Advertisement

A Study of Similarity Measures for In Vivo 3D Ultrasound Volume Registration

  • U. Z. Ijaz
  • R.W. Prager
  • A.H. Gee
  • G.M. Treece
Conference paper
Part of the Acoustical Imaging book series (ACIM, volume 30)

Abstract

Most of the conventional ultrasound machines in hospitals work in two dimensions. However, there are some applications where doctors would like to be able to gather ultrasound data as a three-dimensional (3D) block rather than a two-dimensional (2D) slice. Two different types of 3D ultrasound have been developed to meet this requirement. One type involves a special probe that can record a fixed block of data, either by having an internal sweeping mechanism or by using electronic steering. The other type of 3D ultrasound uses a conventional 2D ultrasound probe together with a position sensor and is called freehand 3D ultrasound. A natural progression of the mechanically-swept 3D ultrasound system is to combine it with the free hand sensor. This results in an extended field of view. There are two major problems with using a position sensor. Firstly, line-of-sight needs to be maintained between the sensor and the reference point. Secondly, the multiple volumes rarely register because of tissue displacement and deformation. Therefore, the objective of this paper is to get rid of the inconvenient position sensor and to use an automatic image-based registration technique. We provide an experimental study of several intensity-based similarity measures for the registration of 3D ultrasound volumes. Rather than choosing a conventional voxel array to represent the 3D blocks, we use corresponding vertical and horizontal image slices from the blocks to be matched. This limits the amount of data thus making the calculation of the similarity measure less computationally expensive.

Keywords

Similarity measures 3D ultrasound Automatic registration 

Notes

Acknowledgements

This work is supported by the UK Engineering and Physical Sciences Research Council (EPSRC) grant EP/F016476/1.

References

  1. 1.
    Pratikakis, J., Barillat, C., Hellia, P.: Robust multi-scale non-rigid registration of 3D ultrasound images. In: Scale-space and Morphology Computer Vision, vol. 2106, pp. 389–397 (2001)Google Scholar
  2. 2.
    Krücker, J.F., Meyer, C.R., LeCarpontier, J.B., Fowlkes, G.L., Carson, P.L.: 3D spatial compounding of ultrasound images using image-based nonrigid registration. Ultrasound Med. Biol. 26(9), 1475–1488 (2000)CrossRefGoogle Scholar
  3. 3.
    Lange, T., Eulenstein, S., Hünerbein, M., Lamecker, H., Schlag, P.M.: Augmenting intra-operative 3D ultrasound with preoperative models for navigation in liver surgery. In: Proceedings of MICCAI 2004, vol. 3217, pp. 534–541 (2004)Google Scholar
  4. 4.
    Blackall, J.M., Rueckert, D., Maurer Jr., C.R., Penney, G.P., Hill, D.L.G., Hawkes, D.J.: An image registration approach to automated calibration for freehand 3D ultrasound. In: Proceedings of MICCAI2000, vol. 1935, pp. 465–471 (2000)Google Scholar
  5. 5.
    Wachinger, C., Wein, W., Navab, N.: Registration strategies and similarity measures for three-dimensional ultrasound mosaicing. Acad. Radiol. 15, 1404–1415 (2008)CrossRefGoogle Scholar
  6. 6.
    Francois, R., Fablet, R., Barillot, C.: Robust statistical registration of 3D ultrasound image using texture information. In: Proceedings of the 2003 International Conference on Image Processing, vol. 1, pp. 581–584 (2003)Google Scholar
  7. 7.
    Gao, S., Xiao, Y., Hu, S.: A comparison of two similarity measures in intensity-based ultrasound image registration. In: Proceedings of the 2004 International Symposium on Circuits and Systems, vol. 4, pp. 61–64 (2004)Google Scholar
  8. 8.
    Xia, G., Brady, M., Noble, J.A., Burcher, M., English, R.: Non-rigid registration of 3-D freehand ultrasound images of the breasts. IEEE Trans. Med. Imag. 21(4), 405–412 (2002)CrossRefGoogle Scholar
  9. 9.
    Abel, T., Morandi, X., Comeau, R.M., Collins, D.L.: Automatic non-linear MRI-ultrasound registration for the correction of intra-operative brain deformation. In: Proceedings of MICCAI2001, vol. 2208, pp. 913–922 (2001)Google Scholar
  10. 10.
    Ionescu, G., Lavalle, S., Demongeat, J.: Automatic registration of ultrasound with CT images: Application to computer assisted prostate radio therapy and orthopedics. In: Proceedings of MICCAI99, vol. 1679, pp. 768–779 (1999)Google Scholar
  11. 11.
    Pennec, X., Cachier, P., Ayache, N.: Tracking brain deformations in time sequences of 3D US images. Pattern Recogn. Lett. 24(4–5), 801–813 (2003)CrossRefGoogle Scholar
  12. 12.
    Zikic, D., Wein, W., Khamene, A., Clevert, D.A., Navab, N.: Fast deformable registration of 3D-ultrasound data using a variational approach. In: Proceedings of MICCAI2006, vol. 4190, pp. 915–923 (2006)Google Scholar
  13. 13.
    Treece, G.M., Prager, R.W., Gee, A.H., Berman, L.: Correction of probe pressure artifacts in freehand 3D ultrasound. Med. Image Anal. 6(3), 199–214 (2002)CrossRefGoogle Scholar
  14. 14.
    Sarrut, D., Miguet, S.: Similarity measures for image registration. In: First European Workshop on Content-Based Multimedia Indexing, October 1999, Toulouse, France, pp. 263–270 (1999)Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  • U. Z. Ijaz
    • 1
  • R.W. Prager
    • 1
  • A.H. Gee
    • 1
  • G.M. Treece
    • 1
  1. 1.Department of EngineeringUniversity of CambridgeCambridgeUK

Personalised recommendations