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Machine Learning in Medical Imaging: Second International Workshop, MLMI 2011, Held in Conjunction with MICCAI 2011, Toronto, Canada, September 18, 2011, Proceedings de Kenji Suzuki

Machine Learning in Medical Imaging: Second International Workshop, MLMI 2011, Held in Conjunction with MICCAI 2011, Toronto, Canada, September 18, 2011, Proceedings: Lecture Notes in Computer Science, cartea 7009

Editat de Kenji Suzuki, Fei Wang, Dinggang Shen, Pingkun Yan
en Limba Engleză Paperback – 13 sep 2011
This book constitutes the refereed proceedings of the Second International Workshop on Machine Learning in Medical Imaging, MLMI 2011, held in conjunction with MICCAI 2011, in Toronto, Canada, in September 2011. The 44 revised full papers presented were carefully reviewed and selected from 74 submissions. The papers focus on major trends in machine learning in medical imaging aiming to identify new cutting-edge techniques and their use in medical imaging.
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Specificații

ISBN-13: 9783642243189
ISBN-10: 3642243185
Pagini: 360
Ilustrații: XIII, 371 p.
Dimensiuni: 155 x 235 x 23 mm
Greutate: 0.54 kg
Ediția:2011
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seriile Lecture Notes in Computer Science, Image Processing, Computer Vision, Pattern Recognition, and Graphics

Locul publicării:Berlin, Heidelberg, Germany

Public țintă

Research

Textul de pe ultima copertă

This book constitutes the refereed proceedings of the Second International Workshop on Machine Learning in Medical Imaging, MLMI 2011, held in conjunction with MICCAI 2011, in Toronto, Canada, in September 2011. The 44 revised full papers presented were carefully reviewed and selected from 74 submissions. The papers focus on major trends in machine learning in medical imaging aiming to identify new cutting-edge techniques and their use in medical imaging.

Caracteristici

State-of-the-art research Fast-track conference proceedings Unique visibility

Cuprins

Transductive Prostate Segmentation for CT Image Guided Radiotherapy.- Model-Driven Centerline Extraction for Severely Occluded Major Coronary Arteries.- MRI Confirmed Prostate Tissue Classification with Laplacian Eigenmaps of Ultrasound RF Spectra.- Hierarchical Ensemble of Multi-level Classifiers for Diagnosis of Alzheimer’s Disease.- Dense Deformation Reconstruction via Sparse Coding.- Group Sparsity Constrained Automatic Brain Label Propagation.- Sparse Patch-Guided Deformation Estimation for Improved Image Registration.- Computer-Aided Detection of Aneurysms in 3D Time-of-Flight MRA Datasets.- Data Driven Constraints for the SVM.- Towards Improving the Accuracy of Sensorless Freehand 3D Ultrasound by Learning.- A Novel 3D Joint MGRF Framework for Precise Lung Segmentation.- Nonlinear Discriminant Graph Embeddings for Detecting White Matter Lesions in FLAIR MRI.- Use of Pattern-Information Analysis in Vision Science: A Pragmatic Examination.- Human Age Estimation with Surface-Based Features from MRI Images.- Biomedical Images Classification by Universal Nearest Neighbours Classifier Using Posterior Probability.- Simultaneous Registration and Segmentation by L1 Minimization.- On the Creation of Generic fMRI Feature Networks Using 3-D Moment Invariants.- Description and Classification of Confocal Endomicroscopic Images for the Automatic Diagnosis of Inflammatory Bowel Disease.- A Localized MKL Method for Brain Classification with Known Intra-class Variability.- Supervised Image Segmentation across Scanner Protocols: A Transfer Learning Approach.- Learning to Locate Cortical Bone in MRI.- Quality Classification of Microscopic Imagery with Weakly Supervised Learning.- Graph-Based Inter-subject Classification of Local fMRI Patterns.- Combining Multiple Image Segmentations by Maximizing Expert Agreement.- Cardiac LV and RV Segmentation Using Mutual Context Information.- Non-parametric Density Modeling and Outlier Detection in MedicalImaging Datasets.- Learning Correspondences in Knee MR Images from the Osteoarthritis Initiative.- Gradient Projection Learning for Parametric Nonrigid Registration.- Learning to Rank from Medical Imaging Data.- Integrating Statistical Shape Models into a Graph Cut Framework for Tooth Segmentation.- A Random Forest Based Approach for One Class Classification in Medical Imaging.- Finding Deformable Shapes by Correspondence-Free Instantiation and Registration of Statistical Shape Models.- Computer Aided Skin Lesion Diagnosis with Humans in the Loop. Model-Driven Centerline Extraction for Severely Occluded Major Coronary Arteries.- MRI Confirmed Prostate Tissue Classification with Laplacian Eigenmaps of Ultrasound RF Spectra.- Hierarchical Ensemble of Multi-level Classifiers for Diagnosis of Alzheimer’s Disease.- Dense Deformation Reconstruction via Sparse Coding.- Group Sparsity Constrained Automatic Brain Label Propagation.- Sparse Patch-Guided Deformation Estimation for Improved Image Registration.- Computer-Aided Detection of Aneurysms in 3D Time-of-Flight MRA Datasets.- Data Driven Constraints for the SVM.- Towards Improving the Accuracy of Sensorless Freehand 3D Ultrasound by Learning.- A Novel 3D Joint MGRF Framework for Precise Lung Segmentation.- Nonlinear Discriminant Graph Embeddings for Detecting White Matter Lesions in FLAIR MRI.- Use of Pattern-Information Analysis in Vision Science: A Pragmatic Examination.- Human Age Estimation with Surface-Based Features from MRI Images.- Biomedical Images Classification by Universal Nearest Neighbours Classifier Using Posterior Probability.- Simultaneous Registration and Segmentation by L1 Minimization.- On the Creation of Generic fMRI Feature Networks Using 3-D Moment Invariants.- Description and Classification of Confocal Endomicroscopic Images for the Automatic Diagnosis of Inflammatory Bowel Disease.- A Localized MKL Method for Brain Classification with Known Intra-classVariability.- Supervised Image Segmentation across Scanner Protocols: A Transfer Learning Approach.- Learning to Locate Cortical Bone in MRI.- Quality Classification of Microscopic Imagery with Weakly Supervised Learning.- Graph-Based Inter-subject Classification of Local fMRI Patterns.- Combining Multiple Image Segmentations by Maximizing Expert Agreement.- Cardiac LV and RV Segmentation Using Mutual Context Information.- Non-parametric Density Modeling and Outlier Detection in Medical Imaging Datasets.- Learning Correspondences in Knee MR Images from the Osteoarthritis Initiative.- Gradient Projection Learning for Parametric Nonrigid Registration.- Learning to Rank from Medical Imaging Data.- Integrating Statistical Shape Models into a Graph Cut Framework for Tooth Segmentation.- A Random Forest Based Approach for One Class Classification in Medical Imaging.- Finding Deformable Shapes by Correspondence-Free Instantiation and Registration of Statistical Shape Models.- Computer Aided Skin Lesion Diagnosis with Humans in the Loop.