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38 2 Despeckle Filters for Medical Ultrasound Images
The same Canny detector is used to extract the edge of these filtered images. Note
that Fo M is normalized where Fo M = 1 is the perfect result. The perfect result means
that the actual edge map is the same with ideal edge map.
The bold face indicates the biggest result among eleven filters, and the underlined
values indicate the biggest result in each category.
(1) Filtered Output of Two Kinds of Simulated Images
The filtered results for the simulated images of eleven filters are shown in Fig. 2.7.It
can be observed that the local adaptive filters, Fig. 2.7b–d, have reduced some part of
speckle. In addition, Frost filter reduces more speckles, while its output result appears
blurry.For anisotropic diffusion filters, Fig. 2.7e, f, OSRAD filter obtains a more obvi-
ous speckle reducing effect than DPAD filter. In addition, OSRAD filtered images
introduce small artifacts to some extent. Among multi-scale filters, Fig. 2.7g–i, GLM
filter performs better speckle suppression than both Gupta and Andria filters. The
nonlocal means filters and the nonlocal means hybrid filter, Fig. 2.7j–l, achieve r ather
well results on speckle reduction. Especially, SAR-BM3-D filter exhibits the impres-
sive despeckle ability. Although PPB filter and OBNLM filter have strong speckle
reduction performances, they introduce some man-made artifact, which is the visible
man-made texture.
(2) Image Quality Metrics: To Analysis the Filters of Five Categories
The PSNR values in Table 2.2 measured from two kinds of filtered simulated images
are averaged over the set of twenty images. Among the local adaptive filters, it
can be observed that Lee and Frost filters have higher PSNR values, while SRBF
obtains the lowest value. From the results, it can be observed that the local adaptive
Table 2.2 Average image quality metrics (PSNR, SSIM, FoM) for simulation images
Category Method PSNR SSIM FoM
Speckle
noise
Field II Speckle
noise
Field II Speckle
noise
Field II
Local adaptive
filter
Lee 27.1520 9.5413 0.5495 0.2246 0.2208 0.0386
Frost 27.8261 9.2995 0.7281 0.3054 0.2442 0.0391
SRBF 25.8125 9.0254 0.4513 0.3343 0.2243 0.0388
Anisotropic
diffusion filter
DPAD 24.1257 9.4315 0.4201 0.1974 0.1916 0.0381
OSRAD 29.4577 9.7851 0.8602 0.3077 0.2142 0.0375
Multi-scale filter GLM 29.1739 9.5727 0.7551 0.1745 0.3415 0.0383
Gupta 24.2002 9.9086 0.5842 0.2355 0.1734 0.0362
Andria 22.4908 9.6032 0.2160 0.1651 0.2062 0.0369
Nonlocal means
filter
OBNLM 26.3802 9.4415 0.4612 0.2233 0.3610 0.0384
PPB 25.9305 8.5357 0.5797 0.4796 0.4701 0.0509
Hybrid filter SAR-
BM3-D
27.6781 9.5643 0.8996 0.2012 0.5338 0.0361
2.3 Experiments for Simulation and Ultrasound Images 39
filters have poor performance. The OSRAD filter outperforms the other filters, which
means that the OSRAD filter has the strongest despeckle ability. Among the multi-
scale filters, GLM filter has higher PSNR values which confirm GLM performs good
speckle suppression. Among the nonlocal means filters, OBNLM filter obtains bigger
values than PPB filter. The hybrid filter SAR-BM3-D (nonlocal means and multi-
scale hybrid Filter) performs better than nonlocal means filters. Table 2.2 shows
the SSIM values of the filtered simulated images, and SSIM quantifies the structure
similarity of the two images. The bigger SSIM (the biggest SSIM is 1) value of the
two comparing images is, the more similar structure they have. By comparing the
adaptive filters, it can be observed that Frost filters have higher SSIM values than
Lee and SRBF filters. OSRAD filter has a better performance with the two kinds of
simulated images than the DPAD filter. Among the multi-scale filters, GLM filter
presents better results, which can be observed in Fig. 2.7g. The nonlocal means
filter PPB achieves more excellent results than OBNLM filter, which means that
the PPB filtered output image has more similar structure. It can be readily observed
that SAR-BM3-D filter outperforms other filters with higher SSIM values in speckle
noise simulated images, while it does not show a good performance with the Field
II simulated images because of different simulating speckle methods. Table 2.2 also
shows the result of FoM values. The FoM metric measures the similarity of the image
edge between the noise-free image and the filtered output image. It is observed that the
nonlocal means filters (OBNLM, PPB) and the nonlocal means hybrid filter (SAR-
BM3-D) do outperform other kinds of filters by comparing the similarity of the image
ideal edge. The next filter is GLM, which has the comparable performance with the
other multi-scale filters, local adaptive filters, and anisotropic diffusion filters.
