50120140503016

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
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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.

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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),

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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

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

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.

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
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
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
© 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
) 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
Median filtered has yielded good results in
and Table 3. Table 1
, PSNR (Figure 7 ) and

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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

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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.
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