Reprinted from

REGULAR PAPER

Detection of Boundaries of Carotid Arterial Wall by Analyzing Ultrasonic RF Signals
Nabilah Ibrahim, Hideyuki Hasegawa, and Hiroshi Kanai

Jpn. J. Appl. Phys. 51 (2012) 07GF07# 2012 The Japan Society of Applied Physics

Person-to-person distribution (up to 10 persons) by the author only. Not permitted for publication for institutional repositories or on personal Web sites.

REGULAR PAPER

Japanese Journal of Applied Physics 51 (2012) 07GF07
DOI: 10.1143/JJAP.51.07GF07

Detection of Boundaries of Carotid Arterial Wall by Analyzing Ultrasonic RF Signals
Nabilah Ibrahim1 , Hideyuki Hasegawa1;2 , and Hiroshi Kanai1;2 
12Graduate School of Engineering, Tohoku University, Sendai 980-8579, Japan
Graduate School of Biomedical Engineering, Tohoku University, Sendai 980-8579, Japan

Received November 18, 2011; accepted January 10, 2012; published online July 20, 2012
In line with the fact that the intima–media thickness (IMT) of the carotid arterial wall is the most frequently used indicator to diagnose
atherosclerosis by ultrasound, it is essential to accurately estimate the thickness of the intima–media complex (IMC) boundaries, i.e., the lumen–
intima boundary (LIB) and media–adventitia boundary (MAB). In this study, an improved adaptive model of an ultrasonic echo was developed forthe model to realize better fitting to the reference RF echo, which is measured from a glass plate, using a Gaussian window for the envelope
function of the adaptive model. Using the mean squared error (MSE) method, the envelope of the improved adaptive model (multiply thesinusoidal wave with the Gaussian window) was fitted with the envelope of an RF echo measured in vivo to estimate the boundaries of the carotid
arterial wall. Firstly, a computer simulation of the carotid arterial wall was conducted to evaluate the accuracy of boundary detection using theenvelope of the improved adaptive model. In the simulation, IMT was set at 0.50 mm in a 7.2-mm-long short segment in the longitudinal direction.
The IMT estimated by the proposed method was 0.54 mm. The 8% error between the true and detected IMTs showed the high accuracy of theenvelope of the improved adaptive model in boundary detection. In the in vivo measurement, for the 4.8-mm-long short segment in the longitudinal
direction, the average IMT automatically estimated by the proposed method was 0.57 mm. The result was compared with those obtained by theprevious method and manually. The IMT estimated by the previous method, which uses an RF adaptive model, was 0.59 mm and the manually
determined IMT was 0.56 mm. The smaller difference between the results obtained by the proposed method and manually verified that boundary
detection by the proposed method was better than that by the previous method. # 2012 The Japan Society of Applied Physics

1. Introduction

Cardiovascular diseases are responsible for over 17.3 million
deaths yearly and are the leading causes of death in theworld.1) Moreover, in 2008, out of the 17.3 million
cardiovascular deaths, 7.3 million were caused by heart
attack and 6.2 million were caused by stroke. The rise in
the prevalence of atherosclerosis or obesity is considered
the main cause of cardiovascular diseases.2–4) Hence, theassessment of the risk of atherosclerosis and its diagnosis at
an early stage have become very important. Early-stage
atherosclerosis changes the structure of vessels and arteries,
where smooth muscle cells in media (middle layer of thearterial wall) are associated with atherosclerosis lesions.5)
The migration of smooth muscle cells to the intima(innermost layer of the arterial wall) also occurs, resulting
in the thickening of the arterial wall. As a result, bloodvessels constrict and the risk of cardiovascular diseases
increase. Parallel to the fact that the intima–media thickness(IMT) of the carotid arterial wall is the most frequently usedindicator to diagnose atherosclerosis by ultrasound,6) it isessential to accurately estimate the intima–media complex(IMC) boundaries, i.e., the lumen–intima boundary (LIB)
and media–adventitia boundary (MAB). Additionally, Daviset al.7) have reported that IMT measurement in the carotid
artery is more reproducible than the IMT measurement in theabdominal aorta because of the smaller amount of tissue
between the ultrasonic probe and the carotid arterial wall.
Various techniques for detecting the boundaries andestimating the IMT have been proposed.5–14) Invasive andmanual techniques for boundary detection8,9) are notdesirable because the former requires surgical operationand the latter lacks reproducibility. Thus, noninvasive andautomated techniques are necessary.10–14) Standard medical
imaging tools such as computed tomography (CT) andmagnetic resonance imaging (MRI) are widely used forthe assessment of the IMT because they produce images
with good spatial resolution. Boussel et al.10) compared theE-mail address: kanai@ecei.tohoku.ac.jp

