Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

access icon free Modified ultrasound despeckling assessment index for the Field II simulated cyst image

  • Author(s): Murat Alparslan Gungor 1  and  Irfan Karagoz 2
    • View affiliations
  • Source: Volume 11, Issue 9, September 2017, p. 667 – 671
    DOI:  10.1049/iet-ipr.2016.0582 , Print ISSN 1751-9659, Online ISSN 1751-9667
© The Institution of Engineering and Technology
Received 17/07/2016, Accepted 28/05/2017, Revised 28/04/2017, Published 01/09/2017

Various methods have been proposed to reduce speckle noise, which decreases image quality in ultrasound images. The Field II simulated cyst image consists of three classes and is used to compare a proposed despeckle filter with other well-known filters. The ultrasound despeckling assessment index (USDSAI) is a metric used to evaluate the proposed despeckling filters for the cyst image. This metric should be used when different regions are properly defined. In this study, the authors first analysed the performance of USDSAI for the cyst image. Then, the authors modified the USDSAI by proposing a new metric for the background class of the cyst image and evaluated its performance. The results show that the authors’ proposed metric has better performance than USDSAI.

Inspec keywords: medical image processing; biomedical ultrasonics; image filtering

Other keywords: Field II simulated cyst image; USDSAI; modified ultrasound despeckling assessment index

Subjects: Optical, image and video signal processing; Patient diagnostic methods and instrumentation; Filtering methods in signal processing; Biology and medical computing; Sonic and ultrasonic radiation (medical uses); Computer vision and image processing techniques; Sonic and ultrasonic radiation (biomedical imaging/measurement)