2.3.5 Experiments for Clinical Ultrasound Breast Images
The experiments for clinical ultrasound breast images are presented in this section,
which are the major part of this chapter. 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 dis-
played 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. Con-
sidering that no noise-free clinical ultrasound images exist, the blind/no reference
image quality assessment metric is applied to evaluate the image quality. The blind
image quality assessment metric is to predict the quality of distorted images (noise,
ringing, blur, or blocking), almost without any prior knowledge of the reference
images or their distortions. The experiments for the ultrasound breast images are
performed using the eleven filters with the same parameters as the experiments of
the simulated images [30, 31].
40 2 Despeckle Filters for Medical Ultrasound Images
(1) Filtered Clinical Images
A group of representative filtered images of 200 ultrasound breast images are shown
in Fig. 2.8. (The resolutions of these clinical images are 350 × 340 pixels with 256
gray levels, approximately representing 36 × 35 mm). Among local adaptive filters,
it can be seen that the Frost (Fig. 2.8c) and the SRBF filters (Fig. 2.8d) have a better
speckle reduction performance. However, the edges appear somewhat blurred. With
careful observation of these images, both DPAD and OSRAD filters show a strong
speckle reduction as shown in Fig. 2.8e–f. Among the multi-scale filters, GLM filter
(Fig. 2.8g) can suppress some speckle noise, while Gupta filter (Fig. 2.8h) only
(a) Original image (b) Lee (c) Frost (d) SRBF
(e) DPAD (f) OSRAD (g) GLM (h) Gupta
(i) Andria (j) OBNLM (k) PPB (l) SAR-BM3-D
Fig. 2.8 Filtered outputs of medical ultrasound breast image
2.3 Experiments for Simulation and Ultrasound Images 41
Table 2.3 The NIQE values
and the average scores of
visual evaluation by experts
Category Method NIQE Visual score
Local adaptive filter Lee 6.6353 60
Frost 7.4899 58
SRBF 6.6925 64
Anisotropic diffusion
filter
DPAD 4.5600 65
OSRAD 4.8022 68
Multi-scale filter GLM 5.0913 69
Gupta 7.6998 70
Andria 6.8539 64
Nonlocal means filter OBNLM 6.6960 75
PPB 8.1190 79
Nonlocal means filter SAR-BM3-D 7.4700 81
reduces a little of speckle. Both OBNLM filter and SAR-BM3-D filters have very
fine speckle suppression performance. The PPB filter has suppressed more speckle
than other filters. However, it has over-smoothed the structure of the image.
(2) The Blind Image Quality Evaluator: NIQE
The natural image quality evaluator (NIQE) is used for evaluating the quality of an
image, which is a “completely blind” image quality analyzer proposed by Mittal
[10]. NIQE performs better over some full-reference image quality evaluator, such
as PSNR and SSIM.
Because no noise-free clinical ultrasound images exist, the noise-free ultrasound
breast images are supposed to be natural grayscale images. The average NIQE values
of filtered images are listed in Table 2.3. The patch size was set to 96 × 96. It can
be seen that the PPB filter achieves the highest average NIQE value because it has
the smoothest filtered output image (Fig. 2.8k). The SAR-BM3-D, Gupta, and Frost
filters obtain similar and higher value than the other filters in each category. However,
the anisotropic diffusion filters DPAD and OSRAD perform poorly in NIQE metric
due to the reason that some obvious artifact (as the massive mosaics) is appeared in
the filtered images (Fig. 2.8e, f).
(3) The Removed Noise Images
Figure 2.9 demonstrates the removed noise images of the experiment. The removed
noise image means the difference between clinical ultrasound image and the cor-
responding filtered image. Removed noise images can present the visible traces of
man-made structures and the smoothing of sharp boundaries of the eleven filters. A
removed noise image only contains speckle if the filter performs ideal despeckling
performance. On the contrary, the geometric structures exist, indicating an unwanted
smoothing of sharp boundaries. The removed noise images of the local adaptive fil-
ters (Lee, Frost and SRBF filters) present the trace of man-made structures at the
high amplitude region in the images, where the trace is marked by linear structures.
42 2 Despeckle Filters for Medical Ultrasound Images
(a) Original image (b) Lee (c) Frost (d) SRBF
(e) DPAD (f) OSRAD (g) GLM (h) Gupta
(i) Andria (j) OBNLM (k) PPB (i) SAR-BM3-D
Fig. 2.9 The removed noise images of the filtered ultrasound images
The removed noise images of the anisotropic filters (DPAD and OSRAD filters)
exhibit less trace at the low amplitude region than that at the high amplitude region.
