1. Home
  2. Issues
  3. Volume 30, Issue 1 (2019)
  4. Non-Subsampled Shearlet Transform and Lo ...

Informatica


Non-Subsampled Shearlet Transform and Log-Transform Methods for Despeckling of Medical Ultrasound Images
Volume 30, Issue 1 (2019), pp. 1–19
Pub. online: 1 January 2019      Type: Research Article      Open accessOpen Access
Received
1 September 2017
Accepted
1 November 2018
Published
1 January 2019

Abstract

Medical Ultrasound is a diagnostic imaging technique based on the application of ultrasound in various branches of medical sciences. It can facilitate the observation of structures of internal body, such as tendons, muscles, vessels and internal organs such as male and female reproductive system. However, these images usually degrade by a special kind of multiplicative noise called speckle. The main effects of speckle noise in the ultrasound images appear in the edges and fine details which lead to reduce their resolution and consequently make difficulties in medical diagnosing. Therefore, reducing of speckle noise seriously plays an important role in image diagnosing. Among the various methods that have been proposed to reduce the speckle noise, there exists a class of approaches that firstly convert multiplicative speckle noise into additive noise via log-transform and secondly perform the despeckling process via a directional filter. Usually, the additive noises are mutually uncorrelated and obey a Gaussian distribution. On the other hand, non-subsampled shearlet transform (NSST), as a multi scale method, is one of the effective methods in image processing, specially, denoising. Since NSST is shift invariant, it diminishes the effect of pseudo-Gibbs phenomena in the denoising. In this paper, we describe a simple image despeckling algorithm which combines the log-transform as a pre-processing step with the non-subsampled shearlet transform for strong numerical and visual performance on a broad class of images. To illustrate the efficiency of the proposed approach, it is applied on a sample image and two real ultrasound images. Numerical results illustrate that the proposed approach can obtain better performance in term of peak signal to noise ratio (PSNR) and structural similarity (SSIM) index rather than existing state-of-the-art methods.

References

 
Abazari, R., Lakestani, M. (2018a). Fourier based discrete shearlet transform for speckle noise reduction in medical ultrasound images. Current Medical Imaging Reviews, 14(3), 477–483.
 
Abazari, R., Lakestani, M. (2018b). A hybrid denoising algorithm based on shearlet transform method and Yaroslavsky’s filter. Multimedia Tools and Applications, 77(14), 17829–17851.
 
Abbott, J.G., Thurstone, F.L. (1979). Acoustic speckle: theory and experimental analysis. Ultrason Imaging, 1, 303–324.
 
Averbuch, A., Coifman, R.R., Donoho, D.L., Israeli, M., Shkolnisky, Y. (2008). A framework for discrete integral transformations I – the pseudo-polar Fourier transform. SIAM Journal on Scientific Computing, 30(2), 764–784.
 
Bhuiyan, M.I.H., Ahmad, M.O., Swamy, M.N.S. (2009). Spatially adaptive thresholding in wavelet domain for despeckling of ultrasound images. IET Image Process, 3, 147–162.
 
Burt, P.J., Adelson, E. (1983). The Laplacian pyramid as a compact image code. IEEE Transactions on Communications, 31(4), 532–540.
 
Candes, E.J. (1998). Ridgelets: Theory and Applications. PhD thesis, Department of Statistics, Stanford University.
 
Candes, E.J., Donoho, D.L. (2000). Curvelets: a surprisingly effective nonadaptive representation for objects with edges. Tech. report, Department of Statistics, Stanford University.
 
Colonna, F., Easley, G.R., Guo, K., Labate, D. (2010). Radon transform inversion using the shearlet representation. Applied and Computational Harmonic Analysis, 29(2), 232–250.
 
Cunha, A.L., Zhou, J., Do, M.N. (2006). The nonsubsampled contourlet transform: theory, design, and applications. IEEE Transactions on Image Processing, 15(10), 3089–3101.
 
Deka, B., Bora, P.K. (2013). Wavelet-based despeckling of medical ultrasound images. IETE Journal of Research, 59(2), 97–108.
 
Do, M.N., Vetterli, M. (2005). The contourlet transform: an efficient directional multiresolution image representation. Transactions on Image Processing, 14(12), 2091–2106.
 
