New Noise Reduction Technique for Medical Ultrasound Imaging using Gabor Filtering

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1 New Noise Reuction Technique for Meical Ultrasoun Imaging using Gabor Filtering Mehei Hasan Taluker Course of Inustrial Engineering an Management Grauate School of Engineering, Kanagawa University Yokohama, , Japan Mitsuhara Ogiya Department of Inustrial Engineering an Management Faculty of Engineering, Kanagawa University Yokahama, , Japan Masato Takanokura Department of Inustrial Engineering an Management Faculty of Engineering, Kanagawa University Yokahama, , Japan Abstract Ultrasoun (US) imaging is an important meical iagnostic metho, as it allows the examination of several internal boy organs. However, its usefulness is iminishe by signal epenent noise known as speckle noise. Speckle noise egraes target etectability in ultrasoun images an reuces contrast an resolution, affecting the ability to ientify normal an pathological tissue. For accurate iagnosis, it is important to remove this noise from ultrasoun images. In this stuy, a new filtering technique is propose for removing speckle noise from meical ultrasoun images. It is base on Gabor filtering. Specifically, a preprocessing step is ae before applying the Gabor filter. The propose technique is applie to various ultrasoun images, an certain measurement inexes are calculate, such as signal to noise ratio, peak signal to noise ratio, structure similarity inex, an root mean square error, which are use for comparison. In particular, five wiely use image enhancement techniques were applie to three types of ultrasoun images (kiney, abomen an ortho). The main objective of image enhancement is to obtain a highly etaile image, an in that respect, the propose technique prove superior to other wiely use filters. Keywors: Ultrasoun Images, Speckle Noise, Ege Preservation, Performance Evaluation, Gabor Filter. 1. INTRODUCTION Digital ultrasoun (US) imaging is a wiely use meical iagnosis technique, owing to its safe noninvasive nature, low cost, capability of generating real time images, an continuing improvement in image quality [1] [4]. It is estimate that one out of every four meical iagnostic image stuies (such as X-Ray, CT, MRI, PET, an US) in the worl involves US techniques. Ultrasoun imaging is use for visualizing muscles an several internal organs an thus revealing certain pathological abnormalities using real-time tomographic images [3], [4]. It is also use for visualizing the fetus uring routine an emergency prenatal care. Obstetric sonography is commonly use uring pregnancy. It has no known long-term sie effects an rarely causes any iscomfort to the patient. As it oes not use ionizing raiation, ultrasoun involves no risks to the patient. It provies live images from which the operator can select the most useful section, thus facilitating quick iagnoses. International Journal of Image Processing (IJIP), Volume (12) : Issue (1) :

2 The usefulness of ultrasoun imaging is iminishe by signal epenent noise known as speckle noise. This is multiplicative noise that egraes image quality; hence, it reuces the ability of an observer to iscriminate fine etails in iagnostic examination an reners treatment ecisionmaking ifficult. Ege preservation [5] an noise reuction are important for accurate iagnosis [6] [9]. Speckle reuction is the process of removing speckle noise from US. Speckle noise ecreases the efficiency of further image processing such as ege etection [5], [6]. De-noising techniques shoul reuce speckle without blurring or changing the location of the eges, which are those points at which the luminous intensity changes sharply an usually reflect important changes in the properties of the image. Thus, ege etection is highly important in ientifying an unerstaning the entire image. Ege etection is primarily the measurement an etection of gray change, an in ultrasoun images, it is a ifficult task because the relate algorithms may be sensitive to noise [9], [10]. Speckle noise also egraes the spee an accuracy of US image processsing operations, such as segmentation an registration [11], [12]. Thus, the enhancement of image quality is an important an emaning research fiel, an this stuy aims at suppressing speckle noise by preserving eges in ultrasoun images. 2. RELATED STUDIES De-noising techniques for US images have been extensively stuie, owing to the nee for accurate iagnosis. Richar et al. [2] propose a novel aaptation of meian filters for bounarypreserving speckle reuction. Specifically, a set of short lines passing through the center of a square-shape kernel was consiere. Following the sticks technique, the meian along each line was compute, an the largest meian value was taken for the central pixel. Patier et al. [3] use meian, mean, an Wiener filters for removing noise. In this regar, aaptive filter esign has attracte consierable attention. Bhattacharya et al. [4] emonstrate the pertinence of Gabor filtering in brain image segmentation, namely, the ientification analysis of the output of noisy an filtere images by Gabor filtering. An algorithm was evelope that implemente all filtering types on the input image, an arithmetic parameters were calculate accoring to the comparison between the output an input images. Negi et al. [5] propose a Gabor base wavelet transform for ege etection in ultrasoun as well as normal images. Gabor base etection performs filtering in ifferent irections an scales to etermine the eges of the texture at optimal frequency. To reuce the effect of noise, the eges were etecte in smooth images instea of the original images, an it was emonstrate that Gabor wavelet-base ege etection is highly effective for preserving eges. Karaman et al. [6] propose an aaptive filtering technique for removing speckle pattern from ultrasoun images. Smoothing operators (mean or meian) are applie in regions where the tissue is assume homogeneous. These regions are obtaine by region growing that is constraine only by statistical properties an the istance from the central pixel. Similarly, Joseph et al. [7] propose a new weighte linear filtering approach that uses local binary patterns (LBP) for reucing speckle noise in ultrasoun images. This approach performs effective enoising without affecting image content. It uses LBP, which is a gray scale invariant that escribes local primitives, such as curve eges, points, spot, an flat areas, an plays a vital role in texture analysis. It is wiely use in various computer vision problems, such as face recognition, motion analysis, meical image analysis, fingerprint recognition, palmprint recognition, an vessel extraction in conjunctive imaging. In this process, the center pixel value is subtracte from that of each neighboring pixel. To generate the LBP coe for a neighborhoo, the weight assigne to each pixel is multiplie by a numerical threshol. The process is repeate for a set of circular samples. International Journal of Image Processing (IJIP), Volume (12) : Issue (1) :

