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3.4 Experimental Studies of the Despeckling Method 69
Table 3.2 The parameters of filters for experiments
Method Date Category Experiment parameters
SRBF [17] 2010 Local adaptive Window size 5 × 5
DPAD [18] 2006 Anisotropic diffusion Window size 5 × 5, time step
t = 0.2,
Iteration times t = 100
Andria [19] 2012 Multi-scale Window size 5 × 5,
decomposition level J = 1
OBNLM [10] 2009 Nonlocal means Window size 7 × 7, search
area size 15 × 15,
Smoothing parameter h = 0.6
SAR-BM3-D [20] 2012 Hybrid Window size 8 × 8, search
area size 39 × 39,
decomposition level J = 3
Wavelet-Soft Shrinkage [13] 1995 Multi-scale Window size 3 × 3, wavelet =
‘coif1’, decomposition level
J = 4
3.4.4 Experiments for Medical Ultrasound Images
The experiments for clinical ultrasound breast images are presented in this section.
The clinical ultrasound breast images are acquired using Vivid 7 ultrasound unit
(GE Medical System) with an operating frequency range of 2.3–10.0 MHz. The
ultrasound breast images are recorded as displayed on the monitor of Vivid 7 after
logarithmic compression. The image resolution is 350 × 340 pixels with 256 gray
levels,approximately representing 36 ×35 mm. There are 200 asymptomatic and 200
symptomatic ultrasound breast images obtained for the clinical experiment, which
are provided by ultrasound medical experts. The result is shown in Fig. 3.16 and
Table 3.4.
3.4.5 Interpretations of Experimental Results
(1) Simulation Experiments
Figure 3.14 and Table 3.1 show the denoised results of the traditional bilateral filter
and the fast bilateral filter with the same settings. According to the evaluation criteria,
the fast bilateral filter not only has the good denoising effect, but also greatly reduce
the running time compared with the traditional bilateral filter.
In the experiment, some strong speckle reducing filters which belong to dif-
ferent categories are applied to the simulated images, and the filtered results of
these filters for the simulated image are shown in Fig. 3.15 respectively. It can be
observed that the Frost filter (Fig. 3.15b) reduces more speckles while its output result
70 3 Wavelet and Fast Bilateral Filter Based Despeckling …
(d) Andria (e) OBNLM
(f) SAR-BM3-D
(g) Wavelet-Soft Shrinkage
(h) Wavelet+bilateral (i) Wavelet+fast bilateral
(a) Noisy image (b) SRBF (c) DPAD
Fig. 3.15 De-noised results of simulation experiment 2
appears blurry. For the DPAD filter (Fig. 3.15c), it obtains an obvious speckle reduc-
ing effect, but introduces small artifacts to some extent. Among multi-scale filters,
Andria (Fig. 3.15d) and Wavelet-Soft Shrinkage (Fig. 3.15g) cannot obtain satisfied
speckle reducing effecst. The OBNLM filter (Fig. 3.15e) and the SAR-BM3-D filter
(Fig. 3.15f) achieve rather well results on speckle reduction. Especially,SAR-BM3-
D filter exhibits the impressive despeckle ability. Although OBNLM filter presents
strong speckle reduction performance, but introduces some man-made artifact, which
is the visible man-made texture. The developed wavelet transform and fast bilateral
3.4 Experimental Studies of the Despeckling Method 71
Table 3.3 Performance comparison of simulation experiment 2
De-noising method PSNR SSIM FoM Running time (s)
Noisy image 24.7141 0.4593 0.1763 –
SRBF 26.1535 0.9022 0.1574 10.2188
DPAD 27.8729 0.9316 0.2265 12.7031
Andria 25.0185 0.5113 0.1662 0.6094
OBNLM 27.0275 0.9039 0.3512 30.0082
SAR-BM3-D 27.4333 0.9236 0.2192 130.8637
Wavelet-Soft Shrinkage 26.2004 0.7459 0.1808 1.7188
Wav e l et + bilateral 32.7396 0.9032 0.2929 9.6406
Wav e l et + fast bilateral 33.4424 0.9072 0.3175 1.1224
filter based method shows a strong speckle reducing ability for the speckle noise,
and at the same time keeps the major edge information.
