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Computational and Applied Mathematics Journal 2015; 1(3): 79-87
Published online April 30, 2015 (http://www.aascit.org/journal/camj)
Keywords Ultrasound Imaging,
Field-II Simulation,
Computing Paradigm,
Image Quality,
Acquisition System
Received: March 29, 2015
Revised: April 14, 2015
Accepted: April 15, 2015
Evaluation of Ultrasound Imaging for Biomedical and Industrial Applications: A Computing Perspective
G. N. Ezeh, K. C. Okafor, G. H. Elisha, J. N. Iloabachie
Dept. of Electrical Electronic Engineering, Federal University of Technology, Owerri, Imo State,
Nigeria
Email address [email protected] (G. N. Ezeh), [email protected] (K. C. Okafor),
[email protected] (G. H. Elisha), [email protected] (J. N. Iloabachie)
Citation G. N. Ezeh, K. C. Okafor, G. H. Elisha, J. N. Iloabachie. Evaluation of Ultrasound Imaging for
Biomedical and Industrial Applications: A Computing Perspective. Computational and Applied
Mathematics Journal. Vol. 1, No. 3, 2015, pp. 79-87.
Abstract Ultrasound systems are widely used modalities for real-time imaging. This is due to its
non-invasive and non-ionizing nature as such offering better flexibility. This paper
presented classical theories on ultrasound imaging processing techniques from one
dimensional to multidimensional systems. Theoretical concepts were explored while
observing that the usability of the image obtained depends on the quality of the image
displayed which is a function of the acquisition parameters. An optimization algorithm
was developed for the system in this paper. The acquisition parameters are autotuned
using an initialized algorithm to obtain the optimize parameters for image quality
comparison. This approach gives an image quality rated 9 (90%) or 10(100%) on a scale
of 10 (100%), which is widely acceptable. Also, this work addressed ultrasound
acquisition system, as well as its modes of operation. In this regard, the system
characterization was carried. Field-II simulation was used for result synthesis. Finally,
the application areas of ultrasound imaging were discussed.
1. Introduction
Ultrasound is acoustic (sound) energy in the form of waves having a frequency
beyond the capacity of normal human hearing. Since the highest frequency that the
human ear can respond to is approximately 20 thousand cycles per second (20 KHz),
anything beyond this marks the end of the sonic range and now begins the ultrasonic
range [1].
Typically, ultrasound systems operate in the 2MHz to 20 MHz frequency range,
although some systems are approaching 40MHz for harmonic imaging (multiple
frequency of the fundamental signal). Due to its non-invasive and non-ionizing nature
and its flexibility, ultrasound (US) systems are a widely used modality for real time
imaging. A high number of ultrasound systems applications are found in the biomedical
fields. However, electronic, navigational, and security applications remain areas of
important use. Ultrasound imaging is continuously growing in each of these fields. This
is namely due to three reasons. The first one is linked to important advances in
transducers technology. The second is the improvement brought by advances in digital
technologies, and lastly advances in signal and image processing methods and
technologies. The last one is the wide variety of applications in medical as well as in
industrial areas [2], [3], [4].
80 G. N. Ezeh et al.: Evaluation of Ultrasound Imaging for Biomedical and Industrial Applications: A Computing Perspective
Basically, an Ultrasound image is obtained when a pulse of
ultrasound encounters a medium transition. In this case, part
of itis reflected forming echoes, while the rest penetrates.
Ultrasound pulses are assumed to propagate in straight lines,
resulting in echoes of various magnitudes along this line.
Different parts of a body can be interrogated by scanning in
different directions. An ultrasound image is created from an
array of such scan lines, revealing cross-sectional
information of the body noninvasively.
Thus, to sample a scan line of depth d into the object, each
ultrasound pulse needs to travel the same path twice, to and
from the transducer. With the propagation speed c of
ultrasound in the object, the time t for sampling of a scan line
of depth d is:
t � 2�
� (1)
Because each scan line cannot be sampled simultaneously
due to their interference with each other, the total sampling
time of an image with n scan lines is:
� � � � 2�
� (2)
In medical applications, the propagation speed of
ultrasound through soft tissue is approximately 1540m/s.
