DOI QR코드

DOI QR Code

간 초음파 영상에서의 스페클 노이즈 제거를 위한 필터들의 비교 평가

Comparative Evaluation of Filters for Speckle Noise Reduction in a Clinical Liver Ultrasound Image

  • 김하진 (가천대학교 일반대학원 보건과학과) ;
  • 이영진 (가천대학교 방사선학과)
  • Hajin Kim (Department of Health Science, General School of Gachon University) ;
  • Youngjin Lee (Department of Radiological Science, Gachon University)
  • 투고 : 2023.10.10
  • 심사 : 2023.11.15
  • 발행 : 2023.12.31

초록

This study aimed to compare filters for reducing speckle noise in ultrasound images using clinical liver images. We acquired the clinical liver ultrasound images, and noisy images were obtained by adding 0.01, 0.05, 0.10, and 0.50 intensity levels of speckle noise to the liver images. The Wiener filter, median modified Wiener filter, gamma filter, and Lee filter were designed for the noisy images by setting window sizes at 3×3, 5×5, and 7×7. The coefficient of variation (COV) and contrast to noise ratio (CNR) were calculated to evaluate noise reduction and various filters. Moreover, the filter with the highest image quality was selected and quantitatively compared to a noisy image. As a result, COV and CNR showed the noise improved result when the Lee filter was applied. Furthermore, the Lee filter image with a window size of 7×7 was noted to possess approximately a minimum of 1.28 to a maximum of 3.38 times better COV and a minimum of 2.18 to a maximum of 5.50 times better CNR than the noisy image. In conclusion, we confirmed that the Lee filter was effective in reducing speckle noise and proved that an appropriate window size needs to be set considering blurring.

키워드

과제정보

This paper is supported by the academic activities grant from the society of Incheon Radiological Technologists Association (IRTA) of the Korean Radiological Technologists Association (KRTA) in 2023.

