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  • 1. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 5, Issue 3, March (2014), pp. 138-147 © IAEME 138 A COMBINATIONAL APPROACH FOR NOISE REMOVING AND SMOOTHING ULTRA SOUND KIDNEY IMAGES Dr. G.M. Nasira Assistant Professor, Department of Computer Science, Chikkana Government Arts College, Tirupur, Tamilnadu-641 602, India Ranjitha. M Faculty in Department of Information Technology, CMR Institute of Management Studies, Bangalore, India ABSTRACT Interference of noise, influences the decision making of radiologists. A new technique of smoothing and reducing the speckle noise, and thereby improving the clarity of the US(Ultra Sound) images is introduced through this paper. Recognizing kidney stones and gall stones from an US scanned image is a challenging problem. There are different types of noises present in the US images due to various reasons. The speckle noises present in the images have to be removed before starting further stages of analysis. In this paper we have worked on a new approach of using Gaussian over median and Wiener over Median filtering technique for noise removal and smoothing. These approaches were tested on ultra sound scanned kidney images and both the techniques were compared based on their statistical properties. Keywords: Noise Removing, Ultrasound Images, Median, Gaussian, Wiener, Speckle, Threshold. I. INTRODUCTION Ultrasound scan can be used to detect clear uric acid stones and obstruction in the urinary tract. Researchers indicate this as the first diagnostic step in emergency to predict the likelihood of a stone by indicating some suspected stones. Though US image is adaptable, transferable and comparatively safe, this type of image is often full of acoustic interferences (speckle noise) and artifacts. The word artifact itself means something artificial and something produced by outside forces over which one has no control. A sonographic artifact is the artificial information on the INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & TECHNOLOGY (IJCET) ISSN 0976 – 6367(Print) ISSN 0976 – 6375(Online) Volume 5, Issue 3, March (2014), pp. 138-147 © IAEME: www.iaeme.com/ijcet.asp Journal Impact Factor (2014): 8.5328 (Calculated by GISI) www.jifactor.com IJCET © I A E M E
  • 2. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 5, Issue 3, March (2014), pp. 138-147 © IAEME 139 image that is produced by physical and/or imaging phenomena over which the operator has little control, if any. The production and display of this information may be related to variations in the propagation speed of sound in different tissues; it may be related to bending and vibrations encountered by ultrasound energy as it traverses complex anatomical structures or it may be the improper registration and display of information on the image due to limitations of the transducer, receiver or other components of the imaging system. Regardless of the cause, false information on a Scanned image must be corrected to prevent misdiagnosis [2]. Rahman [1] has suggested that Speckle is a complex phenomenon, which degrades delectability of target organ and reduces the contrast, resolutions with back-scattered wave appearance which originates from many microscopic diffused reflections. The complex physical interactions of the ultrasound beam with the human tissues results in some unexpected results in the ultrasound scanned images. Sonographic images depend on several assumptions and on failure of any of these assumptions, a noise or an artifact occurs. Some of the common assumptions are i) The transmitted waves from a scanning machine should travel in straight line ii) The dimensions of the beam are very small in all dimensions iii) The echoes originate along the axis of the transducer iv) The echoes are derived from the most recent pulse v) The amplitude of the echo is directly proportional to reflective strength vi) Sound waves travel at 1540 m/s in soft human tissues. Artifacts can be of various types - ring down, attenuation, comet tail, speckle, shadowing etc. The most common ones are speckle, which is created when patterns of waves in sound beam close to the surface of the transducer reduces image resolution and these noise like fill in of image pixels results in speckle noise. Ultrasound beam is a three dimensional beam and hence the width varies at different depths in the body. Due to this, some misregistration artifacts are added. As the Ultrasound scanner registers echoes as a function of