Thus, an ultrasound image with 512 scan lines of depth 10cm
will take at least 66.5ms to sample, restricting the maximum
frame rate to about 15 frames per second (fps). Also, in
recent years, more advanced image acquisition methods have
arisen, such as spatial compound imaging and coded
excitation, which involve different timing parameters.
Nevertheless, these calculations are a good approximation of
the frame rate for most ultrasound system [5].
2. Literature Review
Prior to World War II, Sonar (Sound Navigation and
Ranging), the technique of sending sound waves through
water and observing the returning echoes to characterize
submerged objects, inspired early ultrasound investigators to
explore ways to apply the concept to medical diagnosis. In
1929 and 1935, Sokolov studied the use of ultrasonic waves
in detecting metal objects. Mulhauser, in 1931, obtained a
patent for using ultrasonic waves, using two transducers to
detect flaws in solids. Firestone (1940) and Simons (1945)
developed pulsed ultrasonic testing using a pulse-echo
technique. Shortly after the close of World War II,
researchers in Japan began to explore the medical diagnostic
capabilities of ultrasound. The first ultrasonic instruments
used an A-mode presentation with blips on an oscilloscope
screen. This was followed by a B-mode presentation with a
two dimensional, gray scale image. Researchers learnt to use
ultrasound to detect potential cancer and to visualize tumors
in living subjects and in excised tissue [6]. Real-time imaging
is another significant diagnostic tool for physicians which
present ultrasound images directly on the system's Cathode
Ray Tube (CRT) screen at the time of scanning. The
introduction of spectral doppler and later color doppler
depicted blood flow in various colors to indicate the speed
and direction of the flow [7].
In [8], the authors investigated an aura based technique for
enhancing the quality of medical ultrasound images. An
algorithm was developed using aura transformation whose
performance was evaluated on a series of diseased and
normal ultrasound images. Various techniques have been
investigated for image processing in literature
[9],[11],[12],[13]. Most works are based on Basic Gray-Level
Aura Matrices (BGLAM) mathematical framework [14],
which is developed based on the aura concepts (i.e., aura sets,
aura measures, and aura matrices) [15].
In [16], the authors worked on ultrasound speckle
reduction in ultrasound imaging. This speckle reduces the
usefulness of ultrasound imaging due to the presence of this
signal dependant noise. Relevant models were articulated and
simulated. `
From the exiting works, imaging modes found in today’s
ultrasound system lacks homogeneous distribution. This
work will then use harmonic imaging as a new modality
where the B-mode imaging is performed on the second or
Nth harmonics of the imaging. It has been concluded that due
to the usual high frequency of the harmonic, these images
have higher resolution than conventional imaging [17].
However, due to higher loss, the depth of imaging is limited.
But, some modern ultrasound systems switch between
harmonic and conventional imaging based on depth of
scanning. This system imposes stringent linearity
requirements on the signal chain components [17].
3. Theoretical Concepts
3.1. Ultrasound Mode
A mode is an operational state that a system has been
switched to. A normal mode occurs when all parts of a
system oscillate with the same frequency. For ultrasound
imaging, different modes are used for examination be it for
medical or industrial use. The different types of mode can be
controlled by the operator or technician [6].
Fig. 1. A-Mode Display of an Ultrasound image [6]
i. A-Modeor Amplitude Mode: This is the display of
amplitude spikes of different heights. It is used for
Computational and Applied Mathematics Journal 2015; 1(3): 79-87 81
ophthalmology studies to detect finding in the optic nerve. A-
Mode consists of x and y axis, where x represents the depth
and y represents the amplitude. Attenuation due to high
frequency is not a problem as the desired imaging depth is
small. The image in Fig1 shows an example of A-Mode
display.
ii. B-Mode (Brightness Mode): This involves transmitting
small pulses of ultrasound echo from a transducer into the
body. Unlike A-Mode, B-Mode is based on brightness with
the absence of vertical spikes. Therefore, the brightness
depends upon the amplitude or intensity of the echo. There is
no y-axis in B-Mode instead; there is a z-axis, which
represents the echo intensity or amplitude, and an x-axis,
which represents depth. B-Mode will display an image of
large and small dots, which represents strong and weak
echoes respectively. Fig 2 shows a typical B-Mode imaging.
