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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5837_Библиотеки_им_академика_М_И_Перельмана.pdf
8 1 Introduction
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Fig. 1.11 The result of convolution
image convolution, one can separate t he elements of the image that would otherwise
be closely intertwined, and can operate on specific elements easily. The processes of
wavelet transformation and shearlet transformation presented in later chapters will
also involve convolution operations.
1.2 Motivations
Medical ultrasound imaging techniques have become an indispensable technology in
medical diagnosis. Because of the fact that the random scattering phenomenon occurs
when the size of the organ tissue is close to the wavelength of the ultrasonic wave,
ultrasonic image always contains speckle noise as Fig. 1.12 shows. These speckle
noise will seriously affect the doctor’s diagnosis of ultrasound image. The ultrasonic
speckle noises are some bright and dark grains appearing on the images resulted
from the intervening perturbations between different scattered-backward ultrasonic
waves, while random scattering phenomenon are stemed from the similitude between
scales of human tissue structures and ultrasonic wavelengths. Speckle noises often
hide the dominant image features useful in medical diagnoses, and may drastically
degrade the image qualities. Therefore, it is really necessary to find an effective
denoising method for medical ultrasound image. In the past decade, we did a lot of
research work firstly on wavelet transformation based denosing of medical ultrasonic
image, and then did some research work on shearlet transformation based denosing
of medical ultrasonic image. We hope that our research work can, to some extent,
1.2 Motivations 9
Fig. 1.12 Ultrasound images with speckle noisy
improve the quality of the ultrasound medical image, and current ultrasound medical
imaging technology.
1.2.1 The Tasks
The ultrasonic image is a digital signal. Because of the limitations of ultrasonic
imaging mechanism and speckle noises (multiplicative noises) during the imag-
ing processes, the quality of ultrasonic images is usually relatively poor. Several
factors degrade the image quality, and may result in poor image contrast degree,
inadequate and non-identifiable characteristics of the tissue and structure features,
etc. Moreover, compared with multiplicative noises, some ultrasonic image itself
might exist a few random additive Gaussian noises as well. The existence of speckle
noises is a constant phenomenon in the processes of coherent imaging. The ultrasonic
speckle noises are some bright and dark grains appearing on the images resulted from
the intervening perturbations between different scattered-backward ultrasonic waves
while random scattering phenomenon are caused by the similitude between scales
of human tissue structures and ultrasonic wavelengths. Speckle noises often hide the
dominant image features useful in medical diagnoses, and may drastically degrade
the image qualities. Although computer aided diagnosis methods have appeared in
recent years, automatically diagnostic instruments still confront with the problem of
a relatively poor ultrasonic imaging quality, and speckle noises might as well exert
an enormously negative influences on the accuracy of diagnoses in several clinical
applications. Hence the diagnosis for some diseases depends much more on previous
experiences and professional trainings. However, even an ultrasonic clinician expert
cannot make a precise diagnosis from the ultrasonic images with the totality of his
confidences. In order to tackle this problem, many researchers have devised various
appropriate denoising methods to address the specific features of ultrasonic images.
10 1 Introduction
In the past several decades, more and more experts have devoted to work on the field
of despeckling of medical ultrasonic images.
The denoising of image, to put it intuitively, is to find out the disturb information
in the image and filter it out. It means that we are changing the information of a
real image. However, when we remove the disturb information from the image, we
will inevitably change at the same time the image information that we really want to
keep. Figure 1.13 shows the visual effects of denoised images obtained with different
denoising methods.
As Fig. 1.13d–f shows, the choice of threshold is very important for a denoising
method. Therefore, in the study of improvement of denoising method, there is a
considerable effort to invest in the choice of threshold. Based on the unique structure
of medical image, it is more important to keep the structure information of medical
image in the processing of de-noising. The distinct features of speckle noises in the
ultrasonic medical images result in specific de-noising implementations. At first,
speckle noises may reveal the large-grainy features in images. Then, the details
of diseased regions are often represented in edges of images. To satisfy the real-
time requirements of the imaging system, the de-noising method must have a high
efficiency. In order to accomplish these goals, the study of denoising methods of this
book will focus on the following objectives:
(a) (b) (c)
(d) (e) (f)
Fig. 1.13 a Ultrasound image with speckle noise, b de-noised ultrasound image using frost filter,
c denoised ultrasound image using OBNLM filter, d Lena image with speckle noise, e denoised
version by NSST with appropriate threshold, f denoised version by NSST with excessive threshold
1.2 Motivations 11
(a) Have a relatively strong ability of denoising speckle noises. This is the most
essential demand for a de-noising method. To provide a high-quality images
relies on a relatively strong de-noising powers of the method.
