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A01=Moo K. Chung
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Author_Moo K. Chung
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Statistical and Computational Methods in Brain Image Analysis

English

By (author): Moo K. Chung

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 authors 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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Current price €53.19
Original price €55.99
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A01=Moo K. ChungAge Group_UncategorizedAuthor_Moo K. Chungautomatic-updateCategory1=Non-FictionCategory=MQWCategory=PBTCategory=PSANCategory=TCOP=United KingdomDelivery_Pre-orderLanguage_EnglishPA=Not yet availablePrice_€50 to €100PS=Forthcomingsoftlaunch

Will deliver when available. Publication date 14 Oct 2024

Product Details
  • Weight: 800g
  • Dimensions: 156 x 234mm
  • Publication Date: 14 Oct 2024
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: United Kingdom
  • Language: English
  • ISBN13: 9781032919959

About Moo K. Chung

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 Editors 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.

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