REGULAR PAPER

Japanese Journal of Applied Physics 53, 07KF09 (2014)
http://dx.doi.org/10.7567/JJAP.53.07KF09

Optimization of feature extraction for automated identiﬁcationof heart wall regions in different cross sections
Kohei Nakahara1, Hideyuki Hasegawa1,2, and Hiroshi Kanai1,2*
1Graduate School of Engineering, Tohoku University, Sendai 980-8579, Japan
Graduate School of Biomedical Engineering, Tohoku University, Sendai 980-8579, Japan
E-mail: kanai@ecei.tohoku.ac.jp
2

Received November 29, 2013; accepted March 5, 2014; published online June 13, 2014
In most current methods of evaluating the cardiac function based on echocardiography, the heart wall in an ultrasonic image is currently identiﬁed
manually by an operator. However, this task is very time-consuming and leads to inter- and intraobserver variability. To facilitate the analysis andeliminate operator dependence, automated identiﬁcation of heart wall regions is essential. We previously proposed a method of automatic
identiﬁcation of heart wall regions using multiple features based on information of the amplitude and phase of the ultrasonic RF echo signal by
pattern recognition. In the present study, we investigate a new method of segmenting an ultrasonic image into the heart wall, lumen, and external
tissues (includes pericardium) by two-step pattern recognition. Also, parameters in the proposed classiﬁcation method were examined for
application to different cross sections, i.e., long-axis and short-axis views, by considering differences in the motion and echogenicity of the heart
walls. Furthermore, moving target indicator (MTI) ﬁltering to suppress echoes from clutters was improved to enhance the separability in the shortaxis view. © 2014 The Japan Society of Applied Physics

1.

Introduction

Echocardiography is an indispensable modality for the
diagnosis of cardiac diseases because it is noninvasive, costeffective, and easy to use. Also, the echocardiographic
methods for the early detection of cardiac diseases (e.g.,
myocardial ischemia), have been developed.1) In conventional echocardiography, the structural change and macroscopic motion of the heart are diagnosed by measuring crosssectional images, and, owing to the high temporal resolution,
quantitative evaluation of intracardiac blood ﬂow and cardiac
wall motion can also be done by ultrasound Doppler
measurements.1,2) Furthermore, various valuable methods
for evaluating the cardiac function on the basis ofechocardiography have been developed, such as the evaluation of two-dimensional (2D) cardiac wall motion and strain
rate by the speckle tracking method,3–7) measurement of the
propagation of vibration caused by the closure of heart
valves,8) and strain rate imaging at high spatial and hightemporal resolution, which measures the transient of
myocardial contraction and relaxation for about 10 ms.9,10)
In addition, it was reported that the myocardial strain rate,estimated from the temporal change of the integrated
backscatter signal, which is obtained by averaging theultrasonic scattering power from a region of interest in
myocardial tissue, reﬂects the contractile and relaxant
characteristics fairly well.11)
In most of the above-mentioned methods, the heart wall(myocardium), which is the object to be analyzed, is currently
identiﬁed manually by an operator on a cross-sectional
image of the heart. However, this task is very tedious andtime-consuming, and it causes inter- and intraobserver
variability.12–14) To eliminate operator dependence andfacilitate analysis, automated identiﬁcation of the heart wallis demanded.
Various studies on segmenting regions in ultrasonic images
have been conducted. Ibrahim et al. proposed techniques
for the detection of arterial wall boundaries for assessing
the intima-media thickness of the carotid arterial wall.15,16)
Chang et al. classiﬁed the thyroid gland and directly
estimated its volume in ultrasonic images.17) In the citedstudies, objects in an ultrasonic image are segmented using
features extracted from the magnitude of the received ultrasonic signal. Furthermore, Torp et al. proposed a method forautomatic detection of several candidate points in the apicalview of the left ventricle.18) This method also yields featuresfrom the gray-scale value (intensity of an ultrasound image),the velocity, and the depth of each image point to detect thecandidate points.
On the other hand, a method has been reported for theidentiﬁcation of an object of interest, i.e., heart wall or lumen,on an echocardiographic image by pattern recognition using
the information of the ultrasonic echo signal. Nillesen et al.
introduced a method in which echogenicity was smoothedin each local area to suppress speckle noise in the lumen.19)
However, the method using only echogenicity would
misclassify a region with low echogenicity inside the heart
wall as lumen. To overcome this limitation, Kinugawa et al.
proposed a method of identifying the heart wall using themagnitude-squared coherence (MSC) function, which evaluates the variance of the phase shift of the RF signal amongconsecutive frames,20) and it shows a greater separability of
the heart wall and lumen. Takahashi et al. further improved
the segmentation by using multiple features including
echogenicity and magnitude-squared coherence.21,22) The
separabilities of multiple features change depending on the
behavior of the heart and, thus, the optimal cardiac phase forfeature extraction was determined by quantitative evaluationof the separability. As a result, a more accurate segmentation
in a frame in the optimal cardiac phase was achieved. The
heart wall region in each frame throughout an entire cardiac
cycle can be identiﬁed by tracking the points in the heart wallregion identiﬁed by proposed pattern recognition in a certain
frame using a tracking method.
However, the above-mentioned method can be applied toonly the ultrasonic data obtained in the long-axis view of theheart [Fig. 1(a)]. The echogenicity of the lateral wall of theleft ventricle in the short-axis view [Fig. 1(b)] is low owing
to the small angle between the ultrasonic beam and the
direction of the myocardial ﬁber.23) In addition, the MSC of
the heart wall is low owing to the movement of the heart
wall in the direction perpendicular to the ultrasonic beam.

