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A Motion Correction Framework for Time Series Sequences in Microscopy Images

Published online by Cambridge University Press:  15 February 2013

Ankur N. Kumar ,
Kurt W. Short  and
David W. Piston
Ankur N. Kumar
Affiliation:
Department of Electrical Engineering, 367 Jacobs Hall, Vanderbilt University, Nashville, TN 37212, USA
Kurt W. Short
Affiliation:
Department of Molecular Physiology & Biophysics, 747 Light Hall, Vanderbilt University, Nashville, TN 37232, USA
David W. Piston*
Affiliation:
Department of Molecular Physiology & Biophysics, 747 Light Hall, Vanderbilt University, Nashville, TN 37232, USA
*
*Corresponding author. E-mail: [email protected]
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Abstract

With the advent of in vivo laser scanning fluorescence microscopy techniques, time-series and three-dimensional volumes of living tissue and vessels at micron scales can be acquired to firmly analyze vessel architecture and blood flow. Analysis of a large number of image stacks to extract architecture and track blood flow manually is cumbersome and prone to observer bias. Thus, an automated framework to accomplish these analytical tasks is imperative. The first initiative toward such a framework is to compensate for motion artifacts manifest in these microscopy images. Motion artifacts in in vivo microscopy images are caused by respiratory motion, heart beats, and other motions from the specimen. Consequently, the amount of motion present in these images can be large and hinders further analysis of these images. In this article, an algorithmic framework for the correction of time-series images is presented. The automated algorithm is comprised of a rigid and a nonrigid registration step based on shape contexts. The framework performs considerably well on time-series image sequences of the islets of Langerhans and provides for the pivotal step of motion correction in the further automatic analysis of microscopy images.

Keywords

nonrigid registrationmotion correctionshape contextsvessel filterdeconvolutionpoint-based registrationthin plate splinesimage processingin vivo microscopy
Type
Software, Techniques, and Equipment Development
Information
Microscopy and Microanalysis , Volume 19 , Issue 2 , April 2013 , pp. 433 - 450
Copyright
Copyright © Microscopy Society of America 2013

