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Statistical and Computational Methods in Brain Image Analysis.
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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hoboken :
CRC Press,
2013.
|
| Colección: | Chapman & Hall/CRC Mathematical and Computational Imaging.
|
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 049 | |a UAMI | ||
| 100 | 1 | |a Chung, Moo K. | |
| 245 | 1 | 0 | |a Statistical and Computational Methods in Brain Image Analysis. |
| 260 | |a Hoboken : |b CRC Press, |c 2013. | ||
| 300 | |a 1 online resource (432 pages). | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 490 | 1 | |a Chapman & Hall/CRC Mathematical and Computational Imaging | |
| 505 | 0 | |a 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. | |
| 505 | 8 | |a 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. | |
| 520 | |a 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. T. | ||
| 588 | 0 | |a Print version record. | |
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Brain mapping |x Statistical methods. | |
| 650 | 0 | |a Brain |x Imaging. | |
| 650 | 0 | |a Brain |x Imaging |x Statistical methods. | |
| 650 | 6 | |a Cartographie cérébrale |x Méthodes statistiques. | |
| 650 | 6 | |a Cerveau |x Imagerie. | |
| 650 | 6 | |a Cerveau |x Imagerie |x Méthodes statistiques. | |
| 650 | 7 | |a Brain |x Imaging |2 fast | |
| 758 | |i has work: |a Statistical and computational methods in brain image analysis (Text) |1 https://id.oclc.org/worldcat/entity/E39PCGhrfpHmQc9BtPHXJvQFVd |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a Chung, Moo K. |t Statistical and Computational Methods in Brain Image Analysis. |d Hoboken : CRC Press, ©2013 |z 9781439836354 |
| 830 | 0 | |a Chapman & Hall/CRC Mathematical and Computational Imaging. | |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=1222354 |z Texto completo |
| 938 | |a ProQuest Ebook Central |b EBLB |n EBL1222354 | ||
| 994 | |a 92 |b IZTAP | ||