Medical Computer Vision

1. Front Matter

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2. Shape, Geometry and Registration

   1. Conditional Point Distribution Models

      • Kersten Petersen, Mads Nielsen, Sami S. Brandt

      Pages 1-10

   2. Deformable Registration of Organic Shapes via Surface Intrinsic Integrals: Application to Outer Ear Surfaces

      • Sajjad Baloch, Alexander Zouhar, Tong Fang

      Pages 11-20

   3. Iterative Training of Discriminative Models for the Generalized Hough Transform

      • Heike Ruppertshofen, Cristian Lorenz, Sarah Schmidt, Peter Beyerlein, Zein Salah, Georg Rose et al.

      Pages 21-30

   4. Topology Noise Removal for Curve and Surface Evolution

      • Chao Chen, Daniel Freedman

      Pages 31-42

   5. Exploring Cortical Folding Pattern Variability Using Local Image Features

      • Rishi Rajalingham, Matthew Toews, D. Louis Collins, Tal Arbel

      Pages 43-53

3. Markov Models for Image Reconstruction and Analysis

   1. Surgical Phases Detection from Microscope Videos by Combining SVM and HMM

      • Florent Lalys, Laurent Riffaud, Xavier Morandi, Pierre Jannin

      Pages 54-62

   2. Motion Artifact Reduction in 4D Helical CT: Graph-Based Structure Alignment

      • Dongfeng Han, John Bayouth, Sudershan Bhatia, Milan Sonka, Xiaodong Wu

      Pages 63-73

   3. Comparative Validation of Graphical Models for Learning Tumor Segmentations from Noisy Manual Annotations

      • Frederik O. Kaster, Bjoern H. Menze, Marc-André Weber, Fred A. Hamprecht

      Pages 74-85

4. Automatic Anatomy Localization via Classification

   1. Localization of 3D Anatomical Structures Using Random Forests and Discrete Optimization

      • René Donner, Erich Birngruber, Helmut Steiner, Horst Bischof, Georg Langs

      Pages 86-95

   2. Detection of 3D Spinal Geometry Using Iterated Marginal Space Learning

      • B. Michael Kelm, S. Kevin Zhou, Michael Suehling, Yefeng Zheng, Michael Wels, Dorin Comaniciu

      Pages 96-105

   3. Regression Forests for Efficient Anatomy Detection and Localization in CT Studies

      • Antonio Criminisi, Jamie Shotton, Duncan Robertson, Ender Konukoglu

      Pages 106-117

   4. Correcting Misalignment of Automatic 3D Detection by Classification: Ileo-Cecal Valve False Positive Reduction in CT Colonography

      • Le Lu, Matthias Wolf, Jinbo Bi, Marcos Salganicoff

      Pages 118-129

   5. Learning Adaptive and Sparse Representations of Medical Images

      • Alessandra Staglianò, Gabriele Chiusano, Curzio Basso, Matteo Santoro

      Pages 130-140

   6. Feature Selection for SVM-Based Vascular Anomaly Detection

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

      Pages 141-152

5. Texture Analysis

   1. Multiple Classifier Systems in Texton-Based Approach for the Classification of CT Images of Lung

      • Mehrdad J. Gangeh, Lauge Sørensen, Saher B. Shaker, Mohamed S. Kamel, Marleen de Bruijne

      Pages 153-163

   2. Imaging as a Surrogate for the Early Prediction and Assessment of Treatment Response through the Analysis of 4-D Texture Ensembles (ISEPARATE)

      • Peter Maday, Parmeshwar Khurd, Lance Ladic, Mitchell Schnall, Mark Rosen, Christos Davatzikos et al.

      Pages 164-173

   3. A Texture Manifold for Curve-Based Morphometry of the Cerebral Cortex

      • Maxime Boucher, Alan Evans, Kaleem Siddiqi

      Pages 174-183

   4. Semisupervised Probabilistic Clustering of Brain MR Images Including Prior Clinical Information

      • Annemie Ribbens, Frederik Maes, Dirk Vandermeulen, Paul Suetens

      Pages 184-194

6. Segmentation

   1. Simultaneous Multi-object Segmentation Using Local Robust Statistics and Contour Interaction

      • Yi Gao, Allen Tannenbaum, Ron Kikinis

      Pages 195-203

This book constitutes the thoroughly refereed post-workshop proceedings of the International Workshop on Medical Computer Vision, MCV 2010, held in Beijing, China, in September 2010 as a satellite event of the 13th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2010. The 10 revised full papers and 11 revised poster papers presented were carefully reviewed and selected from 38 initial submissions. The papers explore the use of modern image recognition technology in tasks such as semantic anatomy parsing, automatic segmentation and quantification, anomaly detection and categorization, data harvesting, semantic navigation and visualization, data organization and clustering, and general-purpose automatic understanding of medical images.

• 3D imaging
• 3D shape modeling
• CT
• classification
• computer vision
• digital microscope
• discriminative training
• feature detection
• generalized hough transform
• graph cuts
• image analysis
• image registration
• machine learning
• neurosurgery
• object localization
• optimal model generation

• Computer Science and Artificial Intelligence Laboratory, MIT, USA

  Bjoern Menze

• CSAIL - Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, USA

  Georg Langs

• Laboratory of Neuroimaging, University of California, Los Angeles

  Zhuowen Tu

• Machine Learning and Perception Group, Microsoft Research Cambridge, UK

  Antonio Criminisi

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