Wavelet Transform Based Reduction of Speckle in Ultrasound Images

0974-0627 International Journal of Imaging, 4 (3), June 2011, pp. 92-99
92
Wavelet Transform Based Reduction of Speckle in Ultrasound Images
P.S.Jagadeesh Kumar1
, J.Ruby2
, J.Tisa3
, J.Lepika4
, J.Nedumaan 5
1
Associate Professor, Department of Compute Science, University of Cambridge, United Kingdom
2
Medical Professional, Department of Surgery, University of Cambridge, United Kingdom
3, 4, 5
Scholars, Malco Vidyalaya Matriculation Higher Secondary School, Mettur Dam, Tamil Nadu, India
ABSTRACT:
Ultrasonography is often more desirable than another medical imaging formations because it
is non-invasive, portable and utilizable. It does not use ionizing radiations and it is
comparatively low-cost. In time, the main disadvantage of medical ultrasonography is the
poor quality of images, which are mainly originated by multiplicative speckle noise. For
efficient speckle suppression of images of the kidney a comparison of some noise reduction
method is introduced in this paper. The main intention of this paper is to analyse and to
provide most significant content descriptive parameters to identify and classify the kidney
stones with ultrasound scan. The intentions of the present study were to compare quantitative
and qualitative ultrasound highly improved images by reducing speckle noise while improving
anatomical features, so that the images may be acceptable for the diagnosis. For efficient
image improvement we adopt a multi-resolution approach. For speckle reduction, many
algorithms and procedures are used. Some filtering technique enhances edges and speckle
indiscriminately, while others manages to remove considerable amounts of speckle but also
tends to over smooth the boundaries of important image features. For 2-dimensional B-mode
ultrasound images, we use an image enhancement algorithm based on filtering and noise
reduction procedures from the pinguid to fine resolution images that are obtained from the
wavelet-transformed data. In this paper, a comparative study with other de-speckling
techniques (median and Wiener filtering) is made employing quantitative indices and visual
evaluation that demonstrates that our method achieved superior speckle reduction
performance.
Keywords: Ultrasonography, Speckle noise, Filtering, Wavelet transform
1 INTRODUCTION
Most of the times medical images are deteriorated by noise because of various sources
of interferences and other phenomena that affects the measurement processes of images and
acquisition systems. Speckle noise is the appearance of uneven spots in the image with bright
as well as dark spots, which conceals fine details and reduces the detectability of low-contrast
lesions. The occurrence of speckle noise is actually undesirable as it affects the task of human
interpretation and diagnosis. Besides, its texture carries useful information about the imaged
tissue. Speckle filtering is, therefore, a critical preprocessing step in kidney ultrasound
imagery which provides the features of interest for diagnosis that are not lost. The small
dissimilarities that may be found between normal and abnormal tissues are mixed-up by noise
and artifacts, sometime making direct analysis of the gathered images a bit difficult. Basically,
image enhancement methods are mathematical techniques that are planned to make real
improvement in the quality of a given image. The result reflects on another image that shows
certain features in a manner which is better in some cases as compared to their presence in the
original image. However, ultrasonography is much more operator-dependent. Well-trained
and experienced radiologists are always required to read ultrasound images. Even well-trained