(b) Have the capability of preserving the edges of an image. Although the traditional
de-noising methods have relatively strong de-noising ability, they may often
smoothened the edges of an image so that the de-noised image is rather blurred.
This is very inconvenient for the ultrasonic diagnosis specifically in diseased
regions, and may influence negatively and enormously for the right justifications
made by medical practitioners.
(c) Have the non-existence of gradient losses. This aim is raised by t he situation
that bilateral filters exist certain gradient losses.
(d) Have the non-iteratively. There are many iterative based methods in the most
cutting-edge research work of denoising. Although having relatively strong
denoising powers, they often depend on many times of iterative loops, and thus
cost a large time for implementation. And therefore, they can not be applied to
the real-time imaging systems.
(e) Have the low complexity. This aim is similar to that of above (d), i.e. to speed
up the efficiency of the denoising algorithm so that it could satisfy the real-time
requirement of the imaging system.
1.3 Introduction of Ultrasound Image
Sound waves are the waves that cause sound sensation in the auditory organ. The
frequency range of sound waves that humans can sense is about 20 Hz to 20 kHz.
The sound wave which has a frequency of higher than 20 kHz, human sensory
organs will not feel it, and thus the sound wave is called ultrasound. Ultrasonic wave
is a longitudinal wave and it can be propagated in solids, gases and liquids. And
ultrasound has the same physical properties as sound waves. Its physical parameters
also include wavelength, frequency and propagation speed. But because of its high
frequency, it has a short wavelength and some of its own characteristics, and it is
more widely used than other sound waves.
Ultrasonic wave has beam-emitting property. It can propagate in one direction,
and has strong directivity, and propagates in a straight line in the medium. Linear
propagation is a prerequisite for ultrasonic detection of human organs. When the
ultrasonic wave is transmitted to the interface of two adjacent media with differ-
ent acoustic impedance, an echo occurs. The amplitude of an echo depends on the
differences of the acoustic impedance between two adjacent media.
12 1 Introduction
1.3.1 Basic Principle of Ultrasound Image
Ultrasound is a sound wave that is higher than the auditory range of the human ear.
The frequency of ultrasound used in medical diagnosis is generally in the range of
1–100 MHz. The important role of ultrasound in medicine is that it not only penetrates
the human body, but also interacts with body tissue.
Ultrasound in clinical application is mainly divided into two aspects: diagnosis
and treatment. Ultrasound with a higher frequency and a lower intensity of ultrasound
is used in diagnosis. High frequency can improve the resolution of texture to obtain
clear and detailed images, while low intensity can reduce the side effects of tissue
injury. Ultrasound at a lower frequency is used in t reatment. The high sound intensity
of and the low frequency ultrasound can increase the penetration rate of the tissue.
The high intensity, especially the focused ultrasound, can produce biological effect
on the tissue, and it is used to selectively destroy focal lesions.
Ultrasonic diagnosis is mainly based on the good directionality of ultrasound
and the physical properties of reflection, scattering, attenuation and Doppler effect.
It uses a variety of scanning methods to send ultrasound into the human body and
spread it through the tissues. When the acoustic impedance of normal or pathological
tissues is different, they form an interface where reflection and scattering occur. After
receiving and detecting the echo signal, the signal is displayed as waveform, curve
or image. Due to the interface modality of various tissues, the movement condition
of the tissue organ and the degree of absorption of the ultrasound, its echo has certain
commonness and some characteristics. Combined with physiological, pathological
anatomy knowledge and clinical medicine, observation, analysis, location, nature,
or degree of dysfunction of the disease can be judged [5].
Ultrasonic diagnosis uses the reflection characteristics of ultrasound to obtain
the relevant information in human tissues, so as to diagnose diseases. When the
ultrasonic beam propagates in human tissues and encounters t wo adjacent media
interfaces with different acoustic impedance, it generates a reflected echo on the
interface. Each interface encounters an echo. The echo is displayed as a wave on the
screen of the oscilloscope. The greater the difference of acoustic impedance between
the media on both sides of the interface, the greater the amplitude of the echo. On
the contrary, the smaller the difference of acoustic impedance between the two sides
of the interface, the lower the echo amplitude. If the ultrasonic wave propagates
in a homogeneous medium without interface, that is, when the acoustic impedance
difference is zero, it will appear an echo-free phase. According to the echo amplitude,
number and shape, the tissue state is judged (Fig. 1.14).
1.3 Introduction of Ultrasound Image 13
Fig. 1.14 Principle diagram of B ultrasonic imaging
1.3.2 Several Kind of Diasonograph
At present, one of the most widely used ultrasonic diagnostic instruments is pulse
reflection type. According to the different display and exploration methods, it can be
divided into many types.