참고문헌

  1. World Health Organization. The top 10 causes of death. [cited 2023 Sep 4]. Retrieved from https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death
  2. Statistics Korea. Causes of death statistics in 2019. [cited 2023 Sep 4]. Retrieved from https://kostat.go.kr/board.es?mid=a10301060200&bid=218&act=view&list_no=385219 
  3. Brancatelli G, Federle MP, Vilgrain V, Vullierme MP, Marin D, Lagalla R. Fibropolycystic liver disease: CT and MR imaging findings. RadioGraphics. 2005;25(3):659-70. DOI: https://doi.org/10.1148/rg.253045114 
  4. Boll DT, Merkle EM. Diffuse liver disease: Strategies for hepatic CT and MR imaging. RadioGraphics. 2009;29(6):1591-614. DOI: https://doi.org/10.1148/rg.296095513 
  5. Ballestri S, Romagnoli D, Nascimbeni F, Francica G, Lonardo A. Role of ultrasound in the diagnosis and treatment of nonalcoholic fatty liver disease and its complications. Expert Rev. Gastroenterol. Hepatol. 2015;9(5):603-27. DOI: https://doi.org/10.1586/17474124.2015.1007955 
  6. Gerstenmaier JF, Gibson RN. Ultrasound in chronic liver disease. Insights Imaging. 2014;5:441-55. DOI: https://doi.org/10.1007/s13244-014-0336-2 
  7. Goyal N, Jain N, Rachapalli V, Cochlin DL, Robinson M. Non-invasive evaluation of liver cirrhosis using ultrasound. Clin. Radiol. 2009;64(11):1056-66. DOI: https://doi.org/10.1016/j.crad.2009.05.010 
  8. Solbiati L, Ierace T, Tonolini M, Cova L. Guidance and monitoring of radiofrequency liver tumor ablation with contrast-enhanced ultrasound. Eur. J. Radiol. 2004;51:S19-23. DOI: https://doi.org/10.1016/j.ejrad.2004.03.035 
  9. Lupsor-Platon ML, Stefanescu H, Muresan D, Florea M, Szasz ME, Maniu A, et al. Noninvasive assessment of liver steatosis using ultrasound methods. Med. Ultrason. 2014;16(3):236-45. DOI: https://doi.org/10.11152/mu.2013.2066.163.1mlp 
  10. Schwenzer NF, Springer F, Schraml C, Stefan N, Machann J, Schick F. Non-invasive assessment and quantification of liver steatosis by ultrasound, computed tomography and magnetic resonance. J. Hepatol. 2009;51(3):433-45. DOI: https://doi.org/10.1016/j.jhep.2009.05.023 
  11. Acharya UR, Raghavendra U, Fujita H, Hagiwara Y, Koh JE, Jen Hong T, et al. Automated characterization of fatty liver disease and cirrhosis using curvelet transform and entropy features extracted from ultrasound images. Comput. Biol. Med. 2016;79:250-8. DOI: https://doi.org/10.1016/j.compbiomed.2016.10.022 
  12. Nicolau C, Bru C, Carreras E, Bosch J, Bianchi L, Gilabert R, et al. Sonographic diagnosis and hemodynamic correlation in veno-occlusive disease of the liver. J. Ultrasound Med. 1993;12(8):437-40. DOI: https://doi.org/10.7863/jum.1993.12.8.437 
  13. Sagi R, Reif S, Neuman G, Webb M, Phillip M, Shalitin S. Nonalcoholic fatty liver disease in overweight children and adolescents. Acta Paediatr. 2007;96(8):1209-13. DOI: https://doi.org/10.1111/j.1651-2227.2007.00399.x 
  14. Amitrano L, Guardascione MA, Brancaccio V, Margaglione M, Manguso F, Iannaccone L, et al. Risk factors and clinical presentation of portal vein thrombosis in patients with liver cirrhosis. J. Hepatol. 2004;40(5):736-41. DOI: https://doi.org/10.1016/j.jhep.2004.01.001 
  15. Mwanza T, Miyamoto T, Okumura M, Hagio M, Fujinaga T. Ultrasonography and angiographic examination of normal canine liver vessels. Jpn. J. Vet. Res. 1996;44(3):179-88. DOI: https://doi.org/10.14943/jjvr.44.3.179 
  16. Zhou, JH, Li AH, Cao LH, Jiang HH, Liu LZ, Pei XQ, et al. Haemodynamic parameters of the hepatic artery and vein can detect liver metastases: Assessment using contrast-enhanced ultrasound. Br. J. Radiol. 2008;81(962):113-9. DOI: https://doi.org/10.1259/bjr/25294912 
  17. Marzouni HZ, Davachi B, Rezazadeh M, Milani MS, Matinfard S. Diagnostic value of hepatic vein ultrasound in early detection of liver cirrhosis. Galen Med. J. 2018;7:e1140. 
  18. Jeyalakshmi TR, Ramar K. A modified method for speckle noise removal in ultrasound medical images. Int. J. Comput. Electr. Eng. 2010;2(1):54. 
  19. Karthikeyan K, Chandrasekar C. Speckle noise reduction of medical ultrasound images using bayesshrink wavelet threshold. Int. J. Comput. Appl. 2011;22(9):8-14. DOI: https://doi.org/10.5120/2614-3646 
  20. Krissian K, Kikinis R, Westin CF, Vosburgh K. Speckle-constrained of ultrasound images. 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition(CVPR' '05). 2005;2:547-52. 
