Abstract
Quantification of object elasticity properties has important technical implications as well as significant practical applications, such as civil structural integrity inspection, machine fatigue assessment, and medical disease diagnosis. In general, given noisy measurements on the kinematic states of the objects from imaging or other data, the aim is to recover the elasticity parameters for assumed material constitutive models of the objects. Various versions of the least-square (LS) methods have been widely used in practice, which, however, do not perform well under reasonably realistic levels of disturbances. Another popular strategy, based on the extended Kalman filter (EKF), is also far from optimal and subject to divergence if either the initializations are poor or the noises are not Gaussian. In this paper, we propose a robust system identification paradigm for the quantitative analysis of object elasticity. It is derived and extended from the \(\mathcal{H}_\infty\) filtering principles and is particularly powerful for real-world situations where the types and levels of the disturbances are unknown. Specifically, we show the results of applying this strategy to synthetic data for accuracy assessment and for comparison to LS and EKF results, and using canine magnetic resonance imaging data for the recovery of myocardial material parameters.
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References
Bathe, K.: Finite Element Procedures in Engineering Analysis. Prentice-Hall, Englewood Cliffs (1982)
Creswell, L.L., Moulton, M.J., Wyers, S.G., Pirolo, J.S., et al.: An Experimental Method for Evaluting Constitutive Models of Myocardium in vivo Hearts. American J. Physio. 267, H853–H853 (1994)
Didinsky, G., Pan, Z., Basar, T.: Parameter Identification for Uncertain Plants using H ∞ Methods. Automatica 31, 1227–1250 (1995)
Fung, Y.C.: A First Course in Continuum Mechanics: for Physical and Biological Engineers and Scientists, 3rd edn. Prentice Hall, Englewood Cliffs (1994)
Kallel, F., Bertrand, M.: Tissue Elasticity Reconstruction Using Linear Perturbation Method. IEEE Trans. Med. Imag. 15, 299–313 (1996)
Muthupilla, R., Lomas, D.J., Rossman, P.J., Greenleaf, J.F., Manduca, A., Ehman, R.L.: Magnetic Resonance Elastography by Direct Visualization of Propagating Acoustic Strain Waves. Science 269, 1854–1857 (1995)
Raghavan, R.H., Yagle, A.: Forward and Inverse Problems in Imaging the Elasticity of Soft Tissue. IEEE Trans. Nucl. Sci. 41, 1639–1647 (1994)
Shapo, B.M., Crowe, J.R., Skovoroda, A.R., et al.: Displacement and Strain Imaging of Coronary Arteries with Ultraluminal Ultrasound. IEEE Trans. Ultrason. Ferroelect. and Freg. Cont. 43, 234–246 (1996)
Shi, P., Liu, H.: Stochastic Finite Element Framework for Simultaneous Estimation of Cadiac Kinematic Functions and Material Parameters. Medical Image Analysis 7, 445–464 (2003)
Skovoroda, A.R., Emelianov, S.Y., O’Donnel, M.: Tissue Elasticity Reconstruction Based on Ultrasonic Displacement and Strain Images. IEEE Trans. Ultrason. Ferroelect. and Freg. Cont. 42, 747–765 (1995)
Tsap, L.V., Goldgof, D.B., Sarkar, S.: Nonrigid Motion Analysis Based on Dynamic Refinement of Finite Element Models. IEEE. PAMI 22, 526–543 (2000)
Weiss, H., Moore, J.B.: Improved Extended Kalman Filter Design for Passive Tracking. IEEE Trans. Auto. Contr. 25, 807–811 (1980)
Wong, L.N., Liu, H., Sinusas, A.J., Shi, P.: Spatio-temporal Active Region Model for Simultaneous Segmentation and Motion Estimation of the heart. In: ICCVVLSM 2003, Nice, France, pp. 193–200 (2003)
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, 59–63 (1988)
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© 2004 Springer-Verlag Berlin Heidelberg
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Liu, H., Shi, P. (2004). Robust Identification of Object Elasticity. In: Sonka, M., Kakadiaris, I.A., Kybic, J. (eds) Computer Vision and Mathematical Methods in Medical and Biomedical Image Analysis. MMBIA CVAMIA 2004 2004. Lecture Notes in Computer Science, vol 3117. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27816-0_37
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DOI: https://doi.org/10.1007/978-3-540-27816-0_37
Publisher Name: Springer, Berlin, Heidelberg
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