measurement of the IMT and thickening of the carotid wallusing MRI with that using ultrasound and found a very good
agreement in results between these techniques. However, themethod using MRI is time-consuming and cost-ineﬀective
compared with that using ultrasound. Because ultrasonicdevices are inexpensive, portable, and provides real-time
results, IMT measured by the ultrasonic technique is morepractical. Savithri and Purushothaman11) estimated thearterial wall boundaries using a conventional intensity
gradient method. However, their method is signiﬁcantly
inﬂuenced by ultrasonic scattering in in vivo measurement.
For the measurement of the IMT using a dynamic
programming method, Liang et al.12) were able to detect
the above boundaries precisely. However, their method
requires retraining when the image characteristics change.
Loizou et al.13) used an active contour method for carotid
segmentation and IMT measurement using ultrasonicimages. The disadvantage of this method is that the solution
often falls into the local minima rather than into the global
minima. To automatically localize boundaries and estimate
the IMT, Rossi et al.14) proposed a novel algorithm that uses
a sustain-attack ﬁlter and a multi-scale barycenter ﬁlter. The
detection of MAB position performed using a sustain-attack
ﬁlter requires the determination of the diameter of the artery.
Thus, the near wall adventitia-media interface and far wall
media–adventitia interface should be carefully positioned.
However, on the near wall, echo from the adventitia-media
interface overlaps with the high echo from the adventitia
and reverberates from the surrounding tissue owing to thesidelobe. Therefore, the estimation of the MAB position by
this method is inaccurate.
For the accurate assessment of the LIB and MAB
positions, a technique using template matching between
the adaptive model and the RF echo is used.15) In thistechnique, the assessment of the boundary positions isanalyzed in the arterial longitudinal section because it
provides a clearer description of the border between thelumen and the arterial wall. Moreover, in the longitudinal
section, the averaging of the IMT over a short segment of theartery is applicable. However, in this method, the diﬀerences

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between the adaptive model and the RF echo measured
in vivo and even those between the adaptive model and themeasured reference RF echo (echo from a point scatterer)
are signiﬁcant. The template matching method was also
reported for the accurate estimation of boundaries, but it
only detects the MAB.16) To overcome and/or reduce suchdrawbacks, ﬁrst, we consider an improved adaptive model toobtain better ﬁtting to the reference RF echo by taking theenvelope signal into account and by using a Gaussian
window as an envelope function of the adaptive model. Notethat an ultrasonic (US) pulse is transmitted with a Gaussianenvelope,17) and that it retains its form during the propagation. In this study, applying the noninvasive ultrasonic
template-matching technique, we automatically estimatedthe LIB and MAB positions accurately using the envelope of
the improved adaptive model. To conﬁrm the reliability ofthe proposed method and the accuracy of the improved
adaptive model, a computer simulation of the carotid arterial
wall is conducted and boundary positions are detected.
Moreover, the correlation between the improved adaptivemodel signal and the reference RF signal and also thatbetween their envelopes are presented and compared. In
addition, we showed the comparative results between the
proposed automated method and the manual method.

