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Speckle noise removal in medical ultrasonic image using spatial filters and DnCNN

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Abstract

Medical ultrasonic imaging is affected by an inherent phenomenon called speckle noise, which prevents the identification of details in images. While several state-of-the-art methods have been already proposed for speckle noise reduction, they often suffer from blurring, artifacts, and losing the useful details and features of image which limits the accuracy of medical diagnosis. To address such challenges, in this paper, taking the advantage of convolutional neural network (CNN), a hybrid algorithm composed of anisotropic spatial filter and denoising CNN (DnCNN) is proposed for speckle noise reduction. To further eliminate the blurring effect and increase the contrast of image edges, we incorporate Wiener filter and fast local Laplacian filter as post-processing. The experimental results on medical images show that the proposed method, in addition to an effective noise suppression, can preserve the edges and structural details of the image. The proposed algorithm outperforms state-of-the-art noise removal filters, including Frost, Lee, Median, the speckle reducing anisotropic diffusion (SRAD) filter, Wiener filter, DnCNN, and fusion filters including SRAD + DnCNN, and SRAD + DnCNN + Wiener, in terms of PSNR, SSI, and SSIM metrics.

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Data availability

The datasets generated during and/or analyzed during the current study are available in the Brest ultrasound image dataset repository, https://www.kaggle.com/datasets/aryashah2k/breast-ultrasound-images-dataset and http://www.imageprocessingplace.com

References 

  1. Al-Asad JF (2018) Medical ultrasound image: A pre-processing approach towards reconstruction. In 2018 International Symposium on Advanced Electrical and Communication Technologies (ISAECT), pp 1-6. https://doi.org/10.1109/ISAECT.2018.8618769

  2. Bafaraj AS (2019) Performance analysis of best speckle filter for noise reduction in ultrasound medical images. Int J Appl Eng Res 14:1340–1351

    Google Scholar 

  3. Yu H, Ding M, Zhang X, Wu J (2018) PCANet based nonlocal means method for speckle noise removal in ultrasound images. PLoS ONE 13:e0205390. https://doi.org/10.1371/journal.pone.0205390

    Article  Google Scholar 

  4. Yahia M, Mortula MM, Awwad A, Albasha L, Ali T (2020) Ultrasound water leakage image denoising by the iterative MMSE filter abstract. In 2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), pp 1–5. https://doi.org/10.1109/ATSIP49331.2020.9231848

  5. Duarte-Salazar CA, Castro-Ospina AE, Becerra MA, Delgado-Trejos E (2020) Speckle noise reduction in ultrasound images for improving the metrological evaluation of biomedical applications: an overview. IEEE Access 8:15983–15999. https://doi.org/10.1109/ACCESS.2020.2967178

    Article  Google Scholar 

  6. Karthikeyan S, Manikandan T, Nandalal V, Iqbal JM, Babu JJ (2019) A survey on despeckling filters for speckle noise removal in ultrasound images. In 2019 3rd International conference on Electronics, Communication and Aerospace Technology (ICECA), pp 605-609.https://doi.org/10.1109/ICECA.2019.8822052

  7. Pal SK, Bhardwaj A, Shukla A (2021) A review on despeckling filters in ultrasound images for speckle noise reduction. In 2021 International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), pp 973-978.https://doi.org/10.1109/ICACITE51222.2021.9404638

  8. Zhao Y, Chu KK, Eldridge WJ, Jelly ET, Crose M, Wax A (2018) Real-time speckle reduction in optical coherence tomography using the dual window method. Biomed Opt Express 9:616–622. https://doi.org/10.1364/BOE.9.000616

    Article  Google Scholar 

  9. Zhang P, Manna SK, Miller EB, Jian Y, Meleppat RK, Sarunic MV et al (2019) Aperture phase modulation with adaptive optics: a novel approach for speckle reduction and structure extraction in optical coherence tomography. Biomed Opt Express 10:552–570. https://doi.org/10.1364/BOE.10.000552

    Article  Google Scholar 

  10. Li D, Kelly DP, Sheridan JT (2013) Speckle suppression by doubly scattering systems. Appl Opt 52:8617–8626. https://doi.org/10.1364/AO.52.008617

