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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Chang, S., Yu, B., Vetterli, M.: Adaptive wavelet thresholding for image denoising and compression. IEEE Trans. Image Processing 9(9), 1532–1546 (2000)
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)
Donoho, D.L., Johnstone, I.M.: Adapting to unknown smoothness via wavelet shrinkage. Journal of American Statistical Association 90(432), 1200–1224 (1995)
Finn, S., Glavin, M., Jones, E.: Echocardiographic speckle reduction comparison. IEEE Trans. Ultrasonics, Ferroelectrics Freq. Control 58(1), 82–101 (2011)
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)
Jensen, J.: Simulation of advanced ultrasound systems using field ii. In: International Symposium on Biomedical Imaging: Nano to Macro, pp. 636–639 (2004)
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)
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)
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)
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)
Loizou, C., Pattichis, C.: Despeckle filtering algorithms and software for ultrasound imaging. Synthesis Lect. Algorithms Soft. Engineering 1(1), 1–166 (2008)
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)
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)
Ortiz, S., Chiu, T., Fox, M.D.: Ultrasound image enhancement: A review. Biomedical Signal Processing and Control 7(5), 419–428 (2012)
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)
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)
Wang, Z., Li, Q.: Information content weighting for perceptual image quality assessment. IEEE Transactions on Image Processing 20(5), 1185–1198 (2011)
Weickert, J.: Coherence-enhancing diffusion filtering. International Journal of Computer Vision 31(2–3), 111–127 (1999)
Yu, Y., Acton, S.T.: Speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 11(11), 1260–1270 (2002)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-11758-4_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11757-7
Online ISBN: 978-3-319-11758-4
eBook Packages: Computer ScienceComputer Science (R0)