Lumen
IntimaMedia

Adventitia

Fig. 1. (Color online) Schematic of longitudinal section of arterial wall(left) and ultrasound echo (right).

SSD-6500 ALOKA
Ultrasonic diagnostic
equipment
lateral, xPC (for data
analysis)linear probe (10 MHz)axis, ydglass plate

2. Principle

The structure of the arterial wall consists of three layers, i.e.,
the intima, media, and adventitia, which enable arteries tomaintain their function and strength. The very thin intima
layer consists of a single layer of endothelial cells lining thearterial wall and the media layer, which is the middle layerof the artery consisting of a complex network of smoothmuscle cells, elastin, and collagen ﬁbrils. Meanwhile, thethick adventitia layer, which is the mechanically strong outerlayer, consists mainly of ﬁbroblasts, ﬁbrocytes, and bundles
of collagen ﬁbrils.18) These diﬀerent characteristics of therespective layers produce a particular ultrasonic echo
composed of reﬂected echoes from the LIB and MAB, as
shown in the schematic view of the far-wall longitudinal
section in Fig. 1. In an echo signal from the far wall, anultrasonic echo reﬂected from the LIB is found, followed bya slightly low amplitude region in the media, and anotherecho is found around the MAB.19) Since the distance
between the LIB and the MAB, which is deﬁned as the IMT,
is very small, the initial positions of the LIB and MAB
should be determined accurately. Subsequently, in thepresent study, we modeled the echo from the arterial wallusing those from the LIB and MAB. The model echoes
imitated a reference echo from a glass plate.
2.1 Reference-RF echoFirst, echoes from a glass plate are measured in a water tank,considering the depths of the glass plate from a linear
transducer of 10 MHz (ALOKA UST-5410), which isconnected to the modiﬁed ultrasonic diagnostic equipment(ALOKA SSD-6500), as shown in Fig. 2. The glass plate is
set horizontally in the middle of the water tank, ﬁxed by aholder that is connected to an automated XYZ stage. The
depth of the carotid arterial wall from the skin diﬀers fromperson to person.20) The purpose of this experiment is to
conﬁrm that the same waveforms (without taking amplitude

water tank

automatic xyz-stage

Fig. 2. (Color online) Schematic of experimental setup for measurementof echoes from glass plate (reference echo).

into account) can be obtained even at diﬀerent distancesfrom the transducer to the glass plate. Therefore, the depth
denoted as d was changed by moving the glass plate in thevertical direction. Considering the typical depths of the
carotid arterial wall from the skin, d was set from 13.7 to18.7 mm at 1 mm intervals. In addition, the acoustical focusof the transducer was set at 20 mm. Figure 3 shows theamplitude-normalized RF echoes from the glass plate. To
evaluate the similarity between echoes, the correlation
between all the combinations of echoes is calculated. To
obtain the maximum correlation coeﬃcient between all the
combinations of echoes accurately, a higher time resolution
is needed. Therefore, the interpolation was applied tothe echo signals before the correlation is calculated. By
employing an interpolation technique,21) the maxima of
correlation functions are obtained, as shown in Fig. 4. For all
the calculations, the maximum values of the correlation
functions exceed 0.97. From these results, it is conﬁrmedthat the waveforms of echoes can be assumed to be the samefor all the diﬀerent depths.
2.2 Optimum adaptive model

In a previous method,15) the adaptive model was ﬁtted to thereference RF echo from a wire of 35 m diameter. However,there are still signiﬁcant diﬀerences that would aﬀect
boundary detection. In this study, the improved adaptivemodel signal x^i ðnTs Þ is deﬁned by multiplying the sinusoidal
wave with the Gaussian window wðnTs Þ, and the envelopee^i ðnTs Þ of the model signal is estimated using the Hilberttransform as

