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- 1. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 4, July-August (2013), © IAEME 283 SPECKLE NOISE REDUCTION FROM MEDICAL ULTRASOUND IMAGES USING WAVELET THRESHOLDING AND ANISOTROPIC DIFFUSION METHOD Ratil Hasnat Ashique1 , Md Imrul Kayes2 , M T Hasan Amin3 , Badrun Naher Lia4 1 Primeasia University, Department of EEE, 12, Kamal Atartuk Avenue, Banani, Dhaka 2 International Islamic University Chittagong, Department of CSE, Chittagong 3 University of Surrey, Department of Electronics Engineering, Surrey, UK 4 Primeasia University, Department of EEE, 12, Kamal Atartuk Avenue, Banani, Dhaka ABSTRACT Medical Images are very often corrupted by various types of noise including speckle noise, salt and pepper noise etc. This corruption of noise is introduced to the original image during image acquisition and transmission. The various image denoising techniques that are proposed from time to time are offering denoising techniques preserving the original image features. The denoising is so important because ultrasound imaging today has gained wide acceptance due to its safety, easy imaging procedure, low cost and adaptability. However the main shortcomings of this process is poor quality of images which is further degraded due to the presence of speckle noise and other types of noise. Hence it has become vital to remove noise while preserving important datails and features of the image. This paper will introduce a unique method to speckle noise filtering using median filters, wavelet and SRAD filters. Keyword: Ultrasound Image, Ultrasonography, Speckle Noise, Wavelet, Hard Threshold, Soft threshold, SNR, PSNR, MSE, RMSE, Median filter, SRAD filter. 1. INTRODUCTION Compared to other medical imaging techniques ultrasound images suffers from lower image contrast, low Signal to noise ratio, signal dropouts, shadowing of structures, variable intensity problem etc. Moreover very often they contain high noise contents against poor contrast. This ultimately results in blurred image, missing edge points, fake edge points etc. Hence due to complex and changing shapes it becomes difficult to obtain a correct edge map which is vital for diagnosis purpose. Speckle noise comes up as a result of interference between multiple scattering beams and main reflected signal. Speckle noise is a granular noise that inherently exists in and degrades the quality of the images. Generally it is found in ultrasound image and radar image. This noise is, in INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATION ENGINEERING & TECHNOLOGY (IJECET) ISSN 0976 – 6464(Print) ISSN 0976 – 6472(Online) Volume 4, Issue 4, July-August, 2013, pp. 283-290 © IAEME: www.iaeme.com/ijecet.asp Journal Impact Factor (2013): 5.8896 (Calculated by GISI) www.jifactor.com IJECET © I A E M E
- 2. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 4, July-August (2013), © IAEME 284 fact, caused by errors in data transmission. The corrupted pixels are either set to the maximum value, which is something like a snow in image or have single bits flipped over. This kind of noise affects the ultrasound images. Speckle noise has the characteristic of multiplicative noise. Speckle noise follows a gamma distribution and is given as Where, α is variance and g is the gray level. 2. NECESSITY TO REMOVE SPECKLE NOISE As most prevalent artifact in ultrasound image which makes object detection and recognition more difficult, reduction of speckle directly improves the value of the sonogram. Because Ultrasound Images have very little contrast, edge detection is essential to object detection. Ultrasounds depend more heavily on edge detection than other medical imaging modalities. Speckle noise can distort or hide edges making object detection less reliable. Objects such as tumors or birth defects can go undetected and thus untreated 3. SPECKLE NOISE STATISTICS The Rayleigh density function, and its extension, the Rice density function, provide a good starting point for the model for the statistics of the envelope signal. The Rayleigh density function provides a good model for the backscattered echo signals when the scatterer density is very large (>10 scatterers per resolution cell). This model has been used extensively for such fully formed speckle situation. Similarly, the Rice model provides a good model for the presence of coherent backscatter. 4. REMOVING SPECKLE NOISE To develop a despeckling algorithm to filter out speckle noise we have know the mathematical model of the speckle noise .The simplified model can be described as follows: As we know speckle noise is a multiplicative noise, the logarithm of the noisy image is taken to convert the noise function to an additive one .Hence for a possible imaging let F1(m,n)=F2(m,n)*N(m,n) --------------------------(1) where F1,F2 and N are the noisy image , original image without noise and noise function respectively.By taking log on both sides eqn (1) becomes Log{F1(m,n)}=Log{F2(m,n)}+Log{N(m,n)}---(2) In eqn (2) as we observe the noise becomes an additive noise which is processed by various noise removing filters. Denoised Image is obtained by taking the exponential of the image matrix. 5. MEDIAN FILTER Median filter is a nonlinear spatial filter which is good at removing pulse and spike noise. The filtering process is described here-
