000			a
999			_c29886 _d29886
008			191213b xxu||||| |||| 00| 0 eng d
020			_a9780128147252
082			_a616.075401516373 _bPEN
100			_aPennec, Xavier
245			_aRiemannian geometric statistics in medical image analysis
260			_bAcademic Press _c2020 _aLondon
300			_axix, 614 p. _bill. _c24 cm
365			_b125.00 _c75.10 _dUSD
520			_aOver the past 15 years, there has been a growing need in the medical image computing community for principled methods to process nonlinear geometric data. Riemannian geometry has emerged as one of the most powerful mathematical and computational frameworks for analyzing such data. Riemannian Geometric Statistics in Medical Image Analysis is a complete reference on statistics on Riemannian manifolds and more general nonlinear spaces with applications in medical image analysis. It provides an introduction to the core methodology followed by a presentation of state-of-the-art methods. Content includes: The foundations of Riemannian geometric methods for statistics on manifolds with emphasis on concepts rather than on proofs Applications of statistics on manifolds and shape spaces in medical image computing Diffeomorphic deformations and their applications As the methods described apply to domains such as signal processing (radar signal processing and brain computer interaction), computer vision (object and face recognition), and other domains where statistics of geometric features appear, this book is suitable for researchers and graduate students in medical imaging, engineering and computer science. A complete reference covering both the foundations and state-of-the-art methods Edited and authored by leading researchers in the field Contains theory, examples, applications, and algorithms Gives an overview of current research challenges and future applications.
650			_aDiagnostic imaging
650			_aDigital techniques
700			_aSommer, Stefan
700			_aFletcher, Tom
942			_2ddc _cBK