Machine Learning in Medical Imaging

1. Random Forest-Based Manifold Learning for Classification of Imaging Data in Dementia

   • Katherine R. Gray, Paul Aljabar, Rolf A. Heckemann, Alexander Hammers, Daniel Rueckert

   Pages 159-166

2. Probabilistic Graphical Model of SPECT/MRI

   • Stefano Pedemonte, Alexandre Bousse, Brian F. Hutton, Simon Arridge, Sebastien Ourselin

   Pages 167-174

3. Directed Graph Based Image Registration

   • Hongjun Jia, Guorong Wu, Qian Wang, Yaping Wang, Minjeong Kim, Dinggang Shen

   Pages 175-183

4. Improving the Classification Accuracy of the Classic RF Method by Intelligent Feature Selection and Weighted Voting of Trees with Application to Medical Image Segmentation

   • Mohammad Yaqub, M. Kassim Javaid, Cyrus Cooper, J. Alison Noble

   Pages 184-192

5. Network-Based Classification Using Cortical Thickness of AD Patients

   • Dai Dai, Huiguang He, Joshua Vogelstein, Zengguang Hou

   Pages 193-200

6. Anatomical Regularization on Statistical Manifolds for the Classification of Patients with Alzheimer’s Disease

   • Rémi Cuingnet, Joan Alexis Glaunès, Marie Chupin, Habib Benali, Olivier Colliot

   Pages 201-208

7. Rapidly Adaptive Cell Detection Using Transfer Learning with a Global Parameter

   • Nhat H. Nguyen, Eric Norris, Mark G. Clemens, Min C. Shin

   Pages 209-216

8. Automatic Morphological Classification of Lung Cancer Subtypes with Boosting Algorithms for Optimizing Therapy

   • Ching-Wei Wang, Cheng-Ping Yu

   Pages 217-224

9. Hot Spots Conjecture and Its Application to Modeling Tubular Structures

   • Moo K. Chung, Seongho Seo, Nagesh Adluru, Houri K. Vorperian

   Pages 225-232

10. Fuzzy Statistical Unsupervised Learning Based Total Lesion Metabolic Activity Estimation in Positron Emission Tomography Images

    • Jose George, Kathleen Vunckx, Sabine Tejpar, Christophe M. Deroose, Johan Nuyts, Dirk Loeckx et al.

    Pages 233-240

11. Predicting Clinical Scores Using Semi-supervised Multimodal Relevance Vector Regression

    • Bo Cheng, Daoqiang Zhang, Songcan Chen, Dinggang Shen

    Pages 241-248

12. Automated Cephalometric Landmark Localization Using Sparse Shape and Appearance Models

    • Johannes Keustermans, Dirk Smeets, Dirk Vandermeulen, Paul Suetens

    Pages 249-256

13. A Comparison Study of Inferences on Graphical Model for Registering Surface Model to 3D Image

    • Yoshihide Sawada, Hidekata Hontani

    Pages 257-264

14. A Large-Scale Manifold Learning Approach for Brain Tumor Progression Prediction

    • Loc Tran, Deb Banerjee, Xiaoyan Sun, Jihong Wang, Ashok J. Kumar, David Vinning et al.

    Pages 265-272

15. Automated Detection of Major Thoracic Structures with a Novel Online Learning Method

    • Nima Tajbakhsh, Hong Wu, Wenzhe Xue, Jianming Liang

    Pages 273-281

16. Accurate Regression-Based 4D Mitral Valve Surface Reconstruction from 2D+t MRI Slices

    • Dime Vitanovski, Alexey Tsymbal, Razvan Ioan Ionasec, Michaela Schmidt, Andreas Greiser, Edgar Mueller et al.

    Pages 282-290

17. Tree Structured Model of Skin Lesion Growth Pattern via Color Based Cluster Analysis

    • Sina KhakAbi, Tim K. Lee, M. Stella Atkins

    Pages 291-299

18. Subject-Specific Cardiac Segmentation Based on Reinforcement Learning with Shape Instantiation

    • Lichao Wang, Su-Lin Lee, Robert Merrifield, Guang-Zhong Yang

    Pages 300-307

19. Faster Segmentation Algorithm for Optical Coherence Tomography Images with Guaranteed Smoothness

    • Lei Xu, Branislav Stojkovic, Hu Ding, Qi Song, Xiaodong Wu, Milan Sonka et al.

    Pages 308-316

20. Automated Nuclear Segmentation of Coherent Anti-Stokes Raman Scattering Microscopy Images by Coupling Superpixel Context Information with Artificial Neural Networks

    • Ahmad A. Hammoudi, Fuhai Li, Liang Gao, Zhiyong Wang, Michael J. Thrall, Yehia Massoud et al.

    Pages 317-325

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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