07KF09-1© 2014 The Japan Society of Applied Physics

Jpn. J. Appl. Phys. 53, 07KF09 (2014)(a) echo aquisition

K. Nakahara et al.

(b)

2.

area

RV

RV
IVS

IVS

lateral

LV

LV

Principles

2.1
lateral

LVPW
LVPW

Fig. 1. (Color online) Illustrations of measurement by transthoracic
echocardiography in (a) long-axis view and short-axis view of left ventricle(RV: right ventricle, LV: left ventricle, IVS: interventricular septum, LVPW:
left ventricular posterior wall).

RF data

Selecting a frame of interestin one cardiac cycle with high separability

Assigning ROIs in heart wall, lumen,
and external tissue as training data
for AM filtering and EM algorithm

MTI filtering for RF signals

Improved in this study
Extracting features(Echogenicity and MSC)

Identification of external tissue

Proposed method
AM filtering of MSC

Identification of the heart wall in the frame of interest
using EM alrorithm

Feature extraction
Figure 2 shows the procedure proposed in the present study
for automated identiﬁcation of the heart wall in ultrasounddata throughout an entire cardiac cycle. At the beginning, theframe of interest (FOI) is selected from the optimal cardiac
phase21) for classiﬁcation. Then, the ultrasonic data in the
FOI is classiﬁed into the heart wall, lumen, and external
tissue by two-step pattern recognition. To perform patternrecognition, small regions of interest (ROIs) were manuallyassigned in the heart wall, lumen, and external tissue in the
FOI, as shown in Fig. 3(a), to obtain training data. After theclassiﬁcation in the FOI, the heart wall regions in each framein an echocardiographic image sequence during one cardiac
cycle is identiﬁed by tracking the points in the heart wallregion identiﬁed in the FOI by 2D tracking methods. In thestep of pattern recognition, the information of the amplitude
and phase of the ultrasonic RF echo signal is used. The ﬁrst
feature is echogenicity, which is equivalent to information of
the amplitude of the ultrasonic RF signal. The magnitudesquared coherence (MSC) function, which evaluates the
temporal variance in the phase shifts in ultrasonic echoes, canbe used to improve the separability.20) Using the extracted
multiple features, the heart wall regions in the FOI are
identiﬁed by automatically determining the threshold between features of the classes (heart wall and lumen regions)using the expectation-maximization (EM) algorithm.24)
In this method, features, which were extracted with respect
to all points of received RF signals, were calculated fromsignals in each spatial window in the axial direction, with asize of 1.44 mm (= 28 points) corresponding to the length of
an ultrasonic pulse. Similar to conventional segmentation of
echocardiographic images, the echogenicity was used as theﬁrst feature in the present study. The echogenicity Gn ði; jÞ ofa discrete point at the j-th sampled point in the axial directionin the i-th beam and the n-th frame was extracted from thereceived ultrasonic RF echo signal sði; jÞ as follows:Gn ði; jÞ ¼ log10 ½LPFj ½ðsn ði; jÞÞ2 ;
Tracking points classified into heart wall by tracking method

Identification of heart wall regions
throughout an entire cardiac cycle

Fig. 2. (Color online) Schematic diagram of procedure of identiﬁcation of
the heart wall proposed in the present study.

Therefore, the classiﬁcation in the short-axis view is difﬁcult.
In the present study, the extraction of features from ultrasonicechoes was optimized also for measurement in the short-axis
view. Furthermore, the optimum cardiac phase was examinedby evaluating the separability of the extracted features.

where LPFj[0] denotes low-pass ﬁltering in the direction of
depth with a cut-off frequency of 0.3 MHz (nominal transmit
ultrasonic center frequency: 3.75 MHz).
The heart wall is not completely distinguished from thelumen using only the echogenicity because there are regionswith low echo levels inside the heart wall. Hete and Shungfound that the magnitude of the ultrasonic backscatteredsignals were changed depending on the orientation of muscle
collagen ﬁbers.25) Wickline et al. suggested that the magnitude of ultrasonic backscatter is determined by local
differences in acoustic impedance, which are affected bymyocardial contraction and relaxation.26) Thus, an additional
second feature, i.e., the MSC function, was adopted in our
method. The MSC is deﬁned as follows:

2

N 1

X



Ynþk ði; j þ bmðn þ kÞ; fÞYnþkþ1 ði; j þ bmðn þ k þ 1Þ; fÞ


 k¼0

jn ði; j; fÞj2 ¼ N1
X
k¼0

ð1Þ

jYnþk ði; j þ bmðn þ kÞ; fÞj2

N1
X

;

ð2Þ

jYnþkþ1 ði; j þ bmðn þ k þ 1Þ; fÞj2

k¼0

07KF09-2© 2014 The Japan Society of Applied Physics

Jpn. J. Appl. Phys. 53, 07KF09 (2014)

(a)

10 mm

K. Nakahara et al.

(b)

RV

heart wall
lumen
external tissues

(c)

IVS
center of heart

misclassification

LV
LVPW
external
tissuesFig. 3. (Color online) Result of identiﬁcation of external tissue: (a) B-mode image with gravity center estimated using echo amplitude and manuallyassigned areas for heart wall, lumen, and external tissue. (b) Region-identiﬁed image. (c) Region-identiﬁed image corrected using distance from gravity center.

Imultrasonic beamreceived
signal....

high consistency

....

Re
heart wall

lumen

region ofinterestnn+1
n+2
Spectra in heart wall

traceableIm

depth [m]dispersion
blood cell

untraceableframe
number:n a few milliseconds

n+1

Re

n+2

n+2

n+1

time [s]nSpectra in lumen

Fig. 4. (Color online) Illustration of changes in waveforms of echo signals in ROI.

where + and N denote a complex conjugate and the number offrames used for estimating the MSC, respectively, and bmðnÞcorresponds to the displacement of the point of interest ði; jÞin the n-th frame estimated by the phased-tracking method.27)
The details of the estimation of the MSC is described inreferences,20,28) and the frequency f was set at 4.82 MHz. As
illustrated in Fig. 4, the temporal changes in waveforms of
RF echo signals in an ROI placed in the heart wall region
are fairly small and can be tracked. Therefore, the MSC in
the heart wall becomes high. On the other hand, blood cells in
the cardiac lumen are difﬁcult to track because they are
moving fast owing to blood ﬂow and slip off the ultrasonicbeam. Therefore, the temporal change in the waveform of
an ultrasonic echo in an ROI assigned in the lumen issigniﬁcant. Therefore, the MSC becomes high in the heart
wall region and low in the lumen region. However, the MSC
of RF signals, the second feature, is also increased because of
the components of echoes from stationary or slowly moving
clutter, such as the ribs.20) In addition, there is a considerable
difference between the acoustic impedances of clutter andblood particles. Therefore, the echogenicity and MSC
become higher when echoes from clutters are included.
Thus, reduction of the clutter component is necessary toaccurately extract the features, particularly from the cardiac
lumen. In the present study, in order to reduce echoes fromclutters, a high-pass ﬁlter with respect to the frame29) wasused as a moving target indicator (MTI) ﬁlter30) for RF
signals before calculating the ﬁrst and second features,Gn ði; jÞ and j n ði; jÞj2 .21,22) There are clutter echoes partic-

ularly in shallow regions, e.g., clutter echoes from ribs, and,thus, the clutter echoes dominantly inﬂuence the echoesfrom a shallow region in the heart, e.g., the right ventricle.
Therefore, in the present study, MTI ﬁltering was applied to
the region with depth limited to 33 mm to suppress reductionof the necessary signal, i.e., echoes from the heart wall andlumen.
2.2 Improvement of estimated MSC distribution by
adaptive mean ﬁltering

In the present study, adaptive mean (AM) ﬁltering, whichsmooths spatial features, is applied to reduce the undesired
spatial ﬂuctuation in the estimated MSC distribution andimprove the separability of the second feature, i.e., MSC. The
AM ﬁltering is expressed as follows:c^ ¼  þ kðc  Þ;ð3Þ  min;ð4Þ
k¼
max  min
0 if k < 0
k¼;
ð5Þ
1 if k > 1
where ® and · are the mean and standard deviation of MSC
in each 6 © 6 mm2 (in horizontal and vertical directions)region (window) containing 30 © 30 data points (center ofthe window is the point of interest). The original ultrasonic
RF data were obtained in a sector format and, thus, theoriginal ultrasonic data were interpolated so that 30 © 30 data
points were included in each 6 © 6 mm2 window. In thisinterpolation, the value (MSC) of the nearest original sampled

07KF09-3© 2014 The Japan Society of Applied Physics

Jpn. J. Appl. Phys. 53, 07KF09 (2014)(a) 10 mm

K. Nakahara et al.

(b)Standard deviationof MSC(c)

MSC

Fig. 5. (Color online) Example of MSC image of short-axis view of left ventricle: (a) B-mode image. (b) MSC image. (c) Standard deviation of MSC ineach local region.