Amplitude Mode
Amplitude-mode Ultrasound (A-mode Ultrasound) diagnostic instrument belongs
to amplitude modulation display type, which is the first diagnostic instrument used
in clinical diagnosis. The probe emits and receives sound waves from the human
body in a fixed position and direction without scanning the sound beam. When
ultrasound propagates in human body, it meets the interface with different acoustic
impedance, and produces reflection. The probe receives the reflected echo, converts
it into electrical signal, and sends it to the display after processing. The longitudinal
coordinates of the display screen show the amplitude waveform of the reflected echo,
and the abscissa represents the depth of the echo source. According to the location
of the echo, the echo amplitude, the shape, the number and the presence of the echo,
we can determine the information about the lesion or the anatomical location of the
subject. It has high accuracy in measuring thickness or distance, and is often used
in ophthalmic diagnosis. However, the specificity of type A echo in some lesions is
not prominent, and it lacks anatomical characteristics. With the wide application of
real-time B-mode tomography technology, A-mode ultrasound has been relegated to
the secondary position.
14 1 Introduction
Brightness Mode
B-mode ultrasound is widely used in clinic and has the advantages of simplicity and
convenience. At present, there are many types of ultrasound diagnostic instrument
and various scanning methods. Signals such as reflection and scattering are collected,
and various tissues and pathological changes are presented in the form of images.
Relying on the professional knowledge of pathology and clinical medicine, we can
find out the specific reflex rules on the basis of observation and analysis, so as to
accurately judge the location and nature of the lesion.
We can get clear cross-sectional figures of human internal organs by B-mode
ultrasound. B-mode ultrasound has been widely used because of its low price and
few adverse reactions, especially for the detection and diagnosis of liver, gallbladder,
kidney and ovary, uterus and other gynecological organs.
B-mode ultrasound diagnostic instrument uses brightness modulation to display
the reflected echoes of all interfaces in depth direction. The vertical plane two-
dimensional ultrasound tomography image, i.e. line scan tomography image, can be
obtained by transmitting and receiving ultrasonic echoes one by one in the horizontal
direction with a fast scanning method. If the angle of the ultrasonic beam is changed
rapidly, the vertical sector two-dimensional ultrasound tomography image, namely
sector scan tomography, can be obtained.
Brightness Mode ultrasound displays the echo information in a two-dimensional
form. The scattering and reflection echo information in the tissue is displayed in
a luminosity modulation mode. When the echo is strong, the spot is bright. The
spot moves and scans with the movement of the probe. Because the scanning is
continuous, the anatomical section of the organ can be scanned by points and lines.
According to the different imaging speed, it can be divided into slow imaging and
fast imaging. Slow imaging can only display the static anatomical images of viscera.
The images are clear and the spatial range of scanning is large. Rapid imaging can
show the active state of viscera, also known as real-time imaging diagnostic method,
but the display area is small (Fig. 1.15).
Although there are many kinds of ultrasonic diagnostic instruments, their appli-
cation fields are different. At present, the most commonly used ultrasonic diagnostic
instrument in the world is two-dimensional B ultrasonic diagnostic instrument.
The basic principles of B-Ultrasound Imaging are as follows. Send a group of
ultrasonic waves to the human body and scan them in a certain direction. The distance
and nature of viscera can be judged by monitoring the delay time and intensity of
echo. After processing of the echo signals with electronic circuit and computer, we
see the formation of images. The key component of the ultrasound is what we call
an ultrasonic probe. There is a group of ultrasonic transducers, which are made of a
group of special crystals with piezoelectric effect. This kind of piezoelectric crystal
has special properties, that is, if the electric voltage is applied in a particular direction
of the crystal, the crystal will deform. When the crystal is deformed, the voltage is
generated in the corresponding direction, so that the conversion between the electric
signal and the ultrasonic wave is realized.
1.3 Introduction of Ultrasound Image 15
Fig. 1.15 Ultrasonic image
of Brightness Mode
Motion Mode
M-mode echocardiography uses a single beam to scan the heart. M-mode echocar-
diography, guided by two-dimensional echocardiography, shows the fine structure
and movement of local tissues, and observes the interface distribution, echo intensity
and activity on the sampling line. At present, the size and function of each chamber
of the heart are measured. Ultrasound images of the heart and blood vessels are pre-
sented in the form of curves formed by the change of light points over time. M-mode
echocardiography is formed when the probe is relatively fixed on the chest wall and
the heart or great vessels move up and down under the scanning line. It is named
M-mode echocardiography because it shows the movement of the heart and blood
vessels. When the M-type sampling line passes through the atrium and ventricle in
turn, the ability of myocardial activity can be understood and the type of arrhythmia
can be judged.
