Automated Tag Detection

  • Thomas S. DenneyJr.
Part of the Computational Imaging and Vision book series (CIVI, volume 23)


Tagged MRI [4, 30] is an excellent technique for measuring tissue deformations. For the deformation to be quantified, however, the tags must be identified and tracked through the image sequence. Tag identification and tracking is the most time consuming step in the analysis of tagged MR images because most techniques require that the myocardium is segmented from the background before the tags are identified and tracked. This segmentation requires user interaction in each image in the study.


Black Blood Snake Algorithm Black Blood Image Image Noise Statistic Image Pixel Spacing 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    Amini, A., Chen, Y., Curwen, R., Mani, V., and Sun, J. (1998). Coupled B-snake grids and constrained thin-plate splines for analysis of 2-D tissue deformations from tagged MRI. IEEE Transactions on Medical Imaging, 17(3):344–356.PubMedCrossRefGoogle Scholar
  2. [2]
    Amini, A., Curwen, R., Constable, R., and Gore, J. (1994). MR physics-based snake tracking and dense deformation from tagged cardiac images. In American Association for Artificial Intelligence (AAAI) Spring Symposium Series. Applications of Computer Vision in Medical Image Processing, pages 126–129. The AAAI Press.Google Scholar
  3. [3]
    Atalar, E. and McVeigh, E. (1994). Optimization of tag thickness for measuring position with magnetic resonance imaging. IEEE Transactions on Medical Imaging, 13(1):152–160.PubMedCrossRefGoogle Scholar
  4. [4]
    Axel, L. and Dougherty, L. (1989). MR imaging of motion with spatial modulation of magnetization. Radiology, 171:841–845.PubMedGoogle Scholar
  5. [5]
    Besag, J. (1986). On the statistical analysis of dirty pictures. J. Royal Stat. Soc. B, 48:259–302.Google Scholar
  6. [6]
    Cohen, L. (1991). Note on active contour models and balloons. CVGIP: Image Understanding, 53(2):211–218.CrossRefGoogle Scholar
  7. [7]
    Crum, W., Berry, E., Ridgeway, J., Sivananthan, U., Tan, L.-B., and Smith, M. (1997). Simulation of two-dimensional tagged MRL Journal of Magnetic Resonance Imaging, 7(2):416–24.PubMedCrossRefGoogle Scholar
  8. [8]
    Denney, T. (1999). Estimation and detection of myocardial tags in MR images without user-defined myocardial contours. IEEE Transactions on Medical Imaging, 18(4):330–344.PubMedCrossRefGoogle Scholar
  9. [9]
    Denney, T. and Yan, L. (2000). Unsupervised reconstruction of left ventricular strain from tagged cardiac MR images. In 8th Meeting of the International Society for Magnetic Resonance in Medicine, Denver, CO.Google Scholar
  10. [10]
    Edelman, R., Chien, D., and Kim, D. (1991). Fast selective black blood MR imaging. Radiology, 181(3):655–60.PubMedGoogle Scholar
  11. [11]
    Fischer, S., McKinnon, G., Maier, S., and Boesiger, P. (1993). Improved myocardial tagging contrast. Magnetic Resonance in Medicine, 30(2): 191–200.PubMedCrossRefGoogle Scholar
  12. [12]
    Golub, G. H. and Loan, C. F V. (1989). Matrix Computations. Johns Hopkins University Press, Baltimore, 2nd ed. edition.Google Scholar
  13. [13]
    Guillemaud, R. and Brady, M. (1997). Estimating the bias field of MR images. IEEE Transactions on Medical Imaging, 16(3):238–251.PubMedCrossRefGoogle Scholar
  14. [14]
    Guttman, M., Prince, J., and McVeigh, E. (1994). Tag and contour detection in tagged MR images of the left ventricle. IEEE Transactions on Medical Imaging, 13(l):74–88.PubMedCrossRefGoogle Scholar
  15. [15]
