Skip to main content
SpringerLink
Log in

White Matter Supervoxel Segmentation by Axial DP-Means Clustering

  • Conference paper
  • First Online:
Medical Computer Vision. Large Data in Medical Imaging (MCV 2013)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8331))

Included in the following conference series:

Abstract

A powerful aspect of diffusion MR imaging is the ability to reconstruct fiber orientations in brain white matter; however, the application of traditional learning algorithms is challenging due to the directional nature of the data. In this paper, we present an algorithmic approach to clustering such spatial and orientation data and apply it to brain white matter supervoxel segmentation. This approach is an extension of the DP-means algorithm to support axial data, and we present its theoretical connection to probabilistic models, including the Gaussian and Watson distributions. We evaluate our method with the analysis of synthetic data and an application to diffusion tensor atlas segmentation. We find our approach to be efficient and effective for the automatic extraction of regions of interest that respect the structure of brain white matter. The resulting supervoxel segmentation could be used to map regional anatomical changes in clinical studies or serve as a domain for more complex modeling.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Azoury, K.S., Warmuth, M.K.: Relative loss bounds for on-line density estimation with the exponential family of distributions. Mach. Learn. 43(3), 211–246 (2001)

    Article  MATH  Google Scholar 

  2. Banerjee, A., Merugu, S., Dhillon, I.S., Ghosh, J.: Clustering with Bregman divergences. J. Mach. Learn. Res. 6, 1705–1749 (2005)

    MATH  MathSciNet  Google Scholar 

  3. Bloy, L., Ingalhalikar, M., Eavani, H., Schultz, R.T., Roberts, T.P., Verma, R.: White matter atlas generation using HARDI based automated parcellation. Neuroimage 59(4), 4055–4063 (2012)

    Article  Google Scholar 

  4. Dhillon, I.S., Marcotte, E.M., Roshan, U.: Diametrical clustering for identifying anti-correlated gene clusters. Bioinformatics 19(13), 1612–1619 (2003)

    Article  Google Scholar 

  5. Jiang, K., Kulis, B., Jordan, M.: Small-variance asymptotics for exponential family Dirichlet process mixture models. In: NIPS 2012 (2012)

    Google Scholar 

  6. Kaden, E., Kruggel, F.: Nonparametric Bayesian inference of the fiber orientation distribution from diffusion-weighted MR images. Med. Image Anal. 16(4), 876–888 (2012)

    Article  Google Scholar 

  7. Kulis, B., Jordan, M.I.: Revisiting k-means: new algorithms via Bayesian nonparametrics. In: ICML-12, pp. 513–520 (2012)

    Google Scholar 

  8. Lucchi, A., Smith, K., Achanta, R., Lepetit, V., Fua, P.: A fully automated approach to segmentation of irregularly shaped cellular structures in EM images. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010, Part II. LNCS, vol. 6362, pp. 463–471. Springer, Heidelberg (2010)

    Google Scholar 

  9. Mori, G.: Guiding model search using segmentation. In: ICCV 2005, vol. 2, pp. 1417–1423 (2005)

    Google Scholar 

  10. Rathi, Y., Michailovich, O., Shenton, M.E., Bouix, S.: Directional functions for orientation distribution estimation. Med. Image Anal. 13(3), 432–444 (2009)

    Article  Google Scholar 

  11. Schwartzman, A., Dougherty, R.F., Taylor, J.E.: Cross-subject comparison of principal diffusion direction maps. Magnet. Reson. Med. 53(6), 1423–1431 (2005)

    Article  Google Scholar 

  12. Sra, S., Karp, D.: The multivariate Watson distribution: maximum-likelihood estimation and other aspects. J. Multivar. Anal. 114, 256–269 (2013)

    Article  MATH  MathSciNet  Google Scholar 

  13. Veksler, O., Boykov, Y., Mehrani, P.: Superpixels and supervoxels in an energy optimization framework. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part V. LNCS, vol. 6315, pp. 211–224. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  14. Vinh, N.X., Epps, J., Bailey, J.: Information theoretic measures for clusterings comparison: variants, properties, normalization and correction for chance. J. Mach. Learn. Res. 11, 2837–2854 (2010)

    MATH  MathSciNet  Google Scholar 

  15. Wachinger, C., Golland, P.: Spectral label fusion. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part III. LNCS, vol. 7512, pp. 410–417. Springer, Heidelberg (2012)

    Google Scholar 

  16. Watson, G.S.: Statistics on spheres, vol. 6. Wiley, New York (1983)

    MATH  Google Scholar 

  17. Wiegell, M.R., Tuch, D.S., Larsson, H.B., Wedeen, V.J.: Automatic segmentation of thalamic nuclei from diffusion tensor magnetic resonance imaging. Neuroimage 19(2), 391–401 (2003)

    Article  Google Scholar 

  18. Wu, K., Otoo, E., Suzuki, K.: Optimizing two-pass connected-component labeling algorithms. Pattern Anal. Appl. 12(2), 117–135 (2009)

    Article  MathSciNet  Google Scholar 

  19. Zhang, H., Yushkevich, P.A., Rueckert, D., Gee, J.C.: A computational white matter atlas for aging with surface-based representation of fasciculi. In: Fischer, B., Dawant, B., Lorenz, C. (eds.) WBIR 2010. LNCS, vol. 6204, pp. 83–90. Springer, Heidelberg (2010)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Science Department, Brown University, Providence, RI, USA

    Ryan P. Cabeen & David H. Laidlaw

Authors
  1. Ryan P. Cabeen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. David H. Laidlaw
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ryan P. Cabeen .

Editor information

Editors and Affiliations

  1. Technical University of Munich, ETH, Zürich, Switzerland, Munich, Germany

    Bjoern Menze

  2. Medical University of Vienna, Vienna, Austria

    Georg Langs

  3. GE Global Research, Niskayuna, New York, USA

    Albert Montillo

  4. Siemens AG, Erlangen, Germany

    Michael Kelm

  5. University of Applied Sciences Western Switzerland, Sierre, Switzerland

    Henning Müller

  6. University of California, La Jolla, California, USA

    Zhuowen Tu

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Cabeen, R.P., Laidlaw, D.H. (2014). White Matter Supervoxel Segmentation by Axial DP-Means Clustering. In: Menze, B., Langs, G., Montillo, A., Kelm, M., Müller, H., Tu, Z. (eds) Medical Computer Vision. Large Data in Medical Imaging. MCV 2013. Lecture Notes in Computer Science(), vol 8331. Springer, Cham. https://doi.org/10.1007/978-3-319-05530-5_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-05530-5_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-05529-9

  • Online ISBN: 978-3-319-05530-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation