Milindkumar V. Sarode
Department of Computer Science and Engineering Jawaharlal Darda Institute of Engineering & Technology, Yavatmal (M.S.) India. Email: [email protected]
Prashant R. Deshmukh
Department of Computer Science and Engineering & IT, SIPNA’s College of Engineering, Amravati(M.S) India. Email: [email protected]
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Reducing noise from the medical images, a satellite image etc. is a challenge for the researchers in digital image processing. Several approaches are there for noise reduction. Generally speckle noise is commonly found in synthetic aperture radar images, satellite images and medical images. This paper proposes filtering techniques for the removal of speckle noise from the digital images. Quantitative measures are done by using signal to noise ration and noise level is measured by the standard deviation.
Speckle noise, SNR, SAR, Sampling Enhancement, Ultrasound images
Medical images, Satellite images are usually degraded by noise during image acquisition and transmission process. The main purpose of the noise reduction technique is to remove speckle noise by retaining the important feature of the images. This section offers some idea about various noise reduction techniques. Synthetic Aperture Radar (SAR) imagery uses microwave radiation so that it can illuminate the earth surface. Synthetic Aperture Radar provides its own illumination. It is not affected by cloud cover or radiation in solar illumination. ISUKF technique , which uses sampling to incorporate the Discontinuity-adaptive Markov random field for the reduction of speckle noise. Context-based adaptive wavelet thresholding  method introduced a simple context-based method for the selection of adaptive threshold. Coherent filtering , is a speckle noise reduction technique of the ultrasound images. This technique is based on Coherent Anisotropic Diffusion for real time adaptive ultrasound Speckle noise reduction.
In our work, we introduced a novel method which reduces speckle noise in ultrasound images and SAR images, retaining the original content of these images. This method enhances the Signal to Noise ratio and perceives the original features of the images. The paper is organized as follows.
Section II presents the model of speckle noise and noise in ultrasound images as well as noise in SAR images. Novel method is described in section III. Section IV presents some experimental results in both graphical and tabular forms. Section V describes conclusion.
Model of Speckle Noise
An inherent characteristic of ultrasound imaging is the presence of speckle noise. Speckle noise is a random and deterministic in an image. Speckle has negative impact on ultrasound imaging, Radical reduction in contrast resolution may be responsible for the poor effective resolution of ultrasound as compared to MRI. In case of medical literatures, speckle noise is also known as texture. Generalized model of the speckle  is represented as,
Where, g(n,m) is the observed image, u(n,m) is the multiplicative component and (n,m) is the additive component of the speckle noise. Here n and m denotes the axial and lateral indices of the image samples.
For the ultrasound imaging, only multiplicative component of the noise is to be considered and additive component of the noise is to be ignored. Hence, equation (1) can be modified as;
Noise in Ultrasound Images
Ultrasound imaging system is widely used diagnostic tool for modern medicine. It is used to do the visualization of muscles, internal organs of the human body, size and structure and injuries. Obstetric sonography is used during pregnancy. In an ultrasound imaging speckle noise shows its presence while doing the visualization process.
Medical Ultrasound Speckle Pattern 
Nature of Speckle pattern depends on the number of scatters per resolution cell or scatter number density. Spatial distribution and the characteristics of the imaging system can be divided into three classes:
a) The fully formed speckle pattern occurs when many random distributed scattering exists within the resolution cell of the imaging system. Blood cells are the example of this class.
b) The second class of tissue scatters is no randomly distributed with long-range order . Example of this type is lobules in liver parenchyma.
c) The third class occurs when a spatially invariant coherent structure is present within the random scatter region like organ surfaces and blood vessels .
Noise in Synthetic Apertures Radar (SAR) Images
Synthetic Apertures Radar (SAR) technique is popular because of its usability under various weather conditions, its ability to penetrate through clouds and soil . A SAR image is a mean intensity estimate of the radar reflectivity of the region which is being imaged. Speckle noise in such system is to be referred as the difference between a measurement and the true mean value. Degraded image with speckle noise in ultrasound imaging is given by the equation;
Where, d(X,Y) is the degraded ultrasound image with speckle, I (X,Y) is the original image and S(X,Y) is the speckle noise. Where (X,Y) denotes the pixel location. The multiplicative nature of speckle complicates the noise reduction process .
Noise Reduction in SAR Images
Speckle noise reduction in SAR images has been done using described algorithm below. An algorithm which use sampling to introduced the Discontinuity Adaptive Prior and Moment Estimation  within the ISUKF framework for speckle noise reduction. The stepwise algorithm is as given below:
1) Do the modeling of the original image f (m,n)with probability density function
2) Do the estimation of mean µ and variance σ2 using the mathematical model.
Where, is the estimation of non-Gaussian probability Density Function, q(z) is the sampler PDF that includes non-zero support of target PDF.