- 3. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – Here a NxN window is centered around each pixel. Generally N is a small odd number (~5 to 50). Intensity values of each pixel in the window are sorted in an array. The pi window is replaced in the final image with the median value of the pixels in the window. It is simple filter to implement and good at removing “salt and pepper” type noise. graphically below Here following thing are done a. Window centers on target pixel. b. Intensity values are ordered for each pixel in the w c. Mean Value is selected for new image 6. ANISOTROPIC DIFFUSION FILTERS Anisotropic diffusion is an contrast enhancement and noise reduction. It smoothes homogeneous image regions and retains image edges. The main concept of anisotropic diffusion is the introduction of a function that inhabits smoothing at the image edges. This function is called diffusion coefficient. The diffusion coefficient is chosen to vary spatially in such a way to encourage intra region smoothing in preference to inter region smoothing. To smooth image on a continuous dom onal Journal of Electronics and Communication Engineering & Technology (IJECET), – 6472(Online) Volume 4, Issue 4, July-August (2013), © IAEME 285 Here a NxN window is centered around each pixel. Generally N is a small odd number (~5 to 50). Intensity values of each pixel in the window are sorted in an array. The pixel in the center of the window is replaced in the final image with the median value of the pixels in the window. It is simple filter to implement and good at removing “salt and pepper” type noise. Median filtering is shown e ordered for each pixel in the window. ean Value is selected for new image. ANISOTROPIC DIFFUSION FILTERS Anisotropic diffusion is an efficient nonlinear technique for simultaneously performing contrast enhancement and noise reduction. It smoothes homogeneous image regions and retains image edges. The main concept of anisotropic diffusion is the introduction of a function that inhabits moothing at the image edges. This function is called diffusion coefficient. The diffusion coefficient is chosen to vary spatially in such a way to encourage intra region smoothing in preference to inter region smoothing. To smooth image on a continuous domain: onal Journal of Electronics and Communication Engineering & Technology (IJECET), August (2013), © IAEME Here a NxN window is centered around each pixel. Generally N is a small odd number (~5 to xel in the center of the window is replaced in the final image with the median value of the pixels in the window. It is simple Median filtering is shown efficient nonlinear technique for simultaneously performing contrast enhancement and noise reduction. It smoothes homogeneous image regions and retains image edges. The main concept of anisotropic diffusion is the introduction of a function that inhabits moothing at the image edges. This function is called diffusion coefficient. The diffusion coefficient is chosen to vary spatially in such a way to encourage intra region smoothing in preference to inter
- 4. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 4, July-August (2013), © IAEME 286 Where ∇ is the gradient operator, div is the divergence operator, || is the magnitude, c(x) is the diffusion coefficient, and I0 is the initial image. For c(x), they have two coefficients options: Where k is the edge magnitude parameter. c(x) is the conduct coefficient along four directions. In practical design, the diffusion coefficient c(∇I ) is anisotropic, and thus it’s called anisotropic diffusion. The option 1 of the diffusion coefficient favors high contrast edges over low contrast ones. The option 2 of the diffusion coefficient favors wide regions over smaller ones. The edge magnitude parameter k controls conduction as a function of gradient. If k is low, then small intensity gradients are able to block conduction and hence diffusion across step edges. A large value of k can overcome the small intensity gradient barrels and reduces the influence of intensity gradients on conduction. Usually k ~ [20,100]. This method can be iteratively applied to the output image, and the iteration equation is: where I (n) is the output image after n iterations. λ is the diffusion conducting speed, usually we set λ<=0.25. 7. WAVELET BASED DENOISING Wavelet transform(WT) is another transformation method like Fourier transform(FT) except that time and frequency information is obtained simultaneously in the later. FT has the drawback that it cannot provide time information of the frequency bands which is specially important in case of non stationary signals. Though STFT although provides time frequency information of the signal ,it suffers from resolution problem. WT removes this resolution problem also. As most of the practical signals are non stationary type , it is crystal clear that WT has higher preference in signal analysis. The continuous wavelet transform is given by Xw(a,b)=(1/√a)∫[h*{(t-b)/a}x(t)] Where h(t) is the wavelet basis function and x(t) is the original signal. Wavelet techniques are widely used for image denoising and image compression. The wavelet denoising method decomposes the signal image into wavelet basis and shrink the wavelet coefficients to remove speckle. At every level the coefficients are run through soft thresholding process. After thresholding inverse wavelet transform is performed to reconstruct the image. In short, • Decomposition: A wavelet chosen with level N. The wavelet decomposition of the signal is computed at level N. • Threshold detail coefficients: For each level from 1 to N, a threshold selected and soft thresholding applied to the detail coefficients. • Reconstruction: Wavelet reconstruction using the original approximation coefficients of level N and the modified detail coefficients of levels from 1 to N is performed.
