Abstract
Speckle phenomenon strongly affects Ultra-Sound (US) images. Over the past last years, several efforts have been done in order to provide an effective denoising methodology. Although good results have been achieved in terms of noise reduction effectiveness, most of the proposed approaches are characterized by a high computational load and require the supervision of an external operator for tuning the input parameters. Within this manuscript, a novel approach for noise reduction is investigated, based on Wiener filter. With respect to classical Wiener filter, the proposed Enhanced Wiener filter is able to locally adapt itself. By automatically tuning its kernel a good combination of edges and details preservation with effective noise reduction can be reached. This behavior is achieved by implementing a Local Gaussian Markov Random Field for modeling the image. Due to its intrinsic characteristics, the computational burden of the algorithm is sensibly low compared to other widely adopted filters and the parameter tuning effort is minimal. Results on a simulated dataset are reported, showing the interesting performances of the approach.
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Loupas T., McDicken W. N., Allan P. L.. An adaptive weighted medianfilter for speckle suppression in medical ultrasonic images IEEETransactions on Circuits and Systems. 1989;36:129–135.
Karaman M., Kutay M. A., Bozdagi G.. An adaptive speckle suppressionfilter for medical ultrasonic imaging IEEE Transactions on MedicalImaging. 1995;14:283–292.
Mateo Juan L., Fernndez-Caballero Antonio. Finding out generaltendencies in speckle noise reduction in ultrasound images ExpertSystems with Applications. 2009;36:7786–7797.
Wiener N.. Extrapolation, Interpolation, and Smoothing of StationaryTime Series. Wiley, New York 1949.
Perona P., Malik J.. Scale-space and edge detection using anisotropicdiffusion IEEE Transactions on Pattern Analysis and MachineIntelligence. 1990;12:629–639.
Baselice F., Ferraioli G., Pascazio V.. A 3D MRI denoising algorithmbased on Bayesian theory BioMedical Engineering OnLine. 2017;16:25.
Yu Yongjian, Acton S. T.. Speckle reducing anisotropic diffusion IEEETransactions on Image Processing. 2002;11:1260–1270.
Baselice F., Ferraioli G., Pascazio V., Sorriso A.. Bayesian MRIdenoising in complex domain Magnetic Resonance Imaging.2017;38:112–122.
Baselice F., Ferraioli G., Pascazio V., Schirinzi G.. EnhancedWiener Filter for Desplecking Ultra-Sound Images in 2016IEEE Nuclear Science Symposium and Medical Imaging Conference 2016.
Blackledge J.M.. Quantitative Coherent Imaging. Academic, London1989.
Lim J.S.. Two dimensional signal and image processing. Prentice Hall, Englewood Cliffs (NJ) 1990.
Kay Steven M.. Fundamentals of statistical signal processing: estimation theory. Prentice-Hall PTR.1 ed. 2010.
Baselice Fabio, Ferraioli Giampaolo, Pascazio Vito. A BayesianApproach for Relaxation Times Estimation in MRI Magnetic ResonanceImaging. 2016;34:312–325.
Baselice Fabio, Caivano Rocchina, Cammarota Aldo, FerraioliGiampaolo, Pascazio Vito. T1 and T2 estimation in complex domain:First results on clinical data Concepts in MagneticResonance Part A. 2014;43:166–176.
Ambrosanio M., Baselice F., Ferraioli G., Pascazio V., Schirinzi G.. Enhanced Wiener filter for ultrasound image restorationyComputer Methods and Programs in Biomedicine. 2017;underreview.
Ramos-Llordn G., Vegas-Snchez-Ferrero G., Martin-Fernandez M.,Alberola-Lpez C., Aja-Fernndez S.. Anisotropic Diffusion Filter WithMemory Based on Speckle Statistics for Ultrasound Images IEEETransactions on Image Processing. 2015;24:345–358.
Dabov K., Foi A., Katkovnik V., Egiazarian K.. Image Denoising bySparse 3-D Transform-Domain Collaborative Filtering IEEETransactions on Image Processing. 2007;16:2080–2095.
Jensen J. A., Svendsen N. B.. Calculation of pressure fields fromarbitrarily shaped, apodized, and excited ultrasound transducersIEEE Transactions on Ultrasonics, Ferroelectrics, and FrequencyControl. 1992;39:262–267.
Tay P. C., Acton S. T., Hossack J. A.. Ultrasound Despeckling Usingan Adaptive Window Stochastic Approach in 2006 InternationalConference on Image Processing:2549-2552 2006.
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Baselice, F., Ferraioli, G., Pascazio, V., Schirinzi, G. (2018). Enhanced Wiener Filter for Ultrasound image denoising. In: Eskola, H., Väisänen, O., Viik, J., Hyttinen, J. (eds) EMBEC & NBC 2017. EMBEC NBC 2017 2017. IFMBE Proceedings, vol 65. Springer, Singapore. https://doi.org/10.1007/978-981-10-5122-7_17
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DOI: https://doi.org/10.1007/978-981-10-5122-7_17
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