Generic placeholder image

Current Medical Imaging

Author(s): Jawad Fawaz Al-Asad, Adil Humayun Khan, Ghazanfar Latif* and Wadii Hajji

DOI: 10.2174/1573405614666180813113914

DownloadDownload PDF Flyer Cite As
QR Based Despeckling Approach for Medical Ultrasound Images

Page: [679 - 688] Pages: 10

  • * (Excluding Mailing and Handling)

Abstract

Background: An approach based on QR decomposition, to remove speckle noise from medical ultrasound images, is presented in this paper.

Methods: The speckle noisy image is segmented into small overlapping blocks. A global covariance matrix is calculated by averaging the corresponding covariances of the blocks. QR decomposition is applied to the global covariance matrix. To filter out speckle noise, the first subset of orthogonal vectors of the Q matrix is projected onto the signal subspace. The proposed approach is compared with five benchmark techniques; Homomorphic Wavelet Despeckling (HWDS), Speckle Reducing Anisotropic Diffusion (SRAD), Frost, Kuan and Probabilistic Non-Local Mean (PNLM).

Results and Conclusion: When applied to different simulated and real ultrasound images, the QR based approach has secured maximum despeckling performance while maintaining optimal resolution and edge detection, and that is regardless of image size or nature of speckle; fine or rough.

Keywords: QR decomposition, despeckling, speckle noise, ultrasound image, image denoising, non-invasive.