(a)

0.8

Dði; jÞ ¼ 1 Gði; jÞ
;
Gmax

ð6Þ

0.6

heart wall
lumen
external tissues

extenal tissues
heart wall

Gauss distributions
fitted by EM algorithm

0.4
0.2

lumen

0
−100 −80 −60 −40 −20
echogenicity [dB]0

Fig. 6. (Color online) (a) Result of classiﬁcation of heart wall, lumen, andexternal tissue by one-step classiﬁcation using EM algorithm.
(b) Distributions of feature vectors of region manually assigned as each tissue
and Gaussian distributions ﬁtted by EM algorithm.

P Pij i  Dði; jÞ;ci ¼ P P
ij Dði; jÞP Pij j  Dði; jÞ
cj ¼ P P;
ij Dði; jÞ

2.3 First-step classiﬁcation: Identiﬁcation of external
tissueIn the present study, we proposed a method by two-steppattern recognition for classiﬁcation of an ultrasonic image
into three regions, i.e., heart wall, lumen, and external tissue.
In the ﬁrst step, external tissue is identiﬁed and excluded
from the second step for segmentation of the heart wall andlumen. In the ﬁrst pattern recognition, the ultrasonic data wasclassiﬁed into the heart wall, lumen, and external tissue by
the EM algorithm using only the echogenicity. Figure 3(b) isan example of the result of classiﬁcation based on theechogenicity. In Fig. 3(b), there are small regions misclassiﬁed as external tissue in the interventricular septum (IVS),
which must be corrected. This misclassiﬁcation was correctedusing the geometrical information of each point. To obtain
the geometrical information, the gravity center cðci ; cj Þ[yellow dot in Fig. 3(a)] was obtained as a rough position
near the center of the left ventricle as follows:

10 mm

(b) 1

MSC

point was used as the value of each interpolated point.
Figures 5(a)–5(c) show examples of a B-mode image, MSC
image unprocessed by AM ﬁltering, and standard deviation of
MSC in each local region (window), respectively. In thepresent study, the standard deviation obtained with respect tothe manually assigned ROI for the heart wall [region
surrounded with red line in Fig. 5(a)] was used as the lower
limit of standard deviation min (corresponding to the casewhen there is no boundary in a window for AM ﬁltering). On
the other hand, the standard deviation obtained from the data
(MSC) included in both the manually assigned ROIs for theheart wall and lumen was used as the upper limit of standard
deviation max (corresponding to the case when there is aboundary between the heart wall and lumen in a window).
Using the original MSC c at the point of interest and avariable coefﬁcient k, which was determined from estimatedstandard deviations, the ﬁltered MSC c^ was obtained. The
AM ﬁlter outputs the mean in a 6 © 6 mm2 window to smooththe MSC distribution when the standard deviation · is low(there is no boundary). On the other hand, the smoothing
effect of the AM ﬁlter is suppressed when the standard
deviation · is high (there is a boundary). Using the AM ﬁlter,
the separability is considered to be improved by informationof surrounding points even when there is a peculiar point,e.g., low MSC in the heart wall owing to a low signal-to-noise
ratio of an echo signal.

ð7Þ
ð8Þ

where Gmax is the maximum of echogenicity in the entire
ultrasonic data and Dði; jÞ corresponds to the darkness of thepoint of interest ði; jÞ in the B-mode image. The echogenicity
in the lumen is, in general, low, and the position of the lumen
can be estimated roughly using the gravity center cðci ; cj Þ
obtained by weights of darkness Dði; jÞ.
The distance between the gravity center cðci ; cj Þ and eachpoint classiﬁed as the heart wall based on echogenicity was
averaged to obtain the mean distance from the gravity centercðci ; cj Þ to the heart wall. Using this mean distance, a point,
which was classiﬁed as external tissue and whose distancefrom the gravity center cðci ; cj Þ is shorter than the mean
distance from the gravity center cðci ; cj Þ to the heart wall, wascorrected as the heart wall.
The advantage of two-step pattern recognition is described
below. Figure 6(a) shows the result obtained by single-step

07KF09-4© 2014 The Japan Society of Applied Physics

Jpn. J. Appl. Phys. 53, 07KF09 (2014)