time, each successive echo returning to the face of the transducer is registered as far away from the transducer. The appearance of reverberatory artifacts on a sonographic image is that of equally spaced, repeating echoes. In this schematic, the first sound pulse (1) leaves the transducer and reflects strongly from the border of the oval structure. The high- amplitude echo returns to the face of the transducer where it is registered on the image as a bright echo (E1). The remaining sound is reflected back into the medium again (2), where it repeats this reflection process creating yet another bright echo in the image (E2) and bouncing back into the medium (3). This back-and-forth reflection between the transducer and high amplitude reflectors in the near field of the image is reverberation [2 - 4] and produces artifacts. There are different categories of artifacts. Ringing Artifact - When ultrasound strikes a strong interface such as gas or stone, one of the two responses may be produced - either there is no sound conduction through the area (resulting in shadowing), or numerous secondary reverberations are produced. These secondary reverberations can result in a series of parallel echogenic lines which extends into the tissues (reverberation artifacts). Associated with these strong reverberatory artifacts are indiscrete echoes that represent a continuous “ringing” of the crystal that are produced as a result of the high amplitude and repetitive striking of the crystal. These indiscrete echoes are represented as fuzzy, gray echoes which are displayed below each reverberation echo [2-4]. Multipath - As the name of this artifact suggests, a pulse that is re-directed along several paths before returning to the transducer will produce image anomalies resulting from incorrect timing assumptions of the imaging system. This artifact typically results when a specular reflector is insonated at an oblique angle, reflecting the pulse obliquely along a different path where it encounters yet another strong interface before being sent back to the transducer. Multipath artifacts are displayed as abnormalities of depth or position of a structure.
  • 3. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 5, Issue 3, March (2014), pp. 138-147 © IAEME 140 Mirror-Image - Mirror-image artifacts are produced when an object is located in front of a highly reflective surface at which near total reflection takes place. Part of the energy is reflected back toward the object and some of the energy is transmitted toward the transducer. This process is repeated so that multiple echoes separated in time are generated from the same object [2-4]. Side Lobes - One of the major problems with electronic arrays is the formation of secondary lobes of ultrasonic energy. Secondary lobes originate at the transducer and radiate outwards at various angles to the main beam. Artifacts in the image are created by false interfaces or untrue positions of interfaces because all returning echoes are assumed to originate along the main beam axis. Comet-Tail: Comet-tail artifacts originate from small, highly reflective surfaces and are similar to ring down artifacts in their physical origin. Reverberation and excessive ringing of the transducer crystal caused by resonating air bubbles or small metal clips creates a high amplitude “tail” distal to the structure. The complexity of the comet-tail pattern depends on the shape, composition and size of the object, as well as the scan orientation and the distance to the transducer face. Comet-tails are usually seen in otherwise echo-free areas on an image [2-4]. Apart from the various artifacts, the electrical sensors respond nonlinearly to its input intensity, and the sensor amplifier introduces additive Gaussian noise independent of the image field. Gaussian noise is the statistical noise that has the probability density function same as that of the normal distribution (also known as the Gaussian distribution). Further, the values of the noise are Gaussian-distributed. A special case is white Gaussian noise, in which the values at any pairs of times are statistically independent (and uncorrelated). In applications, Gaussian noise is most commonly used as additive white noise to yield additive white Gaussian noise. If the white noise sequence is a Gaussian sequence, then it is called a white Gaussian noise (WGN) sequence [5]. To remove this noise, Gaussian filtering technique is used. Gaussian filter is a filter whose impulse response is a Gaussian function [6]. Gaussian filters are designed to give no overshoot to a step function input while minimizing the rise and fall time. This behavior is closely connected to the fact that the Gaussian filter has the minimum possible group delay. Mathematically, a Gaussian filter modifies the input signal by convolution with a Gaussian function. Smoothing is commonly undertaken using linear filters such as the Gaussian function (the kernel is based on the normal distribution curve), which tends to produce good results in reducing the influence of noise with respect to the image. The 2D Gaussian distributions with standard deviation σ for pixel (i, j), is given by Eq. (2) ଵ √ଶπσ eିሺ୶ మ ା୷మሻ/ଶσమ [7]. Our study focuses on removing these artifacts and noises from US images and making the image clearer for further processing. This paper is organized as follows. Section II discusses the previous works in the literature. Section III describes the combinational approach of reducing noise (CARN). Finally, the results and discussion are given in Section IV. II. RELATED WORKS There are various methods of removing noise in ultrasound scanned images. T.Ratha Jeyalakshmi and K.Ramar [8] have introduced a technique for removing noise by designing a structural element. In this method an arbitrary structuring element that resembles the shape of the speckle is used since a speckle does not have a regular shape [10]. For this random speckle samples are taken from different ultrasound images and by thresholding, three structuring elements were designed. For thresholding the image, histogram of the image is used in MIC(Morphological Image Cleaning). In Modified Morphological Image Cleaning (MMIC) [8] instead of histogram, the standard deviation of the pixels in the image is used and from this the threshold is found. The assessment parameters such as Noise Standard Deviation (NSD), Mean Square Error (MSE),
  • 4. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 5, Issue 3, March (2014), pp. 138-147 © IAEME 141 Equivalent Numbers of Looks (ENL), Peak Signal to Noise Ratio (PSNR) and Execution time are used to assess the algorithm. Alka Vishwa et al [9] has classified the whole speckle reduction into two categories i) Incoherent processing techniques ii) Image post processing .The first one recovers the image by summing more than a few observations of the same object which assume that no change or motion of the object happened during the reception of observations. Using the parameter α, the local variance to mean ratio, it is possible to decide whether the processed pixel is within the homogenous region or not. Usually, if the local variance to the mean ratio is larger than the speckle, that corresponding pixel is considered as a resolvable object. Otherwise it is considered to be in homogenous region and is to be subjected to smoothening. Wavelet denoising attempts to remove the noise present in the signal while preserving the signal characteristics, regardless of its frequency content. As the discrete wavelet transform (DWT) corresponds to basis decomposition, it provides a non redundant and unique representation of the signal. One widespread method exploited for speckle reduction is wavelet thresholding procedure which is as follows 1) Calculate the DWT of the image 2) Threshold the wavelet coefficients. (Threshold may be universal or sub band adaptive) 3) Compute the IDWT to get the denoised estimate.Another method suggested by Mohammad Motiur Rahman et al [11] is based on Extra-Energy Reduction (EER) function in the vector field, which is addressed by vector triangular formula . It is reasonable to decide that the calculated energy of a pixel by vector triangular formula is less than the usual absolute distance energy of the same pixel, then the pixel must be noisy. The noisy pixel with higher energy is moved to equilibrium situation by subtracting the extra energy for any type of functional operation (e.g. image segmentation, pattern recognition, objects classification). The implemented method reduces extra- energy from an image and provides a proper gray level distribution into the entire image. Unsharp masking (UM) is one of the popular and high performance technique aimed at improving US image quality both in terms of edge enhancement and contrast enhancement. Many researchers have worked on this method and lot of modifications is proposed on the traditional approach. The main concept of this method is that visual appearance of the image f(x,y) is significantly improved by giving attention