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 74–81Cite as

  1. Home
  2. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012
  3. Conference paper
Thoracic Abnormality Detection with Data Adaptive Structure Estimation

Thoracic Abnormality Detection with Data Adaptive Structure Estimation

  • Yang Song19,
  • Weidong Cai19,
  • Yun Zhou20 &
  • …
  • Dagan Feng19 
  • Conference paper
  • 5467 Accesses

  • 4 Citations

Part of the Lecture Notes in Computer Science book series (LNIP,volume 7510)

Abstract

Automatic detection of lung tumors and abnormal lymph nodes are useful in assisting lung cancer staging. This paper presents a novel detection method, by first identifying all abnormalities, then differentiating between lung tumors and abnormal lymph nodes based on their degree of overlap with the lung field and mediastinum. Regression-based appearance model and graph-based structure labeling are designed to estimate the actual lung field and mediastinum from the pathology-affected thoracic images adaptively. The proposed method is simple, effective and generalizable, and can be potentially applicable to other medical imaging domains as well. Promising results are demonstrated based on our evaluations on clinical PET-CT data sets from lung cancer patients.

Keywords

  • Lung Tumor
  • Reference Image
  • Appearance Model
  • Statistical Shape Model
  • Abnormality Detection

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Download conference paper PDF

References

  1. Saradhi, G., Gopalakrishnan, G., Roy, A., Mullick, R., Manjeshwar, R., Thielemans, K., Patil, U.: A Framework for Automated Tumor Detection in Thoracic FDG PET Images Using Texture-based Features. In: ISBI, pp. 97–100 (2009)

    Google Scholar 

  2. Gubbi, J., Kanakatte, A., Kron, T., Binns, D., Srinivasan, B., Mani, N., Palaniswami, M.: Automatic tumour volume delineation in respiratory-gated PET images. J. Med. Imag. Radia. Oncol. 55, 65–76 (2011)

    CrossRef  Google Scholar 

  3. Feulner, J., Zhou, S.K., Huber, M., Hornegger, J., Comaniciu, D.: Lymph Nodes Detection in 3-D Chest CT Using a Spatial Prior Probability. In: CVPR, pp. 2926–2932 (2010)

    Google Scholar 

  4. Feuerstein, M., Glocker, B., Kitasaka, T., Nakamura, Y., Iwano, S., Mori, K.: Mediastinal Atlas Creation from 3-D Chest Computed Tomography Images: Application to Automated Detection and Station Mapping of Lymph Nodes. Med. Image Anal. 16(1), 63–74 (2011)

    CrossRef  Google Scholar 

  5. Song, Y., Cai, W., Eberl, S., Fulham, M.J., Feng, D.: Discriminative Pathological Context Detection in Thoracic Images Based on Multi-level Inference. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part III. LNCS, vol. 6893, pp. 191–198. Springer, Heidelberg (2011)

    CrossRef  Google Scholar 

  6. Sluimer, I., Prokop, M., van Ginneken, B.: Toward Automated Segmentation of the Pathological Lung in CT. IEEE Trans. Med. Imag. 24(8), 1025–1038 (2005)

    CrossRef  Google Scholar 

  7. Sofka, M., Wetzl, J., Birkbeck, N., Zhang, J., Kohlberger, T., Kaftan, J., Declerck, J., Zhou, S.K.: Multi-stage Learning for Robust Lung Segmentation in Challenging CT Volumes. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part III. LNCS, vol. 6893, pp. 667–674. Springer, Heidelberg (2011)

    CrossRef  Google Scholar 

  8. Chen, T., Vemuri, B.C., Rangarajan, A., Eisenschenk, S.J.: Mixture of Segmenters with Discriminative Spatial Regularization and Sparse Weight Selection. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part III. LNCS, vol. 6893, pp. 595–602. Springer, Heidelberg (2011)

    CrossRef  Google Scholar 

  9. Rousseau, F., Habas, P.A., Studholme, C.: Human Brain Labeling Using Image Similarities. In: CVPR, pp. 1081–1088 (2011)

    Google Scholar 

  10. Wu, D., Lu, L., Bi, J., Shinagawa, Y., Boyer, K., Krishnan, A., Salganicoff, M.: Stratified Learning of Local Anatomical Context for Lung Nodules in CT Images. In: CVPR, pp. 2791–2798 (2010)

    Google Scholar 

  11. Tropp, J.: Greed Is Good: Algorithmic Results for Sparse Approximation. IEEE Trans. Inform. Theory 50, 2231–2242 (2004)

    CrossRef  MathSciNet  Google Scholar 

  12. Kolmogorov, V., Zabih, R.: What Energy Functions Can Be Minimized via Graph Cuts? IEEE Trans. Pattern Anal. Machine Intell. 26(2), 147–159 (2004)

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, University of Sydney, Australia

    Yang Song, Weidong Cai & Dagan Feng

  2. The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, USA

    Yun Zhou

Authors
  1. Yang Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Weidong Cai
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yun Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dagan Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

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

    Nicholas Ayache & Hervé Delingette & 

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

    Polina Golland

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

    Kensaku Mori

Rights and permissions

Reprints and Permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Song, Y., Cai, W., Zhou, Y., Feng, D. (2012). Thoracic Abnormality Detection with Data Adaptive Structure Estimation. 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 7510. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33415-3_10

Download citation

  • .RIS
  • .ENW
  • .BIB
  • DOI: https://doi.org/10.1007/978-3-642-33415-3_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33414-6

  • Online ISBN: 978-3-642-33415-3

  • 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