Advertisement

Accurate Detection in Volumetric Images Using Elastic Registration Based Validation

  • Dominic MaiEmail author
  • Jasmin Dürr
  • Klaus Palme
  • Olaf Ronneberger
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8753)

Abstract

In this paper, we propose a method for accurate detection and segmentation of cells in dense plant tissue of Arabidopsis Thaliana. We build upon a system that uses a top down approach to yield the cell segmentations: A discriminative detection is followed by an elastic alignment of a cell template. While this works well for cells with a distinct appearance, it fails once the detection step cannot produce reliable initializations for the alignment. We propose a validation method for the aligned cell templates and show that we can thereby increase the average precision substantially.

Keywords

Support Vector Machine Linear Support Vector Machine Cell Segmentation Detection Filter Discriminative Classifier 
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.

Notes

Acknowledgements

This work was supported by the Excellence Initiative of the German Federal and State Governments: BIOSS Centre for Biological Signalling Studies (EXC 294) and the Bundesministerium für Bildung und Forschung (SYSTEC, 0101-31P5914).

References

  1. 1.
    Bourdev, L., Maji, S., Brox, T., Malik, J.: Detecting people using mutually consistent poselet activations. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part VI. LNCS, vol. 6316, pp. 168–181. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  2. 2.
    Brox, T., Bourdev, L., Maji, S., Malik, J.: Object segmentation by alignment of poselet activations to image contours. In: IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) (2011)Google Scholar
  3. 3.
    Carreira, J., Sminchisescu, C.: CPMC: automatic object segmentation using constrained parametric min-cuts. IEEE Trans. Pattern Anal. Mach. Intell. 34(7), 1312 (2012)CrossRefGoogle Scholar
  4. 4.
    Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 27:1–27:27 (2011)CrossRefGoogle Scholar
  5. 5.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Schmid, C., Soatto, S., Tomasi, C. (eds.) International Conference on Computer Vision & Pattern Recognition (2005)Google Scholar
  6. 6.
    Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The pascal visual object classes (voc) challenge. Int. J. Comput. Vis. 88(2), 303–338 (2010)CrossRefGoogle Scholar
  7. 7.
    Fernandez, R., Das, P., Mirabet, V., Moscardi, E., Traas, J., Verdeil, J., Malandain, G., Godin, C.: Imaging plant growth in 4D: robust tissue reconstruction and lineaging at cell resolution. Nat. Methods 7(7), 547–553 (2010)CrossRefGoogle Scholar
  8. 8.
    Komodakis, N., Tziritas, G., Paragios, N.: Performance vs computational efficiency for optimizing single and dynamic MRFS: setting the state of the art with primal-dual strategies. Comput. Vis. Image Underst. 112(1), 14–29 (2008)CrossRefGoogle Scholar
  9. 9.
    Liu, K., Schmidt, T., T.Blein, Dürr, J., Palme, K., Ronneberger, O.: Joint 3D cell segmentation and classification in the Arabidopsis root using energy minimization and shape priors. In: IEEE International Symposium on Biomedical Imaging (ISBI) (2013)Google Scholar
  10. 10.
    Mai, D., Fischer, P., Blein, T., Dürr, J., Palme, K., Brox, T., Ronneberger, O.: Discriminative detection and alignment in volumetric data. In: Weickert, J., Hein, M., Schiele, B. (eds.) GCPR 2013. LNCS, vol. 8142, pp. 205–214. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  11. 11.
    Marcuzzo, M., Quelhas, P., Campilho, A., Mendonça, A.M., Campilho, A.: Automated arabidopsis plant root cell segmentation based on SVM classification and region merging. Comput. Biol. Med. 39(9), 785–793 (2009)CrossRefGoogle Scholar
  12. 12.
    Schmidt, T., Pasternak, T., Liu, K., Blein, T., Aubry-Hivet, D., Dovzhenko, A., Dürr, J., Teale, W., Ditengou, F.A., Burkhardt, H., Ronneberger, O., Palme, K.: The irocs toolbox - 3D analysis of the plant root apical meristem at cellular resolution. Plant J. 77(5), 806–814 (2014). http://lmb.informatik.uni-freiburg.de//Publications/2014/SLBR14 CrossRefGoogle Scholar
  13. 13.
    Wu, G., Jia, H., Wang, Q., Shen, D.: Sharpmean: groupwise registration guided by sharp mean image and tree-based registration. NeuroImage 56(4), 968–1981 (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Dominic Mai
    • 1
    • 2
    Email author
  • Jasmin Dürr
    • 3
  • Klaus Palme
    • 2
    • 3
  • Olaf Ronneberger
    • 1
    • 2
  1. 1.Computer Science DepartmentUniversity of FreiburgFreiburgGermany
  2. 2.BIOSS Centre of Biological Signalling StudiesUniversity of FreiburgFreiburgGermany
  3. 3.Institute for Biologie IIUniversity of FreiburgFreiburgGermany

Personalised recommendations