Engineering, 2013, 5, 31-36
doi:10.4236/eng.2013.55B007 Published Online May 2013 (http://www.scirp.org/journal/eng)
Human Brain Microwave Imaging Signal Processing:
Frequency Domain (S-parameters) to
Time Domain Conversion
Kim Mey Chew, Rubita Sudirman, Nasrul Humaimi Mahmood, Norhudah Seman, Ching Yee Yong
Infocomm Research Alliance, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Johor, Malaysia
Email: rubita@fke.utm.my
Received 2013
ABSTRACT
The paper presents the microwave signal processing method using MATLAB based on the result of microwave imaging
system simulation developed using Computer Simulation Technology (CST). The simulation system contains a trans-
mitting/receiving antenna, human brain and a tumor inside the brain model. The source signal, microwave signal oper-
ates from 1 to 10 GHz. The generated scattering parameters (S-parameters) are in frequency domain form. This paper
describes in detail regarding the signal conversion from frequency domain to time domain through proposed Inverse
Fast Fourier Transform (IFFT) method as well as the noise filtering process. Peaks detection process was performed in
order to identify the time delay of the reflection points at different Y-axis positions.
Keywords: Microwave Signal; Signal Processing; Scattering Parameters; Time Domain; IFFT
1. Introduction
Since years, lots of microwave engineers put efforts to
implement non-ionizing electromagnetic waves in medi-
cal field to detect cancer in the human body. The efforts
aim to add another alternative for cancer detection be-
sides X-ray and Magnetic Resonance Imaging (MRI).
In return, there is significant progress in using micro-
waves for breast cancer detection. Microwave imaging
enables seeing of the internal structure for an object
through the illuminating of the object with low power
electromagnetic wave at microwave frequencies. Based
on the achievement so far on breast cancer detection re-
search, this study is performed for deeper investigation.
This is a radar-based microwave imaging research
where a short pulse is transmitted from a single ul-
tra-wideband (UWB) antenna into the human brain
phantom. The back-scattering parameters are detected by
the same antenna. This process is repeated for different
locations around the human brain phantom. The presence
of a tumor would produce strong scattering, and such a
response can be interpreted to estimate the location of the
tumor. The travel times of signals at various locations are
recorded and computed [1].
2. Literature Review
2.1. Scattering Parameters
Microwave imaging is conducted by transmitting a
sequence of electromagnetic waves through the human
brain phantom and measuring the scattered field at the
perimeter of the phantom. The electromagnetic signals fed
to the transmitting antennas and captured by the receiving
antennas are characterized by scattering parameters (S-
parameters) at the terminal planes to which the two-port
vector network analyzer (VNA) is calibrated [2].
The S-parameters measured by the VNA are: Snn and
Snm. Snn refers to the ratio of reflected signal at port n to
the incident signal at port n while Snm refers to the ratio
of transmitted signal measured at port n to the incident
signal at port m. Normally, Snn is known as the reflection
coefficient at port n and Snm is the transmission coeffi-
cient from port m to n.
S11 is known as reflection coefficient at antenna 1 un-
der the condition where antenna 2 is terminated in the
impedance of its connecting cable at 50 to avoid signal
enters the region from antenna 2. With the same condi-
tion for antenna 2, S21 is the forward transmission coeffi-
cient of signals from antenna 1 to antenna 2. S22 is the
reflection coefficient at antenna 2, under the condition
that antenna 1 is terminated in the impedance of its con-
necting cable at 50 to avoid signal enters the region
from antenna 1. With the same condition for antenna 1,
S12 is the reverse transmission coefficient of signals from
antenna 2 to antenna 1. The S-parameters for N antennas
are expressed as [3],
Copyright © 2013 SciRes. ENG
K. M. CHEW ET AL.
32
E1r = S11 E1i + S12 E2i + S13 E3i + …+ S1N ENi
E2r = S21 E1i + S22 E2i + S23 E3i + …+ S2N ENi
E3r = S31 E1i + S32 E2i + S33 E3i + …+ S3N ENi
ENr = SN1 E1i +SN2 E2i + SN3 E3i + …+ SNN ENi (1)
This study focused on S11 using one antenna as both
transmitter and receiver.
2.2. Frequency Domain and Time Domain
The frequency domain is the domain of mathematical
functions or signals with respect to frequency rather than
time in electronics, control systems engineering and sta-
tistics fields [4]. The frequency domain graph shows how
much of the signal lies within each given frequency band
over a range of frequencies. A change of a signal over
time is able to be identified from time-domain graph.
In different field, frequency domain and time domain
represent different entity. But all given functions or sig-
nals can be converted between the time and frequency
domains with a pair of same mathematical operators
called a transform. For example, the Fourier transform.
Fourier transform decomposes a function into the sum of
a potentially infinite number of sine wave frequency
components. The 'spectrum' of frequency components is
the frequency domain representation of the signal. The
frequency domain function can be converted back to time
function using inverse Fourier transform.
