CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Analysis of Speckle Reducing Filters in Ultrasound Images

by Arvinder Kaur, Sukhjeet Kaur Ranade
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 156 - Number 11
Year of Publication: 2016
Authors: Arvinder Kaur, Sukhjeet Kaur Ranade
10.5120/ijca2016912559

Arvinder Kaur, Sukhjeet Kaur Ranade . Analysis of Speckle Reducing Filters in Ultrasound Images. International Journal of Computer Applications. 156, 11 ( Dec 2016), 23-30. DOI=10.5120/ijca2016912559

@article{ 10.5120/ijca2016912559,
author = { Arvinder Kaur, Sukhjeet Kaur Ranade },
title = { Analysis of Speckle Reducing Filters in Ultrasound Images },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2016 },
volume = { 156 },
number = { 11 },
month = { Dec },
year = { 2016 },
issn = { 0975-8887 },
pages = { 23-30 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume156/number11/26754-2016912559/ },
doi = { 10.5120/ijca2016912559 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:02:21.237595+05:30
%A Arvinder Kaur
%A Sukhjeet Kaur Ranade
%T Analysis of Speckle Reducing Filters in Ultrasound Images
%J International Journal of Computer Applications
%@ 0975-8887
%V 156
%N 11
%P 23-30
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Ultrasound is a widely used medical imaging technique used for diagnostic purposes. But the major problem with these images is that they are inherently corrupted by speckle. The presence of speckle severely hampers the interpretation and analysis of medical ultrasonic images. In this paper, a comparison of various speckle reducing spatial and wavelet based methods has been carried out while de-speckling the image. These methods are evaluated and compared in terms of filter assessment parameters namely Peak Signal to Noise Ratio (PSNR), MSSIM (Mean Structural Similarity), FOM (Figure of Merit) and Method noise and consequently classified into three categories- outstanding, average and below average on the basis of their performance.

References
  1. N.Gupta, M.N.S. Swamy, E.Plotkin, “Despeckling of medical ultrasound images using data and rate adaptive lossy compression”, IEEE Trans. Med. Ima. 24(6)(2005) 743-754.
  2. C. Saxena and D. Kourav, “Noises and Image Denoising Techniques: Brief Survey”, International Journal of Emerging Technology and Advanced Engineering, Vol. 4, Issue 3, 2014
  3. M. A. Gungor and I. Karagoz, “The homogeneity map method for speckle reduction in diagnostic ultrasound images”, Measurement 68(2015) 100-110
  4. J.S. Lee, “Digital image enhancement and noise filtering by use of local statistics”, IEEE Trans. Pattern Anal. Mach. Intell. PAMI-2 (1980) 165-168.
  5. V.S. Frost, J.A. Stiles, K.S. Shanmugan, J.C. Holtzman, “A model for radar images and its application to adaptive digital filtering of multiplicative noise”, IEEE Trans. Pattern Anal. Mach. Intell. 4 (2) (1982) 157–166.
  6. D. Kuan, A. Sawchuk, T. Strand, P. Chavel, “Adaptive Noise Smoothing Filter for Images with Signal-Dependent Noise”, IEEE Trans. on Pattern Analysis and Machine Intelligence 7 (2) (1985) 165-177.
  7. R. C. Gonzalez and R. E Woods., Digital Image Processing, Pearson Education, Second Edition, 2005
  8. K. He, J. Sun, X. Tang, “Guided image filtering”, in: Proceedings of 11th European Conference on Computer Vision, Berlin, 2010, pp. 1-14.
  9. Y. Yu, S.T. Acton, “Speckle reducing anisotropic diffusion”, IEEE Trans. Image Process. 11 (11) (2002) 1260–1270.
  10. P. Coupe, P. Hellier, C. Kervrann, C. Barillot,“Nonlocal means-based speckle filtering for ultrasound images”, IEEE Trans. Image Process. 18 (10) (2009) 2221-2229.
  11. D.L. Donoho, I.M. Johnstone, “Ideal spatial adaptation by wavelet shrinkage”, Biometrika 81 (3) (1994) 425–455.
  12. D.L. Donoho, J.M. Johnstone,“Adapting to unknown smoothness via wavelet shrinkage”, J. Am. Statist. Assoc. 90 (432) (1995) 1200–1224.
  13. S.G. Chang, B. Yu, M. Vetterli,“Adaptive wavelet thresholding for image denoising and compression”, IEEE Trans. Image Process. 9 (9) (2000) 1532–1546.
  14. G.Y. Chen, T.D. Bui, A. Krzyzak, “Image denoising using neighbouring wavelet coefficients”, IEEE Trans. Signal Process. 10(10)(2004) 917-920.
  15. Z. Dengwen, C. Wengang,“Image denoising with an optimal threshold and neighboring window”, Pattern Recognition Letters, 29(2008) 1694-1697.
  16. J. Zhang, G. Lin, L. Wu, Y. Cheng, “Speckle filtering of medical ultrasonic images using wavelet and guided filter”, Ultrasonics 65 (2016) 177-193.
  17. M. Sifuzzaman, M.R. Islam and M.Z. Ali, “Application of Wavelet Transform and its Advantages compared to Fourier Transform”, Journal of Physical Sciences, Vol. 13, 2009,121-134.
  18. J. Tian and L. Chen,“Image despeckling using a non-parametric statistical model of wavelet coefficients”, Biomedical Signal Processing and Control 6 (2011) 432-437 D. L. Donoho, “De-noising by soft-thresholding”, IEEE Trans. On Information Theory, Vol 41, No. 3, May 1995.
  19. Z. Wang, A.C. Bovik, H.R. Sheikh, “Image quality accessment: from eror visibility to structural similarity”, IEEE Trans. Image Process. 13(4) (2004) 600-612.
  20. www.ultrasoundcases.info
Index Terms

Computer Science
Information Sciences

Keywords

Ultrasound image Speckle noise Filters Wavelets

Powered by PhDFocusTM