Abstract
Speckle noise reduction is an important area of research in the field of ultrasound image processing. Several algorithms for speckle noise characterization and analysis have been recently proposed in the area. Synthetic ultrasound images can play a key role in noise evaluation methods as they can be used to generate a variety of speckle noise models under different interpolation and sampling schemes, and can also provide valuable ground truth data for estimating the accuracy of the chosen methods. However, not much work has been done in the area of modelling synthetic ultrasound images, and in simulating speckle noise generation to get images that are as close as possible to real ultrasound images. This paper discusses these aspects, presents novel algorithms for speckle simulation and modelling based on three sampling schemes, and also evaluates the quality of the outputs using image quality metrics. Detailed experimental analysis including both quantitative and subjective assessments are also presented.
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References
Zhang, J., Cui, W., Wu, L., Lin, G., Cheng, Y.: A novel algorithm based on wavelet-trilateral filter for de-noising medical ultrasound images. In: Control and Decision Conference (2016). doi:10.1109/CCDC.2016.7531648
Malutan, R., Terebes, R., Germain, C., Borda, M., Cislariu, M.: Speckle noise removal in ultrasound images using sparse code shrinkage. In: IEEE International Conference on E-Health and Bioengineering (2015). doi:10.1109/EHB.2015.7391394
Le, T.: Adaptive noise reduction in ultrasound imaging. In: IEEE Symposium on Signal Processing in Medicine and Biology (2014). doi:10.1109/SPMB.2014.7002961
Zang, X., Bascom, R., Gilbert, C., Toth, J., Higgins, W.: Methods for 2D and 3D endobronchial ultrasound image segmentation. IEEE Trans. Biomed. Eng. 63(7), 1426–1439 (2016). doi:10.1109/TBME.2015.2494838
Cortes, C., Kabongo, L., Macia, I., Ruiz, O.E., Florez, J.: Ultrasound image dataset for image analysis algorithms evaluation. In: Chen, Y.-W., Toro, C., Tanaka, S., Howlett, R.J., Jain, L.C. (eds.) Innovation in Medicine and Healthcare 2015. SIST, vol. 45, pp. 447–457. Springer, Cham (2016). doi:10.1007/978-3-319-23024-5_41
Telmed Ultrasound Medical Systems. http://www.pcultrasound.com/products/products_usimg/index.html
Ultrasound Image Gallery. http://www.ultrasound-images.com/
Loizou, C.P., Pattichis, C.S.: Despeckle Filtering for Ultrasound Imaging and Video, Volume 1: Algorithms and Software. Morgan & Claypool, San Rafael (2015)
Perreault, C., Auclair-Fortier, M.F.: Speckle simulation based on B-mode echographic image acquisition model. In: 4th Canadian Conference on Computer and Robot Vision, pp, 379–386 (2007). doi:10.1109/CRV.2007.61
Singh, P., Mukundan, R., de Ryke, R.: Synthetic models of ultrasound image formation for speckle noise simulation and analysis. In: International Conference on Signals and Systems (ICSigSys-2017) (2017)
Kai-yu, L., Wen-dong, W., Kai-wen, Z., Wen-bo, L., Gui-li, X.: The application of B-spline based interpolation in real-time image enlarging processing. In: 2nd International Conference on Systems and Informatics, pp. 823–827 (2014). doi:10.1109/ICSAI.2014.7009398
Goceri, E., Lomenie, N.: Interpolation approaches and spline based resampling for MR images. In: 5th International Symposium on Health Informatics and Bioinformatics (2010). doi:10.1109/HIBIT.2010.5478891
Somawirata, I.K., Uchimura, K., Koutaki, G.: Image enlargement using adaptive manipulation interpolation kernel based on local image data. In: IEEE International Conference on Signal Processing, Communication and Computing, pp. 474–478 (2012). doi:10.1109/ICSPCC.2012.6335692
Xia, Z.W., Li, Q., Wang, Q.: Quality metrics of simulated intensity images of coherent ladar. In: International Conference on Optoelectronics and Microelectronics (2012). doi:10.1109/ICoOM.2012.6316255
Mirza, S., Kumar, R., Shakher, C.: Study of various preprocessing schemes and wavelet filters for speckle noise reduction in digital speckle pattern interferometric fringes. Opt. Eng. 44(4) (2005). doi:10.1117/1.1886749
Grgic, S., Grgic, M., Mrak, M.: Reliability of objective picture quality measures. J. Elec. Eng. 55(1–2), 3–10 (2004). http://citeseerx.ist.psu.edu/viewdoc/versions?doi=10.1.1.138.6936
Burger, W., Burge, M.J.: Digital Image Processing: An Algorithmic Introduction Using Java. Springer, Heidelberg (2008). doi:10.1007/978-1-84628-968-2
Acknowledgments
We acknowledge the help extended by Dr. Khadijah Hajee Abdoula, Victoria Hospital, Quatre Bornes, Mauritius and Dr. Vivek Aggarwal, Thyroid Clinic, New Delhi, India, in providing their expert advice, valuable inputs and subjective evaluation of synthetic images produced in this research.
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Singh, P., Mukundan, R., de Ryke, R. (2017). Modelling, Speckle Simulation and Quality Evaluation of Synthetic Ultrasound Images. In: Valdés Hernández, M., González-Castro, V. (eds) Medical Image Understanding and Analysis. MIUA 2017. Communications in Computer and Information Science, vol 723. Springer, Cham. https://doi.org/10.1007/978-3-319-60964-5_7
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DOI: https://doi.org/10.1007/978-3-319-60964-5_7
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