2.3. Inverse Fast Fourier Transform (IFFT)
There are advantages of analyzing transformed time do-
main data from frequency domain rather than direct
measurement of time domain data [5]. These include
better signal-to-noise (SNR) ratio due to the narrowband
measurements, the possibility of performing error correc-
tion by measuring known standards, as well as the free-
dom from time jitter and zero-level drift [6]. A variation
of Inverse Fast Fourier Transform (IFFT) is used to
transform the frequency domain to the time domain.
These make the user easier to magnify on their range of
interest of the data for specific time or distance.
3. Methodologies
Simulation Modeling
The proposed human brain model was developed using
Computer Simulation Technology (CST). Characteristics
and specifications of the proposed simulation system are
mentioned in [7]. Figure 1 shows the human brain phan-
tom simulation with and without tumor. In the proposed
simulation system, the antenna model moves up to 19
steps along the Y-axis with each step differs by 10 mm
apart along the Y-axis.
Move up
(a)
Move up
(b)
Figure 1. Simulated human brain phantom: (a) With tumor;
(b) Without tumor.
4. Result
The S-parameters generated from the simulation system
is in frequency domain format, known as frequency do-
main signal. The main goal of this study is to process the
frequency domain signal and transform to time domain
format through IFFT method. The transformed time do-
main results represent the reflection coefficient info for
the human brain phantom simulation over time. The sig-
nal is then filter to eliminate the noise and ripples.
4.1. Frequency Domain and Time Domain Result
Figure 2 shows the generated frequency domain signal
of the human brain phantom simulation with the exis-
tence of tumor at 19 different points of microwave pene-
trating locations along Y-axis. The comparisons for the
frequency domain signal of the human brain phantom
simulation with and without tumor are described in detail
in [7]. Figure 3(a) shows the transformation results after
IFFT process. Before filter was applied, the signal dis-
torted with ripples. Filter is applied to smooth up the time
domain signal for higher accuracy.
Copyright © 2013 SciRes. ENG
K. M. CHEW ET AL.
Copyright © 2013 SciRes. ENG
33
12345678910
-70
-60
-50
-40
-30
-20
-10
0
Frequency (GHz)
S11/Magnitude (dB)
P os iti on 1 (P1)
P os iti on 2 (P2)
P os iti on 3 (P3)
P os iti on 4 (P4)
P os iti on 5 (P5)
P os iti on 6 (P6)
P os iti on 7 (P7)
P os iti on 8 (P8)
P os iti on 9 (P9)
P osi ti o n 10 (P 10)
P osi ti o n 11 (P 11)
P osi ti o n 12 (P 12)
P osi ti o n 13 (P 13)
P osi ti o n 14 (P 14)
P osi ti o n 15 (P 15)
P osi ti o n 16 (P 16)
P osi ti o n 17 (P 17)
P osi ti o n 18 (P 18)
P osi ti o n 19 (P 19)
Figure 2. Frequency domain of the simulated human brain ph antom c ontained tumor at 19 different points along Y-axis.
00.5 11.5 22.5 33.5 44.5 5
0
0.005
0.01
Time (ns)
Reflection coefficient, r
With Tumor at Position 1 (P1)
With Tumor at Position 2 (P2)
With Tumor at Position 3 (P3)
With Tumor at Position 4 (P4)
With Tumor at Position 5 (P5)
With Tumor at Position 6 (P6)
With Tumor at Position 7 (P7)
With Tumor at Position 8 (P8)
With Tumor at Position 9 (P9)
With Tumor at Position 10 (P10)
(a)
00.5 11.5 22.5 33.5 44.5 5
0
0.001
0.002
0.003
0.004
0.005
0.006
0.007
0.008
0.009
0.01
Time (ns)
Reflection coefficient, r
With Tumor at Position 1 (P1)
With Tumor at Position 2 (P2)
With Tumor at Position 3 (P3)
With Tumor at Position 4 (P4)
With Tumor at Position 5 (P5)
With Tumor at Position 6 (P6)
With Tumor at Position 7 (P7)
With Tumor at Position 8 (P8)
With Tumor at Position 9 (P9)
With Tumor at Position 10 (P10)
(b)
Figure 3. Time domain result after IFFT process (a) Without smoothing process; (b) With smoothing process.
K. M. CHEW ET AL.
34
4.2. Noise Filtering
A simple MATLAB smoothing method, mslowess was
applied to filter the time domain result. mslowess filter
the distorted signal using nonparametric method. mslow-
ess assumes the input vector may not have uniformly
spaced separation units and therefore, the sliding window
for smoothing is centered using the closest samples in
terms of the input value but not input index. Figure 3(b)
shows the filtered time domain result after mslowess
smoothing process and Figure 4 shows the zoom plot of
the smoothed signal over the original signal.
4.3. Time Delay and Peaks Detection
Figure 5 shows the time domain result of the human
brain phantom simulation with and without tumor at po-
sition 1, 2, 5, 7, 8, 9 and 19.