on high frequency contents of the image F(x,y). The transitional position between two populations is the edge, which is the high frequency component of the image. The two sets of populations are modeled by Gaussian distribution with means M1 and M2 for lower and higher magnitude population. According to the new interpretation discussed in this method, the edge is enhanced by shifting the mean of two pixel population away from each other. This concept has worked on a probabilistic setting. The bilateral filtering is used for denoising in the flat areas. This method exploits the properties of expectation maximization (EM) in classification, which is the statistical extrapolation of the hidden variables within the model. There are two cases of λEM settings ie λEM ≥ 1 and λEM ≤ 1. The higher magnitude populations fµ2(x,y) is multiplied by λEM and added to the lower magnitude populations fµ1(x,y), the resultant image y(x,y) depend on cases of λEM. In case of λEM ≥ 1, the edge and contrast of the resultant image y(x,y) is enhanced. This is because of the fact that the mean of the higher magnitude populations is increased and shifted far away from the lower one. Whereas, when λEM ≤ 1 the mean of the higher magnitude populations are scaled down and moves down towards the lower one. T.Wiliaprasitporn, et al [12] has smoothened and reduced speckle noise of the image y(x,y) using the above method. Another approach of adaptive homomorphic filtering introduced by J. Nikhil Dhinagar and Mehmet Celenk [13] improves the contrast of the US images by speckle reduction. This technique is based on the principle of reducing the dynamic range and thereby increasing the local contrast. F(x,y)=i(x,y)*r(x,y) where i is the illumination component which is responsible for the dynamic range and r is the reflective component which is responsible for the local contrast. Log function can be used to separate illumination i(x,y) from r(x,y) ie log (f(x,y)) = log ( i(x,y)) + log ( r(x,y)). A low pass filter is applied on log ( I(x,y)) and high pass filtering is applied on log ( r(x,y)). The low pass
  • 5. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 5, Issue 3, March (2014), pp. 138-147 © IAEME 142 filter is used to suppress the noise and high frequency signals from the input image whereas the high pass filter is used to emphasize the back ground noise of the image [14]. III. COMBINATIONAL APPROACH OF REMOVING NOISE (CARN) In this paper we have worked on a combined approach of median filter, Gaussian and Wiener filter. The idea of using these was to identify the position of possible renal calculi by filtering out all unwanted noise. Median filter can help in reducing speckle noise as well as salt and pepper noise [15-18]. Median filter is considered as an excellent rejecter of certain common types of noise like random superimposed variations and “shot” or impulse noise in which individual pixels are corrupted or missing from the image. If a pixel contains an extreme value, it is replaced by a “reasonable” value ie the median value in the neighbourhood. It is able to reduce the noise as well as some bad pixels with minimal distortion or degradation of the image. There are two principal advantages to the median filter as compared to multiplication by weights. First, the method does not reduce or blur the brightness difference across steps, because the values available are only those present in the neighbourhood region, not an average between those values. Second, median filtering does not shift boundaries as averaging may, depending on the relative magnitude of values present in the neighbourhood and the local curvature of the boundary. Overcoming these problems makes the median filter preferred both for visual examination and subsequent measurement of images (Huang, 1979; Yang & Huang, 1981; Weiss, 2006). Because of the minimal degradation to edges from median filtering, it is possible to apply the method repeatedly. The images used for this study, which has complex background, low contrast and deteriorate edges were enhanced using histogram equalization to make it suitable for objective analysis [19]. In CANR the image is first filtered using a 5x5 median filter. The same image is filtered using a 21x21 median filter as well. The difference of these filtered images were taken which gave a clear view of the probable stones (Figure 2 of Section IV), but there were still some noise interference in the images. Gaussian filter is applied to this median filtered difference image to remove