Donoho, D.L., Huo, X. (2001). Beamlets and multiscale image analysis. Tech. report, Stanford University.
 
Elmoniem, K., Youssef, A., Kadah, A. (2002). Real-time speckle reduction and coherence enhancement in ultrasound imaging via nonlinear anisotropic diffusion. IEEE Transactions on Biomedical Engineering, 49, 997–1014.
 
Elyasi, I., Pourmina, M.A. (2016). Reduction of speckle noise ultrasound images based on TV regularization and modified bayes shrink techniques. Optik, 127(24), 11732–11744.
 
Goodman, J.W. (1976). Some fundamental properties of Speckle. Journal of the Optical Society of America, 66, 1145–1150.
 
Guo, K., Labate, D. (2007). Optimally sparse multidimensional representation using shearlets. SIAM Journal on Mathematical Analysis, 39, 298–318.
 
Guo, K., Labate, D. (2012). Optimally sparse representations of 3d data with ${c^{2}}$ surface singularities using parseval frames of shearlets. SIAM Journal on Mathematical Analysis, 44, 851–886.
 
Guo, K., Labate, D. (2013). The construction of smooth parseval frames of shearlets. Mathematical Modelling of Natural Phenomena, 8(1), 82–105.
 
Gupta, S., Chauhan, R.C., Saxena, S.C. (2004). Wavelet-based statistical approach for speckle reduction in medical ultrasound images. Medical & Biological Engineering & Computing, 42, 189–192.
 
Hazarika, D., Nath, V.K., Bhuyan, M. (2015). A lapped transform domain enhanced lee filter with edge detection for speckle noise reduction in sar images. In: IEEE International Conference on Recent Trends in Information Systems (ReTIS), pp. 243–248.
 
Hiremath, P.S., Akkasaligar, P.T., Badiger, S. (2013). Speckle noise reduction in medical ultrasound images, advancements and breakthroughs in ultrasound imaging. G P P Gunarathne, IntechOpen. https://doi.org/10.5772/56519.
 
Hou, B., Zhang, X., Bu, X. (2012). SAR image despeckling based on nonsubsampled shearlet transform. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5(3), 809–823.
 
Huang, Y., Hu, B., Zhu, J. (2016). Study on the use of quantitative ultrasound evaluation of diabetic neuropathy in the rat sciatic nerve. Australasian Physical & Engineering Sciences in Medicine, 39(4), 997–1005.
 
Kabir, S.M., Bhuiyan, M.I.H. (2015). Modeling of log-transformed speckle noise in the contourlet transform domain for medical ultrasound images. Journal of Circuits, Systems, and Computers, 24(2), 1540004 (35 pp.).
 
Khare, A., Khare, M., Jeong, Y., Kim, H., Jeon, M. (2010). Despeckling of medical ultrasound images using Daubechies complex wavelet transform. Signal Processing, 90(2), 428–439.
 
Krissian, K., Westin, C., Kikinis, R., Vosburgh, K. (2007). Oriented speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing, 16, 1412–1424.
 
Kutyniok, G., Lim, W. (2011). Image separation using wavelets and shearlets. In: Curves and Surfaces, Avignon, France, 2010, Lecture Notes in Computer Science, Vol. 6920.
 
Labate, D., Lim, W.-Q., Kutyniok, G., Weiss, G. (2005). Sparse multidimensional representation using shearlets. In: Wavelets XI, San Diego, CA, 2005, SPIE Proc., Vol. 5914. SPIE, Bellingham, WA, pp. 254–262.
 
Lakestani, M., Razzaghi, M., Mousavi, Z. (2016). Combined shearlet shrinkage and total variation minimization for image denoising. Iranian Journal of Science and Technology, Transactions A: Science. https://doi.org/10.22099/IJSTS.2016.3677.
 
Loupas, T., Mcdicken, W., Allan, P.L. (1989). An adaptive weighted median filter for speckle suppression in medical ultrasonic images. IEEE Transactions on Circuits and Systems, 36(1), 129–135.
 
Meyer, F.G., Ronald, R.C. (1997). Brushlets: a tool for directional image analysis and image compression. Applied and Computational Harmonic Analysis, 4(2), 147–187.
 
