Anatomical Landmark Based Registration of Contrast Enhanced T1-Weighted MR Images

  • Ali Demir
  • Gozde Unal
  • Kutlay Karaman
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6204)

Abstract

In many problems involving multiple image analysis, an image registration step is required. One such problem appears in brain tumor imaging, where baseline and follow-up image volumes from a tumor patient are often to-be compared. Nature of the registration for a change detection problem in brain tumor growth analysis is usually rigid or affine. Contrast enhanced T1-weighted MR images are widely used in clinical practice for monitoring brain tumors. Over this modality, contours of the active tumor cells and whole tumor borders and margins are visually enhanced. In this study, a new technique to register serial contrast enhanced T1 weighted MR images is presented. The proposed fully-automated method is based on five anatomical landmarks: eye balls, nose, confluence of sagittal sinus, and apex of superior sagittal sinus. After extraction of anatomical landmarks from fixed and moving volumes, an affine transformation is estimated by minimizing the sum of squared distances between the landmark coordinates. Final result is refined with a surface registration, which is based on head masks confined to the surface of the scalp, as well as to a plane constructed from three of the extracted features. The overall registration is not intensity based, and it depends only on the invariant structures. Validation studies using both synthetically transformed MRI data, and real MRI scans, which included several markers over the head of the patient were performed. In addition, comparison studies against manual landmarks marked by a radiologist, as well as against the results obtained from a typical mutual information based method were carried out to demonstrate the effectiveness of the proposed method.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Modersitzki, J.: Numerical methods for image registration. Oxford University Press, Oxford (2004)MATHGoogle Scholar
  2. 2.
    Zitova, B., Flusser, J.: Image registration methods: a survey. Image and Vision Computing 21, 977–1000 (2003)CrossRefGoogle Scholar
  3. 3.
    Fischer, B., Modersitzki, J.: Ill-posed medicine–an introduction to image registration. Inverse Problems 24, 1–16 (2008)CrossRefMathSciNetGoogle Scholar
  4. 4.
    Maintz, J., Viergever, M.: A survey of medical image registration. Medical Image Analysis 2, 1–36 (1998)CrossRefGoogle Scholar
  5. 5.
    Angelini, E., Clatz, O., Mandonnet, E., Konukoglu, E., Capelle, L., Duffau, H.: Glioma Dynamics and Computational Models: A Review of Segmentation, Registration, and In Silico Growth Algorithms and their Clinical Applications. Current Medical Imaging Reviews 3, 176–262 (2007)CrossRefGoogle Scholar
  6. 6.
    Patriarche, J., Erickson, B.: A Review of the Automated Detection of Change in Serial Imaging Studies of the Brain. J. Digit. Imaging 17, 158–174 (2004)CrossRefGoogle Scholar
  7. 7.
    Ettinger, G.J., Grimson, W.E.L., Lozano-Perez, T., Wells III, W.M., White, S.J., Kikinis, R.: Automatic registration for multiple sclerosis change detection. In: IEEE Workshop on Biomedical Image Analysis, Los Alamitos, CA, pp. 297–306 (1994)Google Scholar
  8. 8.
    Chui, H., Win, L., Schultz, R., Duncan, J.S., Rangarajan, A.: A unified non-rigid feature registration method for brain mapping. Med. Image. Anal. 7(2), 113–130 (2003)CrossRefGoogle Scholar
  9. 9.
    Davatzikos, C., Prince, J.L.: Brain image registration based on curve mapping. In: IEEE Workshop on Biomedical Image Analysis, Los Alamitos, CA, pp. 245–254 (1994)Google Scholar
  10. 10.
    Davatzikos, C., Prince, J.L., Bryan, R.N.: Image registration based on boundary mapping. IEEE Transactions on Medical Imaging 15(1), 112–115 (1996)CrossRefGoogle Scholar
  11. 11.
    Douglas, N.G., Bruce, F.: Accurate and robust brain image alignment using boundary-based registration. Neuroimage (2009)Google Scholar
  12. 12.
    Christensen, G.E.: Inverse consistent registration with object boundary constraints. In: IEEE International Symposium on Biomedical Imaging, vol. 1, pp. 591–594 (2004)Google Scholar
  13. 13.
    Talairach, J., Tournoux, P.: Co-planar stereotaxic Atlas of the Human Brain. Thieme Medical Publishers (1988)Google Scholar
  14. 14.
    Verard, L., Allain, P., Travere, J.M., Baron, J.C., Bloyet, D.: Fully Automatic Identification of AC and PC Landmarks on Brain MRI Using Scene Analysis. IEEE Transactions on Medical Imaging 16(5), 610–616 (1997)CrossRefGoogle Scholar
  15. 15.
    Rohr, K., Stiehl, H., Sprengel, R., Buzug, T., Weese, J., Kuhn, M.: Landmark-based elastic registration using approximating thin-plate splines. IEEE Trans. Med. Img. 20(6), 526–534 (2001)CrossRefGoogle Scholar
  16. 16.
    Thirion, J.P.: New feature points based on geometric invariants for 3d image registration. Int. J. of Computer Vision 18(2), 121–137 (1996)CrossRefGoogle Scholar
  17. 17.
    Viola, P.A., Wells, W.M.: Alignment by maximization of mutual information. International Journal of Computer Vision 24(2), 137–154 (1997)CrossRefGoogle Scholar
  18. 18.
    Penney, G.P., Weese, J., Little, J.A., Desmedt, P., Hill, D.L.G., Hawkes, D.J.: A comparison of similarity measures for use in 2-D-3-D medical image registration. IEEE Trans. on Med. Imaging 17, 586–594 (1998)CrossRefGoogle Scholar
  19. 19.
    Pluim, J., Maintz, J., Viergever, M.: Image registration by maximization of combined mutual information and gradient information. IEEE Trans. on Med. Imaging 19(8), 809–814 (2000)CrossRefGoogle Scholar
  20. 20.
    Fitzpatrick, J.M., West, J.B., Maurer, C.R.J.: Predicting error in rigid-body point-based registration. IEEE Trans. Med. Imaging 17, 694–702 (1998)CrossRefGoogle Scholar
  21. 21.
    Jenkinson, M., Smith, S.: A global optimisation method for robust affine registration of brain images. Med. Image. Anal. 5(2), 143–156 (2001)CrossRefGoogle Scholar
  22. 22.
    Golub, G.H., Van Loan, C.F.: Matrix Computations. JHU Press (1996)Google Scholar
  23. 23.
    Murray, R.M., Li, Z., Sastry, S.: A Mathematical Introduction to Robotic Manipulation. CRC Press, Boca Raton (1994)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Ali Demir
    • 1
  • Gozde Unal
    • 1
  • Kutlay Karaman
    • 2
  1. 1.Sabanci UniversityIstanbulTurkey
  2. 2.Anadolu Medical Center, KocaeliTurkey

Personalised recommendations