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1

d=14.7mm

0.3

σ = 0.075 [μs]σ = 0.1 [μs]σ = 0.125 [μs]σ = 0.15 [μs]0.25

σ = 0.175 [μs]σ = 0.2 [μs]

0.35

–1
1
–1
1

Amplitude

d=15.7mm

d=16.7mm

–1

0.2
0.15

1

d=17.7mm
0.1–11

d=18.7mm

–1
0

50

100

150

200

250

300

350

400

450

0.05
0

500

Time (μs)
Fig. 3. (Color online) Measured glass-plate echoes at distances of 13.7,
14.7, 15.7, 16.7, 17.7, and 18.7 mm between transducer and glass plate.

0.1

0.15

0.2

Time [μs]

0.25

0.3

0.35

0.4

0.95

0.99

0.985

0.98

0.9

0.85

0.8

0.75

0.975

0.97

0.05

1

correlation coefficient R

0.995

0

Fig. 5. (Color online) Gaussian windows at diﬀerent standard
deviations fg.

13.7mm
14.7mm
15.7mm
16.7mm
17.7mm
18.7mm1

Correlation Coefficient

σ = 0.025 [μs]σ = 0.05 [μs]

0.4

–1
1

Normalized Amplitude

0.45

d=13.7mm

14

15

16

17

18

19

0.7
0

20

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

standard deviation σ [μs]Depth (mm), d
Fig. 4. (Color online) Correlation coeﬃcients between glass-plate echoes
measured at diﬀerent depths.

Fig. 6. (Color online) Correlation coeﬃcients between envelopes ofmodel echo and reference echo plotted as functions of standard deviation .

e^i ðnTs Þ ¼ jZi ðnTs Þj;x^i ðnTs Þ ¼ ai  sin½2f0 ðnTs   i Þ  wðnTs   i Þ;1ðnTs  Þ2wðnTs Þ ¼ pﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ exp 
;
2 2
2 2

result in Fig. 6 shows that at  of 0.525 s, the maximum
correlation between the envelopes of the model andreference echoes is 0.98. In addition, the envelope Er ðnTs Þof the reference echo is also estimated by applying
the Hilbert transform to the reference RF echo xr ðnTs Þ
expressed as

ð1Þ
ð2Þ
ð3Þ

where Zi ðnTs Þ is the analytic signal of x^i ðnTs Þ obtained by
the Hilbert transform, Ts is the sampling interval (sampling
frequency of 40 MHz), and the center frequency f0 ¼
7:5 MHz is set by estimating the frequency at the maximumof power spectrum of the reference RF echo. In this paper,
the reference RF echo is an echo from a glass plate shownin Fig. 3. The coeﬃcients a1 and a2 (i ¼ 1: LIB, 2: MAB)
show theirs amplitude,  1 and  2 are the time delays of the
LIB and MAB echoes, respectively,  is the central time of
the model signal, and  is the standard deviation of theGaussian window wðnTs Þ, which is determined so that thecorrelation between the envelope of improved adaptivemodel and the envelope of the reference RF echo ismaximum. In conjunction with setting the parameter of theGaussian window to obtain the optimum envelope of
improved adaptive model, the standard deviation  wasused as a changeable parameter. Figure 5 shows theGaussian windows at diﬀerent standard deviations fg. The

Er ðnTs Þ ¼ jZr ðnTs Þj;

ð4Þ

where Zr ðnTs Þ is the analytic signal of xr ðnTs Þ. Figure 7(a)shows the optimum correlation (0.98) between the envelopesof the improved adaptive model (blue line) and the envelopeof the reference RF echo (black line). The correlationcoeﬃcient is 0.98, which is 7.1% higher than the maximum
correlation (0.91) between the improved adaptive RF modeland the reference RF echo. Also, it is 4.1% higher than the
maximum correlation (0.94) between the adaptive model15)(using Hanning window) (blue line) and the reference RFecho (black line) [Fig. 7(b)]. Therefore, the envelope of the
improved adaptive model should be used for the LIB and
MAB echo models. However, considering the second peak
(supposed to be the reverberation component) that appears in
the envelope of the reference RF echo [Fig. 7(a)], the LIB
and MAB echo models are constructed from the two peaks,

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1
Model Sig.
Reflected Glass Plate Sig.

