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A survey of denoising techniques for multi-parametric prostate MRI

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Abstract

Denoising is one of active area of research in the image-processing domain since last decade. Internal and external conditions of acquisition device are the main source of noise in an image during the procurement process, which is often impossible to avoid in practical situations. Since many different image denoising algorithms have been recommended till date, but the issue of noise elimination remains an undefended challenge. The main objective of this paper is to study and analyze the behavior of different denoising filters for multi-parametric (mp) prostate MRI so that the appropriate filter can be selected unanimously. This study evaluates the performance of fifteen denoising filters (Anisotropic, Median, Wiener, Gaussian, Mean, Wavelet, Contourlet, Bilateral, Curvelet, WHMT, NLM, GFOE, LMMSE, CURE-LET and ARF) w.r.t mp-prostate MRI i.e. T2w, DCE and DWI images in the presence of Gaussian and Rician noise. Evaluation is done in both variable and fixed level of noise. Both subjective and objective quality assessment parameters are considered for determining the final rating of filters executed over 300 mp-MRI images. This study concludes that anisotropic and NLM filter should be opted for denoising task because of their structural and other crucial details preserving capability.

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References

  1. Aja-Fernandez S, Alberola-Lopez C, Westin CF (2008) Noise and signal estimation in magnitude MRI and Rician distributed images: a LMMSE approach. IEEE Trans Image Process 17(8):1383–1398. https://doi.org/10.1109/TIP.2008.925382

    Article  MathSciNet  Google Scholar 

  2. Andersen AH (1995) On the Rician distribution of noisy MRI data. Magn Reson Med 34(6):910–914. https://doi.org/10.1002/mrm.1910360222

    Article  Google Scholar 

  3. Barbu A (2009) Training an active random field for real-time image denoising. IEEE Trans Image Process 18(11):2451–2462. https://doi.org/10.1109/TIP.2009.2028254

    Article  MathSciNet  MATH  Google Scholar 

  4. Blu T, Luisier F (2007) The SURE-LET approach to image denoising. IEEE Trans Image Process 16(11):2778–2786. https://doi.org/10.1109/TIP.2007.906002

    Article  MathSciNet  Google Scholar 

  5. Buades A, Coll B, Morel JM (2005) A non-local algorithm for image denoising. IEEE Comput Soc Conf Comput Vision Pattern Recogn (CVPR) 2:60–65. https://doi.org/10.1109/CVPR.2005.38

    Article  MATH  Google Scholar 

  6. Burrus CS, Gopinath RA, Guo H, Odegard JE, Selesnick IW (1998) Introduction to wavelets and wavelet transforms: a primer.(Vol. 1). Prentice Hall, New Jersey

    Google Scholar 

  7. Cahan A, Cimino JJ (2017) A learning health care system using computer-aided diagnosis. J Med Internet Res 19(3). doi:https://doi.org/10.2196/jmir.6663

  8. Candes EJ, Donoho DL (1999) Curvelets. Available from: http://www.stat.stanford.edu/donoho/Reports/1999/Curvelets.pdf. Accessed on 15 September 2016

  9. Cattin DP (2013) Image restoration: introduction to signal and image processing. MIAC, University of Basel. Retrieved Oct;11:93

  10. Do MN, Vetterli M (2005) The contourlet transform: an efficient directional multiresolu-tion image representation. IEEE Trans Image Process 14(12):2091–2106. https://doi.org/10.1109/TIP.2005.859376

    Article  Google Scholar 

  11. Dosselmann R, Yang XD (2011) A comprehensive assessment of the structural similarity index. SIViP 5(1):81–91. https://doi.org/10.1007/s11760-009-0144-1

    Article  Google Scholar 

  12. Fabijanska A (2016) A novel approach for quantification of time intensity curves in a DCE-MRI image series with an application to prostate cancer. Comput Biol Med 73:119–130. https://doi.org/10.1016/j.compbiomed.2016.04.010

  13. Garg G, Juneja M (2016) Anatomical visions of prostate Cancer in Different modalities. Indian J Sci Technol 9(44). doi:https://doi.org/10.17485/ijst/2016/v9i44/105093

