Statistical and computational methods in brain image analysis / Moo K. Chung.
Material type:
TextSeries: Chapman & Hall/CRC mathematical and computational imaging sciencesPublication details: Boca Raton : CRC Press, c2014.Description: xvi, 400 p. : illustrations ; 25 cmISBN: - 9781439836354 (hardback)
- 23 000SB:616.804754 C559
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Includes bibliographical references (pages 363-396) and index.
1. Introduction to brain and medical images --
2. Bernoulli models for binary images --
3. General linear models --
4. Gaussian kernel smoothing --
5. Random fields theory --
6. Anisotropic kernel smoothing --
7. Multivariate general linear models --
8.Cortical surface analysis --
9. Heat kernel smoothing on surfaces --
10. Cosine series representation of 3D curves --
11. Weighted spherical harmonic representation --
12. Multivariate surface shape analysis --
13. Laplace-Beltrami Eigenfunctions for surface data --
14. Persistent homology --
15. Sparse networks --
16. Sparse shape models --
17. Modeling structural brain networks --
18. Mixed effects models--
Bibliography--
Index.
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.
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