1 Introduction. 2 A review on the studied data. 2.1 Ultrasound

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1 Evaluation of Noise Reduction Techniques in two-dimensional Echocardiography Images in the Left Ventricular by Image Processing Algorithms Using Matlab Software A. Elnaz golchin 1, B. Saeed darvishi 2 1 Department of Biomedical Engineering, Islamic Azad University- Science and research branch, Tehran, Iran 2 Department of Biomedical Engineering, Islamic Azad University- Science and research branch, Tehran, Iran Abstract - Echocardiography images are usually corrupted by speckle noise. This noise reduces the image contrast and blurs the explanation of important spots in medical diagnosis. Since the speckle noise is destructive, the denoising procedure on the image, in comparison to other noises, is more difficult. It seems that the old techniques of noise suppression are not appropriate for removing the speckle noise. This paper presents the comparison of old techniques of improving the two-dimensional cardiograph images quality in the left ventricular by Median, Adaptive wiener and Kaun filters to the latest methods of denoising based on the wavelet transformation and wavelet packets. Then the resulted data are compared with the results of other noise suppression techniques. In the filters comparison phase the PSNR factor has been used and the level of PSNR increase between the noisy image and the filtered image signifies the success rate of filtering.. Keywords: Image Processing, Echocardiography, Speckle Noise, Filters. 1 Introduction Medical images are usually damaged by noise in their acquisition and transmission. The main objective of image denoising techniques is to remove such noises as much as possible while retaining important signal features and increase the physician's care and it is significant in diagnosing diseases. Ultrasonic images, a sample of which is echocardiography, are very important in medicine, and this importance is due to its being economical, transferable, and uses the unionization rays [1]. Speckle filtering is a central pre-processing step for the feature extraction, analysis, and recognition of heart problems. Based on the noise type which is multiplicative (speckle noise) or mass (Gaussian noise), in retrieval procedure, all the efforts is done to reconstruct and restore the image which is degraded under the effect of this knowledge. So the restoration techniques move toward the degradation modeling and applying the reverse processes to restore the original image. The probable model produced for the speckle noise is as follows [5,6]: gnm (, ) = f( nmunm, ) (, ) + ζ ( nm, ) (1) g,f are respectively original and observed image. u shows the multiplicative and ζ shows the mass part of the speckle noise. m,n implicate the vertical and horizontal brandies of sample image.when we are dealing with echocardiograph image, we just pay attention to multiplicative image, like the following relation: gnm (, ) = f( nmunm, ) (, ) (2) Recently wavelet transform is used as a tool for image processing to reduce speckle noise. Speckle noise is a highfrequency component of the image and appears in wavelet coefficients. this paper the wavelet Universal threshold technique (Visushrink) has been used for this purpose.[1] 2 A review on the studied data 2.1 Ultrasound Human ears hear sound waves with frequencies between 20 Hz and 20 khz. Higher frequencies are called ultrasound and

2 in diagnostic ultrasonography which is used in medicine, waves with frequencies that vary between 1 MHz and 20 MHz are used. Echocardiography images are ultrasound images from the heart. 2.2 Transducer and piezoelectric crystal Ultrasound transducers use a piezoelectric crystal to produce and receive ultrasound waves (Figure 1). When electric current enters, the crystal moves and produces an ultrasound wave. The frequency that is released by a transducer depends on the nature and the width of the piezoelectric material. Whenever an ultrasound wave reaches a piezoelectric crystal, an electric current is produced; therefore the crystal is used as a receiver and a transmitter Apical view : The transduceris palced on heart Apex. Apical view has 2 plans, 2 chamber view and 4chamber view.(figure4,5). Figure4: 4chamber view Figure1:Transducer 2.3 Some of The standard views of the transducer s location Parastenal view : Parasternal Long Axis View is often the first view during echocardiography. It is also the most common view used to guide the M- Mode cursor while taking measurements of the left ventricle, aorta and left atrium. The view also gives a good visualistion of mitral and aortic valves.[9] parasternal have 2 palans, short axis plan and long axis plan figure2,3 : Figure2: parasternal long axis plan Figure5: 2chamber view The data studied in this project have been collected according to the Apical method and are from a two dimensional view 3 A Survey on Wavelet Transform Wavelet analysis does analysis of image low frequency which cause the production of subpass A (approximation) by the use of low-pass filter and analysis of image high frequency which cause the production of subband D (decomposition) using high-pass filter (figure6). Low frequency content shows the signal identity and is significant in this regard, while, the significance of the image highfrequency content is because of showing details differences. Passing through high-pass and low-pass filters, in the second step A, D coefficients produces two other bands, which finally result in four bands: HH, HL, LH, LL. LL includes image identity with two low-pass frequency filters, three other bands each with details coefficient of horizontal (H), vertical (V) and other direction (D) in which both filters are high-pass frequency. In this step, we have analyzed the image using DWT process and obtained wavelet coefficients. In order to remove noise, these coefficients have been changed using universal threshold technique, then the coefficients which were lower than threshold were removed, finally the image has been recycled from these coefficients using IDWT wavelet reconstruction process (figure 7). Figure3: parasternal short axis plan.