In order to objectively evaluate the above denoising methods, the comparison of
evaluation criteria is shown in Table 3.3. The PSNR values in Table 3.3 measure
the ability of speckle reduction. From the Table 3.3, the SRBF filter cannot obtain
the s atisfied value. But on the contrary, the DPAD filter outperforms some other
filters, which means that the DPAD filter has a strong despeckle ability. Among the
multi-scale filters, the Andria filter obtains the lowest value and the Wavelet-Soft
Shrinkage also cannot achieve the satisfied value. And thus from the results, it can be
observed that the multi-scale filters have poor performance. For the OBNLM filter
and the SAR-BM3-D filter, they perform better than the SRBF filter and the multi-
scale filters. It also can be observed from the results that the developed method not
only obtains the better result than Wavelet-Soft Shrinkage, but also performs better
than others.
Table 3.3 shows the SSIM values of the filtered simulated images, and SSIM quan-
tifies the structure similarity of the two images. The bigger SSIM (the biggest SSIM
is 1) value of the two comparing images are, the more similar structure they have. By
the comparison of these filters, it can be observed that the multi-scale filters (Andria
and Wavelet-Soft Shrinkage) have the lowest SSIM value, which means that the out-
put images filtered with the Andria filter and the Wavelet-Soft Shrinkage filter have
less similar structure. The DPAD filter obtains a good performance and the highest
SSIM value. It can be readily observed that the SAR-BM3-D filter outperforms other
filters with higher SSIM values in speckle noise simulated images, which means the
output image filtered with the SAR-BM3-D filter has more similar structure. The
other filters (the SRBF filter, the OBNLM filter and the proposed filters) all obtain
the good results.
Table 3.3 also shows the result of FoM values which measure the similarity of the
image edge between the noise-free image and the filtered output image. It is observed
that the OBNLM filter and the developed filter outperform other filters by comparing
72 3 Wavelet and Fast Bilateral Filter Based Despeckling …
(a) Original image (b) SRB F (c) DPAD
(d) Andria (e) OBNLM (f) SAR-BM3-D
(g) Wavelet-Soft Shrinkage
(h) Wavelet+bilateral (i) Wavelet+fast bilateral
Fig. 3.16 Denoised results of clinical ultrasound images
the similarity of the image ideal edge. The other filters do not have the comparable
performances.
For the medical ultrasound images, the de-noising process time is also a factor
to be considered, and a filter which needs too much time to process is difficult to be
applied in real-time system. Table 3.3 also shows the results of the running time. It
can be observed that the OBNLM filter and the SAR-BM3-D spend too much time,
and thus they cannot be suitable to the medical ultrasound images. For the SRBF
filter, the DPAD filter and the wavelet + bilateral filter, the running time is not too
long, but the medical ultrasound image resolutions are usually very large, so these
3.4 Experimental Studies of the Despeckling Method 73
Table 3.4 NIQE of images
in Fig. 3.16
De-noising methods NIQE
Noisy image 5.7915
SRBF 7.3026
DPAD 4.5330
Andria 7.3836
OBNLM 6.6960
SAR-BM3-D 8.2565
Wavelet-Soft Shrinkage 7.3725
Wav e l et + bilateral 9.5018
Wav e l et + fast bilateral 10.1890
filters will spend too much time to process the medical ultrasound images. From the
Table 3.3, only the multi-scale filters (Andria and Wavelet-Soft Shrinkage) and the
Wav e l et + fast bilateral filter can meet the time requirement.
From above analysis, according to requirements of the medical ultrasound images,
it can be seen that the developed method (Wavelet + fast bilateral) performs better
than others. Although the SAR-BM3-D filter and the OBNLM filter have the good
speckle reduction, they spend too much running time. Although the multi-scale filters
spend less running time, but they do not have the desirable speckle reduction per-
formance. Compared with the SRBF filter, the DPAD filter and Wavelet + bilateral
filter, it is obvious that the developed method outperforms other filters considering
the speckle reduction and the running time. The developed method has the maxi-
mum value of PSNR, and this means the method has better de-noising effect for the
simulated image. With other image quality metrics, the developed method has good
structural similarity and good quality of edge detection. What’s more, the developed
method has greatly improved in the running time due to the combination of the fast
bilateral filter.