Among removed noise images of multi-scale filters (GLM, Gupta, and Andria), there
is a little trace of man-made structures. In addition, no trace means that the filters
removed the uniform noise. About the nonlocal means filters, for both OBNLM and
PPB, the removed noise images present obvious artifact relate to the structures of
the filtered images. The obvious trace means that there exists over-smoothing of the
sharp boundaries in the filtered image. The removed noise image of SAR-BM3-D
filter has a little trace of man-made structures, which means that no over-smoothing
of sharp boundaries exists in the filtered image.
2.3 Experiments for Simulation and Ultrasound Images 43
(4) Visual Evaluation of Experts
Three experts with different work experiences are invited to evaluate the filtered
images, and the average scores are listed in Table 2.3. The three aspects (the clear
lesion, the clear surrounding tissue, and the artifact) of filtered clinical images are the
standard for evaluation of experts. The hybrid filter SAR-BM3-D and the nonlocal
means filters PPB achieve quite high scores, and they have similar performances
according to the NIQE metric. The SARBM3-D filtered images have a clear lesion,
clear surrounding tissue, and a little artifact. The PPB filtered images have the clearest
lesion. However, its surrounding tissue is fuzzy. The nonlocal means filter OBNLM
has a clear lesion and the surrounding tissue, while it has some artifact. Among
the multi-scale filters, Gupta filter obtains a little higher score than the GLM and
Andria filters. The scores of anisotropic diffusion filters DPAD and OSRAD are low,
because the surrounding tissue is disturbed by many artifacts. The local adaptive
filters perform poorly, and the reason is that the lesion and surrounding tissue are not
clear.
2.4 Conclusions
In this chapter, comparison studies of eleven despeckle filters are presented to eval-
uate the filtering performances for ultrasound breast images. For simulation experi-
ments, the OSRAD and Gupta filters outperform other filters by comparing the PSNR
values (see Table 2.2). By comparing the SSIM metric, it is shown that SAR-BM3-
D and PPB filters achieve a marvelous despeckling performance, revealing that the
structure of SAR-BM3-D and PPB filtered output is very similar to that of noise-free
image. Because the FOM metric measures the similarity of image edge between
the noise-free image and the filtered one, it can be found that the nonlocal filter
PPB and hybrid filter SAR-BM3-D have more similar edges as that of the noise-free
image. Experiments of clinical images are conducted with 200 asymptomatic and 200
symptomatic ultrasound breast images. To systematically compare the despeckling
performance of each filters, several comparison methods are applied, such as blind/no
reference image quality metrics (NIQE), observing the removed noise images, as well
as the visual evaluation of experts. Although the NIQE is not specifically designed
for ultrasound images originally, the result of evaluating ultrasound breast images
can be proven useful (see Table 2.3). Due to the lack of noise-free medical ultrasound
images, the blind image quality metric is rather interesting for evaluating the medical
ultrasound images. The PPB filter achieves the best results than other filters by com-
paring NIQE values. By observing the removed noise images as shown in Fig. 2.9,
it can be found that there are a little trace of man-made structures at the multi-scale
filtered and anisotropic filtered images. In general, the local adaptive filters (Lee,
Frost, and SRBF filters) are computationally efficient and have some speckle reduc-
tions. However, the local adaptive filters often appear blurred. The anisotropic filters
(DPAD and OSRAD filters) have strong speckle reductions, especially the OSRAD
44 2 Despeckle Filters for Medical Ultrasound Images
filter. The filtered images of multi-scale filters (GLM, Gupta, and Andria filters)
have no trace of man-made structures, and they just have an ordinary despeckling
performance. The nonlocal means filters (OBNLM and PPB filters) have stronger
speckle reduction, while they tend to over-smooth the image structures and have a
high computational complexity. The hybrid filter (SARBM3-D filter) is a method
combined by multi-scale method and nonlocal method, leading to a strong speckle
reduction and a little trace of man-made structures. To determine which method is
optimal to filter the clinical ultrasound breast images, several criteria should be taken
into consideration: (1) If the computational efficiency is the main concern, the local
adaptive filters should be of choice (especially the Lee filter), presenting the speckle
suppression ability and low computational cost. (2) If the main concern is to remove
as much speckle as possible, the hybrid filter and the nonlocal means filters are sug-
gested. The nonlocal means method obtains a strong ability of speckle reduction,
while it has a high computational cost. To remove more speckle, the SAR-BM3-D
or PPB filter are the better choice (see Figs. 2.8, 2.9). (3) If both computational
efficiency and speckle reduction are considered, anisotropic filters can be selected
as a trade-off result, especially the OSRAD filter. (4) If the aim is to preserve the
details of lesions and its surrounding tissue, and to prevent the appearance of a trace
of man-made structures, multi-scale filter (GLM and Gupta) is the optimal choice.
