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Biomedical Engineering / Biomedizinische Technik

Joint Journal of the German Society for Biomedical Engineering in VDE and the Austrian and Swiss Societies for Biomedical Engineering and the German Society of Biomaterials

Editor-in-Chief: Dössel, Olaf

Editorial Board: Augat, Peter / Habibović, Pamela / Haueisen, Jens / Jahnen-Dechent, Wilhelm / Jockenhoevel, Stefan / Knaup-Gregori, Petra / Lenarz, Thomas / Leonhardt, Steffen / Plank, Gernot / Radermacher, Klaus M. / Schkommodau, Erik / Stieglitz, Thomas / Boenick, Ulrich / Jaramaz, Branislav / Kraft, Marc / Lenthe, Harry / Lo, Benny / Mainardi, Luca / Micera, Silvestro / Penzel, Thomas / Robitzki, Andrea A. / Schaeffter, Tobias / Snedeker, Jess G. / Sörnmo, Leif / Sugano, Nobuhiko / Werner, Jürgen /

6 Issues per year


IMPACT FACTOR 2017: 1.096
5-year IMPACT FACTOR: 1.492

CiteScore 2017: 0.48

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Online
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1862-278X
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Volume 63, Issue 5

Issues

Volume 57 (2012)

Intraoperative motion correction in neurosurgery: a comparison of intensity- and feature-based methods

Fang Chen
  • Corresponding author
  • Institute of Circuits and Systems, Faculty of Electrical and Computer Engineering, Technische Universität Dresden, 01062 Dresden, Germany
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  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Jan Müller
  • Institute of Circuits and Systems, Faculty of Electrical and Computer Engineering, Technische Universität Dresden, 01062 Dresden, Germany
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/ Jens Müller
  • Institute of Circuits and Systems, Faculty of Electrical and Computer Engineering, Technische Universität Dresden, 01062 Dresden, Germany
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/ Juliane Müller
  • Carl Gustav Carus Faculty of Medicine, Department of Anesthesiology and Intensive Care Medicine, Clinical Sensoring and Monitoring, Technische Universität Dresden, 01307 Dresden, Germany
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/ Elisa Böhl
  • Carl Gustav Carus Faculty of Medicine, Department of Neurosurgery, Technische Universität Dresden, 01307 Dresden, Germany
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/ Matthias Kirsch
  • Carl Gustav Carus Faculty of Medicine, Department of Neurosurgery, Technische Universität Dresden, 01307 Dresden, Germany
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/ Ronald Tetzlaff
  • Institute of Circuits and Systems, Faculty of Electrical and Computer Engineering, Technische Universität Dresden, 01062 Dresden, Germany
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Published Online: 2018-09-21 | DOI: https://doi.org/10.1515/bmt-2017-0188

Abstract

The intraoperative identification of normal and anomalous brain tissue can be disturbed by pulsatile brain motion and movements of the patient and surgery devices. The performance of four motion correction methods are compared in this paper: Two intensity-based, applying optical flow algorithms, and two feature-based, which take corner features into account to track brain motion. The target registration error with manually selected marking points and the temporal standard deviation of intensity were analyzed in the evaluation. The results reveal the potential of the two types of methods.

Keywords: brain motion; feature matching; intensity temporal standard deviation; optical flow; target registration error

Correction note: In the article version published online on September 21, 2018, three authors were inadvertently omitted from the author list. The names of Juliane Müller, Elisa Böhl and Matthias Kirsch were added on September 26, 2018.

References

  • [1]

    Gorbach AM, Heiss J, Kufta C, Sato S, Fedio P, Kammerer WA, et al. Intraoperative infrared functional imaging of human brain. Ann Neurol 2003;54:297–309.Google Scholar

  • [2]

    Gorbach AM, Heiss JD, Kopylev L, Oldfield EH. Intraoperative infrared imaging of brain tumors. J Neurosurg 2004;101:960.Google Scholar

  • [3]

    Steiner G, Sobottka SB, Koch E, Schackert G, Kirsch M. Intraoperative imaging of cortical cerebral perfusion by time-resolved thermography and multivariate data analysis. J Biomed Opt 2011;16:016001.Google Scholar

  • [4]

    Hollmach J, Hoffmann N, Schnabel C, Küchler S, Sobottka S, Kirsch M, et al. Highly sensitive time-resolved thermography and multivariate image analysis of the cerebral cortex for intrasurgical diagnostics. In: Proceedings of SPIE; 2013:8565: 856550. https://doi.org/10.1117/12.2002342

  • [5]

    Hoffmann N, Hollmach J, Schnabel C, Radev Y, Kirsch M, Petersohn U, et al. Wavelet subspace analysis of intraoperative thermal imaging for motion filtering. In: Campilho A, Kamel M, editors. ICIAR 2014, PART II. Springer, Cham; 2014:411–20.Google Scholar

