Advertisement

Non-rigid Registration of 3D Multi-channel Microscopy Images of Cell Nuclei

  • Siwei Yang
  • Daniela Köhler
  • Kathrin Teller
  • Thomas Cremer
  • Patricia Le Baccon
  • Edith Heard
  • Roland Eils
  • Karl Rohr
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4190)

Abstract

We present an intensity-based non-rigid registration approach for normalizing 3D multi-channel microscopy images of cell nuclei. A main problem with cell nuclei images is that the intensity structure of different nuclei differs very much, thus an intensity-based registration scheme cannot be used directly. Instead, we first perform a segmentation of the images, smooth them by a Gaussian filter, and then apply an intensity-based algorithm. To improve the convergence rate of the algorithm, we propose an adaptive step length optimization scheme and also employ a multi-resolution scheme. Our approach has been successfully applied using 2D cell-like synthetic images, 3D phantom images as well as 3D multi-channel microscopy images representing different chromosome territories and gene regions (BACs). We also describe an extension of our approach which is applied for the registration of 3D+t (4D) image series of moving cell nuclei.

Keywords

Cell Nucleus Step Length Image Series Chromosome Territory Rigid Registration 
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.

References

  1. 1.
    Zitova, B., Flusser, J.: Image registration methods: a survey. Image and Vision Computing 21, 977–1000 (2003)CrossRefGoogle Scholar
  2. 2.
    Mattes, J., Fieres, J., Beaudouin, J., Gerlich, D., Ellenberg, J., Eils, R.: New tools for visualization and quantification in dynamic processes: Application to the nuclear envelope dynamics during mitosis. In: Niessen, W.J., Viergever, M.A. (eds.) MICCAI 2001. LNCS, vol. 2208, pp. 1323–1325. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  3. 3.
    Rieger, B., Mollenaar, C., Dirks, R.W., van Vliet, L.J.: Alignment of the cell nucleus from labeled proteins only for 4d in vivo imaging. Microscopy Research and Technique 64, 142–150 (2004)CrossRefGoogle Scholar
  4. 4.
    Thirion, J.P.: Image matching as a diffusion process: an analogy with Maxwell’s demons. Medical Image Analysis 2, 243–260 (1998)CrossRefGoogle Scholar
  5. 5.
    Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Trans. on Pattern Analysis and Machine Intelligence 12, 629–639 (1990)CrossRefGoogle Scholar
  6. 6.
    Ibanez, L., Schroeder, W., Ng, L., Cates, J.: The ITK Software Guide. Kitware Inc. (2005)Google Scholar
  7. 7.
    Knuth, D.: The Art of Computer Programming, Sorting and Searching, 3rd edn., vol. 3. Addison-Wesley, Reading (1997)Google Scholar
  8. 8.
    Kerdok, A., Cotin, S., Ottensmeyer, M.: Truth cube: Establishing physical standards for soft tissue simulation. Medical Image Analysis 7, 283–291 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Siwei Yang
    • 1
    • 2
  • Daniela Köhler
    • 3
  • Kathrin Teller
    • 3
  • Thomas Cremer
    • 3
  • Patricia Le Baccon
    • 4
  • Edith Heard
    • 4
  • Roland Eils
    • 1
    • 2
  • Karl Rohr
    • 1
    • 2
  1. 1.Biomedical Computer Vision Group, Dept. Theoretical BioinformaticsDKFZ Heidelberg 
  2. 2.Dept. Bioinformatics and Functional GenomicsUniversity of Heidelberg, IPMBHeidelbergGermany
  3. 3.Dept. of Biology II, Anthropology and Human GeneticsLudwig Maximilians University Munich, BiozentrumGermany
  4. 4.CNRS UMR 218Curie InstituteParisFrance

Personalised recommendations