0.8

0.6

0.4

0.2

0

0.6
0.4
0.2
0
–0.2
–0.4
–0.6
–0.8

0

(a)

Model Sig.
Reflected Glass Plate Sig.

0.8

Normalized Amplitude

Normalized Amplitude

1

5

10

15

20

25

30

35

40

45

(b)

–1
0

5

10

15

Time (μs)

20

25

30

35

40

45

Time (μs)
1Model Sig.
Reflected Glass Plate Sig.

in vivo Measured signal
LIB echo model
MAB echo model

0.9
0.8

0.8
0.7

Amplitude

Normalized Amplitude

1

0.6

0.4

0.6
0.5
0.4
0.3

0.2
0.2
0.1

0

(c)

(d)
0

5

10

15

20

25

30

35

40

45

0

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

Time [μs]Time (μs)

Fig. 7. (Color online) (a) Envelope of improved-adaptive model (one peak) and envelope of reference echo, (b) adaptive model and reference RF echo,(c) envelope of improved adaptive model (two peaks) and envelope of reference echo, and (d) LIB and MAB echo models and envelope of echo measured
in vivo, which give maximum correlation coeﬃcients.

Linear array transducer

as shown in eq. (5). The second peak overlaps with the ﬁrst
peak and is ﬁxed to be 10.5 s [Fig. 7(c)].
E^ i ðnTs Þ ¼ e^i ðnTs Þ þ e^i ðnTs  Þ;ysub element 96(active element)

ð5Þ

ð6Þ

Furthermore, to further conﬁrm that the two-peak echomodel is better than the one-peak echo model in boundary
detection, both of the models are used and validated bysimulation and in vivo measurement.
3. Simulations and Experiments
3.1 Simulation experimental results

A longitudinal B-mode image of a carotid artery wall was
simulated using the Field II program.22,23) To create asynthetic carotid artery wall, the parameter of the linear
transducer used in the in vivo measurement was referred to.
The transducer has 192 total elements, 96 sub elements, anda 0.2-mm-interval element pitch.24) The element height and...

zwhere  is the delay time of the second peak. Figure 7(c)shows the correlation between the envelope of the improved
adaptive model that consists of two peaks (blue line) andthe envelope of the reference RF echo (black line). The
correlation coeﬃcient is 0.99, which is higher than the
maximum correlation coeﬃcient of 0.98 between theenvelope of the improved adaptive model that consists of
one peak and the envelope of the reference RF echo.
^ s Þ of an echo
Therefore, the ﬁnal equation of the model EðnTfrom the arterial wall is deﬁned using the LIB E^ 1 ðnTs Þ and
MAB E^ 2 ðnTs Þ echo models as^ s Þ ¼ E^ 1 ðnTs   1 Þ þ E^ 2 ðnTs   2 Þ:EðnTTotal element 192
xheightkerf
direction of scan

16.0 mm
5 mm

pitch

scatterers set as the
LIB and MAB onnear and far walls

IMT = 0.5 mm
Acoustical focus

Fig. 8. (Color online) Schematic of linear array transducer and artery incomputer simulation.

kerf were set at 6 and 0.01 mm, respectively. A boxcar
window was used for apodization in both transmission andreception which had a sub aperture size of 96 elements. The
acoustical focus was set at 16 mm. The excitation of thetransducer consisted of three cycles of a 7.5 MHz sinusoid
wave multiplied by the Gaussian window, which was thesame as the improved adaptive model signal. To construct
the longitudinal section, small point scatterers with 20 mintervals are placed at interfaces of the near and far walls,
considering the typical carotid artery wall depth,20) as shownin Fig. 8. In this simulation, no attenuation is considered.

