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Automatic Registration of Serial Cerebral Angiography: A Comparative Review

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Advances in Visual Computing (ISVC 2018)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11241))

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Abstract

Image registration can play a major role in medical imaging as it can be used to identify changes that have occurred over a period of time, thus mirroring treatment effectiveness, recovery, and detection of diseases onset. While medical image registration algorithms have been largely evaluated on MRI and CT, less attention has been given to Digital Subtraction Angiography (DSA). DSA of the brain is the method of choice for the diagnosis of numerous neurovascular conditions and is used during neurovascular surgeries. Numerous studies have relied on semi-automated registration that involve manual selection of matching features to compute the mapping between images. Nevertheless, there are currently a variety of automatic registration methods which have been developed, although the performance of these methods on DSA have not been fully explored. In this paper, we identify and review a variety of automatic registration methods, and evaluate algorithm performance in the context of serial image registration. We find that intensity-based methods are consistent in performance, while feature-based methods can perform better, but are also more variable in success. Ultimately a combined algorithm may be optimal for automatic registration, which can be applied to analyze vasculature information and improve unbiased treatment evaluation in clinical trials.

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References

  1. Baker, S., Matthews, I.: Lucas-Kanade 20 years on: a unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004)

    Article  Google Scholar 

  2. Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (SURF). Comput. Vis. Image Underst. 110(3), 346–359 (2008)

    Article  Google Scholar 

  3. Brody, W.R.: Digital subtraction angiography. IEEE Trans. Nucl. Sci. 29(3), 1176–1180 (1982)

    Article  Google Scholar 

  4. Brown, L.G.: A survey of image registration techniques. ACM Comput. Surv. 24, 325–376 (1992)

    Article  Google Scholar 

  5. Cao, T., Zach, C., Modla, S., Powell, D., Czymmek, K., Niethammer, M.: Multi-modal registration for correlative microscopy using image analogies. Med. Image Anal. 18(6), 914–926 (2014)

    Article  Google Scholar 

  6. Chum, O., Pajdla, T., Sturm, P.: The geometric error for homographies. Comput. Vis. Image Underst. 97(1), 86–102 (2005)

    Article  Google Scholar 

  7. Dellinger, F., Delon, J., Gousseau, Y., Michel, J., Tupin, F., Tupin, F.: SAR-SIFT: a SIFT-like algorithm for SAR images. IEEE Trans. Geosci. Remote. Sens. 53, 453–466 (2015)

    Article  Google Scholar 

  8. Evangelidis, G.D., Psarakis, E.Z.: Parametric image alignment using enhanced correlation coefficient maximization. IEEE Trans. Pattern Anal. Mach. Intell. 30(10), 1858–1865 (2008)

    Article  Google Scholar 

  9. Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381–395 (1981)

    Article  MathSciNet  Google Scholar 

  10. Font, M.A., Arboix, A., Krupinski, J.: Angiogenesis, neurogenesis and neuroplasticity in ischemic stroke. Curr. Cardiol. Rev. 6(3), 238–244 (2010)

    Article  Google Scholar 

  11. Goshtasby, A.A.: 2D and 3-D Image Registration: For Medical, Remote Sensing, and Industrial Applications. Wiley-Interscience, New York (2005)

    Google Scholar 

  12. Kroon, D.J., Slump, C.H.: MRI modality transformation in demon registration. In: Proceedings of the IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009, pp. 963–966. IEEE Signal Processing Society (2009)

    Google Scholar 

  13. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)

    Article  MathSciNet  Google Scholar 

  14. Lucas, B.D., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: Proceedings of the 7th International Joint Conference on Artificial Intelligence, IJCAI 1981, vol. 2, pp. 674–679 (1981)

    Google Scholar 

  15. Ma, W., et al.: Remote sensing image registration with modified sift and enhanced feature matching. IEEE Geosci. Remote. Sens. Lett. 14(1), 3–7 (2017)