P.S.Jagadeesh Kumar et al. 93
experts can have a high inter-observer variation rate; therefore computer-aided enhancement
is required to look after the radiologists in diseases detection and classification. Many factors
can disturb the ultrasound image, such as: attenuation, distortions, refractions, special
speculative reflections, interferences, non-linear propagation etc. There also have another
issue of tissue movement with its non-homogeneous structure and also the movement of fluid
at the vessel level. The main objective of the present study is to compare quantitative and
qualitative highly improved ultrasound images by discarding speckle noise while improving
anatomical features, so that the images may be diagnosed more comfortably. For efficient
image enhancement we adapt a multi-resolution approach. For speckle reduction, Median
filtering [13], Wiener filtering [10] and Wavelet transform [6], [15] are used. Median filtering
enhances speckle and edges indiscriminately. On the other hand, Wiener filter manages to
discard considerable amounts of noise but most of the time it over smooth the boundaries of
vital image features. Throughout the last decennary a new approach in ultrasound image
despecling has been originated based on the wavelet transform. Some of the wavelet-based
proposed methods for ultrasound image denoising are the Bayesian wavelet method by
Achimet et al. [1] and the multiscale non-linear processing method by Hao et al. [9]. A
wavelet-based method is proposed in this paper for efficient speckle elimination in
ultrssonographic kidney images. Both log transform and exponential transform can be avoided
by this proposed wavelet approach, considering the fully developed speckle as additive signal-
dependent noise with zero mean. The proposed method also has the ability to mix the
information at different frequency bands and also accurately computes the local regularity of
the features of given images.
2 MATERIAL AND METHODS
Wavelets have been developed in applied mathematics for the analysis of the multiscale
image structures. Wavelet functions are more remarkable as compared to other
transformations like Fourier transform as they not only cut the signals section wise into their
fundamental frequencies but also alter the scale of the component frequencies that are being
studied. As a result, wavelets are exceptionally compatible for applications such as noise
reduction, singularity detection and data compression in signals. In order to alter the scale of
the function which addresses different frequencies and makes use of wavelets which are better
suited to signals which possess spikes or discontinuities as compared to traditional
transformations like Fourier transforms. Wavelets are applicable for medical image
enhancement that has been analyzed and recently applied. Speckle reduction techniques can
be segmented into three groups: (1) compounding approaches [2]; (2) filtering techniques
[12], [8]; and (3) wavelet domain techniques [11], [7]. Most filters use traditional techniques
in spatial domain. They can be grouped into linear (mean filter) and nonlinear filters.
The mean filter [3] operates by replacing each pixel value with the average values of
the intensities which are present in its neighborhood. It can locally minimize the variance and
its implementation is quite easy. This results in smoothing and blurring of the images and it
achieves an optimal additive Gaussian noise with respect to mean square error. Speckled
image possess a multiplicative model along with non-Gaussian noise. Hence, the simple mean
filter is of no use in this scenario. Order-statistic filters are well equipped for reducing noise
which has significant probability density function. Median filter [12], [3] is a specialized
order-statistic filter. They possess the edge sharpness as well as produce less blurring as
compared to mean filter. Generally, when the image is affected by impulsive noise, it is
effective. Most of the researchers have studied adaptive median filters which perform better
than the median filters [18], [5]. Adaptive weighted median filters were developed to obtain
maximum speckle reduction wherever the areas are uniform and also conserve the edges as
well as features [4]. However, an operator is used in this algorithm that can result difficulties

94
in improving image features such as line segments. In order to overcome these difficulties,
Czerwinski et. al [4] used several one-dimensional median filters which helped in retaining
the largest value at each point among all the outputs of the filter banks. The directional
median filter minimizes speckle noise retaining the structure of the image, especially, the thin
bright streaks.
The discrete wavelet transform (DWT) converts the image into an approximation sub-
band. It consist of scale coefficients along with a set of detail sub-bands which possess
different orientations as well as the resolution scales are comprised of wavelet coefficients
[16], [20]. DWT separates the noise from an image in an efficient manner. Wavelet transform
is good at energy compaction. The small coefficients of wavelet transform denote noise, and
large coefficients denote important image features. The coefficients that denote features tend
to conserve across the scales and produces spatially connected clusters within each sub-band.
All these properties results in making DWT attractive for denoising. With respect to structural
computation point of view, wavelet denoising consists of three stages: (1) computation of the
discrete wavelet transform; (2) removal of noise by changing the wavelet coefficients; and (3)
applying the inverse discrete wavelet transform (IDWT) to make the despeckled image. In this
study, the best filter solutions for ultrasound images of kidney were performed. Some of the
filters already existed in Matlab-7.1 software were also tested. In order to find the best filter,
the main criterion was the one which can optimize the signal to noise ratio in a broad
spectrum of spatial frequencies.
3 EXPERIMENTAL RESULTS
In this paper, several methods have been used for removing speckled noise. The very
first method is the classical Wiener filter method. This method is mainly designed for the
suppression of additive noise. To explain this issue, Jain et. al [10] had developed a
homomorphic approach, which was done by taking the logarithm of the images, which
converts the multiplicative into additive noise and also consequently applies the Wiener filter.
The adaptive weighted median filter, as discussed by Czerwinski et. al [4], efficiently reduced
speckle but it was unable to retain many useful details, as it is a simple low-pass filter.
Figure.1 reveals experimental data for a 5 MHz kidney image obtained from a convex probe.
The images are acquired from scanning systems named SLE-401 curvilinear probe with
transducer frequency of 5 MHz. Transducers are able to detect renal calculi of size 3 mm if
they are in the range of 6 - 10 MHz. Renal calculus is protected by the presence of highly
echogenic focus along with posterior acoustic shadowing of the stone. The main drawbacks of
ultrasound comprises of poor visualization of calcifications or blocking stones in the ureter as
well as insufficiency of assessment of renal function. In order to achieve speckled images, the
original test image quality is degraded by multiplying it with unit-mean random areas.
The correlation length of the speckle is controlled by properly adjusting the size of the
kernel. It is also used to insert correlation to the underlying Gaussian noise. Practically,
uncorrelatedness of the noise could be obtained by exterminating the image to the resolution
limit of the imaging device obtained theoretically. Hence, a short-term correlation is achieved
with a kernel of size three that was quite acceptable to model reality. We take into
consideration three separate levels of simulated speckle noise (fig. 2 a-c). The result obtained
from Wiener filtering as shown in Figure 3, speckle is minimized as well as structures are
improved. Meanwhile some data are lost and some get over-modified. Whereas, the result
obtained by Median filtering as shown in Figure 4, speckle gets reduced quite well, but the
structures get hazy and few of the artifacts are introduced.