In Figure 5(a), the reflection time decrease when the
antenna is moving up and close to the human brain
model at position 5 to 9. Besides, the reflection points of
the human brain phantom model with and without tumor
also differ. The reflection point of the human brain
model with tumor is slightly delayed from the reflection
point of the human brain model without tumor. In Figure
5(b), the reflection coefficient increases when the an-
tenna moving close to tumor. The amplitude shows tu-
mor contributes to the reflection coefficient. In Figure
5(c), the reflection coefficients at position 1, 2 and 19 for
both with and without tumor model are slightly the same.
Figure 6 shows the enlarged image of the peaks detec-
tion for both human brain model with and without tumor
at position 8. From the graph, the reflection time for the
human brain model with tumor are slower than the hu-
man brain model without tumor.
12345678910
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1x 10
-3
Time (ns)
Reflection coefficient, r
Original signal
S moot hed si gnal
Figure 4. Plot of the smoothed signal over the original signal.
0.5 11.5 22.5 33.5
0
1
2
3
4
5
6
7
8
x 10
-3
Time (ns)
Reflection coefficient, r
P5 Without Tumor
P5 With Tumor
P7 Without Tumor
P7 With Tumor
P8 Without Tumor
P8 With Tumor
P9 Without Tumor
P9 With Tumor
Reflection (Without Tumor) :
- Reflection time decrease when the
antenna moving close to model
(a)
Copyright © 2013 SciRes. ENG
K. M. CHEW ET AL. 35
0.5 11.5 22.5 33.5
0
1
2
3
4
5
6
7
8
x 10
-3
Time (ns)
Reflection coefficient, r
P5 Without Tumor
P5 With Tumor
P7 Without Tumor
P7 With Tumor
P8 Without Tumor
P8 With Tumor
P9 Without Tumor
P9 With Tumor
Reflection (WithTumor) :
- Reflection coefficient increase when the
antenna moving close to tumor
(b)
00.5 11.5 22.5 33.5 44.5 5
0
0.5
1
1.5
2
2.5
3
x 10
-3
Time (ns)
Reflection coefficient,
r
P1 Without Tumor
P1 With Tumor
P2 Without Tumor
P2 With Tumor
P19 Without Tumor
P19 With Tumor
(c)
Figure 5. Comparison of time domain result of the simulated human brain phantom with and without tumor.
0.5 11.5 22.5 33.5 44.5 5
0
1
2
3
4
5
6
7
8
9
x 10
-3
X: 1.06
Y: 0.004438
Time(ns)
X: 2.082
Y: 0.0008043
X: 1.06
Y: 0.005211
X: 3.181
Y: 0.000373
X: 2.121
Y: 0.00081X: 3.221
Y: 0.0003851X: 4.203
Y: 0.0002179X: 4.281
Y: 0.0002351
Reflection coefficient, r
Without Tumor
maximum point
minimum point
With Tumor
maximum point
minimum point
Figure 6. Time domain peaks detection at P8.
Copyright © 2013 SciRes. ENG
K. M. CHEW ET AL.
Copyright © 2013 SciRes. ENG
36
5. Discussion
The S-parameters results were generated by the simula-
tion system using the licensed CST software. The simu-
lation system will be enhanced in future by developing
more models with different specifications for measure-
ments.
As shown in Figure 5(a), the reflection times were
decrease when the antenna is moving up close to the hu-
man brain model. This effect was caused by the spherical
shape of the human brain model which signals are always
reaching to the outer layer of the human brain model at
different point of time. The increment of reflection coef-
ficient in Figure 5(b) shows both models at the same
position were affected by tumor model.
Based on these findings, peaks detection was applied
to the transformed time domain signal to mark the reflec-
tion points. Image processing was applied to the list of
reflection points in order to produce a spatial domain
image of the simulation with clearly describing the loca-
tion of tumor inside the human brain phantom.
6. Conclusions
The obtained S-parameters result was successfully trans-
formed into the time domain format using IFFT method.
The mslowess smoothing process is filtered up all the
noise for a more accurate transformed time domain sig-
nal. IFFT was applied to the reflection points of the sig-
nal since the time domain signal is appeared easier for
visualization and analysis. The reflection points are then
processed to produce a spatial domain image of the hu-
man brain model with an estimated tumor location. The
study is still in a preliminary stage and for future work,
different models are developed and processed to enhance
the current method in order to develop a human brain
tumor detection algorithm.
7. Acknowledgements
The authors are deeply indebted and would like to ex-
press our gratitude to the Universiti Teknologi Malaysia
for supporting and funding this study under Research
Universiti Grant (Q.J130000.2636.05J69). As well as
Ministry of Science, Technology and Innovation (MO-
STI) grant support (4S056) and MyPhd Scholarship
Scheme from Ministry of Higher Education (MOHE).
Our appreciation also goes to the Electronics and Bio-
medical Instrumentation (bMIE) for their cooperation in
the research work.
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