Medical Image Computing and Computer-Assisted Intervention - MICCAI 2011

1. Front Matter

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2. Computer Aided Diagnosis and Machine Learning I

   1. Sliding Window and Regression Based Cup Detection in Digital Fundus Images for Glaucoma Diagnosis

      • Yanwu Xu, Dong Xu, Stephen Lin, Jiang Liu, Jun Cheng, Carol Y. Cheung et al.

      Pages 1-8

   2. Learning from Only Positive and Unlabeled Data to Detect Lesions in Vascular CT Images

      • Maria A. Zuluaga, Don Hush, Edgar J. F. Delgado Leyton, Marcela Hernández Hoyos, Maciej Orkisz

      Pages 9-16

   3. Regularized Tensor Factorization for Multi-Modality Medical Image Classification

      • Nematollah Batmanghelich, Aoyan Dong, Ben Taskar, Christos Davatzikos

      Pages 17-24

   4. Detection, Grading and Classification of Coronary Stenoses in Computed Tomography Angiography

      • B. Michael Kelm, Sushil Mittal, Yefeng Zheng, Alexey Tsymbal, Dominik Bernhardt, Fernando Vega-Higuera et al.

      Pages 25-32

   5. Aggregated Distance Metric Learning (ADM) for Image Classification in Presence of Limited Training Data

      • Gaoyu Xiao, Anant Madabhushi

      Pages 33-40

   6. Sparse Classification for Computer Aided Diagnosis Using Learned Dictionaries

      • Meizhu Liu, Le Lu, Xiaojing Ye, Shipeng Yu, Marcos Salganicoff

      Pages 41-48

   7. A Discriminative-Generative Model for Detecting Intravenous Contrast in CT Images

      • Antonio Criminisi, Krishna Juluru, Sayan Pathak

      Pages 49-57

   8. Classification of Alzheimer’s Disease Using a Self-Smoothing Operator

      • Juan Eugenio Iglesias, Jiayan Jiang, Cheng-Yi Liu, Zhuowen Tu, the Alzheimers Disease Neuroimaging Initiative

      Pages 58-65

   9. An Analysis of Scale and Rotation Invariance in the Bag-of-Features Method for Histopathological Image Classification

      • S. Hussain Raza, R. Mitchell Parry, Richard A. Moffitt, Andrew N. Young, May D. Wang

      Pages 66-74

   10. Robust Large Scale Prone-Supine Polyp Matching Using Local Features: A Metric Learning Approach

       • Meizhu Liu, Le Lu, Jinbo Bi, Vikas Raykar, Matthias Wolf, Marcos Salganicoff

       Pages 75-82

   11. Targeted Optical Biopsies for Surveillance Endoscopies

       • Selen Atasoy, Diana Mateus, Alexander Meining, Guang-Zhong Yang, Nassir Navab

       Pages 83-90

   12. Focal Biologically Inspired Feature for Glaucoma Type Classification

       • Jun Cheng, Dacheng Tao, Jiang Liu, Damon Wing Kee Wong, Beng Hai Lee, Mani Baskaran et al.

       Pages 91-98

   13. The Relevance Voxel Machine (RVoxM): A Bayesian Method for Image-Based Prediction

       • Mert R. Sabuncu, Koen Van Leemput

       Pages 99-106

   14. Assessment of Regional Myocardial Function via Statistical Features in MR Images

       • Mariam Afshin, Ismail Ben Ayed, Kumaradevan Punithakumar, Max W. K. Law, Ali Islam, Aashish Goela et al.

       Pages 107-114

3. Computer Aided Diagnosis and Machine Learning II

   1. Identifying AD-Sensitive and Cognition-Relevant Imaging Biomarkers via Joint Classification and Regression

      • Hua Wang, Feiping Nie, Heng Huang, Shannon Risacher, Andrew J. Saykin, Li Shen et al.

      Pages 115-123

   2. Biological Indexes Based Reflectional Asymmetry for Classifying Cutaneous Lesions

      • Zhao Liu, Lyndon Smith, Jiuai Sun, Melvyn Smith, Robert Warr

      Pages 124-132

   3. Identification of Paediatric Tuberculosis from Airway Shape Features

      • Benjamin Irving, Pierre Goussard, Robert Gie, Andrew Todd-Pokropek, Paul Taylor

      Pages 133-140

   4. Automatic Region-of-Interest Segmentation and Pathology Detection in Magnetically Guided Capsule Endoscopy

      • Philip W. Mewes, Dominik Neumann, Oleg Licegevic, Johannes Simon, Aleksandar Lj. Juloski, Elli Angelopoulou

      Pages 141-148

   5. Simultaneous Segmentation and Grading of Hippocampus for Patient Classification with Alzheimer’s Disease

      • Pierrick Coupé, Simon F. Eskildsen, José V. Manjón, Vladimir Fonov, D. Louis Collins

      Pages 149-157

The three-volume set LNCS 6891, 6892 and 6893 constitutes the refereed proceedings of the 14th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2011, held in Toronto, Canada, in September 2011. Based on rigorous peer reviews, the program committee carefully selected 251 revised papers from 819 submissions for presentation in three volumes. The third volume includes 82 papers organized in topical sections on computer-aided diagnosis and machine learning, and segmentation.

• bioinformatics
• computer aided diagnosis
• image guided surgery
• medical image analysis
• visual simulation

• Queen’s University, Kingston, Canada

  Gabor Fichtinger

• Sunnybrook Hospital, Toronto, Canada

  Anne Martel

• Robarts Research Institute, London, Canada

  Terry Peters

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