Echocardiographic Image Sequence Compression Based on Spatial Active Appearance Model

  • Sándor M. Szilágyi
  • László Szilágyi
  • Zoltán Benyó
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4756)


This paper presents a new method for echocardiographic image sequence compression based on active appearance model. The key element is the intensive usage of all kind of a priori medical information, such as electrocardiography (ECG) records and heart anatomical data that can be processed to estimate the ongoing echocardiographic image sequences. Starting from the accurately estimated images, we could obtain lower amplitude residual signal and accordingly higher compression rate using a fixed image distortion. The realized spatial active appearance model provides a tool to investigate the long term variance of the heart’s shape and its volumetric variance over time.


Echocardiography active appearance model image compression QRS clustering 


  1. 1.
    Bosch, J.G., Mitchell, S.C., Lelieveldt, B.P.F., Nijland, F., Kamp, O., Sonka, M., Reiber, J.H.C.: Automatic segmentation of echocardiographic sequences by active appearance motion models. IEEE Trans. Med. Imag. 21, 1374–1383 (2002)CrossRefGoogle Scholar
  2. 2.
    Chiu, E., Vaisey, J., Atkins, M.S.: Wavelet-Based Space-Frequency Compression of Ultrasound Images. IEEE Trans. Inf. Tech. Biomed. Eng. 5, 300–310 (2001)CrossRefGoogle Scholar
  3. 3.
    Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. IEEE Trans. Patt. Anal. Mach. Intell. 23, 681–685 (2001)CrossRefGoogle Scholar
  4. 4.
    Erickson, B.J., Manduca, A., Palisson, P., Persons, K.R., Earnest, D., Savcenko, V.: Wavelet compression of medical images. Radiology 206, 599–607 (1998)Google Scholar
  5. 5.
    Erickson, B.J.: Irreversible Compression of Medical Images. J. Digit. Imag. 15, 5–14 (2002)CrossRefGoogle Scholar
  6. 6.
    Evans, A.N., Nixon, M.S.: Biased motion-adaptive temporal filtering for speckle reduction in echocardiography. IEEE Trans. Med. Imag. 15, 39–50 (1996)CrossRefGoogle Scholar
  7. 7.
    Fidler, A., Skaleric, U.: The impact of image information on compressibility and degradation in medical image compression. Med. Phys. 33, 2832–2838 (2006)CrossRefGoogle Scholar
  8. 8.
    Hang, X., Greenberg, N.L., Zheng, Y.F., Thomas, J.D.: Compression of 3-D echocardiographic images using a modified 3-D set-partitioning-in-hierarchical-trees algorithm based on a 3-D wavelet packet transform. J. Electr. Imag. 15, 1–13 (2006) art. no. 023016Google Scholar
  9. 9.
    Joshi, D., Li, J., Wang, J.Z: A Computationally Efficient Approach to the Estimation of Two- and Three-Dimensional Hidden Markov Models. IEEE Trans. Imag. Proc. 15, 1871–1886 (2006)CrossRefGoogle Scholar
  10. 10.
    Lagerholm, M., Peterson, C., Braccini, G., Edenbrandt, L., Sörnmo, L.: Clustering ECG complexes using Hermite functions and self-organizing maps. IEEE Trans. Biomed. Eng. 47, 838–848 (2000)CrossRefGoogle Scholar
  11. 11.
    Lelieveldt, B.P.F., van der Geest, R.J., Mitchell, S.C., Bosch, J.G., Sonka, M., Reiber, J.H.C.: 3-D active appearance models: fully automatic detection of endoand epicardial contours in short-axis cardiac MR data. Proc. Int. Soc. Magn. Res. Med (ISMRM) 2, 1668 (2002)Google Scholar
