Medical Image Analysis
Volume 14, Issue 3 , Pages 429-448 , June 2010

A coupled deformable model for tracking myocardial borders from real-time echocardiography using an incompressibility constraint

Received 30 September 2008 ,Revised 6 February 2010 ,Accepted 22 February 2010.

References 

  1. Angelsen BA. A theoretical study of the scattering of ultrasound from blood. IEEE TBME. 1980;27(2):61–67
  2. Binder T, Sussner M, Moertl D, Strohmer H, Baumgartner T, Maurer G, et al. Artificial neural networks and spatial temporal contour linking for automated endocardial contour detection on echocardiograms: a novel approach to determine left ventricular contractile function. Ultrasound Med. Biol. 1999;25(7):1069–1076
  3. Bistoquet A, Oshinski J, Shrinjar O. Myocardial deformation recovery from cine mri using a nearly incompressibile biventricular model. Med. Image Anal. 2008;12:69–85
  4. Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet. 1986;i:307–310
  5. Bosch JG, Mitchell SC, Lelieveldt BP, Nijland F, Kamp O, Sonka M, et al. Automatic segmentation of echocardiographic sequences by active appearance motion model. IEEE TMI. 2002;21(11):1374–1383
  6. Boukerroui D, Baskurt A, Noble JA, Basset O. Segmentation of ultrasound images – multiresolution 2d and 3d algorithm based on global and local statistics. Pattern Recogn. Lett. 2003;24:779–790
  7. Bowman AW, Kovacs SJ. Assessment and consequences of the constant-volume attribute of the four-chamber heart. Am. J. Physiol. Heart Circ. Physiol. 2003;285:H2027–2033
  8. Cerqueira MD, Weissman NJ, Dilsizian V, Jacobs AK, Kaul S, Laskey WK, et al. Standardized myocardial segmentation and nomenclature for tomographic imaging of the heart: a statement for healthcare professionals from the cardiac imaging committee of the concil on clinical cardiology of the american heart association. Circulation. 2002;105:539–542
  9. Chalana V, Linker DT, Haynor DR, Kim Y. A multiple active contour model for cardiac boundary detection on echocardiographic sequences. IEEE TMI. 1996;15(3):290–298
  10. Chan TF, Vese LA. Active contours without edges. IEEE IP. 2001;10(2):266–277
  11. Chen X, Xie H, Erkamp R, Kim K, Jia C, Rubin J, et al. 3-d correlation-based speckle tracking. Ultrason. Imag. 2005;27:21–36
  12. Coppini G, Poli R, Valli G. Recovery of the 3-d shape of the left ventricle from echocardiographic images. IEEE TMI. 1995;14(2):301–317
  13. Davignon F, Deprez J-F, Basset O. A parametric imaging approach for the segmentation of ultrasound data. Ultrasonics. 2005;43:789–801
  14. Dickinson R, Hill C. Measurement of soft tissue motion using correlation between a-scans. Ultrasound Med. Biol. 1982;8(3):263–271
  15. Duan Q, Angelini ED, Herz SL, Ingrassia CM, Costa KD, Holmes JW, et al. Region-based endocardium tracking on real-time three-dimensional ultrasound. Ultrasound Med. Biol. 2009;35(2):256–265
  16. Dutt V, Greenleaf JF. Ultrasound echo envelop analysis using a homodyned K distribution signal model. Ultrason. Imag. 1994;16:265–287
  17. Glass L, Hunter P, McCulloch A. Theory of Heart: Biomechanics, Biophysics, and Nonlinear Dynamics of Cardiac Function. Springer-Verlag; 1990;
  18. Hamilton WF, Rompf JH. Movement of the base of the ventricle and the relative constancy of the cardiac volume. Am. J. Physiol. 1932;132:559–565
  19. Hoffman EA, Ritman EL. Invariant total heart volume in the intact thorax. Am. J. Physiol. Heart Circ. Physiol. 1985;249:H883–H890
  20. Hoffman EA, Ritman EL. Heart-lung interaction: effect of regional lung air content and total heart volume. Ann. Biomed. Eng. 1987;15:241–257
  21. Jacob G, Noble A, Behrenbruch C, Kelion AD, Banning AP. A shape-space-based approach to tracking myocardial borders and quantifying regional left-ventricular function applied in echocardiography. IEEE TMI. 2002;21(3):226–238
  22. Jakeman E. K-distributed noise. J. Opt. A: Pure Appl. Opt. 1999;1:784–789
  23. Jensen J. Field: a program for simulating ultrasound systems. Med. Biol. Eng. Comput. 1996;34(Suppl. 1, Part 1):351–353
  24. Jensen J, Svendsen N. Calculation of pressure fields from arbitrary shaped, apodized, and excited ultrasound transducers. IEEE Trans. Ultrason. Ferroelec., Freq. Contr. 1992;39:262–267
  25. Ledesma-Carbayo MJ, Kybic J, Desco M, Santos A, Suhling M, Hunziker P, et al. Spatio-temporal nonrigid registration for ultrasound cardiac motion estimation. IEEE TMI. 2005;24(9):1113–1126
  26. Lim, J., Yang, M.-H., 2005. A direct method for modeling non-rigid motion with thin plate spline. In: CVPR05, vol. 1, pp. 1196–1202.
