Modified Method for Denoising the Ultrasound Images by Wavelet Thresholding

Full Text (PDF, 531KB), PP.25-30

Views: 0 Downloads: 0


Alka Vishwa 1,* Shilpa Sharma 2

1. Department of Computer Science and Engineering, IIMET, Jaipur, Rajasthan, India

2. Department of Electronics & Communication Engineering, IIMET, Jaipur, Rajasthan, India

* Corresponding author.


Received: 1 Aug. 2011 / Revised: 9 Dec. 2011 / Accepted: 11 Feb. 2012 / Published: 8 Jun. 2012

Index Terms

Ultrasound images, Medical imaging, Speckle noise, Wavelet Thresholding


Medical practitioners are increasingly using digital images during disease diagnosis. Several state-of-the-art medical equipment are producing images of different organs, which are used during various stages of analysis. Examples of such equipment include MRI, CT, ultrasound and X-Ray. In medical image processing, image denoising has become a very essential exercise all through the diagnosis as Ultrasound images are normally affected by speckle noise. The noise in the image has two negative outcomes, the first being the degradation of the image quality and the second and more important, obscures important information required for accurate diagnosis.Arbitration between the perpetuation of useful diagnostic information and noise suppression must be treasured in medical images. In general we rely on the intervention of a proficient to control the quality of processed images. In certain cases, for instance in Ultrasound images, the noise can suppress the information which is valuable for the general practitioner. Consequently medical images can be very inconsistent, and it is crucial to operate case to case. This paper presents a wavelet-based thresholding scheme for noise suppression in Ultrasound images and provides the knowledge about adaptive and anisotropic diffusion techniques for speckle noise removal from different types of images, like Ultrasound.

Cite This Paper

Alka Vishwa, Shilpa Sharma, "Modified Method for Denoising the Ultrasound Images by Wavelet Thresholding", International Journal of Intelligent Systems and Applications(IJISA), vol.4, no.6, pp.25-30, 2012. DOI:10.5815/ijisa.2012.06.03


[1]C. B. Burckhardt. Speckle in Ultrasound B-Mode Scans. IEEE Transactions on Sonics and ultrasonics ,vol. 25, 1978, pp. 1 – 6.

[2]K. Tomiyas. Computer Simulation of Speckle in a Synthetic Aperture Radar Image Pixel, IEEE Transactions on Geoscience and Remote Sensing,vol. 21,1983,pp.357 – 363.

[3]M. O’Donnell and S. D. Silverstein. Optimum Displacement for Compound Image Generation in Medical Ultrasound. IEEE Transactions on Ultrasonics ferroelectronics and frequency control,vol. 35,1988,pp. 470-476.

[4]A. Lopes, R. Touzi and E. Nezry. Adaptive Speckle Filters and Scene Heterogeneity, IEEE Transactions on Geoscience and Remote Sensing, vol. 28,1990,pp. 992-1000.

[5]P. Perona, J. Malik . Scale-Space and Edge Detection Using Anisotropic Diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence,vol. 12, 1990,pp. 629-639. 

[6]A. J. Healey, F. Forsberg and S. Leeman. Processing techniques for speckle reduction in medical ultrasound images. IEE Colloquium on Image Processing in Medicine,vol.6, 1991,pp. 1-4.

[7]E. Steen and B. Olstad. Volume Rendering in Medical Ultrasound Imaging based on Nonlinear Filtering. IEEE winter workshop on Nonlinear Digital Signal Processing, vol.6,1993,pp.1- 101.

[8]N. C. Richard, D. L. Jones and D. O. William. Ultrasound Speckle Reduction by Directional Median Filtering, IEEE International Conference on Image processing, vol.1,1995,pp. 358-361.

[9]M. Karaman, M. A. Kutay and G. Bozdagi. An Adaptive Speckle Suppression Filter for Medical Ultrasonic Imaging. IEEE Transactions on Medical Imaging, vol.14,1995,pp. 283-292.

[10]S. B. Jebara, Z. B. Hadj and H. Maatar. Combined Predictive and Multiresolution Schemes for Speckle Reduction in Radar Images. 6th IEEE International Conference on Electronics, Circuits and Systems,vol.2, 1999,pp. 965-968.

[11]M. P. Wachowiak and R. Smolikova. Classification and Estimation of Ultrasound Speckle Noise with Neural Networks. IEEE International Symposium on Bio-Informatics and Biomedical Engineering, 2000, pp. 245-252.

[12]C. Chinrungruen and A. Suvichakorn.Fast Edge-Preserving Noise Reduction for Ultrasound Images. IEEE Transactions on Nuclear Science, vol.48,2001, pp. 849-854.

[13]A. Achim, A. Bezerianos and P. Tsakalides. Novel Bayesian Multiscale Method for Speckle Removal in Medical Ultrasound Images. IEEE Transactions on Medical Imaging,vol.20, 2001, pp. 772-783.

[14]D. Mazumdar, S. Chatterjee, D. Roy and S. Mitra . Analysis and Statistical Characterization of Signal and Noise in Ultrasonography Images.

[15]C. Loizou, C. Christodoulou, C. S. Pattichis, R. Istepanian, M. Pantziaris and A. Nicolaides . Speckle Reduction In Ultrasound Images Of Atherosclerotic Carotid Plaque.IEEE 14th International Conference on Digital Signal Processing, vol. 2,2002, pp. 525- 528.

[16]Y. Yu and T. A. Scott. Speckle Reducing Anisotropic Diffusion. IEEE Transactions on Image Processing, vol. 11,2002, pp. 1260- 1270.

[17]S. T. Acton, J. A. Molloy and Y. Yu . Three-Dimensional Speckle Reducing Anisotropic Diffusion, IEEE Conference Record of the 37th Asilomar Conference on Signals, Systems and Computers, vol.2,2003, pp. 1987- 1991.