Advertisement

Supervoxel Classification Forests for Estimating Pairwise Image Correspondences

  • Fahdi Kanavati
  • Tong Tong
  • Kazunari Misawa
  • Michitaka Fujiwara
  • Kensaku Mori
  • Daniel Rueckert
  • Ben Glocker
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9352)

Abstract

This paper proposes a general method for establishing pairwise correspondences, which is a fundamental problem in image analysis. The method consists of over-segmenting a pair of images into supervoxels. A forest classifier is then trained on one of the images, the source, by using supervoxel indices as voxelwise class labels. Applying the forest on the other image, the target, yields a supervoxel labelling which is then regularized using majority voting within the boundaries of the target’s supervoxels. This yields semi-dense correspondences in a fully automatic, efficient and robust manner. The advantage of our approach is that no prior information or manual annotations are required, making it suitable as a general initialisation component for various medical imaging tasks that require coarse correspondences, such as, atlas/patch-based segmentation, registration, and atlas construction. Our approach is evaluated on a set of 150 abdominal CT images. In this dataset we use manual organ segmentations for quantitative evaluation. In particular, the quality of the correspondences is determined in a label propagation setting. Comparison to other state-of-the-art methods demonstrate the potential of supervoxel classification forests for estimating image correspondences.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Susstrunk, S.: SLIC superpixels. No. EPFL-REPORT-149300, p. 15, June 2010Google Scholar
  2. 2.
    Breiman, L.: Random forests. Machine learning, 5–32 (2001)Google Scholar
  3. 3.
    Breiman, L., Friedman, J., Stone, C.J., Olshen, R.A.: Classification and regression trees. CRC Press (1984)Google Scholar
  4. 4.
    Coupé, P., Manjón, 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(2), 940–954 (2011)CrossRefGoogle Scholar
  5. 5.
    Criminisi, A., Shotton, J., Konukoglu, E.: Decision forests for classification, regression, density estimation, manifold learning and semi-supervised learning. Learning 7, 81–227 (2011)zbMATHGoogle Scholar
  6. 6.
    Criminisi, A., Shotton, J., Robertson, D., Konukoglu, E.: Regression forests for efficient anatomy detection and localization in CT studies. In: Menze, B., Langs, G., Tu, Z., Criminisi, A. (eds.) MICCAI 2010. LNCS, vol. 6533, pp. 106–117. Springer, Heidelberg (2011) Google Scholar
  7. 7.
    Glocker, B., Zikic, D., Haynor, D.R.: Robust registration of longitudinal spine CT. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014, Part I. LNCS, vol. 8673, pp. 251–258. Springer, Heidelberg (2014) Google Scholar
  8. 8.
    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)CrossRefGoogle Scholar
  9. 9.
    Lucchi, A., Smith, K., Achanta, R., Knott, G., Fua, P.: Supervoxel-based segmentation of mitochondria in em image stacks with learned shape features. IEEE Transactions on Medical Imaging 31(2), 474–486 (2012)CrossRefGoogle Scholar
  10. 10.
    Montillo, A., Shotton, J., Winn, J., Iglesias, J.E., Metaxas, D., Criminisi, A.: Entangled decision forests and their application for semantic segmentation of CT images, pp. 184–196 (2011)Google Scholar
  11. 11.
    Tong, T., Wolz, R., Wang, Z., Gao, Q., Misawa, K., Fujiwara, M., Mori, K., Hajnal, J.V., Rueckert, D.: Discriminative dictionary learning for abdominal multi-organ segmentation. Medical Image Analysis 23(1), 92–104 (2015)CrossRefGoogle Scholar
  12. 12.
    Wang, H., Yushkevich, P.A.: Multi-atlas segmentation without registration: a supervoxel-based approach, pp. 535–542 (2013)CrossRefGoogle Scholar
  13. 13.
    Wolz, R., Chu, C., Misawa, K., Fujiwara, M., Mori, K., Rueckert, D.: Automated abdominal multi-organ segmentation with subject-specific atlas generation. IEEE Transactions on Medical Imaging 32(9), 1723–1730 (2013)CrossRefGoogle Scholar
  14. 14.
    Zikic, D., Glocker, B., Criminisi, A.: Encoding atlases by randomized classification forests for efficient multi-atlas label propagation. Medical image analysis, July 2014Google Scholar
  15. 15.
    Zitova, B., Flusser, J.: Image registration methods: a survey. Image and vision computing 21(11), 977–1000 (2003)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (http://creativecommons.org/licenses/by-nc/2.5/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

Authors and Affiliations

  • Fahdi Kanavati
    • 1
  • Tong Tong
    • 1
  • Kazunari Misawa
    • 2
  • Michitaka Fujiwara
    • 3
  • Kensaku Mori
    • 4
  • Daniel Rueckert
    • 1
  • Ben Glocker
    • 1
  1. 1.Biomedical Image Analysis Group, Department of ComputingImperial College LondonLondonUK
  2. 2.Aichi Cancer CenterNagoyaJapan
  3. 3.Nagoya University HospitalNagoyaJapan
  4. 4.Information and CommunicationsNagoya UniversityNagoyaJapan

Personalised recommendations