3 Base on 2D homogeneity, Yanhui et al. [8] propose irectional averaging whereby homogeneity is checke for pixel values. Pixels with homogeneity above a certain threshol remain unchange. Otherwise, they are processe by their irectional average filter. Ege etection is followe by irectional filtering along the ege with the highest ege-value (vertical or horizontal). This approach can effectively reuce speckle noise without aversely affecting textual information. In conclusion, speckle noise reuction was performe in [2], [3], [6], an [7]; however, ege etection was not consiere. In [4], signal to noise ratio an correlation were improve, but ege preservation was ignore. In [5], the Gabor wavelet transform was use for ege etection, but noise was not consiere. The irectional averaging technique was propose in [8]; however, if averaging is performe in US images, then blur an ege egraation occur. Gabor filtering is suitable for ege preservation, but its performance in noise reuction from US images is poor. In the present stuy, a new technique for removing speckle noise from ultrasoun images is propose that also preserves eges an etail information. 3. SPECKLE NOISE MODEL Speckle noise is multiplicative noise affecting all coherent imaging systems, incluing meical ultrasoun [11]-[13]. The most critical part of eveloping a metho for recovering a signal from its noisy environment is choosing a reasonable statistical (or analytic) escription of the physical phenomena unerlying the ata-formation process. The availability of an accurate an reliable moel of speckle noise formation is a prerequisite for the evelopment of a valuable e-speckling algorithm [14], [15]. However, in ultrasoun imaging, a unifie efinition of such a moel remains arguable. Nevertheless, there exist several possible formulas whose potential has been empirically verifie. A possible generalize moel for speckle imaging is the following: g( n, f ( n, u( n, + ξ ( n, = (1) where g, f, u, an ξ enote the observe image, original image, the multiplicative component, an the aitive component of the speckle noise, respectively. (n, enotes the pair of axial an lateral inices of the image samples or, alternatively, the angular an range inices for B-scan images. In ultrasoun images, only the multiplicative component of the noise is to be consiere, an thus the moel can be consierably simplifie by isregaring the aitive term. The simplifie version of this equation thus becomes g ( n, = f ( n, u( n, (2) 4. GABOR FILTER In image processing, a Gabor filter, name after Dennis Gabor, is a linear filter [4] use for ege etection. Frequency an orientation representations of Gabor filters are similar to those of the human visual system, an they have been foun to be particularly appropriate for texture representation an iscrimination. In the spatial omain, a 2D Gabor filter is a Gaussian kernel function moulate by a sinusoial plane wave. Its impulse response is efine by a harmonic function multiplie by a Gaussian function. Owing to the multiplication-convolution property (convolution theore, the Fourier transform of a Gabor filter's impulse response is the convolution of the Fourier transform of the harmonic function an the Fourier transform of the Gaussian function. The filter has a real an an imaginary component representing orthogonal irections. A Gabor filter can be represente as x + γ y x g( x, y; λ, θ, Ψ, σ, γ ) = exp cos 2Π + Ψ 2 2σ λ (3) International Journal of Image Processing (IJIP), Volume (12) : Issue (1) :

