Ultrasound Image Despeckling using Local Binary Pattern Weighted Linear Filtering

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Author(s)

Simily Joseph 1,* Kannan Balakrishnan 1 M.R. Balachandran Nair 2 Reji Rajan Varghese 3

1. Digital Image Processing Lab, Dept. of Computer Applications, Cochin University of Science and Technology, Kerala, India

2. Ernakulam Scan Center, Kerala, India

3. Dept. of Biomedical Engineering, Co operative Medical College, Kerala, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijitcs.2013.06.01

Received: 26 Sep. 2012 / Revised: 5 Feb. 2013 / Accepted: 12 Mar. 2013 / Published: 8 May 2013

Index Terms

Computer Aided Diagnosis, Filtering, Local Binary Pattern, Speckle Noise, Ultrasound Imaging

Abstract

Speckle noise formed as a result of the coherent nature of ultrasound imaging affects the lesion detectability. We have proposed a new weighted linear filtering approach using Local Binary Patterns (LBP) for reducing the speckle noise in ultrasound images. The new filter achieves good results in reducing the noise without affecting the image content. The performance of the proposed filter has been compared with some of the commonly used denoising filters. The proposed filter outperforms the existing filters in terms of quantitative analysis and in edge preservation. The experimental analysis is done using various ultrasound images.

Cite This Paper

Simily Joseph, Kannan Balakrishnan, M.R. Balachandran Nair, Reji Rajan Varghese, "Ultrasound Image Despeckling using Local Binary Pattern Weighted Linear Filtering", International Journal of Information Technology and Computer Science(IJITCS), vol.5, no.6, pp.1-9, 2013. DOI:10.5815/ijitcs.2013.06.01

Reference

[1]Zhang X, Smith N, Webb A, “Medical Imaging, Biomedical Information Technology”, Academic Press Series in Biomedical Engineering, Elsevier , pp. 1-27,2008.

[2]Michailovich Oleg V, Tannenbaum A, “Despeckling of Medical Ultrasound Images”, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 53, No.1, pp.64-78,2006.

[3]Chinrungrueng C. Suvichakorn A. , “Fast Edge-Preserving Noise Reduction for Ultrasound Images”, IEEE Transactions On Nuclear Science, vol.48, No. 3,pp. 849-854, 2001.

[4]Suvichakorn A, Chinrungrueng C, “Speckle Noise Reduction based on Least Squares Approximation”, Proc. IEEE APCCAS, pp.430 – 433, 2000.

[5]Mohamed S, Mansoor Roomi, R.B.Jayanthi rajee, “Speckle Noise Removal In Ultrasound Images Using Particle Swarm Optimization technique”, Proc. ICRTIT, pp.926-931, 2011.

[6]Loupas T., Mcdicken W.N, Allan P.L, “An Adaptive Weighted Median Filter for Speckle Suppression in Medical Ultrasonic Images” , IEEE Transactions On circuits and systems. Vol.36, No. 1,pp. 129-135. 1989.

[7]Sivakumar R, Gayathri M.K, Nedumaran D, “Speckle Filtering Of Ultrasound B-Scan Images - A Comparative Study Between Spatial And Diffusion Filters”,Proc. ICOS, pp.80-85, 2010.

[8]Ricardo G, Dantas, Eduardo T.C , “Ultrasound Speckle Reduction Using Modified Gabor Filters”, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 54, no. 3, 530-538 ,march 2007

[9]Gavriloaia G,Mariuca R.G , “Improving Quality of Medical Ultrasound Images by Filtering of Frames Sequences”, Proc. HB 2011

[10]Xiaohui H, Shangkai G, Xiaorong G, “A Novel Multiscale Nonlinear Thresholding Method for Ultrasonic Speckle Suppressing”, IEEE Transactions On Medical Imaging, vol.18, No. 9,pp. 787-794,1999.

[11]Alin A, Anastasios B, P. Tsakalides,” Novel Bayesian Multiscale Method for Speckle Removal in Medical Ultrasound Images”, IEEE Transactions on Medical Imaging ,vol.20, No.8,pp. 772-783,2001.

[12]Rabbani H, Vafadust M, Abolmaesumi P, Saeed Gazor, “Speckle Noise Reduction of Medical Ultrasound Images in Complex Wavelet Domain Using Mixture Priors”, IEEE Transactions On Biomedical Engineering, vol.55,N. 9,pp. 2152-2160,2008.

[13]Pizurica A, Philips W,Lemahieu I, Marc Acheroy, “A Versatile Wavelet Domain Noise Filtration Technique for Medical Imaging”, IEEE Transactions On Medical Imaging, vol.22, No. 3,pp.323-331,2003

[14]Gnanadurai D , Sadasivam V , Paul Tiburtius Nishandh , Muthukumaran L, Annamalai C, “Undecimated double density wavelet transform based speckle reduction in SAR images” , Computers and Electrical Engineering vol.35 ,pp. 209–217,2009.

