Riemannian Geometric Statistics in Medical Image Analysis /
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 Geometr...
| Main Authors: | , , , |
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| Format: | software, multimedia |
| Language: | eng |
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San Diego : Academic Press,
2020
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| Subjects: | |
| Online Access: | https://www.sciencedirect.com/book/9780128147252 |
| _version_ | 1826472412233859072 |
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| author | Pennec, Xavier, editor 649121 Sommer, Stefan, editor 649122 Fletcher, Tom, editor 649123 ScienceDirect (Online service) 7722 |
| author_facet | Pennec, Xavier, editor 649121 Sommer, Stefan, editor 649122 Fletcher, Tom, editor 649123 ScienceDirect (Online service) 7722 |
| author_sort | Pennec, Xavier, editor 649121 |
| collection | OCEAN |
| description | 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. |
| first_indexed | 2024-03-05T17:18:07Z |
| format | software, multimedia |
| id | KOHA-OAI-TEST:605737 |
| institution | Universiti Teknologi Malaysia - OCEAN |
| language | eng |
| last_indexed | 2024-03-05T17:18:07Z |
| publishDate | 2020 |
| publisher | San Diego : Academic Press, |
| record_format | dspace |
| spelling | KOHA-OAI-TEST:6057372023-10-04T07:57:28ZRiemannian Geometric Statistics in Medical Image Analysis / Pennec, Xavier, editor 649121 Sommer, Stefan, editor 649122 Fletcher, Tom, editor 649123 ScienceDirect (Online service) 7722 software, multimedia Electronic books 631902 San Diego : Academic Press,2020©2020engOver 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.Includes index.Part 1 Foundations of geometric statistics 1. Introduction to differential and Riemannian geometry 2. Statistics on manifolds 3. Manifold-valued image processing with SPD matrices 4. Riemannian geometry on shapes and diffeomorphisms 5. Beyond Riemannian geometry -- Part 2 Statistics on manifolds and shape spaces 6. Object shape representation via skeletal models (s-reps) and statistical analysis 7. Efficient recursive estimation of the Riemannian barycenter on the hypersphere and the special orthogonal group with applications 8. Statistics on stratified spaces 9. Bias on estimation in quotient space and correction methods 10. Probabilistic approaches to geometric statistics 11. On shape analysis of functional data -- Part 3 Deformations, diffeomorphisms and their applications 12. Fidelity metrics between curves and surfaces: currents, varifolds, and normal cycles 13. A discretize–optimize approach for LDDMM registration 14. Spatially adaptive metrics for diffeomorphic image matching in LDDMM 15. Low-dimensional shape analysis in the space of diffeomorphisms 16. Diffeomorphic density registrationOver 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.Diagnostic imaginghttps://www.sciencedirect.com/book/9780128147252URN:ISBN:9780128147252Remote access restricted to users with a valid UTM ID via VPN. |
| spellingShingle | Diagnostic imaging Pennec, Xavier, editor 649121 Sommer, Stefan, editor 649122 Fletcher, Tom, editor 649123 ScienceDirect (Online service) 7722 Riemannian Geometric Statistics in Medical Image Analysis / |
| title | Riemannian Geometric Statistics in Medical Image Analysis / |
| title_full | Riemannian Geometric Statistics in Medical Image Analysis / |
| title_fullStr | Riemannian Geometric Statistics in Medical Image Analysis / |
| title_full_unstemmed | Riemannian Geometric Statistics in Medical Image Analysis / |
| title_short | Riemannian Geometric Statistics in Medical Image Analysis / |
| title_sort | riemannian geometric statistics in medical image analysis |
| topic | Diagnostic imaging |
| url | https://www.sciencedirect.com/book/9780128147252 |
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