Abstract
In this paper, we consider visualization of displacement fields via optical flow methods in elastographic experiments consisting of a static compression of a sample. We propose an elastographic optical flow method (EOFM) which takes into account experimental constraints, such as appropriate boundary conditions, the use of speckle information, as well as the inclusion of structural information derived from knowledge of the background material. We present numerical results based on both simulated and experimental data from an elastography experiment in order to demonstrate the relevance of our proposed approach.
Supported by the Austrian Science Fund (FWF): project F6807-N36 (ES and OS), project F6805-N36 (SH), and project F6803-N36 (LK and WD).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Aubert, G., Kornprobst, P.: Mathematical Problems in Image Processing, 2nd edn. Springer, New York (2006)
Baker, S., Scharstein, D., Lewis, J.P., Roth, S., Black, M.J., Szeliski, R.: A database and evaluation methodology for optical flow. Int. J. Comput. Vis. 92(1), 1–31 (2011)
Black, M.J., Anandan, P.: The robust estimation of multiple motions: parametric and piecewise-smooth flow fields. Comput. Vis. Image Und. 63, 75–104 (1996)
Brox, T., Malik, J.: Large displacement optical flow: descriptor matching in variational motion estimation. IEEE Trans. Pattern Anal. Mach. Intell. 33(3), 500–513 (2011)
Chen Z., Jin H., Lin Z., Cohen S., Wu Y.: Large displacement optical flow from nearest neighbor fields. In: IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, pp. 2443–2450 (2013)
Duncan, D.D., Kirkpatrick, S.J.: Processing algorithms for tracking speckle shifts in optical elastography of biological tissues. J. Biomed. Opt. 6(4), 418 (2001)
Doyley, M.M.: Model-based elastography: a survey of approaches to the inverse elasticity problem. Phys. Med. Biol. 57(3), R35–R73 (2012)
Glatz, T., Scherzer, O., Widlak, T.: Texture generation for photoacoustic elastography. J. Math. Imaging Vision 52(3), 369–384 (2015)
Haber, E., Modersitzki, J.: A multilevel method for image registration. SIAM J. Sci. Comput. 27(5), 1594–1607 (2006)
Hubmer, S., Sherina, E., Neubauer, A., Scherzer, O.: Lamé parameter estimation from static displacement field measurements in the framework of nonlinear inverse problems. SIAM J. Imag. Sci. 11(2), 1268–1293 (2018)
Lauze, F., Kornprobst, P., Memin, E.: A Coarse to fine multiscale approach for linear least squares optical flow estimation. In: British Machine Vision Conference, pp. 767–776 (2010)
Manduca, A., et al.: Magnetic resonance elastography: non-invasive mapping of tissue elasticity. Med. Image Anal. 5, 237–354 (2001)
Meinhardt-Llopis, E., Sánchez, P.J., Kondermann, D.: Horn-Schunck optical flow with a multi-scale strategy. Image Proc. On Line 3, 151–172 (2013)
Modersitzki, J.: FAIR: Flexible Algorithms for Image Registration. Society for Industrial and Applied Mathematics (SIAM), Philadelphia, PA (2009)
Schmid J., et al.: Texture generation in compressional photoacoustic elastography. In: Photons Plus Ultrasound: Imaging and Sensing 2015, Proceedings of SPIE, p. 93232S (2015)
Schmitt, J.M.: OCT elastography: imaging microscopic deformation and strain of tissue. Opt. Express 3(6), 199–211 (1998)
Schmitt, J.M., Xiang, S.H., Yung, K.M.: Differential absorption imaging with optical coherence tomography. J. Opt. Soc. Amer. A 15, 2288–2296 (1998)
Schnörr, C.: Determining optical flow for irregular domains by minimizing quadratic functionals of a certain class. Int. J. Comput. Vision 6, 25–38 (1991)
Sherina, E., Krainz, L., Hubmer, S., Drexler, W., Scherzer, O.: Displacement field estimation from OCT images utilizing speckle information with applications in quantitative elastography. Inverse Prob. 36(12), 124003 (2020)
Sun, D., Roth, S., Black, M.J.: A quantitative qnalysis of current practices in optical flow estimation and the principles behind them. Int. J. Comput. Vision 106(2), 115–137 (2013)
Wang, S., Larin, K.V.: Optical coherence elastography for tissue characterization: a review. J. Biophotonics 8(4), 279–302 (2015)
Weickert, J., Bruhn, A., Brox, T., Papenberg, N.: A survey on variational optic flow methods for small displacements. In: Scherzer, O. (ed.) Mathematical Models for Registration and Applications to Medical Imaging, vol. 10, pp. 103–136. Springer, Heidelberg (2006). https://doi.org/10.1007/978-3-540-34767-5_5
Wijesinghe, P., Kennedy, B.F., Sampson, D.D.: Chapter 9 - Optical elastography on the microscale. In: Alam, S.K., Garra, B.S. (eds.) Tissue Elasticity Imaging, pp. 185–229. Elsevier, Amsterdam (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Sherina, E., Krainz, L., Hubmer, S., Drexler, W., Scherzer, O. (2021). Challenges for Optical Flow Estimates in Elastography. In: Elmoataz, A., Fadili, J., Quéau, Y., Rabin, J., Simon, L. (eds) Scale Space and Variational Methods in Computer Vision. SSVM 2021. Lecture Notes in Computer Science(), vol 12679. Springer, Cham. https://doi.org/10.1007/978-3-030-75549-2_11
Download citation
DOI: https://doi.org/10.1007/978-3-030-75549-2_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-75548-5
Online ISBN: 978-3-030-75549-2
eBook Packages: Computer ScienceComputer Science (R0)