Figure 1: Conventional ultrasound image depicts an ill-defined low echoic multiple kidney stones (renal
calculi). Renal ultrasound describes echogenic focus along with an associated acoustical shadow. These types
of small stones are easy to be hampered and failed to observe by artifacts and speckles.
To obtain the despeckling results of the algorithm we have used the following parameters
defined as:
MSE (Mean Square Error)
2
1 1
1
[ ( , ) ( , )]
M N
i j
MDE f i j f i j
M  

 
 (1)
PSNR (Peak Signal to Noise Ratio)
2
(2 1)
10log
n
MDE
MSE

 (2)
MAE (Mean Absolute Error)
2
1 1
1
[ ( , ) ( , )]
M N
i j
MAE f i j f i j
M  

 
 (3)
Where original image f (i, j) and despecled image f ′ (i, j) have resolution MxN pixels.
The quantitative studies of the objective results do not depend on investigators. The test value
and evaluation does not always correlate along with the quality of a subjective observation of
the original image. The results deal with the PSNR and MAE coefficients of image as well as
SNR. A higher value of SNR denote larger image enhancement. Hence, this technique better
reveals tissue as well as lesion boundaries which in turn provide more exact images of tissues
and lesions. Here, signal-to-noise ratio enhancement is done by wavelet filtering, improve
contrast resolution as well as improve lesion conspicuity and also diagnostic confidence. In
this proposed method, we improve the performance of median filter and Wiener filter by
47.4% and 34.7% respectively in terms of mean absolute error parameter. For better diagnosis
in medical image processing, the numerical values of the quantitative parameters reveal a
good feature preservation performance of the algorithm. Visual examination is performed.
The results obtained by the proposed method and the Weiner filter are nearly same depending
on the clinical point of view. Median filter performs poorly as compared to the proposed

96
method depending on clinical point of view. Depending on the noise removal capability point
of view, the proposed method achieves better result than other two methods. In our approach,
there are three advantages. First of all, we used larger-size 2D data (the sample data denote
the entire kidney). Secondly, the results were evaluated numerically which means they are
quite objective in nature. Thirdly, the methodology used in the patient study does not require
invasive technique and ultrasound data acquisition can be obtained in very short order.
Moreover, our methodology has a limitation in performing the qualitative analysis of the
ultrasound image processing. This requires well-trained radiologists.
Figure 2: Speckle noise with three different levels are shown here. Image has been degraded
(upper left corner) with simulated speckle noise and its detail

Figure 3: Denoised image obtained by the Wiener filtering (over enhanced structure area)
Figure 4: Denoised image obtained by the Median filtering (area with blurred structure)
Figure 5: Denoised image obtained by the Wavelet filtering (The proposed method)
Both of the images in Fig. 3 and fig. 4 look artificial. Also, Fig. 5 demonstrates that the wavelet transform
performs as a feature detector. It retains the features which are clearly distinguishable in the noised data but
cuts out anything that is assumed to be constituted by speckle.