Gaussian noise and it has been observed that the US image of the Kidney is not very clear for the objective analysis (Figure 3 of Section IV). The 2D Gaussian filter is given by G(x,y)= ଵ √ଶπσ eିሺ୶ మ ା୷మሻ/ଶσమ where x and y are the image coordinates and σ is the standard deviation. Larger values of σ result in greater smoothing but larger kernels are required for this. This results in an increase in the number of pixel accesses, multiplications and additions which in turn increases the time to perform operation on an image. Hence in this approach the value of σ is taken as 0.5. Gaussian can be convolved with arrays of other weights for other purposes like laplacian or sharpening. To remove speckle noise, Wiener filter ( Least Mean Square ) is applied on the same Median filtered difference image .It is assumed that an improved quality image can be obtained by using wiener Filtering technique which we have verified using our combinational approach. It smoothens the sudden peaks by replacing the value with an average value. Wiener filter tries to minimize the differences between the original image and the restored image. The closeness of these two images can be measured by adding the squares of the differences. ∑ሺfሺi, jሻ െ Fሺi, jሻଶ Where all pixels of the images are considered. We have got a clear view of the US images with the positions of renal calculi using wiener on Median approach (Figure 4 in Section IV). Wiener filter performs smoothing of the image based on the computation of local image variance. Wiener filter inverts the blurring and removes the additive noise simultaneously by performing an optimal trade-off between inverse filtering and noise smoothing [20]. When the local variance of the image is large, the smoothing is little. On the other
  • 6. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 5, Issue 3, March (2014), pp. 138-147 © IAEME 143 hand, if the variance is small, the smoothing will be better. This approach often produces better quality results than linear filtering, since the Wiener filtering is adaptive, more selective than a comparable linear filter. It preserves edges and other high-frequency information of the image, but requires more computation time than linear filtering [21]. Wiener filter can improve the image qualities well and can be applied in many situations [22]. Steps followed in CARN are as follows: 1. US Image is read and cropped. 2. A 5x5 mask is applied on this image and 21x21 mask is also applied on the same cropped image. 3. The difference of these images is found. The possible stones are displayed clearly. 4. Gaussian filter is applied on this difference of median filtered image to smoothen the region. 5. To remove speckle noise Wiener filter is applied on the difference of Median filtered image. 6. This Wiener filtered image is thresholded to extract the possible calculus. A comparison is made between the two images which we have obtained using Gaussian on Median and Wiener on Median. Wiener on Median was found to be good for further objective analysis (discussed in Section IV). With the help of Median filter the edges have become clearer and using Wiener filter the speckle noise is removed and the image is smoothened. IV. RESULTS AND DISCUSSION This section discusses the measures normally used to evaluate image qualities. The quality of an image can be quantified for subjective analysis by some computable measures. The effectiveness of the above discussed approaches are compared on the basis of statistical quality measures like: Signal to Noise Ratio (SNR), Peak to Signal noise (PSNR), Root Mean square Error (RMSE) and the Mean Structure Similarity Index (MSSIM). Table l. Statistical Measurement Equations Statistical Quality Measures Formula MSE ∑൫fሺi, jሻ െ Fሺi, jሻ൯ MN ଶ RMSE ඨ∑൫fሺi, jሻ െ Fሺi, jሻ൯ MN ଶ SNR 10log10 σమ σ౛ మ PSNR 20log10 ଶହହ ୖ୑ୗ୉ In the formula above, f(i,j) is the original image with noise and F(i,j) is the image after noise suppression. σଶ is variance of original image and σୣ ଶ is variance of image after noise suppression. A Comparison is done by measuring the quality of the resultant images ie Gaussian on difference image (Figure 3) and Wiener on Median difference image (Figure 4). Experimental results demonstrate that Wiener filtering on Median difference clearly outperforms the other denoising approaches for all noises levels which can be observed in Figure 4. The probable stones (Bright white areas of Figure 4) are clearly visible using our CANR using Wiener on Median difference method.
