Hostname: page-component-586b7cd67f-vdxz6 Total loading time: 0 Render date: 2024-11-26T00:02:48.117Z Has data issue: false hasContentIssue false
  • English
  • Français

Nonrigid Registration of CLSM Images of Physical Sections with Discontinuous Deformations

Published online by Cambridge University Press:  03 November 2011

Jan Michálek ,
Martin Čapek  and
Lucie Kubínová
Jan Michálek*
Affiliation:
Institute of Physiology, Academy of Sciences of the Czech Republic, v.v.i., Department of Biomathematics, Vídeňská 1083, CZ-14220 Prague 4, Czech Republic
Martin Čapek
Affiliation:
Institute of Physiology, Academy of Sciences of the Czech Republic, v.v.i., Department of Biomathematics, Vídeňská 1083, CZ-14220 Prague 4, Czech Republic
Lucie Kubínová
Affiliation:
Institute of Physiology, Academy of Sciences of the Czech Republic, v.v.i., Department of Biomathematics, Vídeňská 1083, CZ-14220 Prague 4, Czech Republic
*
Corresponding author. E-mail: [email protected]
Get access

Abstract

When biological specimens are cut into physical sections for three-dimensional (3D) imaging by confocal laser scanning microscopy, the slices may get distorted or ruptured. For subsequent 3D reconstruction, images from different physical sections need to be spatially aligned by optimization of a function composed of a data fidelity term evaluating similarity between the reference and target images, and a regularization term enforcing transformation smoothness. A regularization term evaluating the total variation (TV), which enables the registration algorithm to account for discontinuities in slice deformation (ruptures), while enforcing smoothness on continuously deformed regions, was proposed previously. The function with TV regularization was optimized using a graph-cut (GC) based iterative solution. However, GC may generate visible registration artifacts, which impair the 3D reconstruction. We present an alternative, multilabel TV optimization algorithm, which in the examined samples prevents the artifacts produced by GC. The algorithm is slower than GC but can be sped up several times when implemented in a multiprocessor computing environment. For image pairs with uneven brightness distribution, we introduce a reformulation of the TV-based registration, in which intensity-based data terms are replaced by comparison of salient features in the reference and target images quantified by local image entropies.

Keywords

confocal microscopyimage registrationtarget imagereference imagetotal variationdata termsmoothness termL1-TV functionE-L1-TV functiongraph cutsmultilabel optimizationlocal entropy
Type
Software and Techniques Development
Information
Microscopy and Microanalysis , Volume 17 , Issue 6 , December 2011 , pp. 923 - 936
Copyright
Copyright © Microscopy Society of America 2011

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Čapek, M., Brůža, P., Janáček, J., Karen, P., Kubínová, L. & Vágnerová, R. (2009). Volume reconstruction of large tissue specimens from serial physical sections using confocal microscopy and correction of cutting deformations by elastic registration. Microsc Res Tech 72, 110–119.Google Scholar
Čapek, M., Janáček, J. & Kubínová, L. (2006). Methods for compensation of the light attenuation with depth of images captured by a confocal microscope. Microsc Res Tech 69, 624–635.Google Scholar
Cherkassky, B.V. & Goldberg, A.V. (1997). On implementing the push-relabel method for the maximum flow problem. Algorithmica 19, 390–410.CrossRefGoogle Scholar
Janáček, J. (2009). Image registration by discontinuous correspondence calculated iteratively using graph cuts. In Microscopy Conference 2009 in Graz, Vol. 2: Life Sciences, Pabst, M.A. & Zellnig, G. (Eds.), pp. 417–418. Graz, Austria: Institute for Electron Microscopy, Graz University of Technology.Google Scholar
Jirkovská, M., Náprstková, I., Janáček, J., Kučera, T., Macásek, J., Karen, P. & Kubínová, L. (2005). Three-dimensional reconstructions from non-deparaffinized tissue sections. Anatomy Embryol 210(3), 163–173.Google Scholar
Karen, P., Jirkovská, M., Tomori, Z., Demjénová, E., Janáček, J. & Kubínová, L. (2003). Three-dimensional computer reconstruction of large tissue volumes based on composing series of high-resolution confocal images by GlueMRC and LinkMRC software. Microsc Res Tech 62, 415–422.Google Scholar
Kolmogorov, V. & Zabih, R. (2004). What energy functions can be minimized via graph cuts? IEEE Trans PAMI 26, 147–159.CrossRefGoogle ScholarPubMed
Michálek, J., Čapek, M.J. & Kubínová, L. (2010). Compensation of inhomogeneous fluorescence signal distribution in 2D images acquired by confocal microscopy. Microsc Res Tech 74, 831–838.CrossRefGoogle ScholarPubMed
Rohlfing, T., Brandt, R., Maurer, R.R. & Menzel, R. (2001). Bee brains, B-splines and computational democracy: Generating an average shape atlas. In Proceedings of IEEE Workshop on Mathematical Methods in Biomedical Image Analysis—MMBIA 2001, Staib, L. (Ed.), pp. 187–194. Los Alamitos, CA: IEEE Computer Society.Google Scholar
Schlesinger, M.I. & Giginyak, V.V. (2007). Solution to structural recognition (MAX, +)-problems by their equivalent transformations. Control Systems and Computers, Part 1, Number 1, pp. 3–15and Part 2, Number 2, pp. 3–18. Kiev, Ukraine: Institute of Cybernetics of National Academy of Sciences of Ukraine.Google Scholar
Schmitt, O., Modersitzki, J., Heldmann, S., Wirtz, S. & Fischer, B. (2007). Image registration of sectioned brains. Int J Comput Vision 73, 5–39.Google Scholar
Stewart, Ch.V., Tsai, Ch.-L. & Amitha Perera, A.G. (2003). A view-based approach to registration: Theory and application to vascular image registration. In Proceedings of the 18th International Conference on Information Processing in Medical Imaging, IPMI 2003, Lecture Notes in Computer Science, 2732, 475–486. Berlin, Germany: Springer.Google Scholar
Thirion, J.-P. (1998). Image matching as a diffusion process: An analogy with Maxwell's demons. Med Image Anal 2, 243–260.Google Scholar
Werner, T. (2005). A linear programming approach to max-sum problem: A review. IEEE Trans PAMI 29, 1165–1179.Google Scholar
Yang, S., Köhler, D., Teller, K., Cremer, T., Le Baccon, P., Heard, E., Eils, R. & Rohr, K. (2006). Non-rigid registration of 3D multi-channel microscopy images of cell nuclei. In MICCAI 2006, LNCS 4190, Larsen, R., Nielsen, M. & Sporring, J. (Eds.), pp. 907–914. Berlin, Heidelberg: Springer-Verlag.Google Scholar
Supplementary material: PDF

Michálek Supplementary Materials

Michálek Supplementary Materials

Download Michálek Supplementary Materials(PDF)
PDF 7.7 MB