Machine Learning in Medical Imaging

1. Front Matter

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2. Learning Statistical Correlation of Prostate Deformations for Fast Registration

   • Yonghong Shi, Shu Liao, Dinggang Shen

   Pages 1-9

3. Automatic Segmentation of Vertebrae from Radiographs: A Sample-Driven Active Shape Model Approach

   • Peter Mysling, Kersten Petersen, Mads Nielsen, Martin Lillholm

   Pages 10-17

4. Computer-Assisted Intramedullary Nailing Using Real-Time Bone Detection in 2D Ultrasound Images

   • Agnès Masson-Sibut, Amir Nakib, Eric Petit, François Leitner

   Pages 18-25

5. Multi-Kernel Classification for Integration of Clinical and Imaging Data: Application to Prediction of Cognitive Decline in Older Adults

   • Roman Filipovych, Susan M. Resnick, Christos Davatzikos

   Pages 26-34

6. Automated Selection of Standardized Planes from Ultrasound Volume

   • Bahbibi Rahmatullah, Aris Papageorghiou, J. Alison Noble

   Pages 35-42

7. Maximum Likelihood and James-Stein Edge Estimators for Left Ventricle Tracking in 3D Echocardiography

   • Engin Dikici, Fredrik Orderud

   Pages 43-50

8. A Locally Deformable Statistical Shape Model

   • Carsten Last, Simon Winkelbach, Friedrich M. Wahl, Klaus W. G. Eichhorn, Friedrich Bootz

   Pages 51-58

9. Monte Carlo Expectation Maximization with Hidden Markov Models to Detect Functional Networks in Resting-State fMRI

   • Wei Liu, Suyash P. Awate, Jeffrey S. Anderson, Deborah Yurgelun-Todd, P. Thomas Fletcher

   Pages 59-66

10. DCE-MRI Analysis Using Sparse Adaptive Representations

    • Gabriele Chiusano, Alessandra Staglianò, Curzio Basso, Alessandro Verri

    Pages 67-74

11. Learning Optical Flow Propagation Strategies Using Random Forests for Fast Segmentation in Dynamic 2D & 3D Echocardiography

    • Michael Verhoek, Mohammad Yaqub, John McManigle, J. Alison Noble

    Pages 75-82

12. A Non-rigid Registration Framework That Accommodates Pathology Detection

    • Chao Lu, James S. Duncan

    Pages 83-90

13. Segmentation Based Features for Lymph Node Detection from 3-D Chest CT

    • Johannes Feulner, S. Kevin Zhou, Matthias Hammon, Joachim Hornegger, Dorin Comaniciu

    Pages 91-99

14. Segmenting Hippocampus from 7.0 Tesla MR Images by Combining Multiple Atlases and Auto-Context Models

    • Minjeong Kim, Guorong Wu, Wei Li, Li Wang, Young-Don Son, Zang-Hee Cho et al.

    Pages 100-108

15. Texture Analysis by a PLS Based Method for Combined Feature Extraction and Selection

    • Joselene Marques, Erik Dam

    Pages 109-116

16. An Effective Supervised Framework for Retinal Blood Vessel Segmentation Using Local Standardisation and Bagging

    • Uyen T. V. Nguyen, Alauddin Bhuiyan, Kotagiri Ramamohanarao, Laurence A. F. Park

    Pages 117-125

17. Automated Identification of Thoracolumbar Vertebrae Using Orthogonal Matching Pursuit

    • Tao Wu, Bing Jian, Xiang Sean Zhou

    Pages 126-133

18. Segmentation of Skull Base Tumors from MRI Using a Hybrid Support Vector Machine-Based Method

    • Jiayin Zhou, Qi Tian, Vincent Chong, Wei Xiong, Weimin Huang, Zhimin Wang

    Pages 134-141

19. Spatial Nonparametric Mixed-Effects Model with Spatial-Varying Coefficients for Analysis of Populations

    • Juan David Ospina, Oscar Acosta, Gaël Dréan, Guillaume Cazoulat, Antoine Simon, Juan Carlos Correa et al.

    Pages 142-150

20. A Machine Learning Approach to Tongue Motion Analysis in 2D Ultrasound Image Sequences

    • Lisa Tang, Ghassan Hamarneh, Tim Bressmann

    Pages 151-158

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.

• artificial neural network
• computer assisted surgery
• graphical model
• multi-modality
• support vector machines
• algorithm analysis and problem complexity

• The University of Chicago, Chicago, USA

  Kenji Suzuki

• IBM Research Almaden, San Jose, USA

  Fei Wang

• School of Medicine, Department of Radiology and Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, USA

  Dinggang Shen

• Xian Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an, China

  Pingkun Yan

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