    Article  Google Scholar 

  11. Chambolle A, Caselles V, Cremers D, Novaga M, Pock T (2010) An introduction to total variation for image analysis. Theor Found Numer Methods Sparse Recover 9:227. https://doi.org/10.1515/9783110226157

    Article  MathSciNet  Google Scholar 

  12. Al-Asad JF, Khan AH, Latif G, Hajji W (2019) QR based despeckling approach for medical ultrasound images. Curr Med Imaging 15:679–688. https://doi.org/10.2174/1573405614666180813113914

    Article  Google Scholar 

  13. Lee J-S (1980) Digital image enhancement and noise filtering by use of local statistics. IEEE Trans Pattern Anal Mach Intell:165–168. https://doi.org/10.1109/TPAMI.1980.4766994

  14. Gai S, Zhang B, Yang C, Yu L (2018) Speckle noise reduction in medical ultrasound image using monogenic wavelet and Laplace mixture distribution. Digital Signal Process 72:192–207. https://doi.org/10.1016/j.dsp.2017.10.006

    Article  Google Scholar 

  15. Joel T, Sivakumar R (2018) An extensive review on Despeckling of medical ultrasound images using various transformation techniques. Appl Acoust 138:18–27. https://doi.org/10.1016/j.apacoust.2018.03.023

    Article  Google Scholar 

  16. Nikesh P, Raju G (2019) Despeckling of ultra sound images using spatial filters-A fusion approach, In International Conference on Data Science and Communication (IconDSC), pp 1–4. https://doi.org/10.1109/IconDSC.2019.8816989

  17. Mia S, Talukder MH, Rahman MM (2023) RobustDespeckling: robust speckle noise reduction method using multi-scale and kernel Fisher discriminant analysis. Biomed Eng Adv 100085. https://doi.org/10.1016/j.bea.2023.100085

  18. Navabian S, YousefiRizi F, Alizadeh Sani Z (2019) A comparative study on despeckling techniques in intravascular ultrasound images. Signal Process Renewable Energy 3(2):9–24

    Google Scholar 

  19. Singh P, Diwakar M, Singh S, Kumar S, Tripathi A, Shankar A (2022) A homomorphic non-subsampled contourlet transform based ultrasound image despeckling by novel thresholding function and self-organizing map. Biocybernetics Biomed Eng 42(2):512–528. https://doi.org/10.1016/j.bbe.2022.03.003

    Article  Google Scholar 

  20. Chen HH, Tsai JC (2019) Speckle noise removal using a two-step weighted robust regression. Optics Communications 452:510–514. https://doi.org/10.1016/j.optcom.2019.07.027

    Article  Google Scholar 

  21. Beevi AS, Ratheesha S (2021) Speckle noise removal using spatial and transform domain filters in ultrasound images," in 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS), pp 291–297. https://doi.org/10.1109/ICACCS51430.2021.9441837

  22. Baboshina VA, Lyakhov PA, Kalita DI (2022) Comparative analysis of despeckle filtering methods for ultrasound images. In International Conference on Quality Management, Transport and Information Security, Information Technologies, pp 66–71. https://doi.org/10.1109/ITQMIS56172.2022.9976753

  23. Mohammadi M, Mokhtari R (2021) A model-based on filtration technique for speckle noise removal from ultrasound images. In 2021 26th International Computer Conference, Computer Society of Iran (CSICC), pp. 1-5.https://doi.org/10.1109/CSICC52343.2021.9420572

  24. Majee S, Ray RK, Majee AK (2022) A new non-linear hyperbolic-parabolic coupled PDE model for image despeckling. IEEE Trans Image Process 31:1963–1977. https://doi.org/10.1109/TIP.2022.3149230

    Article  Google Scholar 

  25. Liu S, Wang Y, Yang X, Lei B, Liu L, Li SX et al (2019) Deep learning in medical ultrasound analysis: a review. Engineering 5:261–275. https://doi.org/10.1016/j.eng.2018.11.020

