Open Access Open Access  Restricted Access Subscription or Fee Access

Removal of Speckle Noise in Focal Liver Lesions in Ultrasound Images

Hitesh H Vandra, Dr. H. N. Pandya, Kinita B Wandra

Abstract


Medical imaging techniques are predominantly used in medical diagnosis and therapy. The success of this technique depends largely on the quality of images. Due to various factors images do not have appropriate contrast and are often overridden by noise, making the interpretation of the images too difficult leading to incorrect diagnosis. This will be a very important and significant contribution to the medical professional. Removing noise from the original image is still a challenging research in image processing. Generally there is no common enhancement approach for noise reduction. Several approaches have been introduced and each has its own assumption, advantages and disadvantages. The speckle noise is commonly found in the ultrasound medical images. This paper presents different filtering techniques based on Statistical methods for the removal of speckle noise in ultrasound images. The quality of the enhanced images is measured by the Statistical quantity measures:  Peak Signal-to Noise Ratio (PSNR), Mean Square Error (MSE) and Correlation coefficient (COC).


Keywords


Ultrasound Image of Focal liver Lesions, Speckle Noise, Diffusion Filter, Speckle Reduction Anisotropic Diffusion Filter, PSNR, MSE, COC(β).

Full Text:

PDF

References


Rafael Gonzalez, Richard E. Woods, “Digital Image Processing” Second Edition, Pears on Education, 2002.

Rafael Gonzalez, Richard E. Woods, “Digital Image Processing using MATLAB” low price Edition, Pears on Education, 2004.

Jain AK, “Fundamentals of Digital Image Processing” Prentice Hall, 1989.

Atam P.Dhavan, H.K.Haung, Dae-Shik Kim “principles and advance methods in medical imaging and image analysis”

J. G. Abbott and F. L. Thurstone, "Acoustic speckle:Theory and experimental analysis," Ultrason. Imag., vol. 1, pp. 303-324, 1979.

Image Processing Fundamentals – Statistics, “Signal to Noise Ratio”, 2001.

S.Kalaivani Narayanan and R.S.D.Wahidabanu: “International Journal of Signal Processing, Image Processing and Pattern Recognition Vol. 2, No.3, September 2009”

L. H. Xie, Pierce, and F. T. Ulaby, “Sar speckle reduction using wavelet denoising mand markov random _eld modeling," IEEE Trans. Geosci. Remote Sensing, vol. 40, no. 10, Oct. 2002.”

Z. Shi and K. B. Fung, “A comparison of digital speckle Filters," in Proc. IEEE on IGARSS ('94), vol. 4, Aug. 1994,

J. Canny, “A computational approach to edge detection,” IEEE Trans.Pattern Anal. Machine Intell., vol. PAMI-8, 1986.

V. S. Frost, J. A. Stiles, K. S. Shanmugan, and J. C. Holtzman, “Amodel for radar images and its application to adaptive digital filteringof multiplicative noise,” IEEE Trans. Pattern Anal. Machine Intell., vol.PAMI-4, pp. 157–165, 1982.

J. S. Jin, Y. Wang, and J. Hiller, “An adaptive nonlinear diffusion algorithm for filtering medical images,” IEEE Trans. Inform. Technol. Biomed., vol. 4, pp. 298–305, Dec. 2000.

D. T. Kuan, A. A. Sawchuk, T. C. Strand, and P. Chavel, “Adaptive restoration of images with speckle,” IEEE Trans. Acoust., Speech, Signal Processing, vol. ASSP-35, pp. 373–383, 1987.

J. S. Lee, “Digital image enhancement and noise filtering by using local statistics,” IEEE Trans. Pattern Anal. Machine Intell., vol. PAM1-2, 1980.

“Refined filtering of image noise using local statistics,” Comput. Graph. Image Process., vol. 15, pp. 380–389, 1981.

“Speckle suppression and analysis for synthetic aperture radar,” Opt. Eng., vol. 25, no. 5, pp. 636–643, 1986.

Lopes, R. Touzi, and E. Nezry, “Adaptive speckle filters and scene heterogeneity,” IEEE Trans. Geosci. Remote Sensing, vol. 28, pp. 992–1000, 1990.

P. Perona and J. Malik, “Scale space and edge detection using anisotropic diffusion,” IEEE Trans. Pattern Anal. Machine Intell., vol. 12, pp. 629–639, 1990.

D. Y. Tsai and S. Watanabe, “A method for optimization of fuzzy reasoning by genetic algorithms and its application to discrimination of myocardial heart disease,” IEEE Trans. Nucl. Sci., vol. 46, pp. 2239–2246, Dec. 1999.

J. Wang and X. Li, “A system for segmenting ultrasound images,” in Proc. 14th Int. Conf. Pattern Recognition, vol. 1, 1998, pp. 456–461.

S. H. Wong, K. L. Chan, and P. W. Fung, “Automati segmentation of ultrasonic images,” in Proc. Computer, Communication, Control, Power Engineering (TENCON’93), vol. 2, IEEE Region 10 Conf., Part: 2, 1993, pp. 910–913.

Mark A. Lubinski, Stanislav Y. Emelianov, and Matthew O’Donnell: “Speckle Tracking Methods for Ultrasonic Elasticity Imaging Using Short-Time Correlation” ieee transactions on ultrasonics, ferroelectrics, and frequency control, vol. 46, no. 1, january 1999.

Aleksandra Piˇzurica, Wilfried Philips, Ignace Lemahieu, and Marc Acheroy “A Versatile Wavelet Domain Noise Filtration Technique for Medical Imaging” published in ieee transactions on medical imaging vol. 22, no. 3, march 2003, pages 323–331.

G.R.Arce and S.A.Fontana, "On the MidrangeEstimator," IEEE Trans. Acoust., Speech and Signal Processing, vol.ASSP-36, no.6, pp.920-922, June1988.

A.C.Bovik, T.S.Huang and D.C.Munson, "A Generalisation of Median Filtering Using Linear Combinations of Order-Statistics," IEEE Trans. Acoust., Speech and Signal Processing, vol.ASSP-31, no.6, pp.1342-1349, Dec.1983.

I.Pitas and A.N. Venetsanopoulos, Nonlinear Digital Filters: Principles and Applications. Boston, MA:Kluwer Academic, 1990.

Y P Gowramma, Dr C N Ravikumar , “Development of novel fast block based trace mean correspondence algorithm for face tracking” International Conference Advanced computing and communications ,2006 Proceedings, IEEE pp 263- 266.

E.D SELEPCHI , O.G DULIU “Image processing and data analysis in computed tomography” PP665-675, Received September 12, 2006.


Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.