Machine Learning in Medical Imaging - Third International Workshop, MLMI 2012, Held in Conjunction with MICCAI 2012, Nice, France, October 1, 2012, Revised Selected Papers

This book constitutes the refereed proceedings of the Third International Workshop on Machine Learning in Medical Imaging, MLMI 2012, held in conjunction with MICCAI 2012, in Nice, France, in October 2012.
The 33 revised full papers presented were carefully reviewed and selected from 67 submissions. The main aim of this workshop is to help advance the scientific research within the broad field of machine learning in medical imaging. It focuses on major trends and challenges in this area, and it presents work aimed to identify new cutting-edge techniques and their use in medical imaging.

List of contents

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.

Summary

This book constitutes the refereed proceedings of the Third International Workshop on Machine Learning in Medical Imaging, MLMI 2012, held in conjunction with MICCAI 2012, in Nice, France, in October 2012.
The 33 revised full papers presented were carefully reviewed and selected from 67 submissions. The main aim of this workshop is to help advance the scientific research within the broad field of machine learning in medical imaging. It focuses on major trends and challenges in this area, and it presents work aimed to identify new cutting-edge techniques and their use in medical imaging.

Product details

• Assisted by: Dinggan Shen (Editor), Dinggang Shen (Editor), Kenji Suzuki (Editor), Fei Wang (Editor), Pingkun Yan (Editor), Pingkun Yan et al (Editor)
• Publisher: Springer, Berlin
• Languages: English
• Product format: Paperback / Softback
• Released: 08.11.2012
• EAN: 9783642354274
• ISBN: 978-3-642-35427-4
• No. of pages: 276
• Dimensions: 155 mm x 236 mm x 15 mm
• Weight: 441 g
• Illustrations: XII, 276 p. 91 illus.
• Series: Lecture Notes in Computer Science; Image Processing, Computer Vision, Pattern Recognition, and Graphics; Lecture Notes in Computer Science; Image Processing, Computer Vision, Pattern Recognition, and Graphics
• Subject: Natural sciences, medicine, IT, technology > IT, data processing > Application software