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RIEMANNIAN GEOMETRIC STATISTICS IN MEDICAL IMAGE ANALYSIS: Materials and Biological Issues - Couverture souple

Pennec, Xavier

 
9780128147252: RIEMANNIAN GEOMETRIC STATISTICS IN MEDICAL IMAGE ANALYSIS: Materials and Biological Issues

Synopsis

Dental Implants and Bone Grafts: Materials and Biological Issues brings together cutting-edge research to provide detailed coverage of biomaterials for dental implants and bone graft, enabling scientists and clinicians to gain a thorough knowledge of advances and applications in this field. As tooth loss and alveolar bony defects are common and pose a significant health problem in dental clinics, this book deals with timely topics, including alveolar bone structures and pathological changes, reviews of indications and advantages of biomaterials for dental implants and bone graft, design and surface modification, biological interaction and biocompatibility of modern dental implants and bone graft, and new frontiers. This book is a highly valuable resource for scientists, clinicians and implantologists interested in biomaterial and regenerative strategies for alveolar bone reconstruction.

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À propos des auteurs

Xavier Pennec's research interest is at the intersection of statistics, differential geometry, computer science and medicine. He is particularly interested in the mathematics involved in computational anatomy: geometric statistics involving statistical computing on Riemannian manifolds and other geometric structures . He has contributed mathematically grounded methods and algorithms for medical image registration, statistics on shapes, and their translation to clinical research applications.

Stefan Sommer's research focus is on modeling and statistics of non-linear data with application to shape spaces, functional data analysis, and image registration. This includes foundational and algorithmic aspects of statistics on manifold valued data, and computational modeling and statistical analysis of deformations occurring in computational anatomy.

Tom Fletcher's research focus is on solving problems in medical image analysis and computer vision through the combination of statistics and differential geometry

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