Fig. 2. B-Mode Display of an Ultrasound image [6]
iii. M-Mode or Motion Mode (also called Time Motion or
TM-Mode): This is the display of a one-dimensional image
that is used for analysing moving body parts commonly in
cardiac and fontal cardiac imaging as shown in Fig 3. This
can be accomplished by recording the amplitude and rate of
motion in real time by repeatedly measuring the distance of
the object from the single transducer at a given moment. The
single sound beam is transmitted and the reflected echoes are
displayed as dotes of varying intensities thus creating lines
across the screen. Overtime, this is analogous to recording a
video in ultrasound.
Fig. 3. M-Mode Display of an Ultrasound Image [6]
Doppler Mode exploits the frequency shift due to relative
motion between two objects. With this approach information
regarding fluid velocity can be obtained. Doppler mode can
be obtained by continuous or pulsed wave; in addition,
velocity data can be shown as overlaying colour on B-Mode
images [7].
3.2. Signal Processing Techniques
Signal Processing involves techniques that improve the
understanding of information contained in received ultrasonic
data. Normally, when a signal is measured with an
oscilloscope, it is viewed in the time domain (vertical axis is
amplitude or voltage and the horizontal is time). For many
signals, this is the most logical and intuitive way to view
them. Simple signal processing often involves the use of
gates to isolate the signal of interest or frequency filters to
smooth or reject unwanted frequencies [18].The different
techniques are as follows:
3.2.1. One Dimensional Imaging- 1D
The main aim of 1D ultrasound (US) signal processing is
to extract some parameters from the ultrasound signal for
detection or estimation purpose. Flow velocity estimation is
an important matter of consideration. Doppler Principle and
Time of Flight Principle are two kinds of techniques used for
this purpose.
Blood flow in vessels and pipe fluid flow in industrial
applications can be accessed through analysis of the signal.
In this case, doppler signals are obtained by directing an
ultrasound beam to a moving flow containing backscattering
particles. The wave reflected from the particle is slightly
Doppler-shifted by the movement of the particles. In time of
flight technique, the velocity estimation does not require the
presence of particles in the flow. Transducers located on each
side of the flow are used to acquire the signal from two areas.
The flow velocity is evaluated by estimating time of flight of
the US in the fluid. Here, the velocity estimation is based
either on narrow band or wide band correlation techniques.
These kinds of velocity estimations are used in industrial area
namely for US flow meters.
Other areas of interest of 1D US signal processing are
Related to media parameter estimation such that US
attenuation or non-linear coefficient estimation through
analysis of radio-frequency signals and their time frequency
contents. Ultimately, rare event tracking also belongs to 1D
US signal processing.
3.2.2. Two Dimensional Imaging - 2D
US Image processing has been mainly centred on 2D
image manipulation. Basically, 2D US imaging systems
operate in Brightness-mode or B-mode. A typical B-mode
image is obtained from of a set individual radio-frequency
(RF) signals by filtering, envelope detection and then log
compression. This kind of processing as well as the
characteristics of the transducer and the materials may result
in low resolution either axially (in the direction of the US
beam) or laterally (in the perpendicular direction) through
blurring effects contributing to a poor quality image.
Improving 2D US image resolution is a major concern which
82 G. N. Ezeh et al.: Evaluation of Ultrasound Imaging for Biomedical and Industrial Applications: A Computing Perspective
has given rise to an important number of works. Many
directions have been followed; the two main being novel
transducer designs and signal processing improvement. If a
part of works use B-mode images, a great number of works
deal with RF signals since they contain the overall
information available on the imaged material and their
improvement leads to improvement of B-mode images. The
problem of 2D US image resolution improvement can be
expressed in terms of convolution.
3.2.3. Three Dimensional Imaging - 3D
The 2D US imaging has some intrinsic limitations
(although offers very acute understanding of the imaged
media); the main of which being 2D viewing of 3D structures.
This requires a reconstruction of actual 3D structures, which
is in some situations difficult to achieve. This can be
overcome by taking multiple scans of some particular
locations and to quantitatively analyzing them afterwards-
this is time consuming. 3D may thus be used to overcome
these limitations. The basic principle consists in the
acquisition of a volume data set. Most current methods
consist of acquiring a series of 2D images in a volume of
interest while moving the transducer using a motor. Many
relevant imaging techniques have been developed recently.
One important use of these techniques is currently the field of
“telemedicine”
3.2.4. Four Dimensional Imaging - 4D
The 4D US imaging is real-time 3D US imaging in which
time is taken as the fourth coordinate. The starting point of
3D/4D US imaging is, like in the 2D case, the acquisition
system [4], [19].