    Article  Google Scholar 

  26. Latif G, Iskandar D, Alghazo J, Butt M, Khan AH (2018) Deep CNN based MR image denoising for tumor segmentation using watershed transform. Int J Eng Technol 7:37–42. https://doi.org/10.1109/ASAR.2018.8480289

    Article  Google Scholar 

  27. Zhang L, Zhang J (2022) Ultrasound image denoising using generative adversarial networks with residual dense connectivity and weighted joint loss. Peer J Computer Science 8:e873. https://doi.org/10.7717/peerj-cs.873

    Article  Google Scholar 

  28. Zhang K, Zuo W, Chen Y, Meng D, Zhang L (2017) Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising. IEEE Trans Image Process 26:3142–3155. https://doi.org/10.1109/TIP.2017.2662206

    Article  MathSciNet  Google Scholar 

  29. Bao Z, Zhang G, Xiong B, Gai S (2020) New image denoising algorithm using monogenic wavelet transform and improved deep convolutional neural network. Multimedia Tools Appl 79(11):7401–7412. https://doi.org/10.1007/s11042-019-08569-y

    Article  Google Scholar 

  30. Feng D, Wu W, Li H, Li Q (2020) Speckle noise removal in ultrasound images using a deep convolutional neural network and a specially designed Loss function. Multiscale Multimodal Medical Imaging, Switzerland: Springer, pp 85–92. https://doi.org/10.1007/978-3-030-37969-8_11

  31. Chang Y, Yan L, Chen M, Fang H, Zhong S (2020) Two-stage convolutional neural network for medical noise removal via image decomposition. IEEE Trans Instrum Meas 69:2707–2721. https://doi.org/10.1109/TIM.2019.2925881

    Article  Google Scholar 

  32. Latif G, Butt MO, Al Anezi FY, Alghazo J (2020) Ultrasound image despeckling and detection of breast cancer using deep CNN. In 2020 RIVF International Conference on Computing and Communication Technologies (RIVF), pp 1–5. https://doi.org/10.1109/RIVF48685.2020.9140767

  33. Sawant S, Kasar M, Saha A, Gore S, Birwadkar P, Kulkarni S (2022) Medical image de-speckling using fusion of diffusion-based filters and CNN. In 8th International Conference on Advanced Computing and Communication Systems (ICACCS), pp 1197–1203. https://doi.org/10.1109/ICACCS54159.2022.9785110

  34. Frost VS, Stiles JA, Shanmugan KS, Holtzman JC (1982) A model for radar images and its application to adaptive digital filtering of multiplicative noise. IEEE Trans Pattern Anal Mach Intell:157–166. https://doi.org/10.1109/TPAMI.1982.4767223

  35. Kapoor A, Singh T (2016) Speckle reducing filtering for ultrasound images. Int J Eng Trends Technol 37:283–285. https://doi.org/10.14445/22315381/IJETT-V37P249

    Article  Google Scholar 

  36. El-Said SA, Azar AT (2012) Speckles suppression techniques for ultrasound images. Journal of medical imaging and radiation sciences 43:200–213. https://doi.org/10.1016/j.jmir.2012.06.001

    Article  Google Scholar 

  37. Singh I, Neeru N (2014) Performance comparison of various image denoising filters under spatial domain. Int J Comput Appl 96:21–30

    Google Scholar 

  38. Yu Y, Acton ST (2002) Speckle reducing anisotropic diffusion. IEEE Trans Image Process 11:1260–1270. https://doi.org/10.1109/TIP.2002.804276

    Article  MathSciNet  Google Scholar 

  39. Karthikeyan K, Chandrasekar C (2011) Speckle noise reduction of medical ultrasound images using Bayesshrink wavelet threshold. Int J Comput Appl 22:8–14

    Google Scholar 

  40. Lee H, Lee MH, Youn S, Lee K, Lew HM, Hwang JY (2022) Speckle reduction via deep content-aware image prior for precise breast tumor segmentation in an ultrasound image. IEEE Trans Ultrason Ferroelectr Freq Control 69(9):2638–2650. https://doi.org/10.1109/TUFFC.2022.3193640