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References

Belongie, S., Malik, J. & Puzicha, J. (2002). Shape matching and object recognition using shape contexts. IEEE T Pattern Anal 24, 509–522.Google Scholar
Biggs, D.S. (2010). 3D deconvolution microscopy. In Current Protocols in Cytometry, Robinson, J.P. (Ed.), Chap. 12, Unit 12.19, 1–20. Hoboken, NJ: John Wiley & Sons, Inc. Google Scholar
Bookstein, F.L. (1989). Principal warps: Thin-plate splines and the decomposition of deformations. IEEE T Pattern Anal 11, 567–585.Google Scholar
Cannell, M.B., McMorland, A. & Soeller, C. (2006). Image enhancement by deconvolution. In Handbook of Biological Confocal Microscopy, Pawley, J. (Ed.), pp. 488–500. New York: Springer Press.Google Scholar
Dey, N., Blanc-Feraud, L., Zimmer, C., Roux, P., Kam, Z., Olivo-Marin, J.-C. & Zerubia, J. (2006). Richardson-Lucy algorithm with total variation regularization for 3D confocal microscope deconvolution. Microsc Res Tech 69, 260–266.Google Scholar
Dusch, E., Dorval, T., Vincent, N., Wachsmuth, M. & Genovesio, A. (2007). Three-dimensional point spread function model for line-scanning confocal microscope with high-aperture objective. J Microsc 228, 132–138.Google Scholar
Fitzpatrick, J.M., Hill, D.L.G. & Maurer, C.R. (2000). Image registration. In Handbook of Medical Imaging, Sonka, M. & Fitzpatrick, J.M. (Eds.), Vol. II, Chap. 8, pp. 447–514. Bellingham, WA: SPIE Press.Google Scholar
Forsyth, D.A. & Ponce, J. (2002). Edge detection. In Computer Vision: A Modern Approach, Chap. 8, pp. 230–236. Upper Saddle River, NJ: Prentice Hall.Google Scholar
Frangi, A.F., Niessen, W.J., Vincken, K.L. & Viergever, M.A. (1998). Multiscale vessel enhancement filtering. In MICCAI'98: Medical Image Computing and Computer-Assisted Intervention, Lecture Notes in Computer Science, Wells, W.M., Colchester, A. & Delp, S. (Eds.), pp. 130–137. Berlin: Springer Verlag.Google Scholar
Greenberg, D.S. & Kerr, J.N.D. (2009). Automated correction of fast motion artifacts for two-photon imaging of awake animals. J Neurosci Methods 176, 1–15.Google Scholar
Hara, M., Wang, X., Kawamura, T., Bindokas, V.P., Dizon, R.F., Alcoser, S.Y., Magnuson, M.A. & Bell, G.I. (2003). Transgenic mice with green fluorescent protein-labeled pancreatic beta-cells. Am J Physiol Endocrinol Metab 284, E177–E183.Google Scholar
Kuhn, H.W. (1955). The Hungarian Method for the assignment problem. Nav Res Logist Q 2, 83–97.Google Scholar
Lee, J., Srinivasan, V., Radhakrishnan, H. & Boas, D.A. (2011). Motion correction for phase-resolved dynamic optical coherence tomography imaging of rodent cerebral cortex. Opt Express 19, 21258–21270.Google Scholar
Lorenz, K.S., Salama, P., Dunn, K.W. & Delp, E.J. (2011). Digital correction of motion artifacts in microscopy image sequences collected from living animals using rigid and nonrigid registration. J Microsc 245, 148–160.Google Scholar
Lucy, L.B. (1974). An iterative technique for the rectification of observed distributions. Astron J 79, 745.CrossRefGoogle Scholar
McNally, J.G., Karpova, T., Cooper, J. & Conchello, J.A. (1999). Three-dimensional imaging by deconvolution microscopy. Methods 19, 372–385.Google Scholar
Miyake, T., Murakami, T. & Ohtsuka, A. (1992). Incomplete vascular casting for scanning electron microscope study of the microcirculatory patterns in the rat pancreas. Arch Histol Cytol 55(4), 397–406.Google Scholar
Mori, G., Belongie, S. & Malik, J. (2005). Efficient shape matching using shape contexts. IEEE T Pattern Anal 27, 1832–1837.Google Scholar
Nyman, L.R., Ford, E., Powers, A.C. & Piston, D.W. (2010). Glucose-dependent blood flow dynamics in murine pancreatic islets in vivo . Am J Physiol Endocrinol Metab 298, E807–E814.Google Scholar
Papadimitriou, C.H. & Steiglitz, K. (1998). Weighted matching. In Combinatorial Optimization: Algorithms and Complexity, Chap. 11, pp. 248–254. Mineola, NY: Dover Publications.Google Scholar
Pisano, E.D., Zong, S., Hemminger, B.M., DeLuca, M., Johnston, R.E., Muller, K., Braeuning, M.P. & Pizer, S.M. (1998). Contrast limited adaptive histogram equalization image processing to improve the detection of simulated spiculations in dense mammograms. J Digit Imaging 11, 193–200.Google Scholar
Quoix, N., Cheng-Xue, R., Guiot, Y., Herrera, P.L., Henquin, J.-C. & Gilon, P. (2007). The GluCre-ROSA26EYFP mouse: A new model for easy identification of living pancreatic alpha-cells. FEBS Lett 581, 4235–4240.Google Scholar
Richardson, W.H. (1972). Bayesian-based iterative method of image restoration. J Opt Soc Am 65, 55–59.Google Scholar
Rohr, K., Stiehl, H.S., Sprengel, R., Buzug, T.M., Weese, J. & Kuhn, M.H. (2001). Landmark-based elastic registration using approximating thin-plate splines. IEEE T Med Imag 20, 526–534.CrossRefGoogle ScholarPubMed
Sandison, D.R. & Webb, W.W. (1994). Background rejection and signal-to-noise optimization in confocal and alternative fluorescence microscopes. Appl Optic 33, 603–615.CrossRefGoogle ScholarPubMed
Suckale, J. & Solimena, M. (2008). Pancreas islets in metabolic signaling—Focus on the beta-cell. Front Biosci 13, 7156–7171.Google Scholar
Thayananthan, A., Stenger, B., Torr, P.H.S. & Cipolla, R. (2003). Shape context and chamfer matching in cluttered scenes. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Madison, WI, June 2003, vol. 1, pp. 127–133.Google Scholar
Verveer, P.J., Gemkow, M.J. & Jovin, T.M. (1999). A comparison of image restoration approaches applied to three-dimensional confocal and wide-field fluorescence microscopy. J Microsc 193, 50–61.Google Scholar
Vonesch, C. & Unser, M. (2008). A fast thresholded landweber algorithm for wavelet-regularized multidimensional deconvolution. IEEE T Imag Process 17, 539–549.Google Scholar
Wolleschensky, R., Zimmermann, B. & Kempe, M. (2006). High-speed confocal fluorescence imaging with a novel line scanning microscope. J Biomed Optic 11, 064011. Google Scholar
Yang, S., Kohler, D., Teller, K., Cremer, T., Le Baccon, P., Heard, E., Eils, R. & Rohr, K. (2008). Nonrigid registration of 3-D multichannel microscopy images of cell nuclei. IEEE T Imag Process 17, 493–499.Google Scholar
Zuiderveld, K. (1994). Contrast limited adaptive histogram equalization. In Graphic Gems IV, pp. 474–485. San Diego, CA: Academic Press.Google Scholar
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