  21. Singh K, Ranade SK, Singh C. A hybrid algorithm for speckle noise reduction of ultrasound images. Comput. Method. Programs Biomed. 2017;148:55-69. DOI: https://doi.org/10.1016/j.cmpb.2017.06.009 
  22. Outtas M, Serir A, Kerouh F. Speckle noise reduction in ultrasound image based on A Multiplicative Multiresolution Decomposition (MMD). Eighth ed. of Int. Symp. On Sugnal, Image, Video and Communications; 2014. 
  23. Leal AS, Paiva HM. A new wavelet family for speckle noise reduction in medical ultrasound images. Measurement. 2019;140:572-81. DOI: https://doi.org/10.1016/j.measurement.2019.03.050 
  24. Podilchuk C, Bajor M, Stoddart W, Barinov L, Hulbert W, Jairaj A, et al. Speckle reduction using stepped-frequency continuous wave ultrasound. 2012 IEEE Signal Processing in Medicine and Biology Symposium(SPMB). 2012:1-4. 
  25. Lopes A, Touzi R, Nezry E. Adaptive speckle filters and scene heterogeneity. IEEE Trans. Geosci. Remote Sensing. 1990;28(6):992-1000. DOI: https://doi.org/10.1109/36.62623 
  26. Sudha S, Suresh GR, Sukanesh R. Speckle noise reduction in ultrasound images by wavelet thresholding based on weighted variance. Int. J. Comput. Theor. Eng. 2009;1(1):7. 
  27. Cannistraci CV, Abbas A, Gao X. Median modified wiener filter for nonlinear adaptive spatial denoising of protein NMR multidimensional spectra. Sci. Rep. 2015;5(1):8017. DOI: https://doi.org/10.1038/srep08017 
  28. Park CR, Kang S, Lee Y. Median modified wiener filter for improving the image quality of gamma camera images. Nucl. Eng. Technol. 2020;52(10):2328-33. DOI: https://doi.org/10.1016/j.net.2020.03.022 
  29. Lee JS. Digital image enhancement and noise filtering by use of local statistics. IEEE Trans. Pattern Anal. Mach. Intell. 1980;2:165-8. DOI: https://doi.org/10.1109/tpami.1980.4766994 
  30. Chen J, Benesty J, Huang Y, Doclo S. New insights into the noise reduction Wiener filter. IEEE Trans. Aud. Speech Lang. Process. 2006;14(4):1218-34. DOI: https://doi.org/10.1109/TSA.2005.860851 
  31. Rodriguez-Molares A, Rindal OMH, D'hooge J, Masoy SE, Austeng A, Lediju Bell MA, et al. The generalized contrast-to-noise ratio: A formal definition for lesion detectability. IEEE Trans. Ultrason. Ferroelectr. Freq. Control. 2019;67(4):745-59. DOI: https://doi.org/10.1109/TUFFC.2019.2956855 
  32. Cho JY, Ye SY. GLCM algorithm image analysis of nonalcoholic fatty liver and focal fat sparing zone in the ultrasonography. Journal of Radiological Science and Technology. 2017;40(2):205-11. DOI: http://dx.doi.org/10.17946/JRST.2017.40.2.04 
  33. Kim DH, Kwon DM. Performance testing of medical US equipment using US phantom(ATS-539)(Focusing on Daegu Region). Journal of Radiological Science and Technology. 2014;37(4):295-305. 
  34. Khan MN, Altalbe A. Experimental evaluation of filters used for removing speckle noise and enhancing ultrasound image quality. Biomed. Signal Process. Control. 2022;73:103399. DOI: https://doi.org/10.1016/j.bspc.2021.103399. 
  35. Jabarulla MY, Lee HN. Speckle reduction in ultrasound liver image based on sparse representation over a learned dictionary. Applied Sciences. 2018;8(6):903. DOI: https://doi.org/10.3390/app8060903 
  36. Ramamoorthy S, Siva Subramanian R, Gandhi D. An efficient method for speckle noise reduction in ultrasound liver images for e-health applications. Distributed Computing and Internet Technology: 10th International Conference, ICDCIT. 2014:311-21. DOI: https://doi.org/10.1007/978-3-319-04483-5_32 
  37. Pregitha RE, Jagathesan V, Selvakumar CE. Speckle noise reduction in ultrasound fetal images using edge preserving adaptive shock filters. Int. J. Sci. Res. Publ. 2012;2(3):1-3. 
  38. Islam MA, Talukder MH, Hasan MM. Speckle noise reduction from ultrasound image using modified binning method and fuzzy inference system. 2013 2nd International Conference on Advances in Electrical Engineering(ICAEE). 2013:359-62. 
  39. Loupas T, McDicken WN, Allan PL. Noise reduction in ultrasonic images by digital filtering. Br. J. Radiol. 1987;60(712):389-92. DOI: https://doi.org/10.1259/0007-1285-60-712-389 
  40. Gupta S, Gupta A. Dealing with noise problem in machine learning data-sets: A systematic review. Procedia Comput. Sci. 2019;161:466-74. DOI: https://doi.org/10.1016/j.procs.2019.11.146