K. Nakahara et al.

classiﬁcation into the heart wall, lumen, and external tissue
using the EM algorithm with two features (i.e., echogenicity
and MSC). Misclassiﬁcation of the heart wall as external
tissue is frequently observed. Figure 6(b) shows the feature
distributions in the regions manually assigned as the heartwall, lumen, and external tissue and the estimated Gaussian
distributions ﬁtted to features of the respective regions. The
reason for misclassiﬁcation of the heart wall as external tissue
is that the Gaussian distribution ﬁnally determined by the
EM algorithm does not appropriately ﬁt the measured
features, as shown in Fig. 6(b), particularly for the heartwall. The separability of MSC to distinguish the heart wall
(red points) from external tissue (green points) is low because
the MSCs of the heart wall and external tissue are similar(high MSCs). Therefore, most of the data in the heart walland external tissue can be explained by the Gaussian
distribution obtained for external tissue, as can be seen in
Fig. 6(b), and the data that cannot be explained by theGaussian distributions obtained for the lumen and external
tissue are explained by the Gaussian distribution obtained forthe heart wall. Such a failure of estimation of the probability
density function (Gaussian distribution) leads to signiﬁcantclassiﬁcation errors in Fig. 6(a). Therefore, the data fromexternal tissue, which have MSCs similar to those in the heartwall, should be removed before applying the EM algorithm
for the classiﬁcation of the heart wall and lumen.
2.4 Second-step classiﬁcation: identiﬁcation of heart walland lumen

In this method, the data points classiﬁed as external tissue inthe ﬁrst classiﬁcation were excluded in the second classiﬁcation. The other data points were classiﬁed into the heart
wall and lumen using two features, i.e., echogenicity and
MSC, using the EM algorithm.21) In the measured RF datasetof each cross section (long-axis and short-axis views), theregions of the heart wall and lumen were manually assigned astraining data for classiﬁcation based on the EM algorithm.24)
Using the EM algorithm, a mixture of two Gaussian
distributions (corresponding to the heart wall and lumen)
was ﬁtted to features of the data points, and the line where theprobabilities of the determined Gaussian distributions were
the same was used as the threshold for the classiﬁcationof each data point. Using the 2D feature vector di;j ¼ðGði; jÞ; j ði; jÞj2 Þ extracted at each discrete point ði; jÞ in an
acquisition area, the mixing coefﬁcient fh g, mean vectors
fh g, and covariance matrices fh g were obtained for classes
f!h g [lumen (h = 0) and heart wall (h = 1)] as follows:
h ¼

Nh
N

ðh 2 f0; 1gÞ;h ¼ Ei;j ½di;j j!h  ðh 2 f0; 1gÞ;h ¼ Ei;j ½ðdi;j  h Þðdi;j  h ÞT j!h 

assigned training data. In the subsequent iterations, covariance matrix h , mean vector h and mixing coefﬁcient hwere updated from feature vectors fdi, jg classiﬁed into thecorresponding class using the previous covariance matrix,
mean vector and h . The iteration maximizes the likelihoodof a mixture of Gaussian distributions, which represents thedegree of ﬁtting, and the iteration was stopped when thelikelihood converged to a local maximum value.
3.

3.1 Determination of optimal cardiac phase and numberof frames for calculation of MSC

We previously reported that the optimal cardiac phase forclassiﬁcation of the heart wall in the long-axis view is theperiod from the rapid-ﬁlling phase to the slow-ﬁllingphase.21) In the present study, the optimal cardiac phase forthe short-axis view was also determined. In addition, thenumber of frames N for the calculation of MSC, which is theimportant parameter for determining the separability of MSC,
was examined. A larger number of frames is desirable forstatistically stable estimation of MSC. However, an excessive
number of frames degrades the separability of MSC because
tracking errors are more likely to occur. Therefore, theoptimum cardiac phase and number of frames should be
examined for each cross section, i.e., long-axis and shortaxis views.
Ultrasonic RF echoes were acquired from the long-axisand short-axis views of the heart of a healthy 25-year-old
male, using a 3.75 MHz phased array probe. By parallel
beamforming (PBF)31,32) with plane wave transmission, highframe-rate (long-axis: 860 Hz, short-axis: 560 Hz) measurements of RF echoes were realized with 0.375° intervals ofscan lines. The sampling frequency of the measured RF
signals was 15 MHz.
To evaluate the separability of the feature vector, theinterclass covariance matrix SB in Eq. (14), which corresponds to the distance between mean vectors of classes (heart
wall and lumen), and the intraclass covariance matrix SW in
Eq. (15), which corresponds to the mean variances of feature
vectors within classes, were deﬁned using these parameters as
follows:Nhðh 2 f0; 1gÞ;N
1X
¼
ph Ei;j ½di;j j!h ;

ph ¼

SB ¼

1
X
h¼0

SW ¼

ð10Þ

where Ei, j[0] denotes spatial averaging in the axial and thelateral directions, and Nh, N, and h are the number of feature
vectors belonging to each class, the total number of vectors in
the area for evaluation, and the mean vector in each class,respectively.
In the ﬁrst iteration, the measured ultrasonic data wereclassiﬁed using covariance matrices fh g, mean vectors fh g
and mixing coefﬁcient fh g obtained from the manually