3.3. Ultrasound Acquisition System
All the devices, components, processors etc, involved in
the generation, detection and display of US signal is known
as Ultrasound Acquisition System as shown in Fig 4.The US
systems consist of five parts namely:
i. Transducer: The transducer is responsible for the
conversion of electrical pulses to mechanical vibrations and
the conversion of returned mechanical vibrations back into
electrical energy which is the basis for ultrasonic testing.
Thus, it is the active element and the heart of the acquisition
system [20]. The transducer may consist of one element
(which has to move) or of multiple elements for
multidimensional signal acquisition.
The typical working frequency depends on the materials
investigated by ultrasound. These frequencies are basically
less than 500 KHz in air and range from 1 MHz in some
liquids or biological tissue up to 50 or 100 MHz in some
special applications [21].
The active element is basically a piece of polarized
material. When an electric field is applied across the material,
the polarized molecules will align themselves with the
electric field, resulting in induced dipoles within the
molecular or crystal structure of the material. This alignment
of molecules will cause the material to change dimensions.
This phenomenon is known as electrostriction.
Fig. 4. Ultrasound Signal Acquisition System [6]
In addition, a permanently-polarized material such as
quartz (SiO2) or barium titanate (BaTiO3) will produce an
electric field when the material changes dimensions as a
result of an imposed mechanical force. This phenomenon is
known as the piezoelectric effect.
The active element of most acoustic transducers used
today is piezoelectric ceramic, due to their good piezoelectric
properties and their ease of manufacture into a variety of
shapes and sizes. The thickness of the active element is
determined by the desired frequency of the transducer. A thin
wafer element vibrates with a wavelength that is twice its
thickness. Therefore, piezoelectric crystals are cut to a
thickness that is 1/2 the desired radiated wavelength. The
higher the frequency of the transducer, the thinner the active
element. The primary reason that high frequency contact
transducers are not produced is because the element is very
thin and too fragile.
Fig. 5. A Piezoelectric Transducer [6]
ii. Transmitter/Receiver Beam former: This is the
electronic part which controls the US signal emission, beam-
forming, reception, and conditioning. It is a front end
processing [22].
Moreover it synchronizes the generation of the sound
waves and the reflected wave measurements. The controller
knows the region of interest in terms of width and depth. This
region gets translated into desired number of focal points per
scan line. The beam-former controller begins with the first
scan line and excites an array of piezo-electric transducers
with a sequence of high voltage pulses via transmit
amplifiers. The process of steering and focusing the sound
beam in an ultrasound system is commonly referred to as
Computational and Applied Mathematics Journal 2015; 1(3): 79-87 83
phased array beam forming. In this mode of operation,
multiple piezo-electric elements are excited with properly
time-delayed pulses and then become sensors to record the
reflected sound waves [11], [13].
Fig. 6. A Cross-Sectional View of US Transducer [17]
ii. Signal Conditioning and Acquisition: Signal
conditioning is defined to be any signal processing that
occurs only on a single scan line of beam formed RF data at a
time. This is known as mid-end processing.
iii. Signal Processing: To form the best quality ultrasound
images, it is often necessary to do a wide variety of
operations before displaying the information for human
observation. The exact processing and their order depend on
the overall system configuration and the processing that has
occurred at other parts of the system.
iv. Display System: The device/monitor that shows the
output/result of the entire US image acquisition system. The
output of the signal processing must be in such a form that is
compatible with the type of display used, otherwise the
essence of the tedious processing will not be achieved.
v. Material or Tissue under consideration: This is the
material or tissue under examination or investigation. The
exact processing depends on the US characteristics of the
materials and the type of information required. However, the
resulting signal or image quality that is its ability to restore
information existing in the material, depends (for a large part)
on the characteristics of the transducers such as central
frequency and bandwidth.