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0.4 mm

9

0.4

10

0.8

12
13
14

1.2

depth [mm]

11

15
16

0.7

1.5

2.2

3.0

3.7

4.5

5.2

6.0

6.7

length [mm]2.0

axial length
[mm]

1.6

17

(a)

9

9

10

10

11

11

depth [mm]depth [mm]Fig. 9. (Color online) Simulated longitudinal B-mode image of carotid artery wall and RF echoes from far wall in beam of interest.

12
13

12
13

14

14

15

15

16

(b) 16

17

0.7

1.5

2.2

3.0

3.7

4.5

5.2

6.0

17

6.7

length [mm]

0.7

1.5

2.2

3.0

3.7

4.5

5.2

6.0

6.7

length [mm]Fig. 10. (Color online) Detected boundaries with minimum errors obtained with (a) one-peak echo model and (b) two-peak echo model used in detecting
LIB and MAB positions, respectively.

However, a number of scatterers are randomly placed below
the adventitia layer of the far wall, which simulated thesurrounding tissues at the outer parts of adventitia. The
speed of sound in the tissue was set at 1540 m/s. Figure 9shows the simulated longitudinal B-mode image of carotid
arterial walls, where the electronic focus was placed on the
far wall. Simulated echoes in the region surrounded by the
red line in the B-mode image are shown in Fig. 9. In thisstudy, the detection of the LIB and MAB positions is carriedout using the normalized mean squared error (MSE) method,which determines the LIB and MAB so as to minimize thediﬀerence between the envelope of the improved adaptive^ s Þ and the envelope of the simulated or measured
model EðnTsignal. Let us deﬁne the envelope of the simulated echo
signal as Ec ðnTs Þ. The RMS error ð1 ; 2 Þ is deﬁned asN
1Xð 1 ;  2 Þ ¼

1 n¼0
N

^ s ; 1 ; 2 Þj2
jEc ðnTs Þ  EðnT
N
1
X

:
jEc ðnTs Þj

ð7Þ

2

n¼0

The results of the boundary detection for the simulated data
are shown in Fig. 10. In Fig. 10(a), the boundaries aredetected by applying the one-peak echo model of the LIB
and MAB, while in Fig. 10(b) the results of the boundary

detection using the two-peak echo model of the LIB and
MAB are shown. By comparing the detected boundaries(LIB: red crosses, MAB: green crosses) plotted on thesimulated B-mode image of the arterial wall image in
Fig. 10(a), the detected boundaries obtained using the twopeak echo model shown in Fig. 10(b) is better than thoseobtained by the one-peak echo model. As shown in
Fig. 10(a), there are a few beams in which the MAB is
misdetected, especially when the one-peak echo model wasused. By using the two-peak echo model, the results of these
positions become closer to the MAB positions set manually[Fig. 10(b)]. In this simulation, IMT was set at 0.5 mm in a
7.2-mm-long short segment in the longitudinal direction.
The estimated average IMT was 0.54 mm. The 8% error
between the true and detected IMTs shows the high accuracyof the envelope of the improved adaptive model in boundary
detection by the proposed method.
3.2 Boundary detection for in vivo ultrasonic data

In the in vivo measurement, the same modiﬁed ultrasonicdiagnostic equipment (ALOKA SSD-6500) was used. The
right carotid artery of a healthy male subject was scanned
using a 10 MHz linear array transducer (ALOKA UST5545), and the RF signals were sampled at 40 MHz at a 16
bit resolution. The acoustical focus of the transducer was

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10logα [dB]
5

11.5

M 80

12.5

70

P

0
-5

τMAB/Tsdepth [mm]

90

13.5

14.5

15.5

60
50

-10

40
-15
30

X

20

16.5

-20

10
17.5

(a)