4 an θ where x = xcosθ + ysinθ y = xsinθ + y cos, λ enotes the wavelength of the cosine factor, θ enotes the orientation of the normal to the parallel stripes of the Gabor function, ψ is the phase offset, σ is the sigma of the Gaussian envelope, an γ is the spatial aspect ratio, which specifies the elasticity of the support of the Gabor function. The Gabor filter is the only filter with orientation selectivity that can be expresse as a sum of only two separable filters. If higher frequency information is chosen, the ege is maximize. Gabor filters are irectly relate to Gabor wavelets, as they can be esigne for a number of ilations an rotations. They are convolve with the signal, an this process is closely relate to processes in the primary visual cortex [4], [5]. Gabor filters have receive consierable attention because they can approximate the characteristics of certain cells in the visual cortex of some mammals. In aition, they have been shown to possess optimal localization properties in both the spatial an the frequency omain. Gabor filters have been use in various applications, such as texture segmentation, target etection, fractal imension management, ocument analysis, ege etection, retina ientification, an image coing an image representation. 5. PROPOSED TECHNIQUE In ultrasoun imaging, it is often esire to remove speckle noise for accurate iagnosis. Moreover, ege preservation is important in ientifying an unerstaning the entire image. Eges are those points at which luminous intensity changes sharply [5]. Gabor filters use orientation selectivity; they choose higher frequency information an thus maximize eges. Their performance is poor for speckle reuction, an in this stuy, this limitation is remove. By moifying the traitional Gabor filter, a new speckle noise reuction technique for ultrasoun images is presente. This is accomplishe by aing certain preprocessing tasks. The steps of this technique are shown in Figure1. FIGURE 1: Flow Diagram of Filtering Technique. International Journal of Image Processing (IJIP), Volume (12) : Issue (1) :

5 The general approach of image enoising is to apply a function for processing each pixel of the image. A basic approach is base on the use of masks, which are also referre to as filters, kernels, templates, or winows. An n n kernel is use for processing each pixel, where a kernel is a small 2-D array in which the values of the coefficients etermine the nature of the filtering process, namely, image sharpening, ege etection, an smoothing. Filtering is performe to etermine the central pixel value of the kernel. As n is normally an o number, the size of the filter kernel is, for example, 3 3, 5 5, 7 7. In Figure 1, an input image is first taken, an its size is calculate. Then, a two-imensional winow of size 3 3 is selecte. This winow is centere at each pixel of the corrupte image for performing the esire operations, as shown in Figure 2. x-1,y-1 x-1,y x-1,y+1 x,y-1 x, y x,y+1 x+1,y-1 x+1,y x+1,y+1 FIGURE 2: 3 3 Region of An Image. The central pixel is replace by the meian value of the 3 3 region in the winow; this preprocessing task is performe to remove noise. Then, the final output image is obtaine by Eq RESULTS AND DISCUSSION Simulation stuies are usually the first step for quantitatively evaluating the performance of an estimation metho. To valiate the efficiency of the propose filtering technique, a simulation stuy was carrie out using the MATLAB an ImageJ software packages. Three ultrasoun images (kiney, abomen, an ortho) were use. An original noise-free image was first selecte. Subsequently, an image contaminate with speckle noise (noise factor 0.04) was selecte, an finally output images were obtaine by image processing operations involving various types of filtering, namely, the existing an the propose techniques. Firstly, preprocessing was performe for various winow sizes. For quantitative assessment, the image quality inex (IMGQ) an the ege preservation factor (EPF) were use. IMGQ an EPF are the most important image quality measurement metrics. Base on the value of IMGQ an EPF the winow size is selecte for preprocessing. IMGQ can etermine the egree of istortion in terms of loss of correlation [16]. The ynamic range of IMGQ is between 1 an 1, an higher values inicate higher image quality. It is calculate by the following equation [16]: 4σ xx X X IMGQ = (4) [ σ + σ ][ X + ( X ) ] x where, x enote the original an filtere images, σ enotes the stanar eviation, an X, X the mean value of original an filtere images. x The ege preservation factor (EPF) is one of the most important measurement metrics [17], [18], which is also use for selecting the winow size. Ege preservation is important in the processing of meical images. Its value facilitates the selection of the best filter [17]. If EPF is higher, the technique is better. It is obtaine by the following equation [17], [18]: x International Journal of Image Processing (IJIP), Volume (12) : Issue (1) :