  • 7. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 ISSN 0976 - 6375(Online), Volume 5, Issue Figure 1: Original Cropped Image Figure 3: Gaussian on Median Filtered Difference Image Figure 1- is the original Image which is noisy applying median filter. Figure 3- is the result of Gaussian on this median filtered image. Gaussian noise is removed by this approach. filtered difference image. Figure 1 is the noisy cropped image artifacts like Comet tail, ringing and noises like mask of 5x5 and 21x21 is applied on taken which is shown in Figure 2. The edges of the Probable stone regions are sharpened using this approach. To this Median filtered difference image, Gaussian filter is applied which is shown in Figure 3. Weiner Filter is also applied to the same is shown in Figure 4. It is been observed that The possible stones are looking brighter in the image. of the calculus can be detected. We Filter and b) Wiener over Median Filter MSE, RMSE is low and the values of SNR and better [22]. Higher PSNR values show better image quality. For identical images, the MSE become zero and the PSNR is undefined .The MSSIM value should be closer to unity for optimal measure of similarity [20]. In our study it is found terms of the clarity for objective analysis and Table 2 demonstrate that RMSE is low International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 6375(Online), Volume 5, Issue 3, March (2014), pp. 138-147 © IAEME 144 Original Cropped Image Figure 2: Difference Image of a 5X5 and 21X21Mask Gaussian on Median Filtered Figure 4: Restored Image- Median Filtered Difference image is the original Image which is noisy Figure 2- is the difference is the result of Gaussian on this median filtered image. Gaussian Figure 4- is the restored image after applying W cropped image of an ultrasound kidney image which contains artifacts like Comet tail, ringing and noises like Gaussian as well as speckle. Median is applied on this noisy image. Difference of these two filtered images is n which is shown in Figure 2. The edges of the Probable stone regions are sharpened using this approach. To this Median filtered difference image, Gaussian filter is applied which is shown in Figure 3. Weiner Filter is also applied to the same difference image (Figure 2) and the restored image It is been observed that Figure 4 is giving a clear view of the possible possible stones are looking brighter in the image. By thresholding this image, the detected. We have also compared two methods of a) Gaussian Filter using the various statistical quality measures. is low and the values of SNR and PSNR are larger, then the enhancement approach is Higher PSNR values show better image quality. For identical images, the MSE become zero and the PSNR is undefined .The MSSIM value should be closer to unity for optimal measure of it is found that Wiener on Median filtered has yielded good results in terms of the clarity for objective analysis which is demonstrated using Table 2 and T that RMSE is low (Figure 6) and SNR (Figure 5 ), PSNR International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), © IAEME Difference Image of a 5X5 and 21X21Mask -Wiener on Median Filtered Difference image is the difference image after is the result of Gaussian on this median filtered image. Gaussian restored image after applying Wiener Median of an ultrasound kidney image which contains various . Median filter with a Difference of these two filtered images is n which is shown in Figure 2. The edges of the Probable stone regions are sharpened using this approach. To this Median filtered difference image, Gaussian filter is applied which is shown in ) and the restored image a clear view of the possible stones. By thresholding this image, the exact position have also compared two methods of a) Gaussian over Median using the various statistical quality measures. If the value of the enhancement approach is Higher PSNR values show better image quality. For identical images, the MSE become zero and the PSNR is undefined .The MSSIM value should be closer to unity for optimal measure of Median filtered has yielded good results in and Table 3. Table 1 , PSNR (Figure 7 ) and
