Advertisement

Structural Representations for Multi-modal Image Registration Based on Modified Entropy

  • Keyvan KasiriEmail author
  • Paul Fieguth
  • David A. Clausi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9164)

Abstract

Registration of multi-modal images has been a challenging task due to the complex intensity relationship between images. The standard multi-modal approach tends to use sophisticated similarity measures, such as mutual information, to assess the accuracy of the alignment. Employing such measures imply the increase in the computational time and complexity, and makes it highly difficult for the optimization process to converge. A new registration method is proposed based on introducing a structural representation of images captured from different modalities, in order to convert the multi-modal problem into a mono-modal one. Structural features are extracted by utilizing a modified version of entropy images in a patch-based manner. Experiments are performed on simulated and real brain images from different modalities. Quantitative assessments demonstrate that better accuracy can be achieved compared to the conventional multi-modal registration method.

Keywords

Multi-modal registration Structural features Entropy 

References

  1. 1.
    Crum, W.R., Hartkens, T., Hill, D.L.G.: Non-rigid image registration: theory and practice. Br. J. Radiol. 2, S140–S153 (2004)CrossRefGoogle Scholar
  2. 2.
    Fitzpatrick, J.M., West, J.B., Maurer Jr., C.R.: Predicting error in rigid-body point-based registration. IEEE Trans. Med. Imag. 17(5), 694–702 (1998)CrossRefGoogle Scholar
  3. 3.
    Ghantous, M., Ghosh, S., Bayoumi, M.: A multi-modal automatic image registration technique based on complex wavelets. In: IEEE International Conference on Image Processing (ICIP), pp. 173–176 (2009)Google Scholar
  4. 4.
    Haber, E., Modersitzki, J.: Intensity gradient based registration and fusion of multi-modal images. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4191, pp. 726–733. Springer, Heidelberg (2006) CrossRefGoogle Scholar
  5. 5.
    Kasiri, K., Clausi, D.A., Fieguth, P.: Multi-modal image registration using structural features. In: International Conference on Engineering in Medicine and Biology Society (EMBC), pp. 5550–5553 (2014)Google Scholar
  6. 6.
    Kasiri, K., Fieguth, P., Clausi, D.A.: Cross modality label fusion in multi-atlas segmentation. In: IEEE International Conference on Image Processing (ICIP), pp. 16–20 (2014)Google Scholar
  7. 7.
    Keller, Y., Averbuch, A.: Multisensor image registration via implicit similarity. IEEE Trans. Pattern Anal. Machine Intell. 28(5), 794–801 (2006)CrossRefGoogle Scholar
  8. 8.
    Kim, Y.S., Lee, J.H., Ra, J.B.: Multi-sensor image registration based on intensity and edge orientation information. Pattern Recogn. 41(11), 3356–3365 (2008)zbMATHCrossRefGoogle Scholar
  9. 9.
    Loeckx, D., Slagmolen, P., Maes, F., Vandermeulen, D., Suetens, P.: Nonrigid image registration using conditional mutual information. IEEE Trans. Med. Imag. 29(1), 19–29 (2010)CrossRefGoogle Scholar
  10. 10.
    Pluim, J.P.W., Maintz, J.B.A., Viergever, M.A.: Mutual-information-based registration of medical images: a survey. IEEE Trans. Med. Imag. 22(8), 986–1004 (2003)CrossRefGoogle Scholar
  11. 11.
    Rivaz, H., Karimaghaloo, Z., Collins, D.L.: Self-similarity weighted mutual information: a new nonrigid image registration metric. Med. Image Anal. 18(2), 343–358 (2014)CrossRefGoogle Scholar
  12. 12.
    Rivaz, H., Karimaghaloo, Z., Fonov, V.S., Collins, D.L.: Nonrigid registration of ultrasound and mri using contextual conditioned mutual information. IEEE Trans. Med. Imag. 33(3), 708–725 (2014)CrossRefGoogle Scholar
  13. 13.
    BrainWeb: simulated brain database. http://www.bic.mni.mcgill.ca/brainweb/
  14. 14.
    ITK: Image Registration and Segmentation Toolkit. www.itk.org
  15. 15.
    RIRE: Retrospective Image Registration Evaluation. http://www.insight-journal.org/rire/
  16. 16.
    Wachinger, C., Navab, N.: Structural image representation for image registration. In: Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 23–30 (2010)Google Scholar
  17. 17.
    Wells, W.M., Viola, P., Atsumi, H., Nakajima, S., Kikinis, R.: Multi-modal volume registration by maximization of mutual information. Med. Image Anal. 1(1), 35–51 (1996)CrossRefGoogle Scholar
  18. 18.
    Wong, A., Clausi, D.A., Fieguth, P.: CPOL: complex phase order likelihood as a similarity measure for MR- CT registration. Med. Image Anal. 14(1), 50–57 (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Vision and Image Processing (VIP) Lab, Systems Design EngineeringUniversity of WaterlooWaterlooCanada

Personalised recommendations