ð12Þ
ð13Þ

h¼0

ð9Þ

ðh 2 f0; 1gÞ; ð11Þ

Experimental results and discussion

1Xph ðh  Þðh  ÞT ;ð14Þ

ph h :

ð15Þ

h¼0

Separability of the feature vector increases in proportion to
the distance between mean vectors of classes, whereas it
decreases in proportion to the variances of vectors withinclasses.24) Therefore, the criterion of separability J is deﬁned
by normalizing the interclass covariance matrix SB by the
intraclass covariance matrix SW as follows:33)
J ¼ tr½S1
W SB ;

ð16Þ

where tr[0] represents the sum of diagonal components.

07KF09-5© 2014 The Japan Society of Applied Physics

Jpn. J. Appl. Phys. 53, 07KF09 (2014)

MSC

(b)

echogenicity
[dB]

(c)

100
50
0
1
0.8
0.6
0.4
0.2
0
−30

0.36
0.27
0.18
0.09
0

IVS
LVPW
LV

(c)

−40

6

100

4

50

2

0
1
0.8
0.6
0.4
0.2
0

0

IVS
LVPW
lateral wall
LV

−30
−40

−50

−50

−60

−60

(d)

−60
−40
−20
0
20
40
60

velocity[mm/s]

velocity[mm/s]

8

150

(b)

−70

(d)

200

criterion of
separability J

150

(a)

number offrames N

0

18
15
12
9
6
3
0

MSC

0.06

200

B C D E

ECG
PCG

echogenicity
[dB]

0.12

A

time length [s]

0.17

E

criterion of
separability J

(a)
0.23

BC D

ECG
PCG

number offrames N

window length [s]A

K. Nakahara et al.

0

0.2

0.4

0.6

0.8

1

−70
−60
−40
−20
0
20
40
60

time from R−wave [s]

0

0.2

0.4

0.6

0.8

1

time from R−wave [s]Figures 7(a) and 8(a) show the estimated separabilities in
the long-axis and short-axis views of the left ventricle withdifferent numbers of frames N for the calculation of MSC.
Periods A, B, C, D, and E denote the ejection phase,isovolumic relaxation phase, rapid-ﬁlling phase, slow-ﬁllingphase, and atrial systole, as indicated in Figs. 7 and 8. The
white lines in Figs. 7(a) and 8(a) indicate the optimal cardiac
phases. The white arrows in Figs. 7(a) and 8(a) show the time
and the number of frames, which give the maximal value of
J. As shown in Fig. 7(a), the maximal value of J is found in
the early slow-ﬁlling phase D when N is 70. The optimal
cardiac phase agreed with that reported by Takahashi et al.,and the reason is the large difference in MSCs between theheart wall and lumen, as shown in Fig. 7(b).21)
On the other hand, as shown in Fig. 8(a), the maximal
value of J is found in the atrial systole when N is 150.
Figure 9 shows mean MSCs in the respective regions in the
short-axis view with different numbers fNg of frames in theearly slow ﬁlling phase D (shown by the solid lines) andearly atrial systole (shown by the dashed lines). As shown in
Fig. 9, MSC of the lateral wall in the slow ﬁlling phasebecomes markedly lower as the number of frames N
increases. The motion velocity of the wall estimated by thephased-tracking method in the slow-ﬁlling phase is higher
than that in the atrial systole, as shown in Fig. 8(d).
Therefore, the separability in the slow-ﬁlling phase is lower
than that in the atrial systole because the MSC of the lateralwall becomes lower mainly as a result of the fast movementof the heart wall in the direction perpendicular to theultrasonic beam. As a result, the optimal cardiac phase for the
short-axis view is the early atrial systole when the movementof the heart wall is slower than that in the slow-ﬁlling phase.

Fig. 8. (Color online) Results obtained in short-axis view of left ventricle:(a) Criterion of separability J. Spatial means of (b) MSC, (c) echogenicity
and (d) velocities.