  • [6]

    Senger V, Hoffmann N, Müller J, Hollmach J, Schnabel C, Radev Y, et al. Motion correction of thermographic images in neurosurgery: performance comparison. In: Biomedical Circuits and Systems Conference (BioCAS), Lausanne, 2014 IEEE; 2014:121–4.Google Scholar

  • [7]

    Zitova B, Flusser J. Image registration methods: a survey. Image Vision Comput 2003;21:977–1000.Google Scholar

  • [8]

    Ji S, Fan X, Roberts DW, Hartov A, Paulsen KD. Tracking Cortical Surface Deformation Using Stereovision. New York, NY: Springer New York; 2013:169–76.Google Scholar

  • [9]

    Ji S, Fan X, Roberts DW, Hartov A, Paulsen KD. Cortical surface shift estimation using stereovision and optical flow motion tracking via projection image registration. Med Image Anal 2014;18:1169–83.Google Scholar

  • [10]

    Faria C, Sadowsky O, Bicho E, Ferrigno G, Joskowicz L, Shoham M, et al. Validation of a stereo camera system to quantify brain deformation due to breathing and pulsatility. Med Phys 2014;41:113502.Google Scholar

  • [11]

    Kumar AN, Miga MI, Pheiffer TS, Chambless LB, Thompson RC, Dawant BM. Automatic tracking of intraoperative brain surface displacements in brain tumor surgery. In: Engineering in Medicine and Biology Society (EMBC), Chicago, 2014 36th Annual International Conference of the IEEE. IEEE; 2014:1509–12.Google Scholar

  • [12]

    Jiang J, Nakajima Y, Sohma Y, Saito T, Kin T, Oyama H, et al. Marker-less tracking of brain surface deformations by non-rigid registration integrating surface and vessel/sulci features. Int J Comput Assist Radiol Surg 2016;11:1687–701.Google Scholar

  • [13]

    Horn BK, Schunck BG. Determining optical flow. Artif Intell 1981;17:185–203.Google Scholar

  • [14]

    Lucas BD, Kanade T. An iterative image registration technique with an application to stereo vision. In: Proceedings of the 7th International Joint Conference on Artificial Intelligence, Vol. 2, IJCAI’81. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc.; 1981:674–9. http://dl.acm.org/citation.cfm?id=1623264.1623280.

  • [15]

    Perona P, Malik J. Scale-space and edge detection using anisotropic diffusion. IEEE Trans Pattern Anal Mach Intell 1990;12:629–39.Google Scholar

  • [16]

    Pizer SM, Amburn EP, Austin JD, Cromartie R, Geselowitz A, Greer T, et al. Adaptive histogram equalization and its variations. Comput Vision Graph Image Process 1987;39:355–68.Google Scholar

  • [17]

    Rosten E, Drummond T. Fusing points and lines for high performance tracking. In: IEEE International Conference on Computer Vision, Vol. 2, October; 2005:1508–15.Google Scholar

  • [18]

    Sdika M, Alston L, Mahieu-Williame L, Guyotat J, Rousseau D, Montcel B. Robust real time motion compensation for intraoperative video processing during neurosurgery. In: Biomedical Imaging (ISBI), 2016 IEEE 13th International Symposium on. IEEE; 2016:1046–9.Google Scholar

  • [19]

    Harris C, Stephens M. A combined corner and edge detector. In: Alvey vision conference, Vol. 15, Citeseer; 1988:10–5244.Google Scholar

  • [20]

    Calonder M, Lepetit V, Strecha C, Fua P. Brief: binary robust independent elementary features. Computer Vision–ECCV 2010;2010:778–92.Google Scholar

  • [21]

    Shepard D. A two-dimensional interpolation function for irregularly-spaced data. In: Proceedings of the 1968 23rd ACM national conference. ACM; 1968;517–24.Google Scholar

About the article

Received: 2017-10-26

Accepted: 2018-08-30

Published Online: 2018-09-21

Published in Print: 2018-10-25


Author Statement

Research funding: This work is funded by the European Social Fund (grant no. 100270108).

Conflict of interest: Authors state no conflict of interest.

Informed consent: Informed consent is not applicable.

Ethical approval: The research related to human use complies with all the relevant national regulations, institutional policies and was performed in accordance with the tenets of the Helsinki Declaration, and has been approved by the authors’ institutional review board or equivalent committee (EK 153052012).


Citation Information: Biomedical Engineering / Biomedizinische Technik, Volume 63, Issue 5, Pages 573–578, ISSN (Online) 1862-278X, ISSN (Print) 0013-5585, DOI: https://doi.org/10.1515/bmt-2017-0188.

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