  27. Lin N, Yu W, Duncan JS. Combinative multi-scale level set framework for echocardiographic image segmentation. Med. Image Anal. 2003;7:529–537
  28. Lynch M, Ghita O, Whelan P. Left-ventricle myocardium segmentation using a coupled level-set with a priori knowledge. Comput. Med. Imag. Graph. 2006;30(4):255–262
  29. Malassiotis S, Strintzis MG. Tracking the left ventricle in echocardiographic images by learning heart dynamics. IEEE TMI. 1999;18(3):282–290
  30. Mansi, T., Peyrat, J.-M., Sermesant, M., Delingette, H., Blanc, J., Boudjemline, Y., Anyache, N., 2009. Physically-constrained diffeomorphic demons for the estimation of 3d myocardium strain from cine-mri. In: FIMH, pp. 201–210.
  31. Mignotte M, Meunier J, Tardif J-C. Endocardial boundary estimation and tracking in echocardiographic images using deformable template and markov random field. Pattern Anal. Appl. 2001;24(4):256–271
  32. Mulet-Parada M, Noble JA. 2d+t acoustic boundary detection in echocardiography. Med. Image Anal. 2000;4:21–30
  33. Myronenko, A., Song, X., Sahn, D.J., 2007. Lv motion tracking from 3d echocardiography using textural and structural information. In: MICCAI, pp. 428–435.
  34. Noble JA, Boukerroui D. Ultrasound image segmentation: a survey. IEEE TMI. 2006;25(8):987–1010
  35. O’Donnell, T., Funka-Lea, G., 1998. 3-D cardiac volume analysis using magnetic resonance imaging. In: Proceedings of the Fourth IEEE Workshop on Applications of Computer Vision (WACV), pp. 240–241.
  36. Orderud, F., Hansgard, J., Rabben, S.I., 2007. Real-time tracking of the left ventricle in 3d echocardiography using a state estimation approach. In: MICCAI, pp. 858–865.
  37. Papademetris, X., Jackowski, M., Rajeevan, N., Okuda, H., Constable, R.T., Staib, L.H., 2006. Bioimage suite: An integrated medical image analysis suite. Section of Bioimaging Sciences, Dept. of Diagnostic Radiology, Yale School of Medicine. <http://www.bioimagesuite.org>.
  38. Paragios N. A level set approach for shape-driven segmentation and tracking of the left ventricle. IEEE TMI. 2003;22(6):773–776
  39. Philips, 2005. Philips Medical Systems Corporate Website. <http://www.medical.philips.com/main/products/ultrasound/cardiology/>.
  40. Qian, X., Tagare, H.D., Tao, Z., 2006. Segmentation of rat cardiac ultrasound images with large dropout regions. In: MMBIA.
  41. Rajpoot, K., Noble, J.A., Grau, V., 2009. Multiview rt3d echocardiographic image fusion. In: FIMH, pp. 134–143.
  42. Saddi, K.A., Chefd’hotel, C., Cheriet, F., 2007. Large deformation registration of contrast-enhanced images with volume-preserving constraint. In: SPIE, vol. 6512.
  43. Sarti A, Corsi C, Mazzini E, Lamberti C. Maximum likelihood segmentation of ultrasound images with rayleigh distribution. IEEE TUFFC. 2005;52(6):947–960
  44. Setarehdan SK, Soraghan JJ. Automatic cardiac lv boundary detection and tracking using hybrid fuzzy temporal and fuzzy multiscale edge detection. IEEE Trans. Biomed. Eng. 1999;46(11):1364–1378
  45. Shankar PM. A general statistical model for ultrasonic backscattering from tissues. IEEE UFFC. 2000;47(3):727–736
  46. Shung KK, Thieme GA. Ultrasonic Scattering in Biological Tissues. CRC; 1992;
  47. Song M, Haralick RM, Sheehan FH, Johnson RK. Integrated surface model optimization for freehand three-dimensional echocardiography. IEEE TMI. 2002;21(9):1077–1090
  48. Tao Z, Tagare H, Beaty JD. Evaluation of four probability distribution models for speckle in clinical cardiac ultrasound images. IEEE TMI. 2006;25(11):1483–1491
  49. Taron, M., Paragios, N., Jolly, M.-P., 2007. From uncertainties to statistical model building and segmentation of the left ventricle. In: MMBIA07.
  50. Wagner RF, Smith SW, Sandrik JM, Lopez H. Statistics of speckle in ultrasound b-scans. IEEE Trans. Sonics Ultrason. 1983;30(3):156–163
  51. Yan, P., Sinusas, A., Duncan, J., 2007. Lv segmentation from 3d echocardiography using fuzzy features and a multilevel ffd model. In: ISBI, pp. 848–851.
  52. Yang, L., Georgescu, B., Zheng, Y., Foran, D.J., Comaniciu, D., 2008. A fast and accurate tracking algorithm of left ventricles in 3d echocardiography. In: ISBI, pp. 221–224.
  53. Zhu, Y., Papademetris, X., Duncan, J., Sinusas, A., 2007a. Cardiac mr segmentation with incompressibility constraint. In: ISBI, pp. 185–188.
  54. Zhu, Y., Papademetris, X., Sinusas, A., Duncan, J., 2007b. Segmentation of myocardial volumes from real-time 3d echocardiography using an incompressibility constraint. In: MICCAI, pp. 44–51.

PII: S1361-8415(10)00020-4

doi: 10.1016/j.media.2010.02.005

Medical Image Analysis
Volume 14, Issue 3 , Pages 429-448 , June 2010