  • 8. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 5, Issue 3, March (2014), pp. 138-147 © IAEME 145 MSSIM (Figure 8 ) is larger for Wiener Filtered Median Difference image which is considered as ideal for a good image. Table - 2 and Table - 3 shows results of our experiments on the approaches followed. Table -2: Gaussian on Median filtered difference Image Method Sample Images RMSE SNR PSNR MSSIM Gaussian on Median US-Image1 146.0119 0.0208 4.8770 0.456 US-Image 2 141.0248 0.0938 5.1789 0.367 US-Image3 149.4653 0.4023 4.6740 0.0752 US-Image4 156.6119 0.1055 4.2683 0.0013 Table -3: Wiener on Median Filtered difference image Figure-5. SNR for Gaussian and Wiener Figure-6. RMSE for Gaussian and Wiener Figure-7: PSNR for Gaussian and Wiener Figure-8: MSSIM for Gaussian and Wiener From Figure (5, 6, 7, 8) it is clearly evident that Wiener on Median is an efficient method of noise removal and smoothing and it outperforms the Gaussian-Median approach and simple Median approach. Method Sample Images RMSE SNR PSNR MSSIM Wiener on Median US-Image1 27.3771 4.8095 19.4171 0.845 US-Image 2 31.1947 8.2514 18.2832 0.864 US-Image3 17.7929 3.8995 23.1599 0.626 US-Image4 21.4143 7.2655 21.5507 0.2962 0 2 4 6 8 10 0 2 4 6 Gaussian Wiener 0 50 100 150 200 0 2 4 6 Gaussian Wiener 0 5 10 15 20 25 0 2 4 6 Gaussian Wiener 0 0.2 0.4 0.6 0.8 1 0 2 4 6 Gaussian Wiener
  • 9. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 5, Issue 3, March (2014), pp. 138-147 © IAEME 146 V. CONCLUSION In Medical field, Sonologists are faced with the problem of getting a clear view of the images from US images. It is a difficult task for suggesting an efficient technique for noise removal and artifact removal while preserving the details of the images for further analysis. The results presented in Section IV using CANR using Wiener on Median Filter proved to be an efficient technique for noise removal and smoothing of ultra sound images. To some extend the artifacts can also be removed using an efficient thresholding technique. ACKNOWLEDGMENTS The authors would like to express their gratitude to Pace Ultra Sound Centre, Bangalore, India for providing the images used for the study. VI. REFERENCES [1] T.Rahman (2013), Speckle Noise Reduction and Segmentation of Kidney Regions from Ultrasound Image, Informatics, Electronics & Vision (ICIEV), 1 – 5. [2] Sumarsono, THE BASIC PRINCIPLES OF ULTRASONOGRAPHY (USG), Chapter- 14 Image Artifacts. [3] Vincent Chan, Anahi Perlas, Basics of Ultrasound Imaging. [4] Ultrasound (Sonography), http://www.radiologyinfo.org. [5] S. Kumar, P. Kumar, M. Gupta and A. K. Nagawat (2010), Performance Comparison of Median and Wiener Filter in Image De-noising, International Journal of Computer Applications, Vol.12, No.4,.27–31. [6] P. Hsiao, S. Chou, and F. Huang (2007), Generic 2-D Gaussian Smoothing Filter for Noisy Image Processing, TENCON IEEE region 10 Conference, 1-4. [7] R. Fisher, S.Perkins (2003), A. Walker, E. Wolfart. Gaussian Smoothing, Hypermedia image Processing Reference (HIPR2). [8] T.Ratha Jeyalakshmi and K.Ramar (2010), A Modified Method for Speckle Noise Removal in Ultrasound Medical Images, International Journal of Computer and Electrical Engineering, Vol. 2, No. 1. [9] Alka Vishwa and Shilpa Sharma (2012), Modified Method for Denoising the Ultrasound Images by Wavelet Thresholding, I.J. Intelligent Systems and Applications, PP.25-30. [10] Gjenna Stippel, Nilifred Philips Ignace Lemahieu (2002), A new denoising technique for ultrasound images using morphological properties of speckle combined with tissue classifying parameters, SPIE, Medical Imaging Conference Proceedings, No. 4687, 324-333. [11] Mohammad Motiur Rahman, P.K. Mithun Kumar, B.Borucki, K.S. Nowinski (2013), Speckle noise reduction of Ultrasound images Using Extra-Energy Reduction function, IEEE conference on Informatics, Electronics & Vision (ICIEV). [12] T.Wiliaprasitporn, C.Chinrungrueng, W. Asdornwised (2013), Ultrasound B-scans Image Denoising via Expectation Maximization-based Unsharp Masking, Electrical Engineering/ Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), 1 – 6. [13] J. Nikhil Dhinagar, Mehmet Celenk (2012), Ultrasound Medical Image Enhancement and Segmentation Using Adaptive Homomorphic Filtering and Histogram Thresholding ,IEEE EMBS International Conference on Biomedical Engineering and Sciences, 349-353.
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