Segmentation and Tracking of Myocardial Boundaries Using Dynamic Programming

  • Athira J. Jacob
  • Varghese Alex
  • Ganapathy Krishnamurthi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10124)


Increasing interest in quantification of local myocardial properties throughout the cardiac cycle from tagged MR (tMR) calls for treatment of the cardiac segmentation problem as a spatio-temporal task. The method presented for myocardial segmentation, uses dynamic programming to choose the optimal contour from a set of possible contours subject to maximizing a cost function. Robust Principle Component Analysis (RPCA) is used to decompose the time series into low rank and sparse components and initialization of the contour is done on the low rank approximation. The 3D nature of the images and tag grid location is incorporated into the cost function to get more robust results. 3D+t segmentation of patient data is achieved by propagating contours spatially and temporally. The method is ideal as a pre-processing step in motion quantification and strain rate mapping algorithms.


Dynamic programming Tagged MR image analysis Robust PCA Deformable contours Tracking 4D cardiac images Tag 


  1. 1.
    Candès, E.J., Li, X., Ma, Y., Wright, J.: Robust principal component analysis? J. ACM (JACM) 58(3), 11 (2011)MathSciNetCrossRefMATHGoogle Scholar
  2. 2.
    Edvardsen, T., Gerber, B.L., Garot, J., Bluemke, D.A., Lima, J.A., Smiseth, O.A.: Quantitative assessment of intrinsic regional myocardial deformation by doppler strain rate echocardiography in humans: validation against three-dimensional tagged magnetic resonance imaging. Circulation 106(1), 50–56 (2002). doi: 10.1161/01.CIR.0000019907.77526.75. CrossRefGoogle Scholar
  3. 3.
    Guttman, M., Prince, J., McVeigh, E.: Tag and contour detection in tagged MR images of the left ventricle. IEEE Trans. Med. Imaging 13(1), 74–88 (1994). doi: 10.1109/42.276146. ISSN 0278-0062CrossRefGoogle Scholar
  4. 4.
    Metaxas, D.N., Axel, L., Qian, Z., Huang, X.: A segmentation and tracking system for 4D cardiac tagged MR images, pp. 1541–1544 (2006)Google Scholar
  5. 5.
    Osman, N.F., Kerwin, W.S., McVeigh, E.R., Prince, J.L.: Cardiac motion tracking using CINE harmonic phase (HARP) magnetic resonance imaging. Magn. Reson. Med. Official J. Soc. Magn. Reson. Med./Soc. Magn. Reson. Med. 42(6), 1048 (1999)CrossRefGoogle Scholar
  6. 6.
    Qian, Z., Metaxas, D.N., Axel, L.: A learning framework for the automatic and accurate segmentation of cardiac tagged MRI images. In: Liu, Y., Jiang, T., Zhang, C. (eds.) CVBIA 2005. LNCS, vol. 3765, pp. 93–102. Springer, Heidelberg (2005). doi: 10.1007/11569541_11 CrossRefGoogle Scholar
  7. 7.
    Tobon-Gomez, C., De Craene, M., Mcleod, K., Tautz, L., Shi, W., Hennemuth, A., Prakosa, A., Wang, H., Carr-White, G., Kapetanakis, S., et al.: Benchmarking framework for myocardial tracking and deformation algorithms: an open access database. Med. Image Anal. 17(6), 632–648 (2013)CrossRefGoogle Scholar
  8. 8.
    Yang, X., Murase, K.: Tagged cardiac MR image segmentation by contrast enhancement and texture analysis, pp. 4–210 (2009)Google Scholar
  9. 9.
    Zerhouni, E.A., Parish, D.M., Rogers, W.J., Yang, A., Shapiro, E.P.: Human heart: tagging with MR imaging-a method for noninvasive assessment of myocardial motion. Radiology 169(1), 59–63 (1988). doi: 10.1148/radiology.169.1.3420283. CrossRefGoogle Scholar
  10. 10.
    Zhou, Z., Li, X., Wright, J., Candes, E., Ma, Y.: Stable principal component pursuit. In: IEEE International Symposium on Information Theory Proceedings (ISIT), pp. 1518–1522. IEEE (2010)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Athira J. Jacob
    • 1
  • Varghese Alex
    • 1
  • Ganapathy Krishnamurthi
    • 1
  1. 1.Indian Institute of Technology-MadrasChennaiIndia

Personalised recommendations