1

mean of MSCs

Fig. 7. (Color online) Results obtained in long-axis view of left ventricle:(a) Criterion of separability J. Spatial means of (b) MSC, (c) echogenicity,and (d) velocities.

slow−filling phaseatrial systole

0.9

IVS
LVPW
lateral wall

0.8
0.7
0.6

0

50

100

150

200

number of frames
Fig. 9. (Color online) Mean of MSCs of short-axis view data in rapid
ﬁlling phase and atrial systole.

The motion velocities of IVS and LVPW are evaluated in theatrial systole because the phased-tracking method cannot
estimate the velocity of the lateral wall in the direction
perpendicular to the ultrasonic beam.
Furthermore, the optimal number of frames N for the
calculation of MSC was determined for each cross section.
The number of frames N showing a high separability forthe long-axis view was 40–100 frames corresponding to
0.05–0.12 s [= 0.074/(heart rate)] in the early slow-ﬁllingphase and 177 frames corresponding to 0.21 s [= 0.187/(heart rate)] in the early atrial systole. For the short-axis view,
the optimal number of frames N showing high separability
was 50–70 frames corresponding to 0.09–0.13 s [= 0.09/(heart rate)] in the slow-ﬁlling phase and 130–150 frames
corresponding to 0.23–0.27 s [= 0.21/(heart rate)] in theatrial systole. Two ultrasonic datasets from the long-axis andshort-axis views were acquired under different conditions,e.g., different frame rates and heart rates. Therefore, the07KF09-6© 2014 The Japan Society of Applied Physics

Jpn. J. Appl. Phys. 53, 07KF09 (2014)

K. Nakahara et al.
8

Cross sectionin left ventricle

Optimal cardiac phase

Optimal numberof frames N

Long-axis view

Early slow ﬁlling phase

70 frame[= 0.08 s, 0.074/(heart rate)]

Short-axis view

Early atrial systole

150 frame[= 0.27 s, 0.22/(heart rate)]

criterion of separability

Table I. Summary of optimal cardiac phase and number of frames N ineach cross section.

6
4
2
0

(a)

20
10
0

IVS

LV

300
40

250

20

200
150

0

100

−20

50
0
10

power [dB]velocity [mm/s]

30

shift frequency [Hz]

RV

40

−40
20

30

40

50

60

70

80

depth [mm]Fig. 10. (Color online) (a) Regions manually assigned for evaluation of
power spectra of RF echoes (RV: right ventricle, LV: left ventricle, IVS:
interventricular septum). (b) Power spectra at each depth obtained byaveraging spectra in all beams in the assigned regions.

optimal number of frames N was expressed by the percentage
to a cardiac cycle. The optimal number of frames N in theslow ﬁlling phase was smaller than that in the atrial systole ineach cross section. The reason is that the frames corresponding to the rapid-ﬁlling phase are included when the number offrames N is larger and the heart wall moves and deforms
rapidly in the rapid-ﬁlling phase. Therefore, a smaller numberof frames N was optimum in the slow-ﬁlling phase.
As described above, in the long-axis view, the optimum
cardiac phase is the early slow-ﬁlling phase and the optimal
number of frames N is 70. In the short-axis view, theoptimum cardiac phase is the early atrial systole and the
optimal number of frames N is 150. Table I summarizes the
optimal cardiac phase and the number of frames N in eachview.
Optimization of MTI ﬁltering
Echoes from the heart wall are likely to be affected by MTI
ﬁltering because the motion of the heart wall is smaller thanthose of blood particles. The suppression of the echo signal
from the heart wall possibly degrades separability because
the echogenicity and MSC in the heart wall become also
lower. Thus, in the present study, MTI ﬁltering was applied to
the region with a limited depth, which includes most of theclutter echoes, to preserve the echo signal from the heart wall.
Figure 10(b) shows the power spectrum at each depth
3.2

80

100

Fig. 11. (Color online) Separability plotted as a function of range appliedwith MTI ﬁltering.

LV

50

60

10 mm

IVS

60

40

range in application of MTI filtering [mm]
RV

(b)