3 3.2 Wavelet & Wavelet Packets In each level, in order to change wavelet, just estimation coefficients are analyzed, so that at the end, we would have just one estimation coefficient and details coefficients of each step (figure 10). However, in the analysis of wavelet packets details coefficients are also analyzed in the same way as approximation coefficients (figure 11). Figure6: production of A and D. Figure10 :Wavelet analysis.. Figure7:Decomposition and Reconstruction. 3.1 The advantage of Wavelet over Short time Fourier Transform(STFT) Wavelet analysis is one stage newer than short time Fourier transforms. Wavelet analysis provides the possibility of using long windows in low frequencies and short windows in high frequencies. foloowing figures show this advantage : (Figure8,9) Figure11:Wavelet packet s analysis. Figure8: Short-time Fourier Transform(STFT) 3.3 Universal Threshold Procedures In universal threshold technique, noise variance is remarkable, and it should be estimated from image. For this purpose, after implementing two-dimensional discrete wavelet (DWT) and computing median wavelet coefficients, these coefficients are computed, then using the following relation, we estimate noise variance (σ). median σ = (3) Figure9:Wavelet Transform Appropriate threshold for wavelet coefficient has been obtained from the following relation: λ = σ 2log( n) (4)

4 n shows the quantity of wavelet coefficient. 4 A review on other filters used for speckle noise reduction 4.1 Median filter classifies central pixel and surrounding pixels by determining noise variance. It substitutes pixels mean in the same window for central pixel. It is the simplest technique for removing pixel noise, but it can destroy a quite a few data. 4.2 Wiener Adaptive filter is a type of linear filter which adapt itself with image local variance, and since small variance do smoothing better, it gives better results as compared with other linear filters. In order to retain edges and other parts, image high frequency is used. One of the disadvantages of Wiener filter is its slowness in computation in comparison with other filters. [2] 5.2 We added speckle noise to image noise, performed denoising using different filters and recorded the result in the following table. Table1: Comparison of PSNRs of different filters for echocardiography image corrupted by speckle noise Filter type Median Adaptive wiener Kaun Wavelet PSNR noisy image PSNR denoisy image Kaun filter Waveletpacket This filter is the pivot of linear speckle noise model. Minimum mean square error (MMSE) has been designed for obtaining it. This filter identifies area with fixed or very low variance, and introduces them as areas for which denoising process should be taken. When signal is active, it passes signal filter with no change. The case has been introduced by the following equation : [3] R= I W + I ( L W ) () t () t () t () t (5) and their weight coefficients is obtained from relation 6: Cu / C Wt () = L (6) 2 1+ Cu 5 results and comparison with PSNR standard 5.1 PSNR (Peak signal to noise ratio) Figure12:Noisy image This parameter is used to measure the difference between two images. Its unit is decibel PSNR = 10 log MSE (7) 10 MSE 1 M N = ( X ( i, j ) Y ( i, j )) (8) i= 1 j= 1 MN Figure13: Result of Median filter

5 Figure14: Result of Adaptive Wiener filter Figure15:Result of Kaun filter 6 Conclusions As observed in table 1 the results obtained from median and Wiener filters shows the increase of PSNR. These filters perform successfully based on this standard; however as we lose image details and so many other useful image data which help the physician s diagnosis when using these filters, the use of these filters for speckle denoising is not suggested. As observed in table 1 Kaun filter reduces PSNR from 21,2591 to 17,3374 and so it does not perform well for speckle denoising in this regard Wavelet and Waveletpackets perform well in view of PSNR standard. As observed in table 1, We see PSNR increase from 19,535 to 20,4746. The increase of PSNR when using Wavelet packet is more remarkable and it could be mentioned as the best technique, because it has increased PSNR by about 13.2 units. As you see filtered image with wavelet packets in figure 17, the image details have been mostly retained and image opaque is very less than ordinary wavelet. Finally, in this research wavelet packets are introduced as the appropriate filters and more reliable as compared with older ones. as the results of the table show, mass noise elimination is easier than multiplicative noise elimination and has less error. A proposal for future work is to implement the log function on the database and therefore transform the multiplicative noise into mass noise as much as possible, and then use as the new database. Figure16: Result of Wavelet filter 7 References [1] S.Sudha 1, GR Suresh 1, R Sukanesh 2, Noise Reduction in Ultrasound Images Using Context-based Adaptive Wavelet Thresholding, date of publicastion5aug2009 [2] Amandeep Kaur, Karamjeet, SPECKLE NOISE REDUCTION BY USING WAVELETS, Singh Punjabi University, Patiala, NCCI National Conference on Computational Instrumentation CSIO Chandigarh, india, March Figure17: Result of Wavelet packet filter [3] Edmund Hui-On Ng, Speckle Noise Reduction via Homomorphic Elliptical Threshold Rotations in the Complex WaveletDomain, University of Waterloo, inful_lment of the

6 thesis requirement for the degree of,master of Applied Science in Electrical and Computer EngineeringWaterloo, Ontario, Canada,2005,c Emund Ng [4] Meritz Alfred, Signal Analysis(Wavelet and filter bank),tehran [5] Rafael C. González, Richard Eugene Woods Digital image processing,pearson/prentice Hall (2008). [6] Rafael C. Gonzalez, Richard E. Woods, Steven L, Digital image processing using MATLAB,2009. [7] Luc Kluine, Benoit Vbzel, Kuceni Chehdi, Adaptive filtering of multiplicative noise by a new by a new differential method,2005. [8] Nadia SOUAG,Images Processing Laboratory, Faculty of Electronics and Computing speckle reduction in echocardiographic images,september4-8,2006. [9]