Abstract
Correlative microscopy is a methodology combining the functionality of light microscopy with the high resolution of electron microscopy and other microscopy technologies for the same biological specimen. In this paper, we propose an image registration method for correlative microscopy, which is challenging due to the distinct appearance of biological structures when imaged with different modalities. Our method is based on image analogies and allows to transform images of a given modality into the appearance-space of another modality. Hence, the registration between two different types of microscopy images can be transformed to a mono-modality image registration. We use a sparse representation model to obtain image analogies. The method makes use of representative corresponding image training patches of two different imaging modalities to learn a dictionary capturing appearance relations. We test our approach on backscattered electron (BSE) Scanning Electron Microscopy (SEM)/confocal and Transmission Electron Microscopy (TEM)/confocal images and show improvements over direct registration using a mutual-information similarity measure to account for differences in image appearance.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Caplan, J., Niethammer, M., Taylor II, R.M., Czymmek, K.J.: The power of correlative microscopy: multi-modal, multi-scale, multi-dimensional. Current Opinion in Structural Biology (2011)
Fronczek, D., Quammen, C., Wang, H., Kisker, C., Superfine, R., Taylor, R., Erie, D.A., Tessmer, I.: High accuracy FIONA-AFM hybrid imaging. Ultramicroscopy (2011)
Yang, S., Kohler, D., Teller, K., Cremer, T., Le Baccon, P., Heard, E., Eils, R., Rohr, K.: Nonrigid registration of 3-D multichannel microscopy images of cell nuclei. IEEE Transactions on Image Processing 17(4), 493–499 (2008)
Hertzmann, A., Jacobs, C.E., Oliver, N., Curless, B., Salesin, D.H.: Image analogies. In: The 28th Annual Conference on Computer Graphics and Interactive Techniques, pp. 327–340. ACM (2001)
Wells III, W.M., Viola, P., Atsumi, H., Nakajima, S., Kikinis, R.: Multi-modal volume registration by maximization of mutual information. Medical Image Analysis 1(1), 35–51 (1996)
Bruckstein, A.M., Donoho, D.L., Elad, M.: From sparse solutions of systems of equations to sparse modeling of signals and images. SIAM Review 51(1), 34–81 (2009)
Elad, M.: Sparse and redundant representations: from theory to applications in signal and image processing. Springer (2010)
Boyd, S., Parikh, N., Chu, E., Peleato, B., Eckstein, J.: Distributed optimization and statistical learning via the alternating direction method of multipliers. Machine Learning 3(1), 1–123 (2010)
Boulanger, J., Kervrann, C., Bouthemy, P., Elbau, P., Sibarita, J.B., Salamero, J.: Patch-based nonlocal functional for denoising fluorescence microscopy image sequences. IEEE Transactions on Medical Imaging 29(2), 442–454 (2010)
Roy, S., Carass, A., Prince, J.: A Compressed Sensing Approach for MR Tissue Contrast Synthesis. In: Székely, G., Hahn, H.K. (eds.) IPMI 2011. LNCS, vol. 6801, pp. 371–383. Springer, Heidelberg (2011)
Combettes, P.L., Pesquet, J.C.: Proximal splitting methods in signal processing. In: Fixed-Point Algorithms for Inverse Problems in Science and Engineering, pp. 185–212. Springer (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cao, T., Zach, C., Modla, S., Powell, D., Czymmek, K., Niethammer, M. (2012). Registration for Correlative Microscopy Using Image Analogies. In: Dawant, B.M., Christensen, G.E., Fitzpatrick, J.M., Rueckert, D. (eds) Biomedical Image Registration. WBIR 2012. Lecture Notes in Computer Science, vol 7359. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31340-0_31
Download citation
DOI: https://doi.org/10.1007/978-3-642-31340-0_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31339-4
Online ISBN: 978-3-642-31340-0
eBook Packages: Computer ScienceComputer Science (R0)