4. Mode of Operation
4.1. Parameter Synthesis
Ultrasound acquisition is a challenging task that requires
simultaneous adjustment of acquisition parameters. If the
acquisition parameters are not properly chosen, the resulting
image will have a poor quality. The parameters include:
• The depth
• The focus
• The frequency
• Its operation mode (General [GEN] or Tissue
Harmonics Imaging [THI])
For good quality image, these parameters should be
autotuned. Autotuning could be achieving by:
i. Hardware based systems for auto-tuning the acquisition
parameters which have been proposed, but were largely
abandoned because they failed to properly account for tissue
in homogeneity and other patient-specific characteristics.
ii. Software Based Systems for autotuning the acquisition
parameters which is based on image analytics inspired by the
autofocus capability of conventional cameras (autofocus in
digital camera optimizes a simple measure of image
sharpness over just one parameter, focal length) but is
significantly more challenging because the number of
acquisition parameters is large and the determination of good
quality images is more difficult to assess. Surprisingly, the set
of acquisition parameters which produce images of good
quality comprise a 1D manifold allowing for a real-time
optimization to maximize image quality. This approach starts
with a poor initial set of parameters and automatically
optimizes the parameters to produce high quality images.
4.2. Probe Design
In the proposal, a curvilinear probe enables larger tissue
penetration at the expense of anatomic image resolution. A
linear arrayed probe provides fine details but can only scan
superficial structures. Other hardware solutions include
introducing new materials to the sensors used in the
transducer and adaptive beam forming with its variations.
4.3. Software Based Design
This approach learns a low-manifold on which lie all
acquisition parameters that result in sonographer preferred
images. A machine learning system is trained to model the
image quality assessment given by experts and show how to
efficiently optimize the image quality (optimization of
acquisition parameters) over the low-dimensional manifold
of sonographer preferred parameters. In ultrasound
autofocusing, there are two challenges that must be addressed
include:
i. The development of a quality Q(I(x)) for the ultrasound
image. Here, let the configuration of ultrasound
parameters be denoted by x.
Let the image Acquired with x be denoted by I(x)
Let the quality of the image be represented by Q(I(x))
Now, the autotuning problem may be described by
Min� Q������ In the Autofocus for digital camera, x is simply the focal
length and Q(I(x)) is the image contrast.
The assessment of ultrasound image quality is a perception
characteristics that is, it is difficult to model with an explicit
formula, since it depends on several factors such as:
• Brightness
• Sharpness
• Contrast
• Resolution
• Whether the organ of interest is focus or not.
In the absence of an explicit formula for Q(I(x)), a
proposal to sample a range of images I(x) and learn the
Q(I(x)) mapping for perceptional quality was used. A support
84 G. N. Ezeh et al.: Evaluation of Ultrasound Imaging for Biomedical and Industrial Applications: A Computing Perspective
vector machine (SVM) regressor based on a set of
biologically inspired features was trained. The feature
extraction scheme uses a hierarchical approach that consists
of four layers of computational units, building an
increasingly complex and invariant feature representation by
alternating between simple S layers and complex C layers.
This hierarchical model is chosen as it emulates the object
recognition in the human visual cortex.
ii. The solution of one (i) above i.e. finding the optimal
parameter configuration x, that produce an image I(x)
with the maximum quality.
The major challenge in designing the ultrasound
autotuning is to optimize the image quality or choose the
parameter configuration that produces the best quality image.
A naïve solution may be to use a grid search for the
parameter configuration that optimizes the image quality.
However, this is very computationally very expensive and
cannot be performed in real-time acquisition systems. A key
insight to optimization is that the known relationship of the
acquisition parameter could be exploited to perform a search
over a lower-dimensional space of virtual parameters. As an
example of this relationship between acquisition parameters,
the physics of ultrasound dictates that a deeper focal depth
should require a lower frequency [23]. This was leveraged in
the optimization parameter consideration as applied in
Algorithm I
Algorithm I
Input: Default acquisition parameters x and the learned
manifold pairs (x,y). y is the representation of x on the
learned 1D manifold
Output: Parameter configuration that produces the best
quality image
Initialize xi to x and calculate Q(I(xi))
while Q(I(xi + 1)) > Q(I(xi)) do
1. Project the set of parameters xi to the lower dimensional
manifold using an interpolation of the k-Nearest Neighbors
(kNN) with k = 5, to obtain lower dimensional configuration
y.
2. Find ym the closest point to y on the manifold.
3. Take a small step t along the manifold to obtain the new
low-d parameters yi+1.
4. From the database of pairings (x,y), obtain the back
projection xi+1 that corresponds to the adjusted low-d
parameters yi+1. xi+1 is the new set of parameters in the
original parameter space.
