Introduction

Xavier Pennec; Stefan Sommer; Tom Fletcher     University Côte d'Azur and Inria, Sophia Antipolis, FranceDIKU, University of Copenhagen, Copenhagen, DenmarkUniversity of Virginia, Charlottesville, VA, United States

Introduction

Over the last two decades, there has been a growing need in the medical image computing community for principled methods to process nonlinear geometric data. Typical examples of data in this domain include organ shapes and deformations resulting from segmentation and registration in computational anatomy, and symmetric positive definite matrices in diffusion imaging. In this context, Riemannian geometry has gradually been established as one the most powerful mathematical and computational paradigms.

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