98
CONCLUSION
The conventional ultrasound is a simple method as diagnosing tool. It brings out the
useful and important information but the only drawback is that most of the task is dependent
on the examiner. Here we represent an ultrasound image enhancement algorithm based on the
wavelet transform. In ultrasound images, the speckle energy is often compared with the signal
energy in a vast range of frequency bands. Thus, in the decomposed image it is very hard to
eliminate speckle from the noised signal only using the magnitude statistics of wavelet
coefficients. In this paper, to separate speckle from noised signal, we acquire the structural
processed data from the wavelet decomposed image. The dataset obtained by this experiment
shows that the advanced algorithm considerably enhances the subjective image quality
without reproducing any noticeable artifact. It also provides better performance as compared
to the existing enhancement schemes. After being tested for several times, our algorithm was
found to be approved for an exact matching of the signal and noise distributions at different
orientations and scales. Computerized testing of the ultrasound data substantiates the
examination and makes more accurate and easier identification of certain diseases which
usually provide similar types of US images. It exhibits a virtual biopsy and offers a more
expressive monitoring of the disease expansion, by discarding maximum possible harmfulness
of invasive diagnostic rules. Finally, it can be noted that the proposed algorithm could be
adapted without any difficulty for the purpose of despeckling of several types of biomedical
images.
REFERENCES
[1] A. Achim, A. Bezerianos and P. Tsakalides, IEEE Transactions on Medical Imaging 20,
(2001), 772–783.
[2] D. Adam, S. Beilin-Nissan, Z. Friedman and V. Behar, Ultrasonics 44, (2006), 166–181.
[3] P.B. Caliope, F.N.S. Medeiros, R.C.P Marques and R.C.S. Costa, Telecommunications and
Networking 3124, (2004), 1035–1040.
[4] R.N. Czerwinski, D.L. Jones and W.D. O’Brien Jr., Proceedings of the International
Conference on Image Processing, vol. 1, Washington D.C (1995), 358–361.
[5] A.N. Evans and M.S. Nixon, IEEE Proceedings on Vision Image and Signal Processing
142, (1995), 87–94.
[6] W. Fisher, Digital Television, A Practical Guide for Engineers, Springer-Verlag, (2004).
[7] S. Gupta, L. Kaur, R.C. Chauhan and S.C. Saxena, Digital Signal Processing 17, (2007),
542–560.
[8] Y.H. Guo, H.D. Cheng, J.W.Tian and Y.T. Zhang, Ultrasound in Medicine and Biology 35
(4), (2009), 628–640.
[9] X. Hao, S.Gao and X.Gao, IEEE Transactions on Medical Imaging 18, (1999), 784–794.
[10] K. Jain, Fundamental of Digital Image Processing, Englewood Cliffs, NJ, Prentice-Hall,
(1989).
[11] A. Khare and U.S. Tiwary, International Journal of Wavelets Multiresolution and
Information Processing 3, (2005), 477–496.
[12] C.P. Loizou, C.S. Pattichis, C.I. Christodoulou , R.S.H. Istepanian ,N. Pantziaris and A.
Nicolaides, IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control
52,(2005), 1653–1669.
[13] T. Loupas, W.N. Mcdicken and P.L. Allan IEEE Transactions on Circuits and Systems 36,
Athens, (1989), 129–135.

[14] H.A.M. Mohamad Forouzanfar and M. Dehghani, proceedings of the IEEE 15th SIU on
Signal Processing and Communications Applications, Eskisehir, Turkey, (2007), 1–4.
[15] S.Papadimitriou and A. Bezerianos, Journal of Systems Architecture 42, (1996), 55–65.
[16] A.M. Pizzeria, E.Wink, W.P. Vansteenkiste and J.Roerdink, Current Medical Imaging
Reviews 2, (2006), 247–260.
[17] M. Popescu, P. Cristea and A. Bezerianos, Future Generation Computer Systems 15,
(1999), 195–209.
[18] R.W. Prager, A.H. Gee, G.M. Treece and L.H. Berman, Ultrasonics 40, (2002), 133–137.
[19] E.P. Simoncelli and E.H. Adelson, Third International Conference Image Processing, vol.
1, Lausanne, (1996), 379–382.
[20] Y. Yong, M.M. Croitoru, A. Bidani, J.B. Zwischenberger and W.J. Clark Jr., IEEE
Transactions on Medical Imaging 25, (2006), 297–311.
[21] Y. Zhang, Y. Wang and W. Wang, IEEE Transactions on Ultrasonics, Ferroelectrics and
Frequency Control 50, (2003), 561–564.
[22] X. Zong, A.F. Laine A F and E.A. Geiser, IEEE Transactions on Medical Imaging 17,
(1998), 532–540.

Wavelet Transform Based Reduction of Speckle in Ultrasound Images

Recommended

Recommended

More Related Content

What's hot

What's hot (17)

Similar to Wavelet Transform Based Reduction of Speckle in Ultrasound Images

Similar to Wavelet Transform Based Reduction of Speckle in Ultrasound Images (20)

More from DR.P.S.JAGADEESH KUMAR

More from DR.P.S.JAGADEESH KUMAR (20)

Recently uploaded

Recently uploaded (20)

Wavelet Transform Based Reduction of Speckle in Ultrasound Images