Skip to main content
Springer Nature Link
Log in

Machine Learning in Medical Imaging

4th International Workshop, MLMI 2013, Held in Conjunction with MICCAI 2013, Nagoya, Japan, September 22, 2013, Proceedings

  • Conference proceedings
  • © 2013

Overview

Editors:
  1. Guorong Wu

    1. Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, USA

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  2. Daoqiang Zhang

    1. Dept. of Computer Science, Nanjing University of Aeronautics and Astronautics, Nanjing, P.R. China

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  3. Dinggang Shen

    1. Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, USA

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  4. Pingkun Yan

    1. XIOPM , Chinese Academy of Sciences, Xi’an, P.R. China

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  5. Kenji Suzuki

    1. Dept. of Radiology, The University of Chicago, Chicago, USA

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  6. Fei Wang

    1. IBM Almaden Research Center, San Jose, USA

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  • Conference proceedings of the International Workshop on Machine Learning in Medical Imaging, MLMI 2013

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 8184)

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

Included in the following conference series:

Conference proceedings info: MLMI 2013.

This is a preview of subscription content, log in via an institution to check access.

Access this book

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

eBook USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

About this book

This book constitutes the refereed proceedings of the 4th International Workshop on Machine Learning in Medical Imaging, MLMI 2013, held in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013, in Nagoya, Japan, in September 2013. The 32 contributions included in this volume were carefully reviewed and selected from 57 submissions. They focus on major trends and challenges in the area of machine learning in medical imaging and aim to identify new cutting-edge techniques and their use in medical imaging.

Similar content being viewed by others

Keywords

Table of contents (32 papers)

  1. Front Matter

    Download chapter PDF

  2. Unsupervised Deep Learning for Hippocampus Segmentation in 7.0 Tesla MR Images

    • Minjeong Kim, Guorong Wu, Dinggang Shen
    Pages 1-8

  3. Integrating Multiple Network Properties for MCI Identification

    • Biao Jie, Daoqiang Zhang, Heung-Il Suk, Chong-Yaw Wee, Dinggang Shen
    Pages 9-16

  4. Learning-Boosted Label Fusion for Multi-atlas Auto-Segmentation

    • Xiao Han
    Pages 17-24

  5. Volumetric Segmentation of Key Fetal Brain Structures in 3D Ultrasound

    • Mohammad Yaqub, Remi Cuingnet, Raffaele Napolitano, David Roundhill, Aris Papageorghiou, Roberto Ardon et al.
    Pages 25-32

  6. Sparse Classification with MRI Based Markers for Neuromuscular Disease Categorization

    • Katerina Gkirtzou, Jean-François Deux, Guillaume Bassez, Aristeidis Sotiras, Alain Rahmouni, Thibault Varacca et al.
    Pages 33-40

  7. Fully Automatic Detection of the Carotid Artery from Volumetric Ultrasound Images Using Anatomical Position-Dependent LBP Features

    • Fumi Kawai, Keisuke Hayata, Jun Ohmiya, Satoshi Kondo, Kiyoko Ishikawa, Masahiro Yamamoto
    Pages 41-48

  8. A Transfer-Learning Approach to Image Segmentation Across Scanners by Maximizing Distribution Similarity

    • Annegreet van Opbroek, M. Arfan Ikram, Meike W. Vernooij, Marleen de Bruijne
    Pages 49-56

  9. A New Algorithm of Electronic Cleansing for Weak Faecal-Tagging CT Colonography

    • Le Lu, Bing Jian, Dijia Wu, Matthias Wolf
    Pages 57-65

  10. A Unified Approach to Shape Model Fitting and Non-rigid Registration

    • Marcel Lüthi, Christoph Jud, Thomas Vetter
    Pages 66-73

  11. A Bayesian Algorithm for Image-Based Time-to-Event Prediction

    • Mert R. Sabuncu
    Pages 74-81

  12. Patient-Specific Manifold Embedding of Multispectral Images Using Kernel Combinations

    • Veronika A. M. Zimmer, Roger Fonolla, Karim Lekadir, Gemma Piella, Corné Hoogendoorn, Alejandro F. Frangi
    Pages 82-89

  13. fMRI Analysis with Sparse Weisfeiler-Lehman Graph Statistics

    • Katerina Gkirtzou, Jean Honorio, Dimitris Samaras, Rita Goldstein, Matthew B. Blaschko
    Pages 90-97

  14. Patch-Based Segmentation without Registration: Application to Knee MRI

    • Zehan Wang, Claire Donoghue, Daniel Rueckert
    Pages 98-105

  15. Flow-Based Correspondence Matching in Stereovision

    • Songbai Ji, Xiaoyao Fan, David W. Roberts, Alex Hartov, Keith D. Paulsen
    Pages 106-113

  16. Thickness NETwork (ThickNet) Features for the Detection of Prodromal AD

    • Pradeep Reddy Raamana, Lei Wang, Mirza Faisal Beg, for The Alzheimer’s Disease Neuroimaging Initiative
    Pages 114-122

  17. Metric Space Structures for Computational Anatomy

    • Jianqiao Feng, Xiaoying Tang, Minh Tang, Carey Priebe, Michael Miller
    Pages 123-130

  18. Discriminative Group Sparse Representation for Mild Cognitive Impairment Classification

    • Heung-Il Suk, Chong-Yaw Wee, Dinggang Shen
    Pages 131-138

  19. Temporally Dynamic Resting-State Functional Connectivity Networks for Early MCI Identification

    • Chong-Yaw Wee, Sen Yang, Pew-Thian Yap, Dinggang Shen
    Pages 139-146

  20. An Improved Optimization Method for the Relevance Voxel Machine

    • Melanie Ganz, Mert R. Sabuncu, Koen Van Leemput
    Pages 147-154
Back to top

Other volumes

  1. Machine Learning in Medical Imaging

Editors and Affiliations

  • Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, USA

    Guorong Wu, Dinggang Shen

  • Dept. of Computer Science, Nanjing University of Aeronautics and Astronautics, Nanjing, P.R. China

    Daoqiang Zhang

  • XIOPM , Chinese Academy of Sciences, Xi’an, P.R. China

    Pingkun Yan

  • Dept. of Radiology, The University of Chicago, Chicago, USA

    Kenji Suzuki

  • IBM Almaden Research Center, San Jose, USA

    Fei Wang

Bibliographic Information

Publish with us

Policies and ethics

Back to top

Search

Navigation