6 EPF = ( I I)( I ( I I) 2 ( I I ) I ) 2 (5) where is the Laplace operator, i.e., the ifferential operator given by the ivergence of the graient of a twice-ifferentiable real value function in the n-imensional Eucliian space. I, I, I, an I enote the action of the Laplace operator on the original image, the filtere image, the mean of the original image, an the mean of the filtere image, respectively. Table 1 shows the quantitative performance for various winow sizes. The corresponing images are shown in Figure 3. Here a 3 3 winow was selecte because larger winows, such as 5 5 or 7 7, result in over-smoothe images with egrae eges an yiel low IMGQI an EPF values. If 3 3 winows are use, only the neighboring pixels are consiere, an thus eges remain unchange. As ege preservation is important for accurate iagnosis, a 3 3 winow was selecte for preprocessing. TABLE 1: Quantitative Performance For Various Winow Sizes. Filter Propose IMGQI EPF Image 3 3 winow 5 5 winow 7 7 winow 3 3 winow 5 5 winow 7 7 winow Kiney Abomen Ortho (a) (b) (c) () (e) (f) (g) (h) FIGURE 3: Abomen image (noise factor is 0.04); a) original image, b) noisy Image, c) output of 5 5 winow, ) output of 3 3 winow, e) intensity profile of original image, f) intensity profile of noisy image, g) intensity profile of the output of 5 5 winow, h) intensity profile of the output of 3 3 winow. International Journal of Image Processing (IJIP), Volume (12) : Issue (1) :

7 By observing Table 1 an Figure 3, it can be seen that the 3 3 winow yiels better results in terms of speckle noise reuction an ege an etail preservation. Moreover, higher values of IMGQ an EPF are obtaine. In Figure 3, (e), (f), (g), an (h) show the intensity profiles, that is, the sets of intensity values taken from regularly space points along a line segment or multiline path. The improfile function was use for creating the intensity profiles. For points that o not fall at the center of a pixel, the intensity values were interpolate. In Figure 3, the output image (), which is the output of the 3 3 winow, approximates the input image (a), an the intensity plot profile (h) approximates the intensity plot profile of the original image. From the intensity plot profile of the output of the 5 5 winow (g), it can be conclue that it is slightly smoother than original image; thus, the 3 3 winow was selecte for preprocessing. Pictorial assessment of the performance of various filtering techniques for speckle noise reuction (with noise factor 0.04) in ultrasoun images was performe for various images an is shown in Figures 4 an 5. FIGURE 4: Input an Output of Kiney image for speckle noise (noise factor is 0.04). FIGURE 5: Input an Output of Ortho image for speckle noise (noise factor is 0.04). Speckle noise is a multiplicative noise that has granular patterns an ultrasoun speckle results from the coherent accumulation of ranom scatterings from the resolution cell. From the noisy image (b) of Figures 4 an 5 one can see that, speckle noise egraes the quality of US image baly. In this case it is very important to remove this noise from these images. Here especkling is performe by using various well known enoising techniques. In this case, especkling is expecte to reconstruct the original image by preserving eges an other image etails. The output of Gabor filter (c) is very poor, but, from Figures 4 () an 5 (), it is clear that the propose filtering technique provies better visual appearances in the case of removing speckle noise from ultrasoun images. International Journal of Image Processing (IJIP), Volume (12) : Issue (1) :

