Integrated Four Dimensional Registration and Segmentation of Dynamic Renal MR Images

  • Ting Song
  • Vivian S. Lee
  • Henry Rusinek
  • Samson Wong
  • Andrew F. Laine
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4191)


In this paper a novel approach for the registration and segmentation of dynamic contrast enhanced renal MR images is presented. This integrated method is motivated by the observation of the reciprocity between registration and segmentation in 4D time-series images. Fully automated Fourier-based registration with sub-voxel accuracy and semi-automated time-series segmen-tation were intertwined to improve the accuracy in a multi-step fashion. We have tested our algorithm on several real patient data sets. Clinical validation showed remarkable and consistent agreement between the proposed method and manual segmentation by experts.


Manual Segmentation Translation Error Gradient Vector Flow Refine Segmentation Kidney Segmentation 
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.
    Lee, V.S., Rusinek, H., Noz, M.E., et al.: Dynamic Three-dimensional MR Renography for the Measurement of Single Kidney Function: Initial Experience. Radiology 227, 289–294 (2003)CrossRefGoogle Scholar
  2. 2.
    Yuksel, S.E., El-Baz, A., Farag, A.A., et al.: Automatic detection of renal rejection after kidney transplantation. International Congress Series, vol. 1281, pp. 773–778 (2005)Google Scholar
  3. 3.
    Grattan-Smith, J.D., Perez-Bayfield, M.R., Jones, R.A., et al.: MR imaging of kidneys functional evaluation using F-15 perfusion imaging. Pediatric Radiology 33, 293–304 (2003)Google Scholar
  4. 4.
    Sun, Y., Moura, J.M.F., Yang, D., et al.: Kidney segmentation in MRI sequences using temporal dynamics. In: IEEE International Symposium on Biomedical Imaging (2002)Google Scholar
  5. 5.
    Boykov, Y., Lee, V.S., Rusinek, H., et al.: Segmentation of dynamic N-D data sets via graph cuts using markov models. In: Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention (2001)Google Scholar
  6. 6.
    Priester, J.A.d., Kessels, A.G., Giele, E.L., et al.: MR renography by semiautomated image analysis: performance in renal transplant recipients. J Magn Reson Imag 14, 134–140 (2001)CrossRefGoogle Scholar
  7. 7.
    Sun, Y., Jolly, M.-P., Moura, J.M.F.: Integrated Registration of Dynamic Renal Perfusion MR Images. In: IEEE International Symposium on Image Processing, ICIP 2004, Singapore (2004)Google Scholar
  8. 8.
    Sun, Y., Moura, J.M.F., Ho, C.: Subpixel Registration in Renal Perfusion MR Image Sequence. In: IEEE International Symposium on BioImaging, ISBI 2004, Crystal City, VA (2004)Google Scholar
  9. 9.
    Gerig, G., Kikinis, R., Kuoni, W., et al.: Semiautomated ROI Analysis in Dynamic MRI-Studies: PartI: Image Analysis Tools for Automatic Correction of Organ Displacement. IEEE Transaction on Image Processing 11, 221–232 (1992)Google Scholar
  10. 10.
    Yim, P.J., Marcos, H.B., McAuliffe, M., et al.: Registration of Time-Series Contrast Enhanced Magnetic Resonance Images for Renography. In: 14th IEEE Symposium Computer-Based Medical Systems (2001)Google Scholar
  11. 11.
    Giele, E.L.W., de Priester, J.A., Blom, J.A., et al.: Movement Correction of the Kidney in Dynamic MRI Scans Using FFT Phase Difference Movement Detection. Journal of Magnetic Resonance Imaging 14, 741–749 (2001)CrossRefGoogle Scholar
  12. 12.
    Shanno, D.F.: Conditioning of Quasi-Newton Methods for Function Minimization. Matheamtics of Computing 24, 647–656 (1970)CrossRefMathSciNetGoogle Scholar
  13. 13.
    Stone, H.S., Orchard, M.T., Chang, E.-C., et al.: A Fast Direct Fourier-Based Algorithm for Subpixel Registration of Images. IEEE Transaction on Geoscience and Remote Sensing 39, 2235–2243 (2001)CrossRefGoogle Scholar
  14. 14.
    Song, T., Lee, V.S., Rusinek, H., et al.: Automatic 4-D Registration in Dynamic MR Renography Based on Over-complete Dyadic Wavelet and Fourier Transforms. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3749, pp. 205–213. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  15. 15.
    Xu, C., Prince, J.: Snakes, Shapes, and Gradient Vector Flow. IEEE Transactions on Image Processing 7, 359–369 (1998)MATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ting Song
    • 1
  • Vivian S. Lee
    • 2
  • Henry Rusinek
    • 2
  • Samson Wong
    • 2
  • Andrew F. Laine
    • 1
  1. 1.Department of Biomedical EngineeringColumbia UniversityNew YorkU.S.A.
  2. 2.Department of RadiologyNew York University Medical CenterNew YorkU.S.A.

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