISBN",null,"0-429-09432-9"]
[{"$nulls":4},"1-4398-3636-1"]
[null,null,"Descrizione fisica",null,"1 online resource (432 p.)"]
[null,null,"Collana",null,"Chapman & Hall/CRC mathematical and computational imaging sciences series"]
[null,null,"Classificazione",null,"MAT029000SCI089000TEC059000"]
[null,null,"Disciplina",null,"612.82"]
[null,null,"Soggetti",null,"Brain - Imaging"]
[{"$nulls":4},"Brain - Imaging - Statistical methods"]
[{"$nulls":4},"Brain mapping - Statistical methods"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","\"A Chapman & Hall book.\""]
[null,null,null,"Nota di bibliografia","Includes bibliographical references."]
[null,null,"Nota di contenuto",null,"Front Cover; Contents; Preface; Chapter 1: Introduction to Brain and Medical Images; Chapter 2: Bernoulli Models for Binary Images; Chapter 3: General Linear Models; Chapter 4: Gaussian Kernel Smoothing; Chapter 5: Random Fields Theory; Chapter 6: Anisotropic Kernel Smoothing; Chapter 7: Multivariate General Linear Models; Chapter 8: Cortical Surface Analysis; Chapter 9: Heat Kernel Smoothing on Surfaces; Chapter 10: Cosine Series Representation of 3D Curves; Chapter 11: Weighted Spherical Harmonic Representation; Chapter 12: Multivariate Surface Shape Analysis"]
[{"$nulls":4},"Chapter 13: Laplace-Beltrami Eigenfunctions for Surface DataChapter 14: Persistent Homology; Chapter 15: Sparse Networks; Chapter 16: Sparse Shape Models; Chapter 17: Modeling Structural Brain Networks; Chapter 18: Mixed Effects Models; Bibliography; Color Insert; Back Cover"]
[null,null,null,"Sommario/riassunto","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 and case study data sets, Statistical and Computational Methods in Brain Image Analysis is the first book to explicitly explain "]
[{"$nulls":4},"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.--"]