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Medical Computer Vision. Large Data in Medical Imaging: Third International MICCAI Workshop, MCV 2013, Nagoya, Japan, September 26, 2013, Revised Selected Papers de Bjoern Menze

Medical Computer Vision. Large Data in Medical Imaging: Third International MICCAI Workshop, MCV 2013, Nagoya, Japan, September 26, 2013, Revised Selected Papers: Lecture Notes in Computer Science, cartea 8331

Editat de Bjoern Menze, Georg Langs, Albert Montillo, Michael Kelm, Henning Müller, Zhuowen Tu
en Limba Engleză Paperback – 10 apr 2014
This book constitutes the thoroughly refereed post-workshop proceedings of the Third International Workshop on Medical Computer Vision, MCV 2013, held in Nagoya, Japan, in September 2013 in conjunction with the 16th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013. The 7 revised full papers and 12 poster papers presented were selected from 25 submissions. They have been organized in topical sections on registration and visualization, segmentation, detection and localization, and features and retrieval. In addition, the volume contains two invited papers describing segmentation task and data set of the VISCERAL benchmark challenge.
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Specificații

ISBN-13: 9783319055299
ISBN-10: 3319055291
Pagini: 229
Ilustrații: XI, 229 p. 93 illus.
Dimensiuni: 155 x 235 x 20 mm
Greutate: 0.35 kg
Ediția:2014
Editura: Springer International Publishing
Colecția Springer
Seriile Lecture Notes in Computer Science, Image Processing, Computer Vision, Pattern Recognition, and Graphics

Locul publicării:Cham, Switzerland

Public țintă

Research

Cuprins

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.

Caracteristici

Includes supplementary material: sn.pub/extras