In conclusion, this chapter has shown the results of comparing eleven despeckle
filters (particularly the nonlocal means filter and hybrid filter) for the ultrasound breast
images. The eleven filters are grouped into five categories, and the experiments as
well as the comparisons are conducted. To objectively and systematically compare
the despeckling performances, several comparing methods are adopted, especially
the blind image quality metric (NIQE). The blind image quality metric may be a
potential metric to compare the medical ultrasound in near future.
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Chapter 3
Wavelet and Fast Bilateral Filter Based
Despeckling Method for Medical
Ultrasound Images
Abstract In this chapter, a despeckling method which is based on the wavelet trans-
formation and fast bilateral filter is introduced. According to the statistical properties
of medical ultrasound image in the wavelet domain, an improved wavelet threshold
function based on the universal wavelet threshold function is considered. The wavelet
coefficients of noise-free signal and speckle noise are modeled as generalized Laplace
distribution and Gaussian distribution, respectively. The Bayesian maximum a pos-
teriori estimation is applied to obtain a new wavelet shrinkage algorithm. High-pass
component speckle noise in the wavelet domain of ultrasound images is suppressed
by the new shrinkage algorithm. Additionally, the coefficients of the low frequency
signal in the wavelet domain are filtered by the fast bilateral filter, since the low-
pass component of ultrasound images also contains some speckle noise. Compared
with other de-speckling methods, experiments show that the proposed method has
improved de-speckling performance for medical ultrasound images. It not only has
better reduction performance than other methods but also can preserve image details
such as the edge of lesions.
3.1 Introduction of Wavelet
3.1.1 Backgrounds and Basics of Wavelet Transform
It is well known that the most commonly used method for time-frequency conversion
is called Fourier transform. Fourier transform is divided into two kinds: continuous
Fourier transform and discrete Fourier transform.
Fourier transform can show the frequency characteristic of stationary signal very
well, but for non-stationary signal this transform seems to be impotent. While Fourier
transform in signal processing, the infinite sine signal with fidelity is used as a basis
function to spread the whole time domain (Fig. 3.1).
It can be stretched and narrowed when it corresponds to high frequency. And it can
also be extended to correspond to low frequency. The essence of Fourier transform
is to calculate the correlation between signal and trigonometric function. When we
need to obtain the frequency signal of a time signal in the engineering, we may would
© 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_3
47
48 3 Wavelet and Fast Bilateral Filter Based Despeckling …
Fig. 3.1 The basis function of Fourier transform
like not only to know how many kind of frequency it contains, but also we would like
to know which frequency a signal has at a particular instant time. We cannot simply
use Fourier transform to achieve these goals.
DTFT is discrete in time domain and periodic in frequency domain. It is generally
used to analyze the spectrum of discrete time signal. DTFT can be regarded as the
discrete version of the Fourier transformation.
In order to use the computer to carry out Fourier transform in the fields of scientific
calculation and digital signal processing, functions must be defined in discrete points
rather than in continuous domain and the functions must satisfy the conditions of
finite or periodic.
To calculate the discrete Fourier transform usually has a large computational
complexity. In this case, fast Fourier t ransform (FFT) was proposed to reduce the
computational complexity. With the development of the computing power of digital
circuits, DFT has become a very practical and important method in signal processing.
The basic idea of FFT is to decompose the original n-point sequence into a series
of short sequences. The symmetric properties and periodic properties of exponential
factors in DFT calculation are fully utilized. The corresponding DFT of these short
sequences are obtained and appropriate combinations are used to eliminate duplicate
computation, reduce multiplication and simplify the structure.
As an improvement, a new method for time-frequency conversion of non-
stationary signals called short-time-Fourier transform was proposed. In this algo-
rithm, the whole time domain process is decomposed into a number of very small
equal-length time domain processes. Each small process is nearly stationary, and then
Fourier transform is carried out on the basis thereof, so that the time point at which
the frequency occurs can be obtained. Nevertheless, this method does not solve the
problem of time-frequency transform very well. Taking the non-stationary signal of
Fig. 3.2 as an example, to window it as shown in Fig. 3.3, and Fourier transform is
performed on each of them, and the time-frequency diagram is obtained as shown in
Fig. 3.4.
The problem is how to select window function. If the window is too narrow, the
signal in the window will be too short. Although the time resolution is high, the
frequency analysis is not accurate enough, and the frequency resolution will be poor.
If the window is too wide and the frequency resolution is high, the time domain will
be not fine enough and the time resolution is too low. Therefore, for the time-varying
non steady state signal, the small window is suitable for the high frequency signal,
and the large window is suitable for the low frequency signal. However, the window
of STFT is fixed, and the width does not change in a short time Fourier transforms.
Therefore, STFT still cannot meet the need of frequency change of unsteady signal.