0.7

1.5

2.2

3.0

3.7

4.5

5.2

6.0

5

L 10

15

20

25

τLIB/Ts

10logα [dB]
τMAB/Ts

L
11.5

depth [mm]

12.5

M

-25

(a)

6.7

length [mm]

5

90
80

0

70
-5

60

13.5

50
14.5

M

-10

30

X

15.5

P

40

-15

20
16.5

10

-20

(b)
17.5

(b)

0.7

1.5

2.2

3.0

3.7

4.5

5.2

6.0

5

6.7

length [mm]

L

10

15

20

25

τLIB/Ts

Fig. 11. (Color online) In vivo RF and envelope signals from the far wallin beam of interest and detected boundaries displayed on B-mode image.
(a) Using one-peak echo model. (b) Using two-peak echo model.

Fig. 12. (Color online) The error distribution  in the beam of interest(indicated in Fig. 11) indicates the optimum positions of LIB and MAB(point P), marked as L and M, respectively. (a) Using one-peak echo model.
(b) Using two-peak echo model.

16 mm. The minimization of the diﬀerence between the^ s Þ and theenvelopes of the improved adaptive model EðnTenvelope of the in vivo RF echo EðnTs Þ determines the LIB
and MAB positions. Since the average IMTs range from 0.5
to 1.0 mm for a healthy person25) and the IMT varies fromperson to person, the distance between LIB and MAB(corresponding to 2  1 ) was restricted from 0.3 to 1.0 mm
in the present study.
The distribution of the normalized MSE 10 log  in the
beam of interest [indicated in Fig. 11(b)] indicates the
optimum positions of LIB and MAB (point P), marked as
L and M, respectively, as shown in Fig. 12. Figure 12(a)shows the distribution of normalized MSE 10 log  between
the one-peak echo model and the envelope of the in vivo
RF echo. Figure 12(b) is the normalized MSE distribution
between the two-peak echo model and the envelope of
the in vivo RF echo. For the respective distributions, thedetected boundaries are plotted on B-mode images[Figs. 11(a) and 11(b)] in the beam of interest indicated by
the straight line. The RF and envelope signals in the beam ofinterest are shown in Fig. 11(b). The detected boundaries in
the in vivo measurement are also better when the two-peakecho model is used. The minimum  of 6% at position P[Fig. 12(b)] shows that the envelope of the improved
adaptive model that consists of the two peaks is in very
good agreement with the envelope of the in vivo echo signal.
The other data are also analyzed and the detected LIB (redcrosses) and MAB (green crosses) are shown in Fig. 11(b).
The average IMT for the short segment of 4.8 mm in thearterial longitudinal direction is 0.57 mm. However, for the

beam at position X, the detected MABs are similar between
both the one- and two-peak echo models. The RF andenvelope signals from the far wall at beam position X areplotted in Fig. 11(a), which corresponds to a low-SNR
signal. Thus, it is conﬁrmed that the low-SNR signal may
lead to misdetection of boundaries. In addition, to further
conﬁrm the improved adaptive model and the proposed
method that considered the envelope signal, Figs. 13(a) and13(b) show the diﬀerences among the boundary positions
detected by the method proposed in this study, which uses
the two-peak echo model and the envelope of the in vivo RFecho, the manual method (from the RF signal), and theprevious method, which uses the adaptive RF model andin vivo RF echo. The average diﬀerences between the
proposed method and the manual method and between theprevious method and the manual method, were 0.05 mm(blue crosses) and 0.07 mm (red crosses), respectively, forthe detection of LIB positions [Fig. 13(a)]. For the detectionof MAB positions [Fig. 13(b)], the diﬀerences were 0.04 and0.08 mm, respectively.
4. Discussion

