Riemannian Geometric Statistics in Medical Image Analysis

Xavier Pennec (1, 2) , Stephan Sommer (3) , Tom Fletcher (4)

1 COMUE UCA - COMUE Université Côte d'Azur (2015-2019)
2 EPIONE - E-Patient : Images, données & mOdèles pour la médeciNe numériquE
3 UCPH - University of Copenhagen = Københavns Universitet
4 University of Virginia

Over 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.

Domaines: Imagerie médicale; Géométrie algorithmique [cs.CG]; Géométrie différentielle [math.DG]; Autres [stat.ML]

Xavier Pennec, Stephan Sommer, Tom Fletcher (Dir.). Riemannian Geometric Statistics in Medical Image Analysis. Pennec, Xavier; Sommer, Stefan; Fletcher, Tom. Academic Press; Elsevier, 2020, ⟨10.1016/C2017-0-01561-6⟩. ⟨hal-02341896⟩