Graphical Abstract

[1]
Goodman JW. Some fundamental properties of speckle. JOSA 1976; 66(11): 1145-50.
[http://dx.doi.org/10.1364/JOSA.66.001145]
[2]
Joel T, Sivakumar R. An extensive review on despeckling of medical ultrasound images using various transformation techniques. Appl Acoust 2018; 138: 18-27.
[http://dx.doi.org/10.1016/j.apacoust.2018.03.023]
[3]
Odegard JE, Guo H, Burrus CS, Baraniuk RG. Joint compression and speckle reduction of SAR images using embedded zerotree models. Rice 1996.
[4]
Hansen M, Yu B. Wavelet thresholding via MDL for natural images. IEEE Trans Inf Theory 2000; 46(5): 1778-88.
[http://dx.doi.org/10.1109/18.857790]
[5]
Gupta N, Swamy MN, Plotkin E. Despeckling of medical ultrasound images using data and rate adaptive lossy compression. IEEE Trans Med Imaging 2005; 24(6): 743-54.
[http://dx.doi.org/10.1109/TMI.2005.847401] [PMID: 15957598]
[6]
Frost VS, Stiles JA, Shanmugan KS, Holtzman JC. A model for radar images and its application to adaptive digital filtering of multiplicative noise. IEEE Trans Pattern Anal Mach Intell 1982; 4(2): 157-66.
[http://dx.doi.org/10.1109/TPAMI.1982.4767223] [PMID: 21869022]
[7]
Kuan DT, Sawchuk AA, Strand TC, Chavel P. Adaptive noise smoothing filter for images with signal-dependent noise. IEEE Trans Pattern Anal Mach Intell 1985; 7(2): 165-77.
[http://dx.doi.org/10.1109/TPAMI.1985.4767641] [PMID: 21869255]
[8]
Tay PC, Acton ST, Hossack JA. A stochastic approach to ultrasound despeckling. In: 3rd IEEE International Symposium on Biomedical Imaging:Nano to Macro 2006; Arlington, VA, USA. 221-4.
[http://dx.doi.org/10.1109/ISBI.2006.1624892]
[9]
Coupé P, Hellier P, Kervrann C, Barillot C. Nonlocal means-based speckle filtering for ultrasound images. IEEE Trans Image Process 2009; 18(10): 2221-9.
[http://dx.doi.org/10.1109/TIP.2009.2024064] [PMID: 19482578]
[10]
Gupta S, Chauhan RC, Saxena SC. Homomorphic wavelet thresholding technique for denoising medical ultrasound images. J Med Eng Technol 2005; 29(5): 208-14.
[http://dx.doi.org/10.1080/03091900412331286396] [PMID: 16126580]
[11]
Wen T, Gu J, Li L, Qin W, Wang L, Xie Y. Nonlocal total-variation-based speckle filtering for ultrasound images. Ultrason Imaging 2016; 38(4): 254-75.
[http://dx.doi.org/10.1177/0161734615600676] [PMID: 26316172]
[12]
Wang S, Huang TZ, Zhao XL, Mei JJ, Huang J. Speckle noise removal in ultrasound images by first-and second-order total variation. Numer Algorithms 2018; 78(2): 513-33.
[http://dx.doi.org/10.1007/s11075-017-0386-x]
[13]
Khan AH, Al-Asad JF, Latif G. Speckle suppression in medical ultrasound images through Schur decomposition. IET Image Process 2017; 12(3): 307-13.
[http://dx.doi.org/10.1049/iet-ipr.2017.0411]
[14]
Wu Y, Tracey B, Natarajan P, Noonan JP. Probabilistic Non-Local Means. IEEE Signal Process Lett 2013; 20(8): 763-6.
[http://dx.doi.org/10.1109/LSP.2013.2263135]
[15]
Yu Y, Acton ST. Speckle reducing anisotropic diffusion. IEEE Trans Image Process 2002; 11(11): 1260-70.
[http://dx.doi.org/10.1109/TIP.2002.804276] [PMID: 18249696]
[16]
Bar-Zion AD, Tremblay-Darveau C, Yin M, Adam D, Foster FS. Denoising of contrast-enhanced ultrasound cine sequences based on a multiplicative model. IEEE Trans Biomed Eng 2015; 62(8): 1969-80.
[http://dx.doi.org/10.1109/TBME.2015.2407835] [PMID: 25730824]
[17]
Gentle JE. Numerical linear algebra for applications in statistics. Springer 2012.
[18]
Riaz MM, Ghafoor A. Through-wall image enhancement using fuzzy and QR decomposition 2014. ScientificWorldJournal 2014; 2014487506
[http://dx.doi.org/10.1155/2014/487506]
[19]
Jain AK. Fundamentals of digital image processing. Englewood Cliffs, NJ: Prentice Hall 1989.
[20]
Al-Asad JF. Transform domain based approach for medical ultrasound image de-speckling through overlapping blocks. IASTED International Conference on Computational Bioscience. Cambridge, United Kingdom. 2011.
[http://dx.doi.org/10.2316/P.2011.742-033]
[21]
Al-Asad JF, Moghadamjoo A. Short-time fourier transform and wigner-ville transform for ultrasound image de-noising through dynamic mask thresholding. In: 2010 4th International Conference on Bioinformatics and Biomedical Engineering Chengdu, China. 2010.
[http://dx.doi.org/10.1109/ICBBE.2010.5517751]
[22]
Michailovich OV, Tannenbaum A. Despeckling of medical ultrasound images. IEEE Trans Ultrason Ferroelectr Freq Control 2006; 53(1): 64-78.
[23]
Hao X, Gao S, Gao X. A novel multiscale nonlinear thresholding method for ultrasonic speckle suppressing. IEEE Trans Med Imaging 1999; 18(9): 787-94.
[http://dx.doi.org/10.1109/42.802756] [PMID: 10571383]
[24]
Wang Z, Bovik AC. Mean squared error: Love it or leave it? A new look at signal fidelity measures. IEEE Signal Process Mag 2009; 26(1): 98-117.
[http://dx.doi.org/10.1109/MSP.2008.930649]
[25]
Jensen JA. Field: A program for simulating ultrasound systems. In: Proceedings of the 10th Nordic-Baltic Conference on Biomedical Engineering 2011; 351-53.
[26]
Jensen JA, Svendsen NB. Calculation of pressure fields from arbitrarily shaped, apodized, and excited ultrasound transducers. IEEE Trans Ultrason Ferroelectr Freq Control 1992; 39(2): 262-7.
[http://dx.doi.org/10.1109/58.139123] [PMID: 18263145]
[27]
Akansu AN, Haddad RA. Multiresolution signal decomposition: transforms, subbands, and wavelets. Academic Press 2001.
[28]
Buckheit J, Chen S, Donoho D, Johnstone I, Scargle J. Wavelab toolkit 2012.
[29]
Antony J. Database of ultrasound images 2015.