  14. Garg G, Juneja M (2018) A survey of prostate segmentation techniques in different imaging modalities. Curr Med Imag Rev 14(1):19–46. https://doi.org/10.2174/1573405613666170504145842

    Article  Google Scholar 

  15. Garg G, Juneja M (2018) A survey on computer-aided detection techniques of prostate Cancer. In: progress in advanced computing and intelligent engineering, springer, Singapore (pp 115-125). doi:https://doi.org/10.1007/978-981-10-6875-112

  16. Garg G, Juneja M (2018) Cancer detection with prostate zonal segmentation - a review. In: proceedings of the international conference on computing and communication systems, springer, Singapore (pp 829-835). doi:https://doi.org/10.1007/978-981-10-6890-479

  17. Gonzalez RC, Woods RE, Eddins SL (2004) Digital image processing using MATLAB. Pearson Prentice Hall, Upper Saddle River, New Jersey

    Google Scholar 

  18. Haddad RA, Akansu AN (1991) A class and image processing. IEEE Trans Fast Gaussian Binomial Filters Speech Signal Process 39(3):723–727. https://doi.org/10.1109/78.80892

    Article  Google Scholar 

  19. Hore A, Ziou D (2010) Image quality metrics: PSNR vs. SSIM. In: IEEE 20th international conference on pattern recognition (icpr) (pp 2366-2369). doi:https://doi.org/10.1109/ICPR.2010.579

  20. Huang T, Yang GJ, Tang G (1979) A fast two-dimensional median filtering algorithm. IEEE Trans Acoust Speech Signal Process 27(1):13–18. https://doi.org/10.1109/TASSP.1979.1163188

    Article  Google Scholar 

  21. Kaur R, Juneja M (2018) A survey of kidney segmentation techniques in CT images. Curr Med Imag Rev 14(2):238–250. https://doi.org/10.2174/1573405613666161221164146

    Article  Google Scholar 

  22. Lemaitre G, Mart R, Freixenet J, Vilanova JC, Walker PM, Meriaudeau F (2015) Computer-aided detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: a review. Comput Biol Med 60:8–31. https://doi.org/10.1016/j.compbiomed.2015.02.009

    Article  Google Scholar 

  23. Lemaitre G, Massich J, Mart R, Freixenet J, Vilanova JC, Walker PM, Sidibe D, Meriaudeau F (2015) A boosting approach for prostate cancer detection using multi-parametric MRI. Proc: SPIE 9534, twelfth international conference on quality control by arti cial vision (pp 95340A). doi:https://doi.org/10.1117/12.2182772

  24. Lemaitre G, Rastgoo M, Massich J, Vilanova JC, Walker PM, Freixenet J, Meyer-Baese A, Meriaudeau F, Mart R (2016) Normalization of t2w-mri prostate images using rician a priori. Proc: SPIE 9785, medical imaging:computer-aided diagnosis (pp 978529). https://doi.org/10.1117/12.2216072

  25. Lim JS (1990) Two-dimensional signal and image processing. Prentice Hall, Englewood Cli s, NJ 710 p

    Google Scholar 

  26. Luisier F, Blu T, Unser M (2007) A new SURE approach to image denoising: Interscale or-thonormal wavelet thresholding. IEEE Trans Image Process 16(3):593–606. https://doi.org/10.1109/TIP.2007.891064

    Article  MathSciNet  Google Scholar 

  27. Luisier F, Blu T, Wolfe PJ (2012) A CURE for noisy magnetic resonance images: Chi-square unbiased risk estimation. IEEE Trans Image Process 21(8):3454–3466. https://doi.org/10.1109/TIP.2012.2191565

    Article  MathSciNet  MATH  Google Scholar 

  28. Macovski A (1996) Noise in MRI. Magn Reson Med 36(3):494–497. https://doi.org/10.1002/mrm.1910360327

    Article  Google Scholar 

  29. Manjon JV (2017) MRI Preprocessing. In: Imaging Biomarkers,Springer International Publishing (pp 53–63). doi:https://doi.org/10.1007/978-3-319-43504-65

  30. Mohan J, Krishnaveni V, Guo Y (2014) A survey on the magnetic resonance image denoising methods. Biomed Sign Survey Process Magnet Contrl Reson 9:56–69. https://doi.org/10.1016/j.bspc.2013.10.007

