Skip to main content

Sparse Patch-Based Label Fusion for Multi-Atlas Segmentation

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNIP,volume 7509)

Abstract

Patch-based label fusion methods have shown great potential in multi-atlas segmentation. It is crucial for patch-based labeling methods to determine appropriate graphs and corresponding weights to better link patches in the input image with those in atlas images. Currently, two independent steps are performed, i.e., first constructing graphs based on the fixed image neighborhood and then computing weights based on the heat kernel for all patches in the neighborhood. In this paper, we first show that many existing label fusion methods can be unified into a graph-based framework, and then propose a novel method for simultaneously deriving both graph adjacency structure and graph weights based on the sparse representation, to perform multi-atlas segmentation. Our motivation is that each patch in the input image can be reconstructed by the sparse linear superposition of patches in the atlas images, and the reconstruction coefficients can be used to deduce both graph structure and weights simultaneously. Experimental results on segmenting brain anatomical structures from magnetic resonance images (MRI) show that our proposed method achieves significant improvements over previous patch-based methods, as well as other conventional label fusion methods.

Keywords

  • Input Image
  • Heat Kernel
  • Majority Vote
  • Segmentation Result
  • Sparse Representation

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.

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (Canada)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   54.99
Price excludes VAT (Canada)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   72.00
Price excludes VAT (Canada)
  • 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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Artaechevarria, X., Munoz-Barrutia, A., Ortiz-de-Solorzano, C.: Combination strategies in multi-atlas image segmentation: application to brain MR data. IEEE Transactions on Medical Imaging 28, 1266–1277 (2009)

    CrossRef  Google Scholar 

  2. Sabuncu, M.R., Yeo, B.T., Van Leemput, K., Fischl, B., Golland, P.: A generative model for image segmentation based on label fusion. IEEE Transactions on Medical Imaging 29, 1714–1729 (2010)

    CrossRef  Google Scholar 

  3. Wang, H., Suh, J.W., Das, S.R., Pluta, J., Altinay, M., Yushkevich, P.: Regression-based label fusion for multi-atlas segmentation. In: CVPR, pp. 1113–1120 (2011)

    Google Scholar 

  4. Zhang, D., Wu, G., Jia, H., Shen, D.: Confidence-Guided Sequential Label Fusion for Multi-atlas Based Segmentation. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part III. LNCS, vol. 6893, pp. 643–650. Springer, Heidelberg (2011)

    CrossRef  Google Scholar 

  5. Coupe, P., Yger, P., Barillot, C.: Fast non local means denoising for 3D MR images. Med. Image Comput. Comput. Assist. Interv. 9, 33–40 (2006)

    Google Scholar 

  6. Coupe, P., Manjon, J.V., Fonov, V., Pruessner, J., Robles, M., Collins, D.L.: Patch-based segmentation using expert priors: application to hippocampus and ventricle segmentation. NeuroImage 54, 940–954 (2011)

    CrossRef  Google Scholar 

  7. Rousseau, F., Habas, P.A., Studholme, C.: A supervised patch-based approach for human brain labeling. IEEE Transactions on Medical Imaging 30, 1852–1862 (2011)

    CrossRef  Google Scholar 

  8. Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S., Ma, Y.: Robust face recognition via sparse representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 31, 210–227 (2009)

    CrossRef  Google Scholar 

  9. Tibshirani, R.: Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society Series B 58, 267–288 (1996)

    MathSciNet  MATH  Google Scholar 

  10. Liu, J., Ji, S., Ye, J.: SLEP: Sparse learning with efficient projections. Arizona State University (2009)

    Google Scholar 

  11. Christensen, G.E., Geng, X., Kuhl, J.G., Bruss, J., Grabowski, T.J., Pirwani, I.A., Vannier, M.W., Allen, J.S., Damasio, H.: Introduction to the Non-rigid Image Registration Evaluation Project (NIREP). In: Pluim, J.P.W., Likar, B., Gerritsen, F.A. (eds.) WBIR 2006. LNCS, vol. 4057, pp. 128–135. Springer, Heidelberg (2006)

    CrossRef  Google Scholar 

  12. Warfield, S.K., Zou, K.H., Wells, W.M.: Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation. IEEE Transactions on Medical Imaging 23, 903–921 (2004)

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Zhang, D., Guo, Q., Wu, G., Shen, D. (2012). Sparse Patch-Based Label Fusion for Multi-Atlas Segmentation. In: Yap, PT., Liu, T., Shen, D., Westin, CF., Shen, L. (eds) Multimodal Brain Image Analysis. MBIA 2012. Lecture Notes in Computer Science, vol 7509. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33530-3_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-33530-3_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33529-7

  • Online ISBN: 978-3-642-33530-3

  • eBook Packages: Computer ScienceComputer Science (R0)