Skip to main content

Advertisement

SpringerLink
Log in
Menu
Find a journal Publish with us
Search
Cart
Book cover

International Conference on Medical Image Computing and Computer-Assisted Intervention

MICCAI 2012: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012 pp 106–113Cite as

  1. Home
  2. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012
  3. Conference paper
Automatic Categorization of Anatomical Landmark-Local Appearances Based on Diffeomorphic Demons and Spectral Clustering for Constructing Detector Ensembles

Automatic Categorization of Anatomical Landmark-Local Appearances Based on Diffeomorphic Demons and Spectral Clustering for Constructing Detector Ensembles

  • Shouhei Hanaoka19,
  • Yoshitaka Masutani19,20,
  • Mitsutaka Nemoto19,
  • Yukihiro Nomura19,
  • Takeharu Yoshikawa21,
  • Naoto Hayashi21 &
  • …
  • Kuni Ohtomo19,20 
  • Conference paper
  • 3973 Accesses

Part of the Lecture Notes in Computer Science book series (LNIP,volume 7511)

Abstract

A method for categorizing landmark-local appearances extracted from computed tomography (CT) datasets is presented. Anatomical landmarks in the human body inevitably have inter-individual variations that cause difficulty in automatic landmark detection processes. The goal of this study is to categorize subjects (i.e., training datasets) according to local shape variations of such a landmark so that each subgroup has less shape variation and thus the machine learning of each landmark detector is much easier. The similarity between each subject pair is measured based on the non-rigid registration result between them. These similarities are used by the spectral clustering process. After the clustering, all training datasets in each cluster, as well as synthesized intermediate images calculated from all subject-pairs in the cluster, are used to train the corresponding subgroup detector. All of these trained detectors compose a detector ensemble to detect the target landmark. Evaluation with clinical CT datasets showed great improvement in the detection performance.

Keywords

  • anatomical landmark
  • diffeomorphic demons
  • spectral clustering

Download conference paper PDF

References

  1. Seifert, S., Barbu, A., Zhou, S.K., Liu, D., Feulner, J., Huber, M., Suehling, M., Cavallaro, A., Comaniciu, D.: Hierarchical parsing and semantic navigation of full body CT data. In: Samei, E., Hsieh, J. (eds.) Medical Imaging 2009: Physics of Medical Imaging. Proceedings of the SPIE, vol. 7258, pp. 725902–725902-8 (2009)

    Google Scholar 

  2. Hanaoka, S., Masutani, Y., Nemoto, M., Nomura, Y., Yoshikawa, T., Hayashi, N., Yoshioka, N., Ohtomo, K.: Probabilistic Modeling of Landmark Distances and Structure for Anomaly-proof Landmark Detection. In: Proceedings of the Third International Workshop on Mathematical Foundations of Computational Anatomy, pp. 159–169 (2011)

    Google Scholar 

  3. Nemoto, M., Masutani, Y., Hanaoka, S., Nomura, Y., Yoshikawa, T., Hayashi, N., Yoshioka, N., Ohtomo, K.: A unified framework for concurrent detection of anatomical landmarks for medical image understanding. In: Proc. SPIE, vol. 7962, p. 79623E (2011)

    Google Scholar 

  4. Vercauteren, T., Pennec, X., Perchant, A., Ayache, N.: Diffeomorphic demons: Efficient non-parametric image registration. Neuroimage 45(1) suppl. 1, S61–S72 (2009)

    Google Scholar 

  5. Ng, A.Y., Jordan, M.I., Weiss, Y.: On spectral clustering: Analysis and an algorithm. In: Advances in Neural Information Processing Systems, vol. 14, pp. 849–856 (2001)

    Google Scholar 

  6. Zelnik-Manor, L., Perona, P.: Self-Tuning Spectral Clustering. In: Advances in Neural Information Processing Systems, vol. 17, pp. 1601–1608 (2004)

    Google Scholar 

  7. Heckemann, R.A., Hajnal, J.V., Aljabar, P., Rueckert, D., Hammers, A.: Automatic anatomical brain MRI segmentation combining label propagation and decision fusion. Neuroimage 33(1), 115–126 (2006)

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan

    Shouhei Hanaoka, Yoshitaka Masutani, Mitsutaka Nemoto, Yukihiro Nomura & Kuni Ohtomo

  2. Division of Radiology and Biomedical Engineering, Graduate School of Medicine, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan

    Yoshitaka Masutani & Kuni Ohtomo

  3. Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan

    Takeharu Yoshikawa & Naoto Hayashi

Authors
  1. Shouhei Hanaoka
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yoshitaka Masutani
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mitsutaka Nemoto
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yukihiro Nomura
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Takeharu Yoshikawa
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Naoto Hayashi
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Kuni Ohtomo
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Project Team Asclepios, Inria Sophia Antipolis, 06902, Sophia-Antipolis, France

    Nicholas Ayache & Hervé Delingette & 

  2. MIT, CSAIL, 02139, Cambridge, MA, USA

    Polina Golland

  3. Information and Communication Headquarters, Nagoya University, 464-8603, Nagoya, Japan

    Kensaku Mori

Rights and permissions

Reprints and Permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Hanaoka, S. et al. (2012). Automatic Categorization of Anatomical Landmark-Local Appearances Based on Diffeomorphic Demons and Spectral Clustering for Constructing Detector Ensembles. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012. MICCAI 2012. Lecture Notes in Computer Science, vol 7511. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33418-4_14

Download citation

  • .RIS
  • .ENW
  • .BIB
  • DOI: https://doi.org/10.1007/978-3-642-33418-4_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33417-7

  • Online ISBN: 978-3-642-33418-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Share this paper

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Search

Navigation

  • Find a journal
  • Publish with us

Discover content

  • Journals A-Z
  • Books A-Z

Publish with us

  • Publish your research
  • Open access publishing

Products and services

  • Our products
  • Librarians
  • Societies
  • Partners and advertisers

Our imprints

  • Springer
  • Nature Portfolio
  • BMC
  • Palgrave Macmillan
  • Apress
  • Your US state privacy rights
  • Accessibility statement
  • Terms and conditions
  • Privacy policy
  • Help and support

167.114.118.210

Not affiliated

Springer Nature

© 2023 Springer Nature