A Virtual Analysis on Effective Speckle Noise Removal Techniques in Medical Images by Various Filtering Methods

International Journal of Computer Trends and Technology (IJCTT)          
© 2016 by IJCTT Journal
Volume-38 Number-3
Year of Publication : 2016
Authors : S.Hariharasudhan, Dr.B.Raghu


S.Hariharasudhan, Dr.B.Raghu "A Virtual Analysis on Effective Speckle Noise Removal Techniques in Medical Images by Various Filtering Methods". International Journal of Computer Trends and Technology (IJCTT) V38(3):138-144, August 2016. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
Acquiring a cost effective and efficient method of removing speckle noise from the images is an extraordinary questioning for the researchers and Scientists. Change of magnitude of speckle noise is the most essential processes to improve the quality of consistent and coherent images. Speckle Noise is a farinaceous noise that integrally exists in and aggrades the quality of the progressive in Medical Images. In Medical image processing, image denoising is essential work all through make a diagnosis. Intercession between the upholding of useful diagnostic knowledge and noise repression must be cherished in medical related images. In Ultrasound medical images, the noise can pin down information which is expensive for the medical practitioner. At the same time medical images are very contradictory, and it is decisive to operate from initial state to Final state. These Medical images may be multi - dimensional representations of the object. Here the Speckle noise is to be removed for the fine quality of an image objects. Noise can aggrade the image at the time of acquiring or transmittance of an image. Before applying image processing techniques to an image, Speckle noise removal from the images is to be done. Wide and Wise algorithms are available and accessible, but they have their own postulates, virtues and disadvantages. Filtering is one of the communal methods which is used to cut down the speckle noises. Here a beam of light is thrown on speckle noise and an analysis is carried forward for the removal of speckle noise . Also Multi-Speckle noise reduction filters are considered on the conditional Images such as real or artificial Images for this analysis. This research paper projects the effects of different applied noise image models and de-noising the selected image noise with the help of different filter models and studies the results of applying various Speckle noise reduction techniques with the help of Images and Diagrams.

[1] Ragesh. N. K, Anil A.R, Rajesh R.: Digital image denoisingin medical ultrasound images: a survey, ICGST AIML-11Conference, Dubai, UAE, 2011, pp 12-14.
[2] Abbott J.G, Thurstone F. L., “Acoustic speckle: Theory andexperimental analysis,” Ultrasonic Imaging, 1979,Vol 1, pp 303–324.
[3] Bamber J.C, Daft C.: Adaptive filtering for reduction of speckle in ultrasound pulse-echo images, Ultrasonics,1986, Vol1, pp 41–44.
[4] Sudha S., Suresh G.R, Sukanesh R: Speckle Noise Reduction in Ultrasound Images by Wavelet Thresholding based on Weighted Variance “International Journal of Computer Theory and Engineering, 2009, Vol 1,pp 1793- 8201.
[5] Loupas T., Mcdicken W., Allan P. L.: An adaptive weighted median filter for speckle suppression in medical ultrasonic images, IEEE Trans. Circuits Syst.,1989, Vol 36 (1), pp 129–135.
[6] Taya P.C., Acton S. T., Hossack J. A.: A wavelet thresholding method to reduce ultrasound artifacts” Computerized Medical Imaging and Graphics 35, 42–50 (2011).
[7] Frost V.S, Stiles J.A, Shanmugam K.S, Holtzman J.C : Amodel for radar image & its application To Adaptive digitalfiltering for multiplicative noise, IEEE Transaction on Pattern Analysis and Machine Intelligence, 1982, pp 175-165.
[8] Lee J.S : Digital image enhancement and noise filtering by using local statistics, IEEE Trans. Pattern Anal. Machine Intelligence 1980.
[9] Kaun. D.T, Sowchauk T., Strand C., Chavel P.: Adaptive noise smoothing filters for signal dependent Noise”, IEEE Transaction on pattern analysis and machine intelligence, 1985, Vol 7, pp 165-177.
[10] Chambolle. A: An algorithm for total variation minimization and applications, Journal of Mathematical Imaging and Vision, 2004, Vol 20,pp 89–97 .
[11] Rudin. L, Osher S., and Fatemi E.: Nonlinear total variation based noise removal, Physica, 1992, Vol 60,pp 259–268. [12] Chan T., Osher S., Shen J.: The digital TV filter andnonlinear denoising, IEEE Trans. Image Proc., 2001, Vol 10,231–241.
[13] Perona P., Malik J.: Scale-space and edge detection using anisotropic diffusion, IEEE Trans. Transactions on Pattern Analysis and Machine Intelligence, PAMI, 1990,Vol 12(7),pp629–639.
[14] Garcia R., Deriche. R, Rousson. M, Lopez. C.A: Tensorprocessing for texture and colour segmentation, Scandinavian conference on image analysis SCIA.2005, Vol 3540, pp 1117-1127.
[15] Tschumperlé D.: Anisotropic diffusion PDE's for image regularization and visualization. Handbook of Mathematical Methods in Imaging, 1st Edition, Springer ,2010.
[16] Tomasi C., Manduchi R.: Bilateral filtering for gray and color images, ICCV,1998.
[17]Anil K.Jain, “Fundamentals of Digital ImageProcessing” first edition, 1989, Prentice – Hall, Inc.
[18] Rafael C. Gonzalez and Richard E. Woods, “Digital Image Processing”,Second Edition, Pearson Education.
[19] S. Sudha, G.R Suresh and R. Suknesh, "Speckle Noise Reduction in Ultrasound images By Wavelet Thresholding Based On Weighted Variance",
[20] Wang Z., Bovik A.C, Sheikh H.R, Simoncelli E.P. : Imagequality assessment: from error visibility to structural similarity,IEEE Transactions on Image Processing, 2004, Vol 13(4),pp 600–612.
[21] Zhenghao Shi and Ko B.Fung,” A comparison of digital speckle filters” Canada centre for Remote Sensing.
[22] Anil K.Jain, “Fundamentals of Digital Image Processing” first edition, 1989, Prentice – Hall, Inc.
[23] Rafael C. Gonzalez and Richard E. Woods, “Digital Image Processing”, Second Edition, Pearson Education
[24] J.S.Lee,”Digital image enhancement and noise filtering by use of local Statistics’” IEEE Trans. Pattern Analysis and Machine Intelligence, vol.2,no. 2, pp. 165-168, March 1980.
[25] D.T.Kaun, A.A. Sawchuk, T.C. Strand, and P.Chavel, ”Adaptive noise Smoothing filter for images with signaldependent noise, ”IEEE Trans. Pattern Analysis and Machine Intelligence, vol.2,no. 2, pp. 165-177, March 1985.

Speckle noise, Medical Images, Medical imaging, De-noising.