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Computer Vision for Biomedical Image Applications

First International Workshop, CVBIA 2005, Beijing, China, October 21, 2005, Proceedings

  • Conference proceedings
  • © 2005

Overview

Editors:
  1. Yanxi Liu

    1. School of Computer Science, Carnegie Mellon University, Pittsburgh, USA

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  2. Tianzi Jiang

    1. National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, People’s Republic of China

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  3. Changshui Zhang

    1. State Key Laboratory for Intelligent Technology and Systems, Department of Automation, Faculty of Information Science and Technology, Tsinghua University, Beijing, China

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 3765)

Part of the book sub series: Image Processing, Computer Vision, Pattern Recognition, and Graphics (LNIP)

Included in the following conference series:

Conference proceedings info: CVBIA 2005.

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Table of contents (56 papers)

  1. Front Matter

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  2. Computational Anatomy and Computational Physiology for Medical Image Analysis

    • Nicholas Ayache
    Pages 1-2

  3. Analyzing Anatomical Structures: Leveraging Multiple Sources of Knowledge

    • Eric Grimson, Polina Golland
    Pages 3-12

  4. Advances on Medical Imaging and Computing

    • Tianzi Jiang, Xiaobo Li, Gaolong Gong, Meng Liang, Lixia Tian, Fuchun Li et al.
    Pages 13-23

  5. Tracking of Migrating and Proliferating Cells in Phase-Contrast Microscopy Imagery for Tissue Engineering

    • Takeo Kanade, Kang Li
    Pages 24-24

  6. Cardiology Meets Image Analysis: Just an Application or Can Image Analysis Usefully Impact Cardiology Practice?

    • J. Alison Noble
    Pages 25-30

  7. Computer Vision Algorithms for Retinal Image Analysis: Current Results and Future Directions

    • Charles V. Stewart
    Pages 31-50

  8. 3D Statistical Shape Models to Embed Spatial Relationship Information

    • Jurgen Fripp, Pierrick Bourgeat, Andrea J. U. Mewes, Simon K. Warfield, Stuart Crozier, Sébastien Ourselin
    Pages 51-60

  9. A Generalized Level Set Formulation of the Mumford-Shah Functional with Shape Prior for Medical Image Segmentation

    • Lishui Cheng, Xian Fan, Jie Yang, Yun Zhu
    Pages 61-71

  10. A Hybrid Eulerian-Lagrangian Approach for Thickness, Correspondence, and Gridding of Annular Tissues

    • Kelvin R. Rocha, Anthony J. Yezzi, Jerry L. Prince
    Pages 72-81

  11. A Hybrid Framework for Image Segmentation Using Probabilistic Integration of Heterogeneous Constraints

    • Rui Huang, Vladimir Pavlovic, Dimitris N. Metaxas
    Pages 82-92

  12. A Learning Framework for the Automatic and Accurate Segmentation of Cardiac Tagged MRI Images

    • Zhen Qian, Dimitris N. Metaxas, Leon Axel
    Pages 93-102

  13. A Local Adaptive Algorithm for Microaneurysms Detection in Digital Fundus Images

    • Ke Huang, Michelle Yan
    Pages 103-113

  14. A New Coarse-to-Fine Framework for 3D Brain MR Image Registration

    • Terrence Chen, Thomas S. Huang, Wotao Yin, Xiang Sean Zhou
    Pages 114-124

  15. A New Vision Approach for Local Spectrum Features in Cervical Images via 2D Method of Geometric Restriction in Frequency Domain

    • Viara Van Raad
    Pages 125-134

  16. Active Contours Under Topology Control Genus Preserving Level Sets

    • Florent Ségonne, Jean-Philippe Pons, Eric Grimson, Bruce Fischl
    Pages 135-145

  17. A Novel Multifaceted Virtual Craniofacial Surgery Scheme Using Computer Vision

    • A. S. Chowdhury, S. M. Bhandarkar, E. W. Tollner, G. Zhang, J. C. Yu, E. Ritter
    Pages 146-159

  18. A Novel Unsupervised Segmentation Method for MR Brain Images Based on Fuzzy Methods

    • Xian Fan, Jie Yang, Yuanjie Zheng, Lishui Cheng, Yun Zhu
    Pages 160-169

  19. A Pattern Classification Approach to Aorta Calcium Scoring in Radiographs

    • Marleen de Bruijne
    Pages 170-177

  20. A Topologically Faithful, Tissue-Guided, Spatially Varying Meshing Strategy for Computing Patient-Specific Head Models for Endoscopic Pituitary Surgery Simulation

    • M. A. Audette, H. Delingette, A. Fuchs, O. Astley, K. Chinzei
    Pages 178-188
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  1. Computer Vision for Biomedical Image Applications

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About this book

With the rapid increase in the variety and quantity of biomedical images in recent years, we see a steadily growing number of computer vision technologies applied to biomedical applications. The time is ripe for us to take a closer look at the accomplishments and experiences gained in this research subdomain, and to strategically plan the directions of our future research. The scientific goal of our workshop, “Computer Vision for Biomedical Image Applications: Current Techniques and Future Trends” (CVBIA), is to examine the diverse applications of computer vision to biomedical image applications, considering both current methods and promising new trends. An additional goal is to provide the opportunity for direct interactions between (1) prominent senior researchers and young scientists, including students, postdoctoral associates and junior faculty; (2) local researchers and international leaders in biomedical image analysis; and (3) computer scientists and medical practitioners. Our CVBIA workshop had two novel characteristics: each contributed paper was authored primarily by a young scientist, and the workshop attracted an unusually large number of well-respected invited speakers (and their papers). We had the good fortune of having Dr. Ayache of INRIA, France to talk about “Computational Anatomy and Computational Physiology,” Prof. Grimson of MIT to discuss “Analyzing Anatomical Structures: Leveraging Multiple Sources of Knowledge,” Dr. Jiang of the Chinese Academy of Sciences to present their work on “Computational Neuroanatomy and Brain Connectivity,” Prof.

Editors and Affiliations

  • School of Computer Science, Carnegie Mellon University, Pittsburgh, USA

    Yanxi Liu

  • National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, People’s Republic of China

    Tianzi Jiang

  • State Key Laboratory for Intelligent Technology and Systems, Department of Automation, Faculty of Information Science and Technology, Tsinghua University, Beijing, China

    Changshui Zhang

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