(2) Experiments for Medical Ultrasound Images
A group of representative filtered images of ultra-
sound breast images are shown in Fig. 3.16. It can be observed that the SRBF
filter (Fig. 3.16b) shows a better speckle reduction performance, but the edges
appear somewhat blurred. In Fig. 3.16c, the DPAD filter shows a strong speckle
reduction but introduces small artifacts. Among the multi-scale filters, the Wavelet-
Soft Shrinkage filter (Fig. 3.16g) can suppress some speckle noise, while the
Andria filter (Fig. 3.16d) only reduces a little of speckle. Both the OBNLM
filter (Fig. 3.16e) and the SAR-BM3-D filter (Fig. 3.16f) have very fine speckle
suppression performance, but considering the running time the two methods are not
desirable. Only the developed method has the strong speckle reduction while keeps
the edge information.
Because of the fact that the ultrasound images without noise do not exist, PSNR
and other image quality metrics cannot be effectively used in the experiment of
74 3 Wavelet and Fast Bilateral Filter Based Despeckling …
the clinical ultrasound images. So no-reference image quality assessment, NIQE, is
introduced to evaluate the quality of denoised images. From Table 3.4,itisshown
that the developed method has the highest NIQE value, which means the developed
method has the best s peckle reduction effect compared with others. In addition, the
DPAD filter perform poorly in NIQE metric due to the reason that some obvious
artifact (as the massive mosaics) appear in the filtered image (Fig. 3.16c).
It can be seen from above, although the SRBF filter has low algorithm complexity,
it tends to blur the details of the image, and the speckle noise suppression effects
are not very satisfied. The DPAD filter has strong denoising ability, but the results
may appear the overly smooth phenomenon and introduce some man-made artifacts.
Among the multi-scale filters (Andria and Wavelet-Soft Shrinkage), although the
speckle reduction effect is not good, they do not need too much running time. From
the results of experiments, the OBNLM filter has the ideal speckle noise suppression
performance, but it is not easy to meet the real-time requirement of medical ultrasonic
imaging system due to the disadvantage of high algorithm complexity.In fact, the aim
of the hybrid filters is to combine the advantages of different filters. As for the SAR-
BM3-D filter, because of the combination of the nonlcoal means filter, it spends too
much running time. The developed method is the combination of wavelet denoising
method and fast bilateral filter, and thus it not only ensures the good denoising effect,
but also greatly shortens the running time.
3.5 Conclusions
In this chapter, the principles and basics of wavelets are introduced first, and then
a despeckling method for medical ultrasound images based on wavelet transform
and fast bilateral filter is developed to solve the problem of speckle noise in medical
ultrasound images. Three main achievements are attained.
(1) Based on the general wavelet threshold function, a novel threshold function
is designed according to the statistical characteristics in the wavelet domain
of medical ultrasound image. The threshold function is closely related to the
wavelet decomposition layers.
(2) The noise-free signal and speckle noise in wavelet domain are modeled as gen-
eralized Laplace distribution model and Gaussian model respectively, and the
Bayesian maximum a posteriori estimation method is used to obtain a shrinkage
algorithm.
(3) Because there still exists speckle noise in the low frequency after wavelet decom-
position, the fast bilateral filter is introduced and adopted to suppress the speckle
noise in the low frequency.
Experiments are conducted and divided into two aspects. For simulation experi-
ment, by comparing the PSNR, SSIM and FoM metrics, it is shown that the devel-
oped method greatly improves the denoising efficiency, and presents satisfied speckle
reduction effectiveness. Experiments on clinical ultrasound images are conducted,
3.5 Conclusions 75
and no-reference image quality metrics, NIQE, are adopted. Although the NIQE is
not specifically designed for ultrasound images originally, the results of NIQE for
evaluating the denoising effects of ultrasound breast images can be proven to be
useful (see Table 3.4).
From the results of simulation experiments and medical ultrasound image experi-
ments, it shows that the developed method can not only suppress speckle noise well,
but also greatly improve the denoising efficiency.
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Chapter 4
Despeckle Filtering of Medical Ultrasonic
Images Using Wavelet and Guided Filter
Abstract In this chapter, a new de-noising method based on an improved wavelet
filter and guided filter is presented. The Bayesian maximum a posteriori estimation is
applied to obtain a wavelet shrinkage algorithm. The coefficients of the low frequency
sub-band in the wavelet domain are filtered by guided filter. The filtered image is
then obtained by using the inverse wavelet transformation. Experiments with the
comparison of the other seven de-speckling filters are conducted. The results show
that the proposed method not only has a strong de-speckling ability, but also keeps
the image details, such as the edge of a lesion.