5. Acquire a new image I(xi+1) and calculate Q(I(xi+1))
end
if Q(I(xi+1)) < Q(I(xi)) and direction of movement has never
been changed then Change the movement direction and
GOTO 1.
else
Terminate
end
Algorithm 1: Steps for automated tuning of ultrasound
acquisition.
5. Simulation
A computing perspective using a composite simulation
was adopted. This work used Field II program software tool
for the simulation of ultrasound imaging system. The
program consists of a C program and a number of MATLAB
m-functions that calls this program. All calculations were
performed by the C program, and all data were kept by the C
program. Three types of m-functions were used for:
i. Initializing the program: The routines are preceded by
“field_’’
ii. Defining and manipulating transducers: The routines are
preceded by “xdc_”
iii. Performing calculations: The routines are preceded by
“calc_”
The Field program system uses the concept of spatial
impulse responses primarily. The approach relies on linear
systems theory to find the ultrasound field for both the pulsed
and continuous wave case. This was done through the spatial
impulse response. This response gives the emitted ultrasound
field at a specific point in space as function of time, when the
transducer is excited by a Dirac delta function. The field for
this kind of excitation can is found by just convolving the
spatial impulse response with the excitation function. The
impulse response will vary as a function of position relative
to the transducer, hence the name spatial impulse response.
The received response from a small oscillating sphere is
found by acoustic reciprocity. In this case, the spatial impulse
response equals the received response for a spherical wave
emitted by a point. The total received response in pulse echo
is thus, be found by convolving the transducer excitation
function with the spatial impulse response of the emitting
aperture, with the spatial impulse response of the receiving
aperture, and then taking into account the electromechanical
transfer function of the transducer to yield the received
voltage trace.
To calculate the spatial impulse response for different
transducer geometries was very challenging, but the
simulation program circumvents this problem by dividing the
transducer surface into squares and then sums the response of
these squares to yield the response.
The time for one simulation was also a major concern. As
the squares making up the transducer aperture are small, it
was appropriate to use a far-field approximation, making
simulation simple. Another issue in keeping the simulation
time down is to use a low sampling frequency. Now, the
spatial impulse responses are calculated using sampling
frequencies in the Giga Hertz range due to the sharp
discontinuities of the responses. These discontinuities were
handled in the Field programs by accurately keeping track of
the time position of the responses and via the use of
integrated spatial impulse response as an intermediate step in
the calculations. As such, no energy is lost in the response,
which is far more important than having an exact shape of
the spatial impulse response. In this work, the Field program
achieves better by using 100 MHz sampling and approximate
the calculations, than using the exact analytic expression and
Computational and Applied Mathematics Journal 2015; 1(3): 79-87 85
GHz sampling. The simulation flowchart is shown in Fig 7.
The MATLAB script that defines each stage check is found
in [24], [25].
Fig. 7. Simulation Flowchart
6. Results
Fig. 8. Image of a cyst phantom scan at f0 = 3.5MHz
Fig. 9. Image of a cyst phantom scan at f0 = 4.0MHz
Figure 8 shows the image of a cyst phantom scan at
various frequencies (aperture centre frequency, f0). As shown
in Fig 8, the images obtained at f0 = 3.5MHz is clear enough
for one to identify the cyst phantom. By visual inspection, the
image with better particle definition is scanned at a centre
frequency, f0 = 4.0MHz; this is capture in Fig. 9.
From these images obtained through a linear transducer
aperture, it is clear that the higher the frequency the better the
quality of image obtained. But in practice there is a limit to
the maximum aperture centre frequency because it translates
to thinner transducer elements which cannot be reduce
beyond certain thickness.