[15]Alka Vishwa, Shilpa Sharma , Modfied Method for Denoising the Ultrasound Images by wavelet Thresholding, I.J. Intelligent Systems and Applications, 2012, 6, 25-30

[16]Yongiian Yu ,Joseph Yadegar,” Regularized Speckle Reducing Anisotropic Diffusion For Feature Characterization”, ICIP ,pp. 1577-1580, 2006.

[17]Ricardo G. Dantas, Eduardo T. Costa, “Ultrasound Speckle Reduction Using Modified Gabor Filters”, IEEE transactions on ultrasonics, ferroelectrics, and frequency control, vol.54, No. 3,pp. 530-538, 2007.

[18]Hong-Ying Yang , Xiang-Yang Wanga, Tian-Xiang Qua, Zhong-Kai Fu , “Image denoising using bilateral filter and Gaussian scale mixtures in shiftable complex directional pyramid domain” , Computers and Electrical Engineering vol.37 ,pp. 656–668,2011.

[19]Bhadauria H.S, M.L. Dewal, “Medical image denoising using adaptive fusion of curvelet transform and total variation “, Computers and Electrical Engineering, Article in press

[20]Abramowicz J.S, Sheiner E, “Ultrasound of the Placenta: A Systematic Approach. Part I: Imaging”, Placenta vol.29 ,pp. 225-240, 2008

[21]Loizou C.P, C.S. Pattichis , “Despeckle Filtering Algorithms and Software for Ultrasound Imaging” , Morgan & Claypool Publishers, CA, USA, 2008.

[22]Leonid Yaroslavsk, Digital Holography and Digital Image Processing:: Principles, Methods, Kluwer academic publishers, U.S.A, 2004

[23]Perona P, Malik J, Scale Space and Edge Detection Using anisotropic Diffusion, IEEE Transactions on Pattern Analysis and Machine Intelligence.12:7(1990).

[24]Klaus D. Toennies, Guide to Medical Image Analysis: Methods and Algorithms, Springer 2012.

[25]T. Ojala, M. Pietikäinen, T. Maenpaa, “Multiresolution Gray-scale and Rotation Invariant Texture Classification with local Binary patterns," IEEE Transactions on Pattern Analysis and Machine " Intelligence, vol.24 pp. 971-987,2002.

[26]Ahonen, T, Hadid, A,Pietikainen M, “Face Description with Local Binary Patterns: Application to Face Recognition”. IEEE Trans. Pattern Analysis and Machine Intelligence vol.28, No.12,pp. 2037-2041,2006.

[27]Hadid, A, Pietikainen, M, “Combining Appearance and Motion for Face and Gender Recognition from Videos”. Pattern Recognition 42:11 pp. 2818-2827,2009.

[28]Baopu Li , Q.H.Max ,Meng, IEEE, “Tumor Recognition in Wireless Capsule Endoscopy Images Using Textural Features and SVM-Based Feature Selection”, IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, vol.16: 3,pp. 323-329,2012

[29]Malathi S, Meena C, “An efficient method for partial fingerprint recognition based on Local Binary Pattern” , Proc of ICCCCT’10, pp. 569-572.

[30]Hanmandlu1 M, Ankit Gureja, Ankur Jain, ” Palm Print Recognition using Local Binary Pattern Operator and Support Vector Machines”, Proc. ICSIP (2010) pp. 158-162

[31]SeyedMohsen Zabihi, Hamid Reza Pourreza, Touka Banaee, “Vessel Extraction of Conjunctival Images Using LBPs and ANFIS”, ISRN Machine Vision, Vol. 2012, Article ID 424671.

[32]Maenpaa Topi, Ojala Timo, Pietikainen Matti, Soriano Maricor, “Robust Texture Classification by Subsets of Local Binary Patterns” , Proc. ICPR , 935-938, 2000.

[33]Liao, S., Law M.W.K. , Chung A.C.S, ” Dominant Local Binary Patterns for Texture Classification”. IEEE Trans. Image Processing 18:5,pp. 1107-1118,2009.

[34]Tan X , Triggs B, “Fusing Gabor and LBP feature Sets for Kernel-based Face Recognition”, Proc.International Workshop on Analysis and Modeling of Face and Gesture, 2007, pp.235-249. 

[35]Heikkilä, M, Pietikäinen, M. “A Texture-Based Method for Modeling the Background and Detecting Moving Objects”. IEEE Trans. Pattern Analysis and Machine Intelligence , vol.28(4) pp.657-662.,2006.

[36]http://www.ultrasound-images.com