    Article  Google Scholar 

  16. Maintz, J.B.A., Viergever, M.A.: A survey of medical image registration. Med. Image Anal. 2, 1–36 (1998)

    Article  Google Scholar 

  17. Mattes, D., Haynor, D.R., Vesselle, H., Lewellyn, T.K., Eubank, W.: Nonrigid multimodality image registration (2001)

    Google Scholar 

  18. Nelder, J.A., Mead, R.: A simplex method for function minimization. Comput. J. 7(4), 308–313 (1965)

    Article  MathSciNet  Google Scholar 

  19. Pennec, X., Cachier, P., Ayache, N.: Understanding the “Demon’s Algorithm”: 3D non-rigid registration by gradient descent. In: Taylor, C., Colchester, A. (eds.) MICCAI 1999. LNCS, vol. 1679, pp. 597–605. Springer, Heidelberg (1999). https://doi.org/10.1007/10704282_64

    Chapter  Google Scholar 

  20. Reddy, B.S., Chatterji, B.N.: An FFT-based technique for translation, rotation, and scale-invariant image registration. IEEE Trans. Image Process. 5(8), 1266–1271 (1996)

    Article  Google Scholar 

  21. Rusinkiewicz, S., Levoy, M.: Efficient variants of the ICP algorithm. In: Proceedings of Third International Conference on 3-D Digital Imaging and Modeling (2001)

    Google Scholar 

  22. Scalzo, F., Liebeskind, D.S.: Perfusion angiography in acute ischemic stroke. Comput. Math. Methods Med. 2016, 2478324 (2016)

    Article  MathSciNet  Google Scholar 

  23. Styner, M., Brechbühler, C., Székely, G., Gerig, G.: Parametric estimate of intensity inhomogeneities applied to MRI. IEEE Trans. Med. Imaging 19, 153–165 (2000)

    Article  Google Scholar 

  24. Szeliski, R.: Image alignment and stitching: a tutorial. Technical report, MSR-TR-2004-92, Microsoft Research, 2004 (2005)

    Google Scholar 

  25. Szeliski, R.: Computer Vision: Algorithms and Applications, 1st edn. Springer, London (2011). https://doi.org/10.1007/978-1-84882-935-0

    Book  MATH  Google Scholar 

  26. Thévenaz, P., Unser, M.: Optimization of mutual information for multiresolution image registration. IEEE Trans. Image Process. 9(12), 2083–2099 (2000)

    Article  Google Scholar 

  27. Thirion, J.P.: Image matching as a diffusion process: an analogy with Maxwell’s demons. Med. Image Anal. 2, 243–260 (1998)

    Article  Google Scholar 

  28. Torr, P.H.S., Zisserman, A.: MLESAC: a new robust estimator with application to estimating image geometry. Comput. Vis. Image Underst. 78, 138–156 (2000)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Neurology and Computer Science, University of California Los Angeles (UCLA), Los Angeles, USA

    Alice Tang, Zhiyuan Zhang & Fabien Scalzo

Authors
  1. Alice Tang
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  2. Zhiyuan Zhang
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  3. Fabien Scalzo
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Corresponding author

Correspondence to Fabien Scalzo .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, USA

    Richard Boyle

  3. University of Nevada, Reno, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, USA

    Darko Koracin

  5. DARPA, Arlington, USA

    Matt Turek

  6. University of Utah, Salt Lake City, USA

    Srikumar Ramalingam

  7. National University of Defense Technology, Changsha, China

    Kai Xu

  8. Microsoft Research Asia, Beijing, China

    Stephen Lin

  9. Bosch Research, Farmington Hills, MI, USA

    Bilal Alsallakh

  10. University of North Carolina at Charlotte, Charlotte, USA

    Jing Yang

  11. Microsoft Research, Redmond, USA

    Eduardo Cuervo

  12. University of Colorado at Colorado Springs, Colorado Springs, USA

    Jonathan Ventura

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Tang, A., Zhang, Z., Scalzo, F. (2018). Automatic Registration of Serial Cerebral Angiography: A Comparative Review. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2018. Lecture Notes in Computer Science(), vol 11241. Springer, Cham. https://doi.org/10.1007/978-3-030-03801-4_1

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  • DOI: https://doi.org/10.1007/978-3-030-03801-4_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-03800-7

  • Online ISBN: 978-3-030-03801-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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