Statistical and Computational Methods in Brain Image Analysis
Language: English
Published by Taylor & Francis Inc Jul 2013, 2013
Series: Book 3 of 8 - Chapman & Hall/CRC Mathematical and Computational Imaging Sciences
- Hardcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Seller Inventory # 9781439836354
- Title
- Statistical and Computational Methods in Brain Image Analysis
- Author
- Moo K. Chung
- Publisher
- Taylor & Francis Inc Jul 2013
- Publication year
- 2013
- Condition
- Neu
- Binding
- Buch
- Language
- English
- ISBN 10
- 1439836353
- ISBN 13
- 9781439836354
- Item weight
- 788 grams
- Dimensions
- 238x162x29 mm
- Series
- Book 3 of 8: Chapman & Hall/CRC Mathematical and Computational Imaging Sciences
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 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.
"Synopsis" may belong to another edition of this title.
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.
"About the title" may belong to another edition of this title.
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