Global Wall Motion Estimation of Echocardiographic Images Based on Temporal Information and Sparse Representations

Gifani, Parisa; Behnam, Hamid; Shojaee Fard, Maryam

doi:10.22041/ijbme.2017.70239.1250

Document Type : Full Research Paper

Authors

¹ Biomedical Engineering Department, School of Electrical Engineering; Iran University of Science and Technology, Tehran, Iran

² Shaheed Rajaei Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran

https://doi.org/10.22041/ijbme.2017.70239.1250

Abstract

In this paper, we introduce a novel framework for illustrating the cardiac movements in echocardiogarphic images by utilizing temporal information and sparse representation. For the proposed method, we first derived temporal information by extracting intensity variation time curves (IVTC) assessed for each pixel. Then an over complete dictionary based on prior knowledge of the temporal signals and a set of pre-specified known functions was designed. The IVTCs can then be described as linear combinations of a few prototype atoms in the dictionary. We used the Bayesian Compressive Sensing (BCS) sparse recovery algorithm to find the sparse coefficients of the signals. By decomposing the IVTCs to different families and extracting proper features based on the sparse information, we attain the color coded images which illustrates the general movements of cardiac segments. The database consists of 21 echocardiography sequence of normal and abnormal volunteers in short axes and 4 chamber views. The results show the great achievement in global wall motion estimations.

Keywords

Main Subjects

Biomedical Image Processing / Medical Image Processing

References

[1] M. R. Lang, et al. "A report from the American Society of Echocardiography's Guidelines and Standards Committee and the Chamber Quantification Writing Group, developed in conjunction with the European Association of Echocardiography, a branch of the European Society of Cardiology." J Am Soc Echocardiogr., vol.18, pp. 1440-1463, 2005.

[2] J. S Suri, “Advances in diagnostic and therapeutic ultrasound imaging”. Artech House, 2008.

[3] S. Mazaheri, W. Rahmita, S. S Puteri, Z. D. Mohd, F. Khalid, and R. M. Tayebi. "Segmentation methods of echocardiography images for left ventricle boundary detection."Journal of Computer Science vol.11, no. 9., pp. 957-970, 2015.

[4] S. M. Alavi, and P. Masaeli. "Automatic detection of coronary artery disease using registration of ultrasound images of the heart (echocardiography)." Turkish Journal of Electrical Engineering & Computer Sciences vol. 24, no. 4 2016.

[5] C.R. Dominguez, N. Kachenoura, and S. Mulé, “Classification of Segmental Wall Motion in Echocardiography Using Quantified Parametric Images”, International Conference Functional Imaging and Modeling of the Heart, Barcelona, Spain, pp. 849- 858, 2005.

[6] N.Reckefuss, T. Butz, D .Horstkotte, and L. Faber, ‘Evaluation of longitudinal and radial left ventricular function by two-dimensional speckle-tracking echocardiography in a large cohort of normal probands’, Int J Cardiovasc Imaging, vol. 27, pp. 515-526, 2011.

[7] P. Gifani, H. Behnam, F. Haddadi, Z. A. Sani, and M. Shojaeifard. "Temporal Super Resolution Enhancement of Echocardiographic Images Based on Sparse Representation." IEEE transactions on ultrasonics, ferroelectrics, and frequency control vol.63, no. 1, pp. 6-19, 2016.

[8] P. Gifani, H. Behnam, and Z. A. Sani, "Noise reduction of echocardiographic images based on temporal information," Ultrasonics, Ferroelectrics and Frequency Control, IEEE Transactions on, vol. 61, no. 4, pp. 620-630, 2014.

[9] B. K. Natarajan,. "Sparse approximate solutions to linear systems."SIAM journal on computing vol. 24 no.2 pp. 227-234. 1995.

[10] J. Wright, et al. "Sparse representation for computer vision and pattern recognition." Proceedings of the IEEE vol. 98, no. 6, pp.1031-1044., 2010.

[11] A. Feuer, and A. Nemirovski. "On sparse representation in pairs of bases." IEEE Transactions on Information Theory vol.49, no.6, pp. 1579-1581. 2003.

[12] R. Rubinstein, A. M. Bruckstein, and M. Elad. "Dictionaries for sparse representation modeling." Proceedings of the IEEE, vol. 98, no. 6, pp. 1045-1057, 2010.

[13] S. R. Nikhil., et al. "Convex approaches to model wavelet sparsity patterns." Image Processing (ICIP), 2011 18th IEEE International Conference on. IEEE, 2011.

[14] D. Laurent, and L. Ying. "Wave atoms and sparsity of oscillatory patterns." Applied and Computational Harmonic Analysis, vol. 23, no.3 pp. 368-387, 2007.

[15] L. Sheng-peng, and Y. Fang. "A contourlet-transform based sparse ICA algorithm for blind image separation." Journal of Shanghai University (English Edition), vol.11, pp.464-468, 2007.

[16] E. Glenn, D. Labate, and W. Q. Lim. "Sparse directional image representations using the discrete shearlet transform." Applied and Computational Harmonic Analysis, vol. 25, no.1 pp. 25-46, 2008.

[17] G. Shamgar, R. Hadani, and N. Sochen. "On some deterministic dictionaries supporting sparsity." Journal of Fourier Analysis and Applications. Vol. 14, no. 5. pp. 859-876, 2008.

[18] A. Michal, M. Elad, and A. M. Bruckstein. "On the uniqueness of overcomplete dictionaries, and a practical way to retrieve them." Linear algebra and its applications, vol 416, no.1 pp. 48-67, 2006.

[19] E. J.Candes, and T. Tao. "Decoding by linear programming."Information Theory, IEEE Transactions on vol.51, no.12 pp. 4203-4215, 2005.

[20] C. Shaobing, and D. Donoho. "Basis pursuit." Signals, Systems and Computers, 1994. 1994 Conference Record of the Twenty-Eighth Asilomar Conference on. Vol. 1. IEEE, 1994.

[21] C. T. Tony, and L. Wang. "Orthogonal matching pursuit for sparse signal recovery with noise." Information Theory, IEEE Transactions on. Vol. 57, no.7, pp.4680-4688, 2011.

[22] Yin, Wotao, et al. "Bregman iterative algorithms for \ell_1-minimization with applications to compressed sensing." SIAM Journal on Imaging Sciences1.vol.1, pp.143-168, 2008.

[23] J. Shihao, Y. Xue, and L. Carin. "Bayesian compressive sensing."Signal Processing, IEEE Transactions on, vol. 56, no.6, pp. 2346-2356, 2008.

Iranian Journal of Biomedical Engineering

Global Wall Motion Estimation of Echocardiographic Images Based on Temporal Information and Sparse Representations

References

References

Volume 10, Issue 4
January 2017
Pages 303-313

Global Wall Motion Estimation of Echocardiographic Images Based on Temporal Information and Sparse Representations

References

References

Volume 10, Issue 4January 2017Pages 303-313

Volume 10, Issue 4
January 2017
Pages 303-313