In the present study, the adaptive model has been improved
to detect the boundaries of IMC more accurately. From thebasic experiment using a glass plate, we conﬁrmed thatsimilar echo signals can be obtained for typical depths of the
carotid arterial wall. By the computer simulation of the
carotid arterial wall, a number of scatterers are randomly
placed below the adventitia layer of the far wall to simulatesurrounding tissues. It is observed that, by using the two-

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N. Ibrahim et al.
0.2
differences using envelope signal
differences using RF signal

difference [mm]

0.15

0.1

0.05

0

(a)

–0.05

10

15

20

25

30

35

40

beam number
0.14
differences using envelope signal
0.12

differences using envelope signal
differences using RF signal

difference [mm]difference [mm]differences using RF signal

0.1

0.8

0.6

0.4

0.2

0.08
0.06
0.04
0.02

0

0
–0.2

10

15

20

25

30

35

–0.02

40

beam number

–0.04
10

(b)

15

20

25

30

35

40

beam number

Fig. 13. (Color online) Diﬀerences between boundaries positions obtained by automated method (proposed method) and manual method (from RF signal)
for beams of interest for (a) LIB and (b) MAB positions. Blue squares are obtained using envelope signals and red diamonds are obtained using RF signals.

Table I. Results obtained by the previous and proposed methods.

RF signalaÞ

PreviousbÞRF signal

Proposed
Envelope signalbÞ

Average IMT (mm)

0.56

0.59

0.57

Minimum error distribution of beam of interest (%)

—

23

6

Average diﬀerence of LIB position from that obtained manually (mm)

—

0.07

0.05

Average diﬀerence of MAB position from that obtained manually (mm)

—

0.08

0.04

a) Manual methodb) Automated method

peak echo model, the boundary positions can be detected
as shown in Fig. 10(b), with only an 8% error between thetrue (0.50 mm) and detected (0.54 mm) IMTs. Figure 13(a)provides an explanation of the evaluation of detected LIB
positions, where the results obtained using envelope signals(blue squares) presented smaller diﬀerences than thoseobtained using RF signals (red diamonds). The average
diﬀerences between the proposed and previous methods arelisted in Table I. To clarify the MAB positions [Fig. 13(b)],as shown in Table I, the method that used envelope signals
showed a smaller average diﬀerence than that using the RF
signal. From the small diﬀerence between the IMT estimatedby the proposed method and that determined manually andthe distribution of minimized error, it is found that the
proposed automated method using the improved adaptivemodel (consists of two peaks) is better than the previous
automated method.15)
By using the proposed template matching method,boundaries were detected automatically. To obtain a better
accuracy, there are a few points that should be further
investigated. In the results obtained in the present study,
there were some beam positions where the MAB was
misdetected. The reason for such misdetection should be
investigated. After the correction of the misdetection of the
MAB, the accuracy of the proposed method should be
evaluated in detail. However, the results obtained in thepresent study using the proposed method showed thatthe method is advantageous for the automatic estimation of
the IMT of the carotid arterial wall, and is useful for the
diagnosis of early-stage atherosclerosis.

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7) P. H. Davis, J. D. Dawson, M. B. Blecha, R. K. Masterbergen, and M.

Sonka: Ultrasound Med. Biol. 36 (2010) 560.

5. Conclusions

In this study, we automatically estimated the boundary
positions of IMT in the far wall of the carotid artery of a 4.8mm-long short segment in the arterial longitudinal direction.
The simulation of the carotid arterial wall was conducted toevaluate the accuracy of the improved adaptive model, andan 8% error between the true (0.50 mm) and detected(0.54 mm) IMTs was achieved. In the in vivo measurement,
an average IMT of 0.57 mm was automatically estimated.
From the manual method of boundary detection that was
carried out by observing RF echoes, an average IMT of
0.56 mm was observed. The IMT estimated by the previous
method that uses the RF echo model was 0.59 mm. Fromthese results, it is concluded that the envelope-based twopeak echo model (improved adaptive model) proposed in thepresent automated method yields better results than theprevious method.

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