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Author:   Guorong Wu ,  Pierrick Coupé ,  Yiqiang Zhan ,  Brent C. Munsell
Publisher:   Springer International Publishing AG
Imprint:   Springer International Publishing AG
Edition:   1st ed. 2016
Volume:   9993
Dimensions:   Width: 15.50cm , Height: 0.80cm , Length: 23.50cm
Weight:   2.409kg
ISBN:  

9783319471174

ISBN 10:   3319471171
Pages:   141
Publication Date:   22 September 2016
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand  
We will order this item for you from a manufactured on demand supplier.

Table of Contents

Automatic Segmentation of Hippocampus for Longitudinal Infant Brain MR Image Sequence by Spatial-Temporal Hypergraph Learning.- Construction of Neonatal Diffusion Atlases via Spatio-Angular Consistency.- Selective Labeling: identifying representative sub-volumes for interactive segmentation.- Robust and Accurate Appearance Models based on Joint Dictionary Learning: Data from the Osteoarthritis Initiative.- Consistent multi-atlas hippocampus segmentation for longitudinal MR brain images with temporal sparse representation.- Sparse-Based Morphometry: Principle and Application to Alzheimer’s Disease.- Multi-Atlas Based Segmentation of Brainstem Nuclei from MR Images by Deep Hyper-Graph Learning.- Patch-Based Discrete Registration of Clinical Brain Images.- Non-local MRI Library-based Super-resolution: Application to Hippocampus Subfield Segmentation.- Patch-based DTI grading: Application to Alzheimer's disease classification.- Hierarchical Multi-Atlas Segmentation using Label-SpecificEmbeddings, Target-Specific Templates and Patch Refinement.- HIST: HyperIntensity Segmentation Tool.- Supervoxel-Based Hierarchical Markov Random Field Framework for Multi-Atlas Segmentation.- CapAIBL: Automated reporting of cortical PET quantification without need of MRI on brain surface using a patch-based method.- High resolution hippocampus subfield segmentation using multispectral multi-atlas patch-based label fusion.- Identification of water and fat images in Dixon MRI using aggregated patch-based convolutional neural networks.- Estimating Lung Respiratory Motion Using Combined Global and Local Statistical Models.

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