Abstract
As a novel multiscale transform, curvelet transform has the ability to give a better sparse representation of images with singularity along curves. After analyzing the second-generation curvelet transform, this work presents a new de-noising technique for echocardiographic images corrupted with speckle noise. We employed this new technique, nonlinear diffusion based on total variation, to suppress artifacts resulting from curvelet transform. The results show that this method gives better performance in noise suppression while preserving the edges of echocardiographic images, compared to existing methods. The application of curvelet transform reveals its great potential in echocardiographic image processing.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bhadauria HS, Dewal ML. Medical image denoising using adaptive fusion of curvelet transform and total variation. Comput Electr Eng. 2013;39(5):1451–60.
Starck JL, Candes EJ, Donoho DL. The curvelet transform for image denoising. IEEE Trans Image Process. 2002;11(6):670–84.
Minhas R, Mohammed AA, Wu QM. Shape from focus using fast discrete curvelet transform. Pattern Recogn. 2011;44(4):839–53.
Josse J, Husson F. Selecting the number of components in principal component analysis using cross-validation approximations. Comput Stat Data Anal. 2012;56(6):1869–79.
Rahman MM, Kumar MPK, Aziz A, Arefin G, Uddin MS. Adaptive anisotropic diffusion filter for speckle noise reduction for ultrasound images. Int J Converg Comput. 2013;1:50–9.
Landi G, Piccolomini EL. An efficient method for nonnegatively constrained total variation-based denoising of medical images corrupted by poisson noise. Comput Med Imaging Graph. 2012;36(1):38–46.
Stoitsis J, Golemati S, Nikita KS. A modular software system to assist interpretation of medical images-application to vascular ultrasound images. IEEE Trans Instrum Meas. 2006;55(6):1944–52.
Hao X, Gao S, Gao X. A novel multiscale nonlinear thresholding method for ultrasonic speckle suppressing. IEEE Trans Med Imaging. 1999;18(9):787–94.
Zhang F, Yoo YM, Mong KL, Kim Y. Nonlinear diffusion in laplacian pyramid domain for ultrasound speckle. IEEE Trans Med Imaging. 2007;26(2):200–11.
Ma J, Plonka G. Combined curvelet shrinkage and nonlinear anisotropic diffusion. IEEE Trans Image Process. 2007;16(9):2198–206.
Acknowledgement
This work was supported by the basic research project of Shanghai Science and Technology Commission (12JC1406600).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Xue, H., Chen, B., Sun, K., Yu, J. (2015). De-noising Method for Echocardiographic Images Based on the Second-Generation Curvelet Transform. In: Wong, W. (eds) Proceedings of the 4th International Conference on Computer Engineering and Networks. Lecture Notes in Electrical Engineering, vol 355. Springer, Cham. https://doi.org/10.1007/978-3-319-11104-9_80
Download citation
DOI: https://doi.org/10.1007/978-3-319-11104-9_80
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11103-2
Online ISBN: 978-3-319-11104-9
eBook Packages: EngineeringEngineering (R0)