    Guttman, M., Zerhouni, E., and McVeigh, E. (1995). Fast, contourless tag segmentation and displacement estimation for analysis of myocardial motion. In Proc. SMR/ESMRMB, volume 1, page 41, Nice. SMR.Google Scholar
  16. [16]
    Guttman, M., Zerhouni, E., and McVeigh, E. (1997). Analysis and visualization of cardiac function from MR images. IEEE Computer Graphics and Applications, 17(l):30–38.PubMedCrossRefGoogle Scholar
  17. [17]
    Henkelman, R. (1985). Measurement of signal intensities in the presence of noise in MR images. Medical Physics, 12(2):232–233.PubMedCrossRefGoogle Scholar
  18. [18]
    Kass, M., Witkin, A., and Terzopoulos, D. (1988). Snakes: active contour models. International Journal of Computer Vision, 1:321–331.CrossRefGoogle Scholar
  19. [19]
    Kumar, S. and Goldgof, D. (1994). Automatic tracking of SPAMM grid and the estimation of deformation parameters from cardiac MR images. IEEE Transactions on Medical Imaging, 13(1): 122–132.PubMedCrossRefGoogle Scholar
  20. [20]
    Laidlaw, D., Fleischer, K., and Barr, A. (1998). Partial-volume Bayesian classification of material mixtures in MR volume data using voxel histograms. IEEE Transactions on Medical Imaging, 17(l):74–86.PubMedCrossRefGoogle Scholar
  21. [21]
    McVeigh, E. and Atalar, E. (1992). Cardiac tagging with breath-hold cine MRI. Magnetic Resonance in Medicine, 28:318–327.PubMedCrossRefGoogle Scholar
  22. [22]
    Radeva, P., Amini, A., Huang, J., and Marti, E. (1996). Deformable B-solids and implicit snakes for localization and tracking of MRI-SPAMM data. In IEEE Workshop on Mathematical Models in Biomedical Image Analysis, San Francisco, CA.Google Scholar
  23. [23]
    Reeder, S. and McVeigh, E. (1994). Tag contrast in breath-hold CINE cardiac MRI. Magnetic Resonance in Medicine, 31(5): 521–5.PubMedCrossRefGoogle Scholar
  24. [24]
    Rice, S. (1948). Statistical properties of a sine-wave plus random noise. Bell Sys. Tech. J., 27:109–157.Google Scholar
  25. [25]
    Shanmugan, K. and Breipohl, A. (1988). Random signals: detection, estimation and data analysis. John Wiley and Sons, Inc.Google Scholar
  26. [26]
    Singleton, H. and Pohost, G. (1997). Automatic cardiac MR image segmentation using edge detection by tissue classification in pixel neighborhoods. Magnetic Resonance in Medicine, 37(3):418–24.PubMedCrossRefGoogle Scholar
  27. [27]
    W.M. Wells, I., Grimson, W., Kikinis, R., and Jolesz, F. (1996). Adaptive segmentation of MRI data. IEEE Transactions on Medical Imaging, 15(4):429–442.PubMedCrossRefGoogle Scholar
  28. [28]
    Yan, L. and Denney, T. (1998). Joint reconstruction of 2-D left ventricular displacement and contours from tagged magnetic resonance images using Markov random field edge prior. In Proceedings of the 1998 IEEE Workshop on Biomedical Image Analysis, San Diego, CA.Google Scholar
  29. [29]
    Young, A., Kraitchman, D., Dougherty, L., and Axel, L. (1995). Tracking and finite element analysis of stripe deformation in magnetic resonance tagging. IEEE Transactions on Medical Imaging, 14(3):413–421.PubMedCrossRefGoogle Scholar
  30. [30]
    Zerhouni, E., Parish, D., Rogers, W., Yangand, A., and Shapiro, E. (1988). Human heart: tagging with MR imaging — a method for noninvasive assessment of myocardial motion. Radiology, 169:59–63.PubMedGoogle Scholar

Copyright information

© Kluwer Academic Publishers 2001

Authors and Affiliations

  • Thomas S. DenneyJr.
    • 1
  1. 1.Department of Electrical and Computer EngineeringAuburn UniversityAuburnUSA

Personalised recommendations