8 Quantitative measurement of the performance of various filtering technique for removing speckle noise from ultrasoun images are represente by the following Table 2. For the quantitative assessment, several performance measures were use to compare the performance of especkling methos. This measurement is performe on basis of the values of signal to noise ratio (SNR), peak signal to noise ratio (PSNR), structure similarity inex (SSIM), an root mean square error (RMSE). Higher SNR an PSNR values inicate better image quality [16] [25]. The luminance, contrast, an structure of two images are compare using SSIM. It can be regare as a similarity measure between the images [16]. The stanar value of SSIM is 1. SSIM can be calculate using the following equation: SSIM ( I, I ) = (2µ µ 2 2 ( µ + µ I I I I + C )(2σ C )( σ + σ 1 1 I II + C I 2 ) + C 2 ) (6) RMSE is use for assessing the performance of image reconstruction relative to the original image. It represents the ifference between original an enoise images. Higher values inicate large ifference an lower values inicate smaller ifference [16]. For ientical images, it is zero. RMSE is calculate by the following equation [18]: 1 M 1 x= 0 N 1 y= 0 e 2 rms = [ I( x, y) I ( x, y)] M N (7) In (6) an (7), I an I enote the original image an the enoise image, respectively. µ I an µ I, σ I an σ I enote the average an the variance of I an I, respectively. σ II is the covariance of I an I. C 1 an C 2 are two variables for stabilizing the enominator. Rows an columns are enote by x an y, respectively, an M N is the image size. TABLE 2: Performance Table For Various Filters. Filter Image SNR RMSE PSNR SSIM Original Filtere Kiney Meian Abomen Ortho Kiney Average Abomen Ortho Kiney Inverse Abomen Ortho Kiney Wiener Abomen Ortho Kiney Gabor Abomen Ortho Propose Kiney Metho Abomen Ortho From Figures 4, an 5, it is clear that the propose filter yiels better visual results an preserves more etail information. From Table 2, it can be seen that the propose filter yiels better results International Journal of Image Processing (IJIP), Volume (12) : Issue (1) :

9 in terms of various measurement metrics as well. The Gabor filter uses orientation selectivity, ensuring ege preservation but not satisfactory speckle noise removal; thus, SNR is consierably low for that filter. By contrast, the corresponing value for the propose filtering technique is significantly higher. Likewise, RMSE is also high for the Gabor filter, which inicates that the ifference between the original image an the enoise image is high. However, the propose technique has low RMSE, which implies that the output image approximates the original image. From Table 2, it is clear that the propose filter has higher SNR an PSNR compare with existing filters. Moreover, its SSIM is close to 1, which inicates that the enoise image approximates the original image. 7. CONCLUSION AND FUTURE WORK Human vision is sensitive to high-frequency information. Image etails (e.g., corners an lines) have high frequency content an carry important information for visual perception. In this stuy, a new filtering technique was propose for removing speckle noise from meical ultrasoun images. In particular, a new metho for enhancing the performance of the existing Gabor filtering metho has been presente. Specifically, a simple preprocessing step was ae with Gabor filter to establish a new technique. The performance of five well known especkling methos was examine in the current stuy. Moreover, in all case the performance of the propose technique for removing speckle noise from various ultrasoun images was compare with that of various traitional speckle noise reuction techniques. In conclusion, it is clear that the propose filter yiele better output for kiney, abomen, an ortho images compare with existing filters. It is note that no attempt was mae to compare the performance of propose technique for segmentation or any other viewpoints. Performance evaluation of propose metho from a ifferent number of viewpoints (e.g., computational efficiency, reliability of recovering ifferent anatomical structures, an ifferent tissue morphologies) well eserves a future stuy. 8. REFERENCES [1] R. G. Dantas, E. T. Costa. Ultrasoun Speckle Reuction Using Moifie Gabor Filter. IEEE Transactions on Ultrasonics, Ferroelectrics, an Frequency Control,Vol. 54, pp , Mar [2] R. N. Czerwinski, D. L. Jones, W. D. O Brien. Ultrasoun Speckle Reuction by Directional Meian Filtering. International Conference on Image Processing, IEEE, Vol. 1, pp , Apr [3] P. Patiar, M. Gupta, S. Srivastava, A.K. Nagawat. Image De-noising by Various Filters for Different Noise. International Journal of Computer Applications, Vol. 9, pp , Nov [4] D. D. Bhattacharya, M. J. Devi, M P. Bhattacherjee. Brain Image Segmentation Technique Using Gabor Filter Parameter. American Journal of Engineering Research, Vol. 2, pp , Jul [5] N. Negi, S. Mathur. An Improve Metho of Ege Detection Base on Gabor Wavelet Transform. Recent Avances in Electrical Engineering an Electronic Devices, Vol. 8, pp , Jul [6] M. Karaman, M. A. Kutay, G. Bozagi. An Aaptive Speckle Suppression Filter for Meical Ultrasonic Imaging IEEE Transactions on Meical Imaging, vol. 14, pp , June [7] S. Joseph, K. Balakrishnan, M.R. B. Nair, R. R. Varghese. Ultrasoun Image Despeckling using Local Binary Pattern Weighte Linear Filtering. I.J. Information Technology an Computer Science, 2013, vol. 06, pp. 1-9, June International Journal of Image Processing (IJIP), Volume (12) : Issue (1) :

