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Taking another lesson from nature, the latest advances in image
processing technology seek to combine image data from several
diverse types of sensors in order to obtain a more accurate view of
the scene: very much the same as we rely on our five senses.
Multi-Sensor Image Fusion and Its Applications is the first text
dedicated to the theory and practice of the registration and fusion
of image data, covering such approaches as statistical methods,
color-related techniques, model-based methods, and visual
information display strategies. After a review of state-of-the-art
image fusion techniques, the book provides an overview of fusion
algorithms and fusion performance evaluation. The following
chapters explore recent progress and practical applications of the
proposed techniques to solving problems in such areas as medical
diagnosis, surveillance and biometric systems, remote sensing,
nondestructive evaluation, blurred image restoration, and image
quality assessment. Recognized leaders from industry and academia
contribute the chapters, reflecting the latest research trends and
providing useful algorithms to aid implementation. Supplying a
28-page full-color insert, Multi-Sensor Image Fusion and Its
Applications clearly demonstrates the benefits and possibilities of
this revolutionary development. It provides a solid knowledge base
for applying these cutting-edge techniques to new challenges and
creating future advances.
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Multimodal Brain Image Analysis and Mathematical Foundations of Computational Anatomy - 4th International Workshop, MBIA 2019, and 7th International Workshop, MFCA 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings (Paperback, 1st ed. 2019)
Dajiang Zhu, Jingwen Yan, Heng Huang, Li Shen, Paul M. Thompson, …
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R1,557
Discovery Miles 15 570
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Ships in 10 - 15 working days
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This book constitutes the refereed joint proceedings of the 4th
International Workshop on Multimodal Brain Image Analysis, MBAI
2019, and the 7th International Workshop on Mathematical
Foundations of Computational Anatomy, MFCA 2019, held in
conjunction with the 22nd International Conference on Medical
Imaging and Computer-Assisted Intervention, MICCAI 2019, in
Shenzhen, China, in October 2019. The 16 full papers presented at
MBAI 2019 and the 7 full papers presented at MFCA 2019 were
carefully reviewed and selected. The MBAI papers intend to move
forward the state of the art in multimodal brain image analysis, in
terms of analysis methodologies, algorithms, software systems,
validation approaches, benchmark datasets, neuroscience, and
clinical applications. The MFCA papers are devoted to statistical
and geometrical methods for modeling the variability of biological
shapes. The goal is to foster the interactions between the
mathematical community around shapes and the MICCAI community
around computational anatomy applications.
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Graphs in Biomedical Image Analysis, Computational Anatomy and Imaging Genetics - First International Workshop, GRAIL 2017, 6th International Workshop, MFCA 2017, and Third International Workshop, MICGen 2017, Held in Conjunction with MICCAI 2017, Quebec City, QC, Canada, September 10-14, 2017, Proceedings (Paperback, 1st ed. 2017)
M. Jorge Cardoso, Tal Arbel, Enzo Ferrante, Xavier Pennec, Adrian Dalca, …
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R2,331
Discovery Miles 23 310
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Ships in 10 - 15 working days
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This book constitutes the refereed joint proceedings of the First
International Workshop on Graphs in Biomedical Image Analysis,
GRAIL 2017, the 6th International Workshop on Mathematical
Foundations of Computational Anatomy, MFCA 2017, and the Third
International Workshop on Imaging Genetics, MICGen 2017, held in
conjunction with the 20th International Conference on Medical
Imaging and Computer-Assisted Intervention, MICCAI 2017, in Quebec
City, QC, Canada, in September 2017. The 7 full papers presented at
GRAIL 2017, the 10 full papers presented at MFCA 2017, and the 5
full papers presented at MICGen 2017 were carefully reviewed and
selected. The GRAIL papers cover a wide range of graph based
medical image analysis methods and applications, including
probabilistic graphical models, neuroimaging using graph
representations, machine learning for diagnosis prediction, and
shape modeling. The MFCA papers deal with theoretical developments
in non-linear image and surface registration in the context of
computational anatomy. The MICGen papers cover topics in the field
of medical genetics, computational biology and medical imaging.
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Spatio-temporal Image Analysis for Longitudinal and Time-Series Image Data - Third International Workshop, STIA 2014, Held in Conjunction with MICCAI 2014, Boston, MA, USA, September 18, 2014, Revised Selected Papers (Paperback, 2015 ed.)
Stanley Durrleman, Tom Fletcher, Guido Gerig, Marc Niethammer, Xavier Pennec
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R1,302
Discovery Miles 13 020
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This book constitutes the thoroughly refereed post-conference
proceedings of the Third International Workshop on Spatio-temporal
Image Analysis for Longitudinal and Time-Series Image Data, STIA
2014, held in conjunction with MICCAI 2014 in Boston, MA, USA, in
September 2014. The 7 papers presented in this volume were
carefully reviewed and selected from 15 submissions. They are
organized in topical sections named: longitudinal registration and
shape modeling, longitudinal modeling, reconstruction from
longitudinal data, and 4D image processing.
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.
Beyond medical image computing, the methods described in this book
may also apply to other domains such as signal processing, computer
vision, geometric deep learning, and other domains where statistics
on geometric features appear. As such, the presented core
methodology takes its place in the field of geometric statistics,
the statistical analysis of data being elements of nonlinear
geometric spaces. The foundational material and the advanced
techniques presented in the later parts of the book can be useful
in domains outside medical imaging and present important
applications of geometric statistics methodology 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.
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