4.1 Backgrounds and Introductions
From the perspective of clinical application, despeckling becomes an important step
prior to further analysis and processing of ultrasound images, and many researchers
are inspired to devote their efforts to this issue. It provides a technical incentive for
the doctors to obtain more accurate diagnosis, and reduces the risk of misdiagnosis.
A number of de-noising methods for medical ultrasound images have been proposed
in literatures. These can be simply classified into five categories, i.e. local adaptive
filter, anisotropic diffusion filter, multi-scale filter, nonlocal means filter and hybrid
filter [1–3].
Although the above methods do not achieve optimal balance between speckle
suppression and feature preservation, wavelet threshold de-noising is a classical
wavelet de-noising method. It has higher efficiency and performs better than others
in the processing of additive noise, which can satisfy general requirements. Since
speckle noise in medical ultrasound images appears larger and “granular”, it is found
in the experiments that speckle noise still exists in the low frequency sub-band of
the wavelet domain. Therefore, the guided filter is implemented to filter speckle
noise in the low frequency sub-band. We have introduced wavelet and fast bilateral
filter based despeckling method for medical ultrasound images to deal with this
problem in previous chapter. The bilateral filter can maintain the edge information and
has desirable de-noising performance, but it suffers from two main disadvantages:
i.e., high complexity and “gradient distortion”, which cannot meet the real-time
© Springer Nature Singapore Pte Ltd. 2020
J. Zhang and Y. Cheng, Despeckling Methods for Medical Ultrasound Images,
https://doi.org/10.1007/978-981- 15-0516-4_4
77
78 4 Despeckle Filtering of Medical Ultrasonic Images …
requirement of medical ultrasonic imaging systems. And thus scholars put forward
the concept of the guided filter in 2010, which has greatly improved the de-noising
performance and efficiency.
Therefore, advantages of the wavelet de-noising method and the guided filter
will be combined in this chapter. The main work of our proposed method is as
follows: On the one hand, in order to balance between speckle suppression and feature
preservation, an improved wavelet threshold function related to the layer number of
wavelet decomposition is designed. In addition, according to the statistical models
of noise and signal in the wavelet domain, where the wavelet coefficients of noise-
free signal and speckle noise are modeled as generalized Gaussian distribution and
Rayleigh distribution [4] respectively, Bayesian maximum a posteriori estimation is
applied to develop a new shrinkage algorithm. On the other hand, in order to filter the
larger speckle noise in the low frequency sub-band, the guided filter is implemented.
In the experiments, the proposed method is compared with other seven de-speckling
filters, and the results show that the proposed method not only has a strong de-
speckling ability, but also maintains the image details, such as the edge of the lesion,
which can enhance the quality of medical ultrasound images [5–9].
As we have introduced in the previous chapters, with logarithmic transformation
and wavelet transformation, the final model of ultrasound image can be represented
by following equation [10–12]:
W
j
l,k
( f ) = W
j
l,k
(g) +W
j
l,k
(n) (4.1)
where j = 1, 2,..., J. l, k ∈ Z
2
. W
j
l,k
( f ), W
j
l,k
(g) and W
j
l,k
(n) represent the wavelet
coefficients of noisy images, noise-free images and speckle noise respectively. The
superscript j represent the decomposition layers of wavelet transformation, and the
subscript (l, k) are wavelet domain coordinates. J denotes the largest decomposition
layers. In order to facilitate the representation, we can rewrite (4.1)as:
F
j
l,k
= G
j
l,k
+ N
j
l,k
(4.2)
Since the prior probability of noise-free signal and speckle noise is the premise of
using the Bayesian maximum a posteriori estimation. Thus, from the above analysis,
we consider that the wavelet coefficients of noise-free signal will obey the general-
ized Gaussian distribution, and the wavelet coefficients of speckle noise will obey
Rayleigh distribution, just like what we have discussed in the previous chapter.
The analysis of noise model is important for de-noising and the main purposes
of modeling can be summarized as the following two points: (1) The statistical dis-
tribution model should be close to the actual distribution of the signal; and (2) The
statistical distribution model can be combined with the corresponding de-noising
algorithm, which can obtain the more satisfactory de-noising performance for ultra-
sonic images.