Detecting Structure in Diffusion Tensor MR Images

  • K. Krishna Nand
  • Rafeef Abugharbieh
  • Brian G. Booth
  • Ghassan Hamarneh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6892)

Abstract

We derive herein first and second-order differential operators for detecting structure in diffusion tensor MRI (DTI). Unlike existing methods, we are able to generate full first and second-order differentials without dimensionality reduction and while respecting the underlying manifold of the data. Further, we extend corner and curvature feature detectors to DTI using our differential operators. Results using the feature detectors on diffusion tensor MR images show the ability to highlight structure within the image that existing methods cannot.

Keywords

Differential Operator Fractional Anisotropy Diffusion Tensor Image Diffusion Tensor Hessian Matrix 
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.

References

  1. 1.
    Alshatti, W., Lambert, P.: Using eigenvectors of a vector field for deriving a second directional derivative operator for color images. In: Chetverikov, D., Kropatsch, W.G. (eds.) CAIP 1993. LNCS, vol. 719, pp. 149–156. Springer, Heidelberg (1993)CrossRefGoogle Scholar
  2. 2.
    Arsigny, V., Fillard, P., Pennec, X., Ayache, N.: Log-Euclidean metrics for fast and simple calculus on diffusion tensors. Magnetic Resonance in Medicine 56(2), 411–421 (2006)CrossRefGoogle Scholar
  3. 3.
    Bailey, R.A., Gower, J.C.: Approximating a symmetric matrix. Psychometrika 55(4), 665–675 (1990)MathSciNetCrossRefMATHGoogle Scholar
  4. 4.
    Bullitt, E., Smith, J., Lin, W.: Designed database of MR brain images of healthy volunteers, http://www.insight-journal.org/midas/community/view/21 (accessed May 2010)
  5. 5.
    Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8(6), 679–714 (1986)CrossRefGoogle Scholar
  6. 6.
    Cumani, A.: Edge detection in multispectral images. Graphical Models and Image Processing 53(1), 40–51 (1991)CrossRefMATHGoogle Scholar
  7. 7.
    Descoteaux, M., Audette, M., Chinzei, K., Siddiqi, K.: Bone enhancement filtering: Application to sinus bone segmentation and simulation of pituitary surgery. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3749, pp. 9–16. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  8. 8.
    Evans, A.N., Liu, X.U.: A morphological gradient approach to color edge detection. IEEE Trans. on Image Processing 15(6), 1454–1463 (2006)CrossRefGoogle Scholar
  9. 9.
    Frangi, A.F., Niessen, W.J., Vincken, K.L., Viergever, M.A.: Multiscale vessel enhancement filtering. In: Wells, W.M., Colchester, A.C.F., Delp, S.L. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 130–137. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  10. 10.
    Frindel, C., Robini, M., Croisille, P., Zhu, Y.-M.: Comparison of regularization methods for human cardiac diffusion tensor MRI. Medical Image Analysis 13(3), 405–418 (2009)CrossRefGoogle Scholar
  11. 11.
    Goodlett, C.B., Fletcher, P.T., Gilmore, J.H., Gerig, G.: Group analysis of DTI fiber tract statistics with application to neurodevelopment. NeuroImage 45(1), 133–142 (2009)CrossRefGoogle Scholar
  12. 12.
    Kindlmann, G., Tricoche, X., Westin, C.-F.: Delineating white matter structure in diffusion tensor MRI with anisotropy creases. Medical Image Analysis 11(5), 492–502 (2007)CrossRefGoogle Scholar
  13. 13.
    Ming, A., Ma, H.: A blob detector in color images. In: CIVR 2007, pp. 364–370. ACM, New York (2007)Google Scholar
  14. 14.
    Mori, S.: John Hopkins Medical Institute: Laboratory of Brain Anatomical MRI, in vivo human database, http://lbam.med.jhmi.edu/ (accessed February 2009)
  15. 15.
    Niethammer, M., Zach, C., Melonakos, J., Tannenbaum, A.: Near-tubular fiber bundle segmentation for diffusion weighted imaging: Segmentation through frame reorientation. NeuroImage 45(1), 123–132 (2009)CrossRefGoogle Scholar
  16. 16.
    Noble, J.A.: Finding corners. Image and Vision Computing 6(2), 121–128 (1988)CrossRefGoogle Scholar
  17. 17.
    Pajevic, S., Aldroubi, A., Basser, P.J.: A continuous tensor field approximation of discrete DT-MRI data for extracting microstructural and architectural features of tissue. Journal of Magnetic Resonance 154(1), 85–100 (2002)CrossRefGoogle Scholar
  18. 18.
    Shi, J., Tomasi, C.: Good features to track. In: CVPR 1994, pp. 593–600 (1994)Google Scholar
  19. 19.
    Shi, L., Funt, B., Hamarneh, G.: Quaternion color curvature. In: IS&T CIC 2008, pp. 338–341 (2008)Google Scholar
  20. 20.
    Tsai, P., Chang, C.-C., Hu, Y.-C.: An adaptive two-stage edge detection scheme for digital color images. Real-Time Imaging 8(4), 329–343 (2002)CrossRefMATHGoogle Scholar
  21. 21.
    Yushkevich, P.A., Zhang, H., Simon, T.J., Gee, J.C.: Structure-specific statistical mapping of white matter tracts. NeuroImage 41(2), 448–461 (2008)CrossRefMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • K. Krishna Nand
    • 1
  • Rafeef Abugharbieh
    • 1
  • Brian G. Booth
    • 2
  • Ghassan Hamarneh
    • 2
  1. 1.Biomedical Signal and Image Computing LabUniversity of British ColumbiaCanada
  2. 2.Medical Image Analysis Lab, School of Computing ScienceSimon Fraser UniversityCanada

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