Abstract
The row-column method received a lot of attention for 3-D ultrasound imaging. This simplification technique reduces the number of connections required to address a 2-D array and therefore reduces the amount of data to handle. However, Row-column ultrasound imaging still has its limitations: the issues of data sparsity, speckle noise, and the spatially varying point spread function with edge artifacts must all be taken into account when building a reconstruction framework. In this work, we introduce a compensated row-column ultrasound imaging system, termed 3D-CRC, that leverages 3-D information within an extended 3-D random field model to compensate for the intrinsic limitations of row-column method. Tests on 3D-CRC and previously published row-column ultrasound imaging systems show the potential of our proposed system as an effective tool for enhancing 3-D row-column imaging.
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References
Smith, R.A., Nelson, L.J.: 2D transmission imaging with a crossed-array configuration for defect detection. Insight J. Br. Inst. NDT 51, 82–87 (2009)
Szabo, T.L.: Diagnostic Ultrasound Imaging: Inside Out. Elsevier Academic Press, Cambridge (2004)
Rasmussen, M., Christiansen, T., Thomsem, E., Jensen, J.: 3-D imaging using row-column-addressed arrays with integrated apodization - part I: apodization design and line element beamforming. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 62(5), 947–958 (2015)
Chen, A., Wong, L., Logan, A., Yeow, J.T.W.: A CMUT-based real-time volumetric ultrasound imaging system with row-column addressing. IEEE Int. Ultrason. Symp. 1, 1755–1758 (2011)
Christiansen, T., et al.: 3-D imaging using row-column-addressed arrays with integrated apodization - part II: transducer fabrication and experimental results. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 62(5), 959–971 (2015)
Daya, I.B., Chen, A.I.H., Shafiee, M.J., Wong, A., Yeow, J.T.W.: Compensated row-column ultrasound imaging system using fisher tippett multilayered conditional random field model. PLoS One 10(12), e0142817 (2015)
Michailovich, O., Tannenbaum, A.: Despeckling of medical ultrasound images. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 53(1), 64–78 (2006)
Jensen, J.A.: Linear descriptions of ultrasound imaging systems. Technical University of Denmark, DK-2800 Lyngby, Denmark (1999)
Black, A., Kohli, P., Rother, C.: Markov Random Fields for Vision and Image Processing. The MIT Press, Cambridge (2011)
Dolui, S.: Variable splitting as a key to efficient image reconstruction. Ph.D. thesis, University of Waterloo (2012)
Sanches, J., Bioucas-Dias, J., Marques, J.: Minimum total variation in 3D-ultrasound reconstruction. IEEE Int. Conf. Image Process. 3, 597–600 (2005)
Lafferty, J.D., McCallum, A., Pereira, F.C.N.: Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proceedings of the Eighteenth International Conference on Machine Learning, pp. 282–289 (2001)
Kazemzadeh, F., Shafiee, M.J., Wong, A., Clausi, D.A.: Reconstruction of compressive multispectral sensing data using a multilayered conditional random field approach. In: SPIE Proceedings, vol. 9217 (2014)
Shafiee, M.J., Wong, A., Siva, P., Fieguth, P.: Efficient Bayesian inference using fully connected conditional random fields with stochastic cliques. In: IEEE International Conference on Image Processing, pp. 4289–4293 (2014)
Broomand, A., et al.: Multi-penalty conditional random field approach to super-resolved reconstruction of optical coherence tomography images. Biomed. Optics Express 4(10), 2032–2050 (2013)
Tanaka, K., Kataoka, S., Yasuda, M.: Statistical performance analysis by loopy belief propagation in Bayesian image modeling. J. Phys: Conf. Ser. 233(1), 012013 (2010)
Yao, F., Qian, Y., Hu, Z., Li, J.: A novel hyperspace remote sensing images classification using Gaussian processes with conditional random fields. In: International Conference on Intelligent Systems and Knowledge Engineering, pp. 197–202 (2010)
Jensen, J.: FIELD: a program for simulating ultrasound systems. In: 10th Nordic-Baltic Conference on Biomedical Imaging Published in Medical Biological Engineering Computing, vol. 34, pp. 351–353 (1996)
Achim, A., Bezerianos, A., Tsakalides, P.: Novel Bayesian multiscale method for speckle removal in medical ultrasound images. IEEE Trans. Med. Imaging 20(8), 772–783 (2001)
Shruthi, G., Usha, B.S., Sandya, S.: A novel approach for speckle reduction and enhancement of ultrasound images. Int. J. Comput. Appl. 45(20), 14–20 (2012)
Wu, S., Zhu, Q., Xie, Y.: Evaluation of various speckle reduction filters on medical ultrasound images. In: Engineering in Medicine and Biology Society, pp. 1148–1151, July 2013
Sivakumar, R., Gayathri, M.K., Nedumaran, D.: Speckle filtering of ultrasound B-scan images- a comparative study between spatial and diffusion filters. In: IEEE Conference on Open Systems, pp. 80–85, December 2010
Nageswari, C., Prabha, K.: Despeckle process in ultrasound fetal image using hybrid spatial filters. In: International Conference on Green Computing, Communication and Conservation of Energy, pp. 174–179, December 2013
Srivastava, R., Gupta, J., Parthasarthy, H.: Comparison of PDE based on other techniques for speckle reduction from digitally reconstructed holographic images. Opt. Lasers Eng. 48(5), 626–635 (2010)
Michailovich, O., Tannenbaum, A.: Blind deconvolution of medical ultrasound images: a parametric inverse filtering approach. IEEE Trans. Image Process. 16(12), 3005–3019 (2007)
Rasmussen, M., Jensen, J.: 3-D ultrasound imaging performance of a row-column addressed 2-D array transducer: a measurement study. In: IEEE International Ultrasonics Symposium, pp. 1460–1463 (2013)
Xu, L., et al.: Oil spill candidate detection from SAR imagery using a thresholding-guided stochastically fully-connected conditional random field model. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 79–86 (2015)
Shafiee, M.J., Chung A.G., Wong, A., Fieguth P.: Improved fine structure modeling via guided stochastic clique formation in fully connected conditional random fields. In: 2015 IEEE International Conference on Image Processing (ICIP), pp. 3260–3264 (2015)
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This research was funded by the Natural Sciences and Engineering Research Council of Canada, the Canada Research Chairs Program, and the Ontario Ministry of Research and Innovation.
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Daya, I.B., Chen, A.I.H., Shafiee, M.J., Wong, A., Yeow, J.T.W. (2017). Compensated Row-Column Ultrasound Imaging System Using Three Dimensional Random Fields. In: Karray, F., Campilho, A., Cheriet, F. (eds) Image Analysis and Recognition. ICIAR 2017. Lecture Notes in Computer Science(), vol 10317. Springer, Cham. https://doi.org/10.1007/978-3-319-59876-5_13
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DOI: https://doi.org/10.1007/978-3-319-59876-5_13
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