Abstract
Scanning acoustic microscopy is an imaging method in which the focused high frequency ultrasound is used to visualize the micro structures. The morphology and acoustic properties of the biological tissues can be evaluated using scanning acoustic microscope system. To determine thin tissues having micrometer thickness, the high acoustic frequency is required for conventional SAM. In practice the acoustic frequency is restricted by the penetration depth through the material. Characterization of thin sliced tissue is difficult, as the reflected signals from top and bottom are superimposed. In order to improve the axial resolution of conventional SAM, a technique based on sparse signal representation in overcomplete time–frequency dictionaries is investigated and among the great number of algorithms for finding sparse representation, we first apply matching pursuit (MP) and basis pursuit (BP) and then propose the orthogonal matching pursuit (OMP) and stagewise orthogonal matching pursuit (StOMP) algorithms to decompose the A-scan signal to an overcomplete Gabor dictionary. Different criteria are used for measuring the performance of these algorithms in C-scan imaging. The proposed method can separate closely space overlapping echoes beyond the resolution of conventional SAM systems and also the final results show that StOMP performs best overall in extracting the specific echo, since this algorithm is precise and fast.
References
Kessler, L. W., & Yuhas, D. (1979). Acoustic microscopy. Proceedings of IEEE, 67(4), 526–536.
Saijo, Y., Hozumi, N., Yamashita, R., Lee, C.-K., Nagao, M., Kobayashi, K., et al. (2006). Ultrasonic speed microscopy for imaging of coronary artery. Ultrasonics, 44, e51–e55.
Katz, J. L., Misra, A., Spencer, P. H., Wang, Y., Bumrerraj, S., Nomura, T., et al. (2007). Multiscale mechanics of hierarchical structure/ property relationships in calcified tissues and tissue/material interfaces. Material Science and Engineering C, 27, 450–468.
Attal, J., & Cambon, G. (1978). Improvement in the resolution of the reflective scanning acoustic microscope application to the non destructive evaluation in microelectronics. Ultrasonics Symposium Proceedings.
Semmens, J. E., & Kessler, L. W. (2002). Application of acoustic frequency domain imaging for the evaluation of advanced micro electronic packages. Microelectronics and Reliability, 42, 1735–1740.
Jhang, K., Jang, H., Park, B., Ha, J., Park, I., & Kim, K. (2002). Wavelet analysis based deconvolution to improve the resolution of scanning acoustic microscope images for the inspection of thin die layer in semiconductor. NDT & E International, 35, 549–557.
Hozumi, N., Yamashita, R., Lee, C.-K., Nagao, M., Kobayashi, K., Saijo, Y., et al. (2004). Time-frequency analysis for pulse driven ultrasonic microscopy for biological tissue characterization. Ultrasonics, 44, 717–722.
Zhang, G. M., Harvey, D. M., & Braden, D. R. (2004). High-resolution AMI technique for evaluation of microelectronic packages. Electronics Letters, 40(6), 399–400.
Zhang, G. M., Harvey, D. M., & Braden, D. R. (2006). Advanced acoustic microimaging using sparse signal representation for the evaluation of microelectronic packages. IEEE Transactions on Advanced Packaging, 29(2), 271–283, (May).
Zhang, G. M., Harvey, D. M., & Braden, D. R. (2006). Resolution improvement of acoustic microimaging by continuous wavelet transform for semiconductor inspection. Microelectronic Reliability, 46, 811–821.
Zhang, G. M., Harvey, D. M., & Braden, D. R. (2007). Microelectronic package characterisation using sparse signal representation. NDT & E International, 40, 609–617.
Mallat, S., & Zhang, Z. (1993). Matching pursuit with time–frequency dictionaries. IEEE Transactions on Signal Processing, 41(12), 3397–3415.
Ferrando, S. E., Kolasa, L. A., & Kovacevic, N. (2002). Algorithm 820: A flexible implementation of matching pursuit for Gabor functions on the interval. ACM Transactions on Mathematical Software, 28(3), 337–353.
Pati, Y., Rezaiifar, R., & Kirishnaprasad, P. (1993). Orthogonal matching pursuit: Recursive function approximation with application to wavelet decomposition. In proceedings of the 27th Annual Asilomar Conference on Signals, Systems, and Computers, Nov.
Chen, S., Donoho, D. L., & Saunders, M. A. (1998). Atomic decomposition by basis pursuit. SIAM Journal on Scientific Computing, 20(1), 33–61.
Donoho, D. L., Tasig, Y., Drori, I., & Starck, J. (2008). Sparse solution to underdetermined linear equations by stagewise orthogonal matching pursuit. IEEE Transactions on Information Theory, submitted.
Michailovich, G. O., & Adam, D. (2002). A high-resolution technique for ultrasound harmonic imaging using sparse representations in Gabor frames. IEEE Transactions on Medical Imaging, 21(12), 1490–1503, (Dec).
Adam, D., & Michailovich, O. (2002). Blind deconvolution of ultrasound sequences using nonparametric local polynomial estimates of the pulse. IEEE Transactions on Biomedical Engineering, 49(2), 118–131, (Feb).
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Mohammadi, R., Mahloojifar, A. Resolution Improvement of Scanning Acoustic Microscopy Using Sparse Signal Representation. J Sign Process Syst Sign Image Video Technol 54, 15–24 (2009). https://doi.org/10.1007/s11265-008-0191-9
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DOI: https://doi.org/10.1007/s11265-008-0191-9