  12. 12.
    Mallat, S., Zhang, Z.: Matching pursuits with time-frequency dictionaries. IEEE Trans. Signal Proc. 41, 3397–3415 (1993)zbMATHCrossRefGoogle Scholar
  13. 13.
    Mitchell, S.C., Bosch, J.G., Lelieveldt, B.P.F., van der Geest, R.J., Reiber, J.H.C., Sonka, M.: 3-D active appearance models: segmentation of cardiac MR and ultrasound images. IEEE Trans. Med. Imag. 21, 1167–1178 (2002)CrossRefGoogle Scholar
  14. 14.
    Neff, R., Zakhor, A.: Very low bit rate video coding based on matching pursuits. IEEE Trans. Circ. Syst. Video Techn. 7, 158–171 (1997)CrossRefGoogle Scholar
  15. 15.
    Said, A., Pearlman, W.A.: A new, fast, and efficient image codec based on set partitioning in hierarchical trees. IEEE Trans. Circ. Syst. Video Techn. 6, 243–250 (1996)CrossRefGoogle Scholar
  16. 16.
    Shapiro, J.: Embedded image coding using zerotrees of wavelet coefficients. IEEE Trans. Sign. Proc. 41, 3445–3462 (1993)zbMATHCrossRefGoogle Scholar
  17. 17.
    Shiao, Y.H., Chen, T.J., Chuang, K.S., Lin, C.H., Chuang, C.C.: Quality of Compressed Medical Images. J. Digit. Imag. 20, 149–159 (2007)CrossRefGoogle Scholar
  18. 18.
    Stegmann, M., Pedersen, D.: Bi-temporal 3D active appearance models with applications to unsupervised ejection fraction estimation. Progr. Biomed. Opt. Imag. Proc. SPIE 5746, 336–350 (2005)Google Scholar
  19. 19.
    Szilágyi, S.M., Szilágyi, L., Benyó, Z.: Support Vector Machine-Based ECG Compression. Ser. Adv. Soft Comput (IFSA 2007) 41, 737–745 (2007)Google Scholar
  20. 20.
    Szilágyi, S.M., Szilágyi, L., Benyó, Z.: Volumetric Analysis of the Heart Using Echocardiography. In: FIMH 2007. LNCS, vol. 4466, pp. 81–90 (2007)Google Scholar
  21. 21.
    Winslow, R.L., Hinch, R., Greenstein, J.L.: Mechanisms and models of cardiac excitation-contraction coupling. Lect. Notes Math. vol. 1867, pp. 97–131 (2005)Google Scholar
  22. 22.
    Wu, X., Memon, N.: Context-based adaptive lossless image coding. IEEE Trans. Comm. 45, 437–444 (1997)CrossRefGoogle Scholar
  23. 23.
    Wu, X., Bao, P.: L-infinity constrained high-fidelity image compression via adaptive context modeling. IEEE Trans. Imag. Proc. 9, 536–542 (2000)zbMATHCrossRefGoogle Scholar
  24. 24.
    Yuan, Y., Evans, A.N., Monro, D.M.: Low Complexity Separable Matching Pursuits. In: Proc. IEEE Int. Conf. Acoust. Speech Sign. Proc. III, pp. 725–728. IEEE Computer Society Press, Los Alamitos (2004)Google Scholar
  25. 25.
    Zhenga, Z., Yang, J.: Supervised locality pursuit embedding for pattern classification. Imag. Vis. Comp. 24, 819–826 (2006)CrossRefGoogle Scholar
  26. 26.
    Zigel, Y., Cohen, A., Katz, A.: ECG Signal Compression Using Analysis by Synthesis Coding. IEEE Trans. Biomed. Eng. 47, 1308–1316 (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Sándor M. Szilágyi
    • 1
  • László Szilágyi
    • 1
    • 2
  • Zoltán Benyó
    • 2
  1. 1.Sapientia - Hungarian Science University of Transylvania, Faculty of Technical and Human Science, Târgu-MureşRomania
  2. 2.Budapest University of Technology and Economics, Dept. of Control Engineering and Information Technology, BudapestHungary

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