Doppler Mode
The D mode ultrasound is designed by using the Doppler-Effect-Principle; it is
also called Doppler ultrasound. Two dimensional images of visceral organs can be
obtained by multi-function color Doppler. Color Doppler ultrasound can show the
direction of the blood flow back. The pseudo color coding technique is used to
indicate the direction and velocity of blood flow in the arterial map, so that the
simulated three-dimensional image of the blood vessel can be displayed.
3D Ultrasound Imaging
The development of computer technology has made 3D ultrasound imaging a reality.
Three-dimensional ultrasound imaging requires special probes and software to col-
lect and generate images. There are several ways to make three-dimensional images.
At present, 3D ultrasound imaging of the most of the instruments on the market are
16 1 Introduction
reconstructed by computer on the basis of 2D images. 3D images make volume mea-
surement more accurate, diagnosis more precise and accurate. Doctors can easily
diagnose tissue abnormalities.
1.4 Book Structure
We will introduce several despeckling methods for medical ultrasonic images based
on wavelet and shearlet transformation in the following chapters [6–10].
The contents of this book will be organized as follows.
In this chapter,we presents an overview of digital image processing, Image denois-
ing technology, the general motivations of despeckling of medical ultrasonic images,
introduction of recent research work on denosing of medical ultrasonic images, basic
principle of ultrasonic image, several kinds of diasonograph and the structure and
organization of the book.
In Chap. 2, we will introduce several kinds of de-nosing methods, and speckle
reduction techniques and their applications in the breast ultrasound imaging are dis-
cussed. Five categories of despeckle filters are presented. This chapter focuses on the
comparison of despeckle filters for the breast ultrasound images. Despeckle filters
which are classified into five categories (local adaptive filter, anisotropic diffusion
filter, multi-scale filter, nonlocal means filter, and hybrid filter) are described. The
comparative experiments of eleven despeckle filters for the two types of simulated
images and clinical ultrasound breast images are presented. To objectively and sys-
tematically compare the performance of eleven despeckle filters, several comparison
methods are used, such as the full-reference image quality metrics, the nonrefer-
ence/blind image quality metrics, observing the removed noise images, as well as
the visual evaluation of experts.
In Chap. 3, a de-speckling method which is based on the wavelet transformation
and fast bilateral filter is introduced. According to the statistical properties of medi-
cal ultrasound image in the wavelet domain, an improved wavelet threshold function
based on the universal wavelet t hreshold function is considered. The wavelet coef-
ficients 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.
In Chap. 4, a new de-noising method based on an improved wavelet filter and
guided filter is presented. The Bayesian maximum a posteriori estimation is applied
1.4 Book Structure 17
to obtain a new 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 and com-
parison with 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.
In Chap. 5, an integrated despeckling approach for medical ultrasound images
based on wavelet and trilateral filter is presented. Firstly, a dynamic additive model
is developed to account for the medical ultrasound signal with speckle noise. Sec-
ondly, in accordance with the statistical property of the additive model, an adaptive
wavelet shrinkage algorithm is applied to the noisy medical signal. Particularly, the
algorithm is significant to the high-frequency component of the speckle noise in
the wavelet domain. Thirdly, but most importantly, the low-frequency component of
the speckle noise is suppressed by a trilateral filter. It simultaneously reduces the
speckle and impulse noise in real set data. Finally, a lot of experiments are con-
ducted on both synthetic images and real clinical ultrasound images for authenticity.
Compared with other existing methods, experimental results show that the proposed
algorithm demonstrates an excellent de-noising performance, offers great flexibility
and substantially sharpens the desirable edge.
In Chap. 6, a novel despeckling method based on nonsubsampled shearlet trans-
formation and a guided filter is presented. First, a nonsubsampled Laplacian pyramid
filter is used to decompose the noisy image thus decomposing the image into high-
frequency and low-frequency subbands. Under the direction of the non-sampling
filter bank, a high frequency subband multi-directional decomposition is obtained.
Next, based on the threshold function and the correlation of the shearlet coefficients
in the transformation domain, an improved threshold shrinkage algorithm is pro-
posed to perform the threshold shrinkage processing on the shearlet coefficients of
the high-frequency subbands. Finally, the low-frequency subbands in the transfor-
mation domain are processed by the guided filter, and a denoised ultrasonic image
is obtained by the inverse transformation of the shearlet. So as to verify the effec-
tiveness of the proposed method, experiments were conducted, and the results were
compared to those of other existing denoising filters. These showed the proposed
method performs more effectively at denoising and delivers clearer image detail.
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