- 5. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 4, July-August (2013), © IAEME 287 8. PROPOSED METHOD In our method we first produce artificial speckle noise which is then combined with synthetic ultrasound image to produce noisy test image. At first, denoising is performed using median filter to move long tailed noise such as negative exponential , salt and pepper noise ,spike or pulse noises etc. This of course causes minimum blurring to the image. The process is specially useful if the image contains strong unusual spikes. Secondly, the noisy image is then processed by Wavelet denoising by passing the noisy signal through a wavelet filter and soft thresholding the detail coefficients for speckle removal. At the third step ,SRAD2 filter is used to enhance the contrast of the image, smoothing homogeneous regions and to retain image edges. The whole process can be shown using block diagram as follows: Figure 1: Block Diagram Finally MSE, RMSE, SNR, PSNR are calculated the proposed method and compared with other types of filters. The process is done for three test images. Window size remains fixed 3×3. 9. SIMULATION Here we have used MATLAB as a simulation software .The filters codes are written and image processing toolbox is also used to enhance the simulation. 10. COMPARISON PARAMETERS To determine the image enhancement we have measured The Root Mean Square Error (RMSE), signal-to-Noise Ratio (SNR), and Peak Signal to Noise Ratio (PSNR) of the noisy image The RMSE, SNR, and PSNR are provided below. Synthetic Ultrasound image +Speckle noise Noisy Image SRAD1 filtering Median Filtering WT Denoising Original Image without noise Process flow direction
- 6. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 4, July-August (2013), © IAEME 288 Figure 2: Measurement Parameters Where, f= original image function F=enhanced image function σ=variance of the original image σe=variance of the enhanced image i,j=position of pixel value of image matrix 11. COMPARISON TABLE TEST IMAGE# 1 Parameter Lee Median SRAD2 Proposed MSE 51.4582 58.9049 94.2244 60.5280 RMSE 7.1734 7.6750 9.7069 6.9737 SNR 12.8863 17.2534 15.7889 15.6327 PSNR 31.0163 30.4293 28.3892 29.0713 TEST IMAGE# 2 TEST IMAGE# 3 Parameter Lee Median SRAD2 Proposed MSE 64.7943 52.6241 96.9385 62.9299 RMSE 8.0495 7.2542 9.8457 7.8399 SNR 11.8560 16.7346 14.3236 11.8973 PSNR 30.0154 30.9190 28.2658 22.5017 Statistical Measurement Formula MSE ∑(f(i,j)-F(i,j)^2)/MN RMSE √(∑(f(i,j)-F(i,j))^2)/MN) SNR 10*log((σ^2)/(σ(e)^2) PSNR 20*log10(255/RMSE) Parameter Lee Median SRAD2 Proposed MSE 59.7539 36.5858 85.7866 51.9390 RMSE 7.7301 6.0486 9.2621 6.2069 SNR 9.9015 16.1473 13.0639 12.1305 PSNR 30.3671 32.4977 28.7966 29.9759
- 7. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 4, July-August (2013), © IAEME 289 step3:enhanced image with proposed filtersalt peepered+speckled image step3:enhanced image with proposed filterspeckled image salt peepered+speckled image step3:enhanced image with proposed filtersalt peepered+speckled imagespeckled image TEST IMAGE 1 NOISY IMAGE DENOISED WITH PROPOSED FILTER TEST IMAGE 2 NOISY IMAGE DENOISED WITH PROPOSED FILTER TEST IMAGE 3 NOISY IMAGE DENOISED WITH PROPOSED FILTER 12. CONCLUSION The tested result shows that the proposed multilevel filtering technique provides better resolution, edge preservation with improved SNR compared with other linear and nonlinear filtering techniques. Moreover, signal to noise ratio(SNR),mean square error(MSE) are significantly improved by using the proposed filtering method. 13. REFERENCES [1] Anil K.Jain, “Fundamentals of Digital Image Processing” first edition, 1989, Prentice – Hall, Inc. [2] Tinku Acharya and Ajoy K. Ray, “Image Processing Principles and Appilications”, 2005 edition A John Wiley & Sons, Mc., Publication. [3] Rafael C. Gonzalez and Richard E. Woods, “Digital Image Processing”, Second Edition, Pearson Education. [4] “IEEE Computational Science and Engineering, summer” 1995, vol. 2, num.2, Published by the IEEE Computer Society, 10662 Los Vaqueros Circle, Los Alamitos, CA 90720, USA.
