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Towards a Comprehensive Evaluation of Ultrasound Speckle Reduction

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Image Analysis and Recognition (ICIAR 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8814))

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Abstract

Over the last three decades, several despeckling filters have been developed to reduce the speckle noise inherently present in ultrasound images without losing the diagnostic information. In this paper, a new intensity and feature preservation evaluation metric for full speckle reduction evaluation is proposed based contrast and feature similarities. A comparison of the despeckling methods is done, using quality metrics and visual interpretation of images profiles to evaluate their performance and show the benefits each one can contribute to noise reduction and feature preservation. To test the methods, noise-free images and simulated B-mode ultrasound images are used. This way, the despeckling techniques can be compared using numeric metrics, taking the noise-free image as a reference. In this study, a total of seventeen different speckle reduction algorithms have been documented based on adaptive filtering, diffusion filtering and wavelet filtering, with sixteen qualitative metrics estimation.

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References

  1. Chang, S., Yu, B., Vetterli, M.: Adaptive wavelet thresholding for image denoising and compression. IEEE Trans. Image Processing 9(9), 1532–1546 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  2. Coupé, P., Hellier, P., Kervrann, C., Barillot, C.: Nonlocal means-based speckle filtering for ultrasound images. IEEE Transactions on Image Processing 18(10), 2221–2229 (2009)

    Article  MathSciNet  Google Scholar 

  3. Donoho, D.L., Johnstone, I.M.: Adapting to unknown smoothness via wavelet shrinkage. Journal of American Statistical Association 90(432), 1200–1224 (1995)

    Article  MathSciNet  MATH  Google Scholar 

  4. Finn, S., Glavin, M., Jones, E.: Echocardiographic speckle reduction comparison. IEEE Trans. Ultrasonics, Ferroelectrics Freq. Control 58(1), 82–101 (2011)

    Article  Google Scholar 

  5. Frost, V., Stiles, J., Shanmugan, K., Holtzman, J.: A model for radar images and its application to adaptive digital filtering of multiplicative noise. IEEE Transactions on Pattern Analysis and Machine Intelligence 4(2), 157–166 (1982)

    Article  Google Scholar 

  6. Jensen, J.: Simulation of advanced ultrasound systems using field ii. In: International Symposium on Biomedical Imaging: Nano to Macro, pp. 636–639 (2004)

    Google Scholar 

  7. Jin, F., Fieguth, P., Winger, L., Jernigan, E.: Adaptive wiener filtering of noisy images and image sequences. In: Proceedings of International Conference on Image Processing, vol. 3, p. III-349 (2003)

    Google Scholar 

  8. Khare, A., Khare, M., Jeong, Y., Kim, H., Jeon, M.: Despeckling of medical ultrasound images using daubechies complex wavelet transform. Signal Processing 90(2), 428–439 (2010)

    Article  MATH  Google Scholar 

  9. Kuan, D., Sawchuk, A., Strand, T., Chavel, P.: Adaptive noise smoothing filter for images with signal-dependent noise. IEEE Transactions on Pattern Analysis and Machine Intelligence 7(2), 165–177 (1985)

    Article  Google Scholar 

  10. Lee, J.-S.: Digital image enhancement and noise filtering by use of local statistics. IEEE Trans. on Pattern Analysis and Machine Intelligence 2(2), 165–168 (1980)

    Article  Google Scholar 

  11. Loizou, C., Pattichis, C.: Despeckle filtering algorithms and software for ultrasound imaging. Synthesis Lect. Algorithms Soft. Engineering 1(1), 1–166 (2008)

    Article  Google Scholar 

  12. Malik, J., Belongie, S., Leung, T., Shi, J.: Contour and texture analysis for image segmentation. International Journal of Computer Vision 43(1), 7–27 (2001)

    Article  MATH  Google Scholar 

  13. Mateo, J.L., Fernández-Caballero, A.: Finding out general tendencies in speckle noise reduction in ultrasound images. Expert Systems with Applications 36(4), 7786–7797 (2009)

    Article  Google Scholar 

  14. Ortiz, S., Chiu, T., Fox, M.D.: Ultrasound image enhancement: A review. Biomedical Signal Processing and Control 7(5), 419–428 (2012)

    Article  Google Scholar 

  15. Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Trans. on Pattern Analysis and Machine Intelligence 12(7), 629–639 (1990)

    Article  Google Scholar 

  16. Rosa, R., Monteiro, F.C.: Speckle ultrasound image filtering: Performance analysis and comparison. In: Computational Vision and Medical Image Processing: VIPIMAGE 2013, pp. 65–70 (2013)

    Google Scholar 

  17. Wang, Z., Li, Q.: Information content weighting for perceptual image quality assessment. IEEE Transactions on Image Processing 20(5), 1185–1198 (2011)

    Article  MathSciNet  Google Scholar 

  18. Weickert, J.: Coherence-enhancing diffusion filtering. International Journal of Computer Vision 31(2–3), 111–127 (1999)

    Article  Google Scholar 

  19. Yu, Y., Acton, S.T.: Speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 11(11), 1260–1270 (2002)

    Article  MathSciNet  Google Scholar 

  20. Zhang, D., Bao, P., Wu, X.: Multiscale lmmse-based image denoising with optimal wavelet selection. IEEE Transactions on Circuits and Systems for Video Technology 15(4), 469–481 (2005)

    Article  Google Scholar 

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Correspondence to Fernando C. Monteiro .

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Monteiro, F.C., Rufino, J., Cadavez, V. (2014). Towards a Comprehensive Evaluation of Ultrasound Speckle Reduction. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2014. Lecture Notes in Computer Science(), vol 8814. Springer, Cham. https://doi.org/10.1007/978-3-319-11758-4_16

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  • DOI: https://doi.org/10.1007/978-3-319-11758-4_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11757-7

  • Online ISBN: 978-3-319-11758-4

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