References

    1. 1)
      • 10. Gupta, S., Chauhan, R.C., Sexana, S.C.: ‘Wavelet-based statistical approach for speckle reduction in medical ultrasound images’, Med. Biol. Eng. Comput., 2004, 42, pp. 189–192.
    2. 2)
      • 16. Yu, Y., Acton, S.T.: ‘Speckle reducing anisotropic diffusion’, IEEE Trans. Image Process., 2002, 11, (11), pp. 1260–1270.
    3. 3)
      • 21. Jensen, J.A.: ‘Field: a program for simulating ultrasound systems’, Med. Biol. Eng. Comput., 1996, 34, (1), pp. 351–353.
    4. 4)
      • 15. Perona, P., Malik, J.: ‘Scale-space and edge detection using anisotropic diffusion’, IEEE Trans. Pattern Anal. Mach. Intell., 1990, 12, (7), pp. 629–639.
    5. 5)
      • 6. Loupas, T., Mcdicken, N., Allan, P.L.: ‘An adaptive weighted median filter for speckle suppression in medical ultrasonic images’, IEEE Trans. Circuits Syst., 1989, 36, (1), pp. 129–135.
    6. 6)
      • 13. Frost, V.S., Stiles, J.A., Shanmugan, K.S., et al: ‘A model for radar images and its application to adaptive digital filtering of multiplicative noise’, IEEE Trans. Pattern Anal. Mach. Intell, 1982, 4, (2), pp. 157–166.
    7. 7)
      • 17. Aja-Fernandez, S., Alberola-Lopez, C.: ‘On the estimation of the coefficient of variation for anisotropic diffusion speckle filtering’, IEEE Trans. Image Process., 2006, 15, (9), pp. 2694–2701.
    8. 8)
      • 24. Tay, P.C., Acton, S.T., Hossack, J.A.: ‘A stochastic approach to ultrasound despeckling’. Proc. 3rd IEEE Int. Symp. Biomedical Imaging: Nano to Macro, 2006, pp. 221–224.
    9. 9)
      • 23. Tay, P.C., Garson, C.D., Acton, S.T., et al: ‘Ultrasound despeckling for contrast enhancement’, IEEE Trans. Image Process., 2010, 19, (7), pp. 1847–1860.
    10. 10)
      • 11. Andria, G., Attivissimo, F., Lanzolla, A.M.L., et al: ‘A suitable threshold for speckle reduction in ultrasound images’, IEEE Trans. Instrum. Meas., 2013, 62, (8), pp. 2270–2279.
    11. 11)
      • 18. Krissian, K., Westin, C.F., Kikinis, R., et al: ‘Oriented speckle reducing anisotropic diffusion’, IEEE Trans. Image Process., 2007, 16, (5), pp. 1412–1424.
    12. 12)
      • 4. Wagner, R.F., Smith, S.W., Sandrik, J.M., et al: ‘Statistics of speckle in ultrasound B scans’, IEEE Trans. Sonics Ultrason., 1983, 30, (3), pp. 156–163.
    13. 13)
      • 7. Donoho, D.L., Johnstone, J.M.: ‘Ideal spatial adaptation by wavelet shrinkage’, Biometrika, 1994, 81, (3), pp. 425–455.
    14. 14)
      • 8. Donoho, D.L., Johnstone, J.M.: ‘Adapting to unknown smoothness via wavelet shrinkage’, J. Am. Stat. Assoc., 1995, 90, (432), pp. 1200–1224.
    15. 15)
      • 20. Gungor, M.A., Karagoz, I.: ‘The homogeneity map method for speckle reduction in diagnostic ultrasound images’, Measurement, 2015, 68, pp. 100–110.
    16. 16)
      • 2. Burckhardt, C.B.: ‘Speckle in ultrasound B-mode scans’, IEEE Trans. Sonics Ultrason., 1978, 25, (1), pp. 1–6.
    17. 17)
      • 5. Huang, T., Yang, G., Tang, G.: ‘A fast two-dimensional median filtering algorithm’, IEEE Trans. Acoust Speech Signal Process., 1979, 27, (1), pp. 13–18.
    18. 18)
      • 26. Wang, Z., Bovik, A.C., Sheikh, H.R., et al: ‘Image quality assessment: from error visibility to structural similarity’, IEEE Trans. Image Process., 2004, 13, (4), pp. 600–612.
    19. 19)
      • 22. Jensen, J.A., Svendsen, N.B.: ‘Calculation of pressure fields from arbitrarily shaped, apodized, and excited ultrasound transducers’, IEEE Trans. Ultrason. Ferroelectr. Freq. Control, 1992, 39, (2), pp. 262–267.
    20. 20)
      • 3. Goodman, J.W.: ‘Some fundamental properties of speckle’, JOSA, 1976, 66, (11), pp. 1145–1150.
    21. 21)
      • 25. Tay, P.C., Acton, S.T., Hossack, J.A.: ‘Ultrasound despeckling using an adaptive window stochastic approach’. Proc. IEEE Int. Conf. Image Processing, 2006, pp. 2549–2552.
    22. 22)
      • 19. Coupe, P., Hellier, P., Kervrann, C., et al: ‘Nonlocal means-based speckle filtering for ultrasound images’, IEEE Trans. Image Process., 2009, 18, (10), pp. 2221–2229.
    23. 23)
      • 9. Chang, S.G., Yu, B., Vetterli, M.: ‘Adaptive wavelet thresholding for image denoising and compression’, IEEE Trans. Image Process., 2000, 9, (9), pp. 1532–1546.
    24. 24)
      • 12. Lee, J.S.: ‘Digital image enhancement and noise filtering by use of local statistics’, IEEE Trans. Pattern Anal. Mach. Intell., 1980, PAMI-2, (2), pp. 165–168.
    25. 25)
      • 14. Kuan, D., Sawchuk, A., Strand, T., et al: ‘Adaptive restoration of images with speckle’, IEEE Trans. Acoust. Speech Signal Process., 1987, 35, (3), pp. 373–383.
    26. 26)
      • 1. Szabo, T.L.: ‘Diagnostic ultrasound imaging: inside out’ (Elsevier Academic Press., 2004).
    27. 27)
      • 27. Sattar, F., Floreby, L., Salomonsson, G., et al: ‘Image enhancement based on a nonlinear multiscale method’, IEEE Trans. Image Process., 1997, 6, (6), pp. 888–895.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-ipr.2016.0582
Loading

Related content

content/journals/10.1049/iet-ipr.2016.0582
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
Print this page
This is a required field
Please enter a valid email address