    Article  Google Scholar 

  41. Dabov K, Foi A, Egiazarian K (2007) Video denoising by sparse 3D transform-domain collaborative filtering. In 2007 15th European Signal Processing Conference, pp 145–149. https://doi.org/10.1109/TIP.2007.901238

  42. Hasan M (2014) BM3D image denoising using SSIM optimized Wiener filter. Electronic Thesis and Dissertation Repository 2547. https://ir.lib.uwo.ca/etd/2547

  43. Choi H, Jeong J (2020) Despeckling algorithm for removing speckle noise from ultrasound images. Symmetry 12:938. https://doi.org/10.3390/sym12060938

    Article  Google Scholar 

  44. Attlas N, Gupta S (2014) Reduction of speckle noise in ultrasound images using various filtering techniques and discrete wavelet transform: comparative analysis. Int J Res (IJR) 1:112–117

    Google Scholar 

  45. Sari S, Shimamura T (2012) Frequency domain Wiener filter for image denoising: Derivation of a new power spectrum estimation method. J Signal Process 16:79–85. https://doi.org/10.2299/jsp.16.79

    Article  Google Scholar 

  46. Tasnim T, Shuvo MMH, Hasan S (2017) Study of speckle noise reduction from ultrasound b-mode images using different filtering techniques. International Conference on Advances in Electrical Engineering, Bangladesh, pp 229–234. https://doi.org/10.1109/ICAEE.2017.8255358

  47. Aubry M, Paris S, Hasinoff SW, Kautz J, Durand F (2014) Fast local laplacian filters: Theory and applications. ACM Trans Graph (TOG) 33:1–14. https://doi.org/10.1145/2629645

    Article  Google Scholar 

  48. Paris S, Hasinoff SW, Kautz J (2011) Local laplacian filters: edge-aware image processing with a laplacian pyramid. ACM Trans Graph 30:68. https://doi.org/10.1145/2723694

    Article  Google Scholar 

  49. Dass R (2018) Speckle noise reduction of ultrasound images using BFO cascaded with wiener filter and discrete wavelet transform in homomorphic region. Procedia Comput Sci 132:1543–1551. https://doi.org/10.1016/j.procs.2018.05.118

    Article  Google Scholar 

  50. 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 

  51. Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In International Conference for Learning Representations,. https://doi.org/10.48550/arXiv.1409.1556

  52. Grubinger M, Clough P, Müller H, Deselaers T (2006) The iapr tc-12 benchmark: A new evaluation resource for visual information systems. In International workshop ontoImage

  53. Ioffe S, Szegedy C (2015) Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International conference on machine learning, pp 448–456. https://doi.org/10.48550/arXiv.1502.03167

  54. Lim JS (1990) Two-dimensional signal and image processing. Prentice Hall, Englewood Cliffs, NJ

    Google Scholar 

  55. Qiang Z, He L, Chen Y, Chen X, Xu D (2019) Adaptive fast local Laplacian filters and its edge-aware application. Multimed Tools Appl 78:619–639. https://doi.org/10.1007/s11042-017-5347-9

    Article  Google Scholar 

  56. Devnani A, Rawat C (2016) Comparative analysis of image quality measures. In 2016 International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC), pp 353–357. https://doi.org/10.1109/ICGTSPICC.2016.7955327

  57. Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13:600–612. https://doi.org/10.1109/TIP.2003.819861

    Article  Google Scholar 

  58. Wang Z, Bovik AC (2006) Modern image quality assessment. Synth Lect Image Video Multimed Process 2:1–156

    Article  Google Scholar 

  59. Al-Dhabyani W, Gomaa M, Khaled H, Fahmy A (2020) Dataset of breast ultrasound images. Data Brief 28:104863. https://doi.org/10.1016/j.dib.2019.104863

    Article  Google Scholar 

  60. http://www.imageprocessingplace.com

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  1. Faculty of Electrical and Computer Engineering, Qom University of Technology, Qom, Iran

    Ali Kavand & Mehdi Bekrani

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Kavand, A., Bekrani, M. Speckle noise removal in medical ultrasonic image using spatial filters and DnCNN. Multimed Tools Appl 83, 45903–45920 (2024). https://doi.org/10.1007/s11042-023-17374-7

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