- 8. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 4, July-August (2013), © IAEME 290 [5] Ingrid Daubechies “Ten lectures on wavelets” Philadelphia, PA: SLAM, 1992’ [6] Georges Oppenheim, “Wavelets and Their Application”. [7] S. Sudha, G.R Suresh and R. Suknesh, "Speckle Noise Reduction in Ultrasound images By Wavelet Thresholding Based On Weighted Variance",International Journal of Computer Theory and Engineering, Vol. 1, No. 1, PP 7-12,2009. [8] S. Sudha, G.R Suresh and R. Suknesh, “Speckle Noise Reduction In ultrasound Images Using Context-Based Adaptive Wavelet Thresholding”, IETE Journal of Research Vol 55 (issue 3), 2009. [9] Zhenghao Shi and Ko B.Fung,” A comparison of digital speckle filters” Canada centre for Remote Sensing. [10] 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. [11] D.T.Kaun, A.A. Sawchuk, T.C. Strand, and P.Chavel,”Adaptive noise Smoothing filter for images with signal-dependent noise, ”IEEE Trans. Pattern Analysis and Machine Intelligence, vol.2,no. 2, pp. 165-177, March 1985. [12] V.S.Frost, J.A Stiles, K.S. Shanmugan, and J.C. Holtzman, “A model for radar Images and its application to adaptive digital filtering of multiplicative noise,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol.2,no. 2, pp. 155-166, March 1980. [13] Yongjian Yu and Scott T. Action, “Speckle Reducing Anisotropic Diffusion”, IEEE Transaction on image processing, Vol. 11, NO. 11,pp. 1260-1270,NOV 2002. [14] J.W. Goodman, Some fundamental properties of speckle, J.Opt. Soc. Am.,66: SS1145 - 1150, Nov 1976. [15] Yong Yue,Mihai M. Croitoru, Akhil Bidani, Joseph B. Zwischenberger and John W Clark,Jr., ”Ultrasound Speckle Suppression and Edge Enhancement Using multiscale nonlinear wavelet diffusion” ,IEEE 27th Annual International Conference of the Engineering in medicine and biology society ,2005, page 6429-6432 [16] Chadsada Chinrungrueng and Aimamorn Suvichakron “Fast edge reduction for ultrasound images” IEEETRANSACTION ON NUCLEAR SCIENCE 2001, VOL 48 ,pages 849-854 [17] Yu Y, Acton S. Speckle reducing anisotropic diffusion. IEEE Trans Image Process 2002;11(11):1260–70. [18] Yu, Y. Ultrasound image enhancement for detection of contours using speckle reducing anisotropic diffusion, PhD dissertation; May 2003. [19] ‘Speckle reducing anisotropic diffusion for 3D ultrasound images’ Qingling Suna, John A. Hossackb,*, Jinshan Tangc, Scott T. Actonb,c Computerized Medical Imaging and Graphics 28 (2004) 461–470. [20] Er. Ravi Garg and Er. Abhijeet Kumar, “Comparasion of SNR and MSE for Various Noises using Bayesian Framework”, International Journal of Electronics and Communication Engineering & Technology (IJECET), Volume 3, Issue 1, 2012, pp. 76 - 82, ISSN Print: 0976- 6464, ISSN Online: 0976 –6472. [21] Krishnan Kutty and Lipsa Mohanty, “An Adaptive Method for Noise Removal from Real World Images”, International Journal of Electronics and Communication Engineering & Technology (IJECET), Volume 4, Issue 1, 2013, pp. 112 - 124, ISSN Print: 0976- 6464, ISSN Online: 0976 –6472. 14. BIBLIOGRAPHY [22] Rafael C Gonzalez , Richard E. Woods (2002),DIGITAL IMAGE PROCESSING, 2nd ed., Prentice Hall ,Upper saddle River, NJ

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