    Article  Google Scholar 

  31. Oza SD, Joshi KR (2016) Performance analysis of Denoising filters for MR images. In: advances in computing applications, springer Singapore (pp 87-96). doi:https://doi.org/10.1007/978-981-10-2630-06

  32. Perona P, Malik J (1990) Scale-space and edge detection using anisotropic diffusion. IEEE Trans Pattern Anal Mach Intell 12(7):629–639. https://doi.org/10.1109/34.56205

    Article  Google Scholar 

  33. Redpath TW (1998) Signal-to-noise ratio in MRI. Br J Radiol 71(847):704–707. https://doi.org/10.1259/bjr.71.847.9771379

    Article  Google Scholar 

  34. Rodriguez AO (2004) Principles of magnetic resonance imaging. Revista mexicana de fsica 50(3):272–286

    Google Scholar 

  35. Romberg JK, Choi H, Baraniuk RG (2001) Bayesian tree-structured image modeling using wavelet domain hidden Markov models. IEEE Trans Image Process 10(7):1056–1068. https://doi.org/10.1109/83.931100

    Article  Google Scholar 

  36. Roth S, Black MJ (2005) Fields of experts: a framework for learning image priors. IEEE Conf Comput Vision Pattern Recogn (CVPR) 2:860–867. https://doi.org/10.1109/CVPR.2005.160

    Article  Google Scholar 

  37. Starck JL, Candes EJ, Donoho DL (2002) The curvelet transform for image denoising. IEEE Trans Image Process 11(6):670–684. https://doi.org/10.1109/TIP.2002.1014998

    Article  MathSciNet  MATH  Google Scholar 

  38. Thakur N, Juneja M (2017) Clustering based approach for segmentation of optic cup and optic disc for detection of glaucoma. Curr Med Imag Rev 13(1):99–105. https://doi.org/10.2174/1573405612666160606124044

    Article  Google Scholar 

  39. Tomasi C, Manduchi R (1998) Bilateral filtering for gray and color images. In: IEEE international conference on computer vision pp 839–846. doi:https://doi.org/10.1109/ICCV.1998.710815

  40. Trigui R, Miteran J, Sellami L, Walker P, Hamida AB (2016) A classification approach to prostate cancer localization in 3T multi-parametric MRI. In: IEEE international conference on advanced Technologies for Signal and Image Processing (ATSIP) (pp 113-118). doi:https://doi.org/10.1109/ATSIP.2016.7523064

  41. Trigui R, Mitran J, Walker PM, Sellami L, Hamida AB (2017) Automatic classification and localization of prostate cancer using multi-parametric MRI/MRS. Biomed Sign Process Contrl 31:189–198. https://doi.org/10.1016/j.bspc.2016.07.015

    Article  Google Scholar 

  42. Weiss Y, Freeman WT (2007) What makes a good model of natural images?. In: IEEE conference on computer vision and pattern recognition (CVPR) (pp 1-8). Doi: http://doi.ieeecomputersociety.org/10.1109/CVPR.2007.383092

  43. Wright GA (1997) Magnetic resonance imaging. IEEE Signal Process Mag 14(1):56–66. https://doi.org/10.1109/79.560324

    Article  MathSciNet  Google Scholar 

  44. Zhu H, Li Y, Ibrahim JG, Shi X, An H, Chen Y, Gao W, Lin W, Rowe DB, Peterson BS (2009) Regression models for identifying noise sources in magnetic resonance images. J Am Stat Assoc 104(486):623–637. https://doi.org/10.1198/jasa.2009.0029

    Article  MathSciNet  MATH  Google Scholar 

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  1. Department of Computer Science and Engineering, University Institute of Engineering and Technology, Panjab University, Chandigarh, India

    Gaurav Garg & Mamta Juneja

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  1. Gaurav Garg
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Correspondence to Gaurav Garg.

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Garg, G., Juneja, M. A survey of denoising techniques for multi-parametric prostate MRI. Multimed Tools Appl 78, 12689–12722 (2019). https://doi.org/10.1007/s11042-018-6487-2

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