7. Application
7.1. Food Technology
Until recently the majority of applications of ultrasound in
food technology involved non-invasive analysis with
particular reference to quality assessment. Such applications
use techniques that are similar to those developed in
diagnostic medicine, or non-destructive testing, using high
frequency low power ultrasound. Examples of the use of such
technologies are found in:
i. The location of foreign bodies in food
ii. The analysis of droplet size in emulsions of edible fats
and oils
iii. The determination of the extent of crystallization in
dispersed emulsion droplets
The relationship between measurable ultrasonic properties
of foods (velocity, attenuation coefficient and impedance)
and their physicochemical properties (composition, structure
and physical state) is the basis of the ultrasonic analysis. This
relationship can be established either empirically by
86 G. N. Ezeh et al.: Evaluation of Ultrasound Imaging for Biomedical and Industrial Applications: A Computing Perspective
preparing a calibration curve relating the property of interest
to the measured ultrasonic property, or theoretically by using
equations describing the propagation of ultrasound through
materials by monitoring the attenuation of an ultrasound
pulse has proved possible to determine the degree of
homogenization of fat within milk. The measurement of
ultrasound velocity in conjunction with attenuation can be
used to estimate the degree of emulsification in such
materials. It is possible to determine factors such as the
degree of “creaming” of a sample, i.e. the movement of solid
particles/fat droplets to the surface. Such information gives
details, for example, of the long term stability of fruit juices
and the stability of emulsions such as mayonnaise. The
combination of velocity and attenuation measurements shows
promise as a method for the analysis of edible fats and oil as
well as for the determination of the extent of crystallization
and melting in dispersed emulsion droplets [26].
7.2. Industrial Application
The applications include ultrasonic measurement of flow,
temperature, density, porosity, pressure, viscosity and other
transport properties, level, position, phase, thickness,
composition, anisotropy and texture, grain size, stress and
strain, elastic properties, bubble, particle and leak detection,
nondestructive testing, acoustic emission, imaging and
holography, and combinations of these. Principles,
techniques, equipment, and application data are summarized
for these areas. Most of the measurements utilize approaches
designed to respond primarily to sound speed, but some
depend on attenuation effects. Most equipment in use
involves intrusive probes, but noninvasive, externally
mounted. Transducers are being promoted in several areas.
Both pulse and resonance techniques are widely used.
Limitations due to the influence of unwanted variables are
identified in some cases [27].
7.3. Medical Application
Ultrasound imaging (sonography) uses high-frequency
sound waves to view soft tissues such as muscles and internal
organs. Because ultrasound images are captured in real-time,
they can show movement of the body's internal organs as
well as blood flowing through blood vessels.
In an ultrasound exam, a hand-held transducer is placed
against the skin. The transducer sends out high frequency
sound waves that reflect off of body structures. The returning
sound waves, or echoes, are displayed as an image on a
monitor. The image is based on the frequency and strength
(amplitude) of the sound signal and the time it takes to return
from the patient to the transducer. Unlike with an x-ray, there
is no ionizing radiation exposure with this test.
Ultrasound imaging is used in many types of examinations
and procedures. Some examples include:
i. Doppler ultrasound (to visualize blood flow through a
blood vessel)
ii. Bone sonography (to diagnose osteoporosis)
iii. Echocardiogram (to view the heart)
iv. Fetal ultrasound (to view the fetus in pregnancy)
v. Ultrasound-guided biopsies
vi. Doppler fetal heart rate monitors (to listen to the fetal
heart beat) [19], [20], [21].
8. Conclusion
Signal and image processing applications in industrial and
biomedical fields is evolving. Ultrasound imaging is a widely
used modality for real-time imaging because of its non-
invasive and non-ionizing nature. This paper has reviewed
the main ultrasound imaging systems and techniques from
1D signal to 4D images. In this work, theoretical and basic
analytical concepts were explored. The beam former design
is an excellent example of scalable approach of the US
design.
A computing algorithm was developed for parameter
optimization. A computing paradigm for US imaging of a
cyst phantom based on harmonic imaging via linear
transducer aperture was achieved. It was observed that this
transformation technique is relatively less expensive, simple,
and promising. The duration for processing the image is very
less. The processed ultrasound images were enhanced in
quality. The enhanced images may be used for predicting the
diseases inside the human body more effectively and
accurately. Future work will focus on:
- Investigating images obtained from other medical
imaging techniques are in the future plan.
- The modality of explicitly defining a function or
otherwise for image quality. In this research, this was based
on visual inspection which is not easy for machines to learn
and replicate the same behavior.
- An improved algorithm that will minimize the simulation
time, optimize significantly the challenging number of
acquisition parameters as well as help in the determination
of good quality images.
Also in the future work, a FPGA Vertex-5 [31] device will
be particularly used for US considering higher speed
sampling rate. Data Acquisition of Ultrasound Beam former
in this case will be achieved by DSP Blocks of FPGA blocks
using VHDL programming [32].
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