10 [8] Y. Guo, H. D. Cheng, J Tian, Y. Zhang. A Novel Approach to Speckle Reuction in Ultrasoun Imaging. Ultrasoun in Meicine an Biology, vol. 35, pp , Apr [9] P. Moulin, J. Liu, Analysis of Multiresolution Image Denoisingschemes Using Generalize Gaussian an Complexity Priors. IEEE Transaction of Information Theory, Vol. 45, pp , Apr [10] S. Suha, G.R. Suresh, R Sukanesh. Speckle Noise Reuction in Ultrasoun Images Using Context-base Aaptive Wavelet Thresholing. IETE Journal of Research, Vol. 55, pp , Sep [11] O. V. Michailovich, A. Tannenbaum. Despeckling of Meical Ultrasoun Images. IEEE Transactions on Ultrasonics, Ferroelectrics, an Frequency Control, vol. 53, pp Jan [12] S. C. Kang, S. H. Hong. A Speckle Reuction Filter Using Waveletbase Methos for Meical Imaging Application. In Proceeings of the 23r Annual International Conference of the IEEE Engineering in Meicine an Biology Society, pp , [13] Y. Yu, S. T. Acton. Speckle Reucing Anisotropic Diffusion. IEEE Transactions on Image Processing, Vol. 11, pp , Nov [14] M. H. Taluker, M. A. Islam, T. K. Ghosh, M. M. Rahman. A New Filtering Technique for Reucing Speckle Noise From Ultrasoun Images. International Journal of Research in Computer an Communication Technology, Vol. 2, pp , Sep [15] K. B. Eom. Speckle Reuction in Ultrasoun Images Using Nonisotropic Aaptive Filtering. Ultrasoun in Meicine an Biology, Vol. 37,pp , Oct [16] R. Sivakumar, M. K. Gayathri, D. Neumara. Speckle Filtering of Ultrasoun B-Scan Images - A Comparative Stuy Between Spatial An Diffusion Filter.IEEE Conference on Open System, pp , Dec [17] I. Njeh, O. B. Sassi, K. Chtourou, A. B. Hami. Speckle Noise Reuction In Breast Ultrasoun Images: Smu (Sra Meian Unsharp) Approch. International Multi-Conference on Systems, Signals & Devices, pp. 1-6, Mar [18] M. M. Rahman, M. Kumar, A. Aziz, M. G. Arefin, M. S. Uin. Aaptive Anisotropic Diffusion Filter for Speckle Noise Reuction for Ultrasoun Images. International Journal of Convergence Computing, Vol. 1, pp.50-59,dec [19] A. Buaes, B.Coll, J. Morel A Non-Local Algorithm for Image Denoising. In Proceeings of IEEE Computer Society Conference on Computer Vision an Pattern Recognition, pp , [20] J. Starck, E. J. Canes, D. L. Donoho. The Curvelet Transform for Image Denoising. IEEE Transactions on image processing, Vol.11,pp ,Jun [21] J. Patra, H. N. Moulick, S. Mallick, A. K. Manna. 3D Wavelet SubBans Mixing for Image De-noising an Segmentation of Brain Images. American Journal of Engineering Research, Vol. 03, pp , Jul [22] S. K. Narayanan an R. S. D. Wahiabanu, A View on Despeckling in Ultrasoun Imaging. International Journal of Signal Processing, Image Processing an Pattern Recognition, Vol. 2, pp , Sep International Journal of Image Processing (IJIP), Volume (12) : Issue (1) :

11 [23] A. Islam, M. H. Taluker, M. M. Hasan, Speckle Noise Reuction From Ultrasoun Image Using Moifie Binning Metho an Fuzzy Inference System International Conference on Avances in Electrical Engineering (ICAEE), pp , Dec [24] M. M. Rahman, M. H. Taluker, M. M. Rahman, Ultrasoun Image Enhancement Using Discrete Wavelet Transform base on Image Sharpening Moel. American International Journal of Research in Science, Technology, Engineering & Mathematics (AIJRSTEM), Vol.15, pp , Aug [25] R. E. Pregitha, D. V. Jegathesan, C. E. Selvakumar, Speckle Noise Reuction in Ultrasoun Fetal Images Using Ege Preserving Aaptive Shock Filters. International Journal of Scientific an Research Publications, Vol. 2, pp. 1-3, Mar International Journal of Image Processing (IJIP), Volume (12) : Issue (1) :

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