Computer Vision Approaches to Medical Image Analysis

1. Front Matter

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2. Clinical Applications

   1. Melanoma Recognition Using Representative and Discriminative Kernel Classifiers

      • Tatiana Tommasi, Elisabetta La Torre, Barbara Caputo

      Pages 1-12

   2. Detection of Connective Tissue Disorders from 3D Aortic MR Images Using Independent Component Analysis

      • Michael Sass Hansen, Fei Zhao, Honghai Zhang, Nicholas E. Walker, Andreas Wahle, Thomas Scholz et al.

      Pages 13-24

   3. Comparing Ensembles of Learners: Detecting Prostate Cancer from High Resolution MRI

      • Anant Madabhushi, Jianbo Shi, Michael Feldman, Mark Rosen, John Tomaszewski

      Pages 25-36

   4. Accurate Measurement of Cartilage Morphology Using a 3D Laser Scanner

      • Nhon H. Trinh, Jonathan Lester, Braden C. Fleming, Glenn Tung, Benjamin B. Kimia

      Pages 37-48

3. Image Registration

   1. Quantification of Growth and Motion Using Non-rigid Registration

      • D. Rueckert, R. Chandrashekara, P. Aljabar, K. K. Bhatia, J. P. Boardman, L. Srinivasan et al.

      Pages 49-60

   2. Image Registration Accuracy Estimation Without Ground Truth Using Bootstrap

      • Jan Kybic, Daniel Smutek

      Pages 61-72

   3. SIFT and Shape Context for Feature-Based Nonlinear Registration of Thoracic CT Images

      • Martin Urschler, Joachim Bauer, Hendrik Ditt, Horst Bischof

      Pages 73-84

   4. Consistent and Elastic Registration of Histological Sections Using Vector-Spline Regularization

      • Ignacio Arganda-Carreras, Carlos O. S. Sorzano, Roberto Marabini, José María Carazo, Carlos Ortiz-de-Solorzano, Jan Kybic

      Pages 85-95

4. Image Segmentation and Analysis

   1. Comparative Analysis of Kernel Methods for Statistical Shape Learning

      • Yogesh Rathi, Samuel Dambreville, Allen Tannenbaum

      Pages 96-107

   2. Segmentation of Dynamic Emission Tomography Data in Projection Space

      • Evgeny Krestyannikov, Jussi Tohka, Ulla Ruotsalainen

      Pages 108-119

   3. A Framework for Unsupervised Segmentation of Multi-modal Medical Images

      • Ayman El-Baz, Aly Farag, Asem Ali, Georgy Gimel’farb, Manuel Casanova

      Pages 120-131

5. Poster Session

   1. An Integrated Algorithm for MRI Brain Images Segmentation

      • Yinghua Lu, Jianzhong Wang, Jun Kong, Baoxue Zhang, Jingdan Zhang

      Pages 132-142

   2. Spatial Intensity Correction of Fluorescent Confocal Laser Scanning Microscope Images

      • Sang-Chul Lee, Peter Bajcsy

      Pages 143-154

   3. Quasi-conformal Flat Representation of Triangulated Surfaces for Computerized Tomography

      • Eli Appleboim, Emil Saucan, Yehoshua Y. Zeevi

      Pages 155-165

   4. Bony Structure Suppression in Chest Radiographs

      • M. Loog, B. van Ginneken

      Pages 166-177

   5. A Minimally-Interactive Watershed Algorithm Designed for Efficient CTA Bone Removal

      • Horst K. Hahn, Markus T. Wenzel, Olaf Konrad-Verse, Heinz-Otto Peitgen

      Pages 178-189

   6. Automatic Reconstruction of Dendrite Morphology from Optical Section Stacks

      • S. Urban, S. M. O’Malley, B. Walsh, A. Santamaría-Pang, P. Saggau, C. Colbert et al.

      Pages 190-201

   7. Modeling the Activity Pattern of the Constellation of Cardiac Chambers in Echocardiogram Videos

      • Shahram Ebadollahi, Shih-Fu Chang, Henry Wu

      Pages 202-213

   8. A Study on the Influence of Image Dynamics and Noise on the JPEG 2000 Compression Performance for Medical Images

      • Peter Michael Goebel, Ahmed Nabil Belbachir, Michael Truppe

      Pages 214-224

Medical imaging and medical image analysis are developing rapidly. While m- ical imaging has already become a standard of modern medical care, medical image analysis is still mostly performed visually and qualitatively. The ev- increasing volume of acquired data makes it impossible to utilize them in full. Equally important, the visual approaches to medical image analysis are known to su?er from a lack of reproducibility. A signi?cant researche?ort is devoted to developing algorithms for processing the wealth of data available and extracting the relevant information in a computerized and quantitative fashion. Medical imaging and image analysis are interdisciplinary areas combining electrical, computer, and biomedical engineering; computer science; mathem- ics; physics; statistics; biology; medicine; and other ?elds. Medical imaging and computer vision, interestingly enough, have developed and continue developing somewhat independently. Nevertheless, bringing them together promises to b- e?t both of these ?elds. This was the second time that a satellite workshop,solely devoted to medical image analysis issues, was held in conjunction with the European Conference on Computer Vision (ECCV), and we are optimistic that this will become a tradition at ECCV. We received 38 full-length paper submissions to the second Computer Vision Approaches to Medical Image Analysis (CVAMIA) Workshop, out of which 10 were accepted for oral and 11 for poster presentation after a rigorous peer-review process. In addition, the workshop included three invited talks. The ?rst was given by Maryellen Giger from the University of Chicago, USA — titled “Multi-Modality Breast CADx”.

• 3D imaging
• Bayesian networks
• Computer Vision
• Ensembl
• Image segmentation
• Scale-invariant feature transform
• classification
• computed tomography (CT)
• image analysis
• image registration
• kernel methods
• object recognition
• pattern recognition
• segmentation
• therapy

• Dept. of Electrical & Computer Engineering and Dept. of Internal Medicine, The University of Iowa, USA

  Reinhard R. Beichel

• Department of Electrical and Computer Engineering, University of Iowa, Iowa City, USA

  Milan Sonka

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