Infinity (∞) is the samples drawn from the sampler PDF “ q ” which concentrate on the points where p ≥ q . When q ≤p , we can use samples from q to determine estimation under p
3) Incorporate the observed noise model as;
4) Calculate sigma points as;
5) Apply the measurement model on each and every sigma points.
Noise Reduction in Ultra Sound Images
Steps for the speckle noise reduction in ultra sound images are carried out as below.
a. Construct Multiplicative noise model
b.Do the transformation of Multiplicative noise model
c. Do Wavelet transform of noisy image
d.Calculate variance of noise
e. Calculate weighted variance of signal
f. Calculate threshold value λ of all pixels and sub band coefficients
g.Take inverse DWT to do the despeckling of Ultrasound images.
h.Calculate PSNR (peak signal to noise ratio) for the evaluation of the algorithm
Design procedure of the above implementation steps is shown in Fig.1 in the form of flow diagram.
The performance of the method that has been proposed is investigated with various simulations. Denoising is carried out for ultrasound image with Speckle noise of variance σ2 = 0.03, 0.04, 0.05, using standard speckle filters and introduced filter. For objective evaluation, the signal to noise ratio (SNR) of each denoised image has been calculated using Signal to Noise Ratio (SNR), which is defined as
PSNR = 10log10(2552 / MSE )
MSE=(1/MN)ΣΣ (X(i, j)-Y(i, j))2
Where X(i,j) and Y(i, j) represent the original and denoised image respectively. The performance of the various denoising methods is compared in Table 1 and we have presented a comparative study of various wavelet filters and standard speckle filters for Ultrasound image in terms of PSNR (see fig. 2 to Fig. 9). The performance of Speckle filters such as Kaun filter, Frost filter, the conventional approach in speckle filtering the homomorphic Wiener filter are measured here. We apply Matlab‟s spatially adaptive Wiener filter. We have done all the simulations in MATLAB tool.
We introduced a Speckle noise reduction model for Ultrasound Sound images as well as Synthetic Aperture Radar (SAR) imagery. Both models preserve the appearances of structured regions. In case of Ultrasound Images, Texture and organ surfaces have been enhanced. The performance of the algorithm has been tested using visual performance measures. Many of the methods are failure to remove speckle noise present in the Ultrasound images, since the information about the variance of the noise may not be able to identify by the methods. Introduced model automatically collect the information about the noise variance. Performance of the Speckle noise reduction model for Synthetic Aperture Radar (SAR) imagery is well as compared to other filters. Histogram results shows very closed equivalency in between SAR original images and SAR denoised i.e. enhanced images.
 G.R.K.S. Subrahmanyam, A. N. Rajagopalan and R. Aravind, “A Recursive Filter for Despeckling SAR Images”, IEEE Transaction on Image Processing, Vol. 17, No. 10, pp. 1969-1974, October 2008.
 K. Thangavel, R. Manavalan, Laurence Aroquiaraj, “Removal of Speckle Noise from Ultrasound Medical Image based on Special Filters: Comarative study”, ICGST-GVIP Journal, Vol. 9, Issue:3, pp. 25-32, June 2009.
 Khaled Z. AbdElmoniem, Yasser M. Kadah and AbouBakr M. Youssef, “Real Time Adaptive Ultrasound Speckle Reduction and Coherence Enhancement”, 078032977/00/$10© 2000 IEEE, pp. 172-175.
 H. Xie, L. E. Pierce and F.T. Ulaby, “ SAR Speckle reduction using wavelet denoising and Markov Random field modeling”, IEEE Trans. Geosci. Remote Sens., vol. 40, No. 10, pp. 2196-2212, Oct. 2002.
 M. Dai, C. Peng, A. K. Chan and D. Loguinov, “ Bayesian wavelet shrinkage with edge detection for SAR image despeckling”, IEEE Trans. Geosci. Remote Sens., vol. 42, No. 8, pp. 1642-1648, Aug. 2004.
 F. Argenti, T. Bianchi and L. Alparone, “ Multiresolution MAP despeckling pdf modeling”, IEEE Trans. Image Process., Vol. 15, No. 11, pp. 3385-3399, Nov. 2006.
 A. Achim, E.E. Kuruoglu and J. Zerubia, “ SAR image filtering based on the heavy – tailed Rayleigh model”, IEEE Trans. Image Process., Vol. 1, No. 9, pp. 2686-2693, Sept. 2006.
 R. F. Wagner, M. F. Insana, “ Analysis of Ultrasound Image Texture via Generalised Rician Statistic”, Proc. SPIE 556, pp. 153-159, 1985.
 M. J. Black, G. Sapiro, D.H. Marimont and D. Heeger, “ Robust anisotropic diffusion”, IEEE Trans. Imag. Proc., vol. 7, No. 3, pp. 412-432, March 1998.
 S. G. Chang, Y. Bin, and M. Vetterli, “ Adaptive wavelet thresholding for image denoising and compression”, IEEE Trans. On Image Processing, vol. 9, No. 9., pp. 1532-1546, Sept. 2006.