Patch-Based Techniques in Medical Imaging: Second International Workshop, Patch-MI 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 17, 2016, Proceedings: Lecture Notes in Computer Science, cartea 9993
Editat de Guorong Wu, Pierrick Coupé, Yiqiang Zhan, Brent C. Munsell, Daniel Rueckerten Limba Engleză Paperback – 22 sep 2016
The 17 regular papers presented in this volume were carefully reviewed and selected from 25 submissions.
The main aim of the Patch-MI 2016 workshop is to promote methodological advances within the medical imaging field, with various applications in image segmentation, image denoising, image super-resolution, computer-aided diagnosis, image registration, abnormality detection, and image synthesis.
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Specificații
ISBN-13: 9783319471174
ISBN-10: 3319471171
Pagini: 148
Ilustrații: X, 141 p. 45 illus.
Dimensiuni: 155 x 235 x 8 mm
Greutate: 2.41 kg
Ediția:1st ed. 2016
Editura: Springer International Publishing
Colecția Springer
Seriile Lecture Notes in Computer Science, Image Processing, Computer Vision, Pattern Recognition, and Graphics
Locul publicării:Cham, Switzerland
ISBN-10: 3319471171
Pagini: 148
Ilustrații: X, 141 p. 45 illus.
Dimensiuni: 155 x 235 x 8 mm
Greutate: 2.41 kg
Ediția:1st ed. 2016
Editura: Springer International Publishing
Colecția Springer
Seriile Lecture Notes in Computer Science, Image Processing, Computer Vision, Pattern Recognition, and Graphics
Locul publicării:Cham, Switzerland
Cuprins
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-Specific Embeddings, 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.
Caracteristici
Includes supplementary material: sn.pub/extras