Hough Space Parametrization: Ensuring Global Consistency in Intensity-Based Registration

  • Mehmet Yigitsoy
  • Javad Fotouhi
  • Nassir Navab
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8673)


Intensity based registration is a challenge when images to be registered have insufficient amount of information in their overlapping region. Especially, in the absence of dominant structures such as strong edges in this region, obtaining a solution that satisfies global structural consistency becomes difficult. In this work, we propose to exploit the vast amount of available information beyond the overlapping region to support the registration process. To this end, a novel global regularization term using Generalized Hough Transform is designed that ensures the global consistency when the local information in the overlap region is insufficient to drive the registration. Using prior data, we learn a parametrization of the target anatomy in Hough space. This parametrization is then used as a regularization for registering the observed partial images without using any prior data. Experiments on synthetic as well as on sample real medical images demonstrate the good performance and potential use of the proposed concept.


Prior Data Partial Image Global Regularization Target Registration Error Rigid Registration 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Ballard, D.H.: Generalizing the hough transform to detect arbitrary shapes. Pattern Recognition 13(2), 111–122 (1981)CrossRefzbMATHGoogle Scholar
  2. 2.
    Gall, J., Lempitsky, V.: Class-specific hough forests for object detection. In: Decision Forests for Computer Vision and Medical Image Analysis, pp. 143–157. Springer (2013)Google Scholar
  3. 3.
    Godec, M., Roth, P.M., Bischof, H.: Hough-based tracking of non-rigid objects. Computer Vision and Image Understanding 117(10), 1245–1256 (2013)CrossRefGoogle Scholar
  4. 4.
    Johnson, S.G.: The nlopt nonlinear-optimization package, (accessed February 21, 2014)
  5. 5.
    Kutter, O., Wein, W., Navab, N.: Multi-modal registration based ultrasound mosaicing. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009, Part I. LNCS, vol. 5761, pp. 763–770. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  6. 6.
    Leibe, B., Leonardis, A., Schiele, B.: Combined object categorization and segmentation with an implicit shape model. In: Workshop on Statistical Learning in Computer Vision (ECCV) (May 2004)Google Scholar
  7. 7.
    Ourselin, S., Roche, A., Prima, S., Ayache, N.: Block matching: A general framework to improve robustness of rigid registration of medical images. In: Delp, S.L., DiGoia, A.M., Jaramaz, B. (eds.) MICCAI 2000. LNCS, vol. 1935, pp. 557–566. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  8. 8.
    Øye, O., Wein, W., Ulvang, D., Matre, K., Viola, I.: Real time image-based tracking of 4d ultrasound data. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part I. LNCS, vol. 7510, pp. 447–454. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  9. 9.
    Schneider, R.J., Perrin, D.P., Vasilyev, N.V., Marx, G.R., del Nido, P.J., Howe, R.D.: Real-time image-based rigid registration of three-dimensional ultrasound. Medical Image Analysis 16(2), 402–414 (2012)CrossRefGoogle Scholar
  10. 10.
    Shams, R., Barnes, N., Hartley, R.: Image registration in hough space using gradient of images. In: 9th Biennial Conference of the Australian Pattern Recognition Society on Digital Image Computing Techniques and Applications, pp. 226–232. IEEE (2007)Google Scholar
  11. 11.
    Toews, M., Wells III, W.M.: Efficient and robust model-to-image alignment using 3d scale-invariant features. Medical Image Analysis 17(3), 271–282 (2013)CrossRefGoogle Scholar
  12. 12.
    Varnavas, A., Carrell, T., Penney, G.: Fully automated initialisation of 2D-3D image registration. In: 2013 IEEE 10th International Symposium on Biomedical Imaging (ISBI), pp. 568–571. IEEE (2013)Google Scholar
  13. 13.
    Wachinger, C., Wein, W., Navab, N.: Three-dimensional ultrasound mosaicing. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part II. LNCS, vol. 4792, pp. 327–335. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  14. 14.
    Yigitsoy, M., Navab, N.: Structure propagation for image registration. IEEE Transactions on Medical Imaging 32(9), 1657–1670 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Mehmet Yigitsoy
    • 1
  • Javad Fotouhi
    • 1
  • Nassir Navab
    • 1
  1. 1.Computer Aided Medical Procedures (CAMP)TUMMunichGermany

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