Validación de un algoritmo robusto para la estimación del movimiento en secuencias de imágenes cardiacas

Autores/as

  • Rubén Medina Universidad de Cuenca
  • Emiro Ibarra
  • Villie Morocho
  • Pablo Vanegas

Resumen

RESUMEN
Este artículo describe la validación n de un algoritmo de flujo óptico basado en esparcidad. Para ello se utilizan dos secuencias de imágenes extraídas de la base de datos Sintel y también imágenes extraídas de una secuencia de imágenes de resonancia magnética etiquetada, las cuales representan una capa el ventrículo izquierdo ubicadas en la zona media y de acuerdo a la orientación de eje corto. Los resultados son prometedores debido a que el error de magnitud promedio para las secuencias de la base de datos Sintel es menor a 3 pixels y menor de 1 mm para las imágenes de resonancia magnética etiquetada.
Palabras clave: Flujo óptico, movimiento cardiaco, imágenes médicas, resonancia magnética etiquetada, base de datos Sintel.

ABSTRACT
This paper describes the validation of a sparse based optical flow algorithm using two sequences extracted from the Sintel database and also images extracted from a tagged magnetic resonance image sequence representing a short–axis slice located at the mid-wall of the left ventricle. Results are promising as the average magnitude error for the Sintel sequences is lower than 3 pixels and lower than 1 mm for the tagged MRI.
Keywords: Optical flow, cardiac motion, medical imaging, tagged MRI, Sintel database.

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Citas

Baker, S., D. Scharstein, J.P. Lewis, S. Roth, M.J. Black, R. Szeliski, 2011. A database and evaluation methodology for optical flow. International Journal of Computer Vision, 92(1), 1-31.

Barron, J., D. Fleet, S. Beauchemin, 1994a. Performance of optical flow techniques. IJCV, 12(1), 43-77.

Barron, J.L., D.J. Fleet, S.S. Beauchemin, 1994b. Performance of optical flow techniques. International Journal of Computer Vision, 12, 43-77.

Black, M.J., P. Anandan, 1996. The robust estimation of multiple motions: Parametric and piecewise-smooth flow fields. Computer Vision and Image Understanding, 63(1), 75-104.

Butler, D.J., J. Wulff, G.B. Stanley, M.J. Black, 2012. A naturalistic open source movie for optical flow evaluation. In: Fitzgibbon et al. (Eds.), European Conf. on Computer Vision (ECCV), Part IV, LNCS 7577, pp. 611-625, Springer-Verlag, Germany.

Candes, E., J. Romberg, T. Tao, 2006. Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information. IEEE Transactions on Information Theory, pp. 489-509.
Carranza, N., G. Cristóbal, M.J. Ledesma-Carbayo, A. Santos, 2006. A new cardiac motion estimation method based on a spatio-temporal frequency approach and hough transform. In: Proceedings of Computers in Cardiology, Vol. 33, pp. 805-808.

Donoho, D., 2006. Compressed sensing. IEEE Transactions on Information Theory, pp. 1289-1306.

Fayad, Z.A., V. Fuster, K. Nikolaou, C. Becker, 2002. Computed tomography and magnetic resonance imaging for noninvasive coronary angiography and plaque imaging current and potential future concepts. Circulation, 106(15), 2026-2034.

Horn, B., B. Schunck, 1981. Determining optical flow. Artificial Intelligence, 185-203.
Ibarra, E., R. Medina, 2013. Sparse based optical flow estimation in cardiac magnetic resonance images. In: IX International Seminar on Medical Information Processing and Analysis, International Society for Optics and Photonics.

Ibarra, E., R. Medina, V. Morocho, P. Vanegas, 2015. Optical flow as a tool for cardiac motion estimation. In: Proceedings of the IEEE 2015 Asia-Pacific Conference on Computer Aided System Engineering, pp. 173-178, Quito, Ecuador.

Liu, X., J.L. Prince, 2010. Shortest path refinement for motion estimation from tagged mr images. IEEE Transactions on Medical Imaging, 29(8), 1560-1572.
Lucas, B., T. Kanade, 1981. An iterative image registration technique with an application to stereo vision. In: Proc. 7th Int. Joint Conf. Artificial Intelligence, pp. 674-679.

Mallat, S., Z. Zhang, 1993. Matching pursuits with time-frequency dictionaries. IEEE Transactions on Signal Processing, 41(12), 3397-3415.

McCane, B., K. Novins, D. Crannitch, B. Galvin, 2001. On benchmarking optical flow. Computer Vision and Image Understanding, 84, 126-143.

Nitzken, M., G. Beache, A. Elnakib, F. Khalifa, G. Gimel’farb, A. El-Baz, 2012. Improving full-cardiac cycle strain estimation from tagged cmr by accurate modeling of 3d image appearance characteristics. In: 9th IEEE International Symposium on Biomedical Imaging (ISBI), pp. 462-465.

Otte, M., H. Nagel, 1995. Estimation of optical flow based on higher-order spatiotemporal derivatives in interlaced and non-interlaced image sequences. Artificial Intelligence, 78, 5-43.

Samuel, M.S., M.L. Richard, 1991. Computation of 3-d velocity fields from 3-d cine ct images of a human heart. IEEE Transactions on Medical Imaging, 10(3), 295-306.

Shen, X., Y. Wu, 2010. Sparsity model for robust optical flow estimation at motion discontinuities. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2456-2463.

Suhling, M., M. Arigovindan, C. Jansen, P. Hunziker, M. Unser, 2005. Myocardial motion analysis from b-mode echocardiograms. IEEE Transactions on Image Processing, 14(4), 525-536.

Sun, D., S. Roth, M.J. Black, 2010. Secrets of optical flow estimation and their principles. In: Proc. of Computer Vision and Pattern Recognition (CVPR), pp. 2432-2439.

Wulff, J., D.J. Butler, G.B. Stanley, M.J. Black, 2012. Lessons and insights from creating a synthetic optical flow benchmark. In: Computer Vision-ECCV 2012. Workshops and Demonstrations, pp. 168-177. Springer-Verlag, Berlin-Heidelberg.

Xavier, M., A. Lalande, P.M. Walker, F. Brunotte, L. Legrand, 2012. An adapted optical flow algorithm for robust quantication of cardiac wall motion from standard cine-mr examinations. IEEE Transactions on Information Technology in Biomedicine, 16, 859-868.

Xu, C., J.J. Pilla, G. Isaac, J.H. Gorman, A.S. Blom, R.C. Gorman, Z. Ling, 2010. Deformation analysis of 3d tagged cardiac images using an optical flow method. Journal of Cardiovascular Magnetic Resonance, 12, 1-14.

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Publicado

2015-12-05

Cómo citar

Medina, R., Ibarra, E., Morocho, V., & Vanegas, P. (2015). Validación de un algoritmo robusto para la estimación del movimiento en secuencias de imágenes cardiacas. Maskana, 6(Supl.), 37–43. Recuperado a partir de https://publicaciones.ucuenca.edu.ec/ojs/index.php/maskana/article/view/696

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