Articoli correlati a Medical Computer Vision. Large Data in Medical Imaging:...
Medical Computer Vision. Large Data in Medical Imaging: Third International MICCAI Workshop, MCV 2013, Nagoya, Japan, September 26, 2013, Revised Selected Papers - Brossura
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Overview of the 2013 Workshop on Medical Computer Vision.- Semi-supervised Learning of Nonrigid Deformations for Image Registration.- Local Regression Learning via Forest Classification For 2D/3D Deformable Registration.- Flexible Architecture for Streaming and Visualization of Large Virtual Microscopy Images.- 2D-PCA Shape Models: Application to 3D Reconstruction of the Human Teeth from a Single Image.- Class-Specific Regression Random Forest for Accurate Extraction of Standard Planes from 3D Echocardiography.- Accurate Whole-Brain Segmentation for Alzheimer's Disease Combining an Adaptive Statistical Atlas and Multi-atlas.- Integrated Spatio-Temporal Segmentation of Longitudinal Brain Tumor Imaging Studies.- Robust Mixture-Parameter Estimation for Unsupervised Segmentation of Brain MR Images.- White Matter Supervoxel Segmentation by Axial DP-Means Clustering.- Semantic Context Forests for Learning-Based Knee Cartilage Segmentation in 3D MR Images.- Local Phase-Based Fast Ray Features for Automatic Left Ventricle Apical View Detection in 3D Echocardiography.- Automatic Aorta Detection in 3D Cardiac CT Images Using Bayesian Tracking Method.- Organ Localization Using Joint AP/LAT View Landmark Consensus Detection and Hierarchical Active Appearance Models.- Pectoral Muscle Detection in Digital Breast Tomosynthesis and Mammography.- Multilevel Image Feature Learning for Computer-Aided Diagnosis on Large-Scale Evaluation.- Shape Curvature Histogram: A Shape Feature for Celiac Disease Diagnosis.- 2D-Based 3D Volume Retrieval Using Singular Value Decomposition of Detected Regions.- Feature Extraction with Intrinsic Distortion Correction in Celiac Disease Imagery: No Need for Rasterization.- A Novel Shape Feature Descriptor for the Classification of Polyps in HD Colonoscopy.- Multi-Structure Atlas-Based Segmentation Using Anatomical Regions of Interest.- Using Probability Maps for Multi-organ Automatic Segmentation.
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