Non-subsampled contourlet transform (2008). MATLAB source, Retrieved from http://http://minhdo.ece.illinois.edu/software/.
 
Patel, V.M., Easley, G., Healy, D.M. (2009). Shearlet-based deconvolution. IEEE Transactions on Image Processing, 18, 2673–2685.
 
Ragesh, N.K., Anil, A.R., Rajesh, R. (2011). Digital image denoising in medical ultrasound images: a survey. In: ICGST AIML-11 Conference, Dubai, UAE, pp. 12–14.
 
Rajeshwar, D. (2018). Speckle noise reduction of ultrasound images using BFO cascaded with wiener filter and discrete wavelet transform in homomorphic region. Procedia Computer Science, 132, 1543–1551.
 
Ritenour, E.R., Nelson, T.R., Raff, U. (1984). Application of median filter to digital readiographic images. In: Proc. 7th International Conference on Acoustics, Speech and Signal Processing, Vol. 23, pp. 1–4.
 
ShearLab (2008). Computer Software, MATLAB Source. Retrieved from http://www.shearlet.org.
 
Siemens Healthcare GmbH (2019). Available: http://www.healthcare.siemens.com/ultrasound.
 
Vard, A., Jamshidi, K., Movahhedinia, N. (2012). An automated approach for segmentation of intravascular ultrasound images based on parametric active contour models. Australasian Physical & Engineering Sciences in Medicine, 35(2), 135–150.
 
Vese, L., Osher, S. (2003). Modeling textures with total variation minimization and oscillating patterns in image processing. Journal of Scientific Computing, 19(11), 553–572.
 
Vishwakarma, A., Bhuyan, M.K., Iwahori, Y. (2018). Non-subsampled shearlet transform-based image fusion using modified weighted saliency and local difference. Multimedia Tools and Applications, 77(24), 32013–32040.
 
Wang, G., Xu, J., Pan, Z., Diao, Z. (2014). Ultrasound image denoising using backward diffusion and framelet regularization. Biomedical Signal Processing and Control, 13, 212–217.
 
Wang, Z., Simoncelli, E.P., Bovik, A.C. (2004). Multiscale structural similarity for image quality assessment. In: Conference Record of the Thirty-Seventh Asilomar Conference on Signals, Systems and Computers, Vol. 2, pp. 1398–1402.
 
Yu, Y., Acton, S. (2002). Speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing, 11, 1260–1270. 2002.
 
Zong, X., Laine, A.F., Geiser, E.A. (1998). Speckle reduction and contrast enhancement of echocardiograms via multiscale nonlinear processing. IEEE Transactions on Medical Imaging, 17, 532–540.
 
Zhang, Y., Wang, Y., Wang, W., Liu, B. (2001). Doppler ultrasound signal denoising based on wavelet frames. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 48, 709–716.

Biographies

Abazari Reza
abazari-r@uma.ac.ir

R. Abazari received the MS degree in applied mathematics from University of Mohaghegh Ardabili, Ardabil, Iran, in 2008 and PhD degree in applied mathematics in 2017 from University of Tabriz, Tabriz, Iran. He is now an adjunct lecturer in applied mathematics at University of Mohaghegh Ardabili since September 2009. He is also an research associate at the Young Researchers and Elite Club, Ardabil, Iran. His research interests include mathematical method in physics, medical sciences and image analysis.

Lakestani Mehrdad

M. Lakestani received the BS degree in applied mathematics from University of Tabriz, Iran, in 1998 and the MSc and PhD degree both in applied mathematics from Amirkabir University of Technology – Tehran Polytechnic, Tehran, Iran, in 2000 and 2005, respectively. Since 2005, he is a professor at University of Tabriz. His research interests include numerical analysis, with special emphasis on wavelet theory, time-frequency analysis and image processing.


Full article Cited by PDF XML
Full article Cited by PDF XML

Copyright
© 2019 Vilnius University
Open access article under the CC BY license.

Keywords
ultrasound image discrete shearlet transform non-subsampling log-transform speckle noise

Metrics (since January 2020)
55

Article info
views

 146

Full article
views
678

PDF
downloads

 204

XML
downloads


Share


RSS