20

obtained by applying DFT with respect to the frame [window
size: 50 frames (= 0.09 s)] to RF echoes from manuallyassigned regions of RV, IVS, and LV shown in Fig. 10(a) at
the best frame for classiﬁcation. The power spectra wereaveraged for all scan lines. As shown in Fig. 10(b), thecomponents with low velocity and high magnitude could beclutter echoes, and the clutter component becomes lower as
the position becomes deeper. The optimal cut-off frequency isdetermined to be 50 Hz because the clutter components exist
under approximately 50 Hz. Although echo signals in IVS
should include echoes from the heart wall (IVS) and clutter
echoes, it is difﬁcult to distinguish between them, as shownin Fig. 10(b). Therefore, the range (depth), to which the MTI
ﬁlter was applied, was changed from 20 to 100 mm, and thecriterion of separability J was evaluated. Figure 11 shows thecriterion of separability J plotted as a function of the range
for MTI ﬁltering. The result shows that the range of 33 mm
was the optimum range for MTI ﬁltering. Figures 12(a),12(b), and 12(c) show the spatial distributions of MSC
without the MTI ﬁlter, MSC with MTI ﬁltering (cut-off
frequency: 10 Hz),21) and MSC with MTI ﬁltering applied
to the limited range, respectively. Using the previsously
determined MTI ﬁltering [Fig. 12(b)], MSCs lower thanthose without MTI ﬁltering in the heart wall are found andthe clutter components are not sufﬁciently suppressed, as
shown in Fig. 12(b). On the other hand, MTI ﬁltering applied
to the limited range resulted in the reduction of clutter
components in the right ventricle while preserving MSC in
the heart wall, as shown Fig. 12(c).
3.3 Effect of AM ﬁltering and segmentation of in vivo
ultrasonic data

Figures 13 and 14 show MSCs obtained from the long-axisand short-axis views, respectively. In Figs. 13 and 14,
original MSC images (a) and AM-ﬁltered MSC images (b)are shown. Particularly in the short-axis view, spotlike
regions with low MSCs are observed in the lateral wall, as
shown in Fig. 14(a). By AM ﬁltering, such regions were
eliminated because of smoothing of MSC using theinformation of surrounding points. By applying AM ﬁltering,the criterion of separability J improved from 11.3 to 15.7 forthe data of the long-axis view and from 9.0 to 12.5 for the
short-axis view.
Figures 15 and 16 show the results of classiﬁcations in thelong-axis and short-axis views, respectively: (a) B-mode
images, (b) results of the identiﬁcation of the heart wall

07KF09-7© 2014 The Japan Society of Applied Physics

Jpn. J. Appl. Phys. 53, 07KF09 (2014)(a)K. Nakahara et al.

(b)cluttercompornents

limit of depth(c) for MTI filtering

MSC

10 mm

decreased by
MTI filtering

Fig. 12. (Color online) Spatial distribution of MSC applied by MTI ﬁltering limited in depth: (a) MSC image without MTI ﬁlter. (b) MSC image withpreviously determined ﬁltering (cut-off frequency: 10 Hz).21) (c) MSC image with MTI ﬁltering applied to limited range.

MSC(a) 10 mm

Fig. 13. (Color online) Result of applying AM ﬁltering to MSC obtained
in long-axis view of left ventricle: (a) Original MSC image. (b) Filtered MSC
image.

(a)MSC(a) 10 mm

(b)

(b)
10 mm

(b)

Fig. 14. (Color online) Result of applying AM ﬁltering to MSC obtainedin short-axis view of left ventricle: (a) Original MSC image. (b) Filtered
MSC image.

heart wall
lumen

(c)

0.8

MSC

for supervised
data

external
tissues1

0.6
Gaussian distributions
fitted by EM algorithm

0.4
0.2
0
−100

−80

−60

−40

−20

0

echogenicity [dB]Fig. 15. (Color online) Result of segmentation in long-axis view of left ventricle: (a) B-mode image. (b) Result of identiﬁcation of the heart wall regions.
(c) Distribution of features of region manually assigned as each tissue in 2D feature space and Gaussian distribution ﬁtted into features of each distribution.

(b)heart wall
lumen

for supervised
data

external
tissues

(c) 1
0.8
MSC

(a) 10 mm

0.6

Gaussian distributions
fitted by EM algorithm

0.4
0.2
0
−100 −80 −60 −40 −20
echogenicity [dB]0

Fig. 16. (Color online) Result of segmentation in short-axis view of left ventricle: (a) B-mode image. (b) Result of identiﬁcation of the heart wall regions.
(c) Distribution of features of region manually assigned as each tissue in 2D feature space and Gaussian distribution ﬁtted into features of each distribution.

regions in the optimal cardiac phase with the optimum numberof frames N, and (c) feature distributions of the heart wall andlumen extracted from manually assigned regions with thecorresponding Gaussian distributions estimated using the EM
algorithm.24) The red regions in Figs. 15(b) and 16(b) are theregions identiﬁed as the heart wall, and the results show that
accurate segmentation was possible in both long-axis andshort-axis views by the proposed method. As can also be seenin Figs. 15(c) and 16(c), the features extracted under the
optimal condition show high separabilities.

07KF09-8© 2014 The Japan Society of Applied Physics

Jpn. J. Appl. Phys. 53, 07KF09 (2014)

4.

K. Nakahara et al.

Conclusion

The aim of the present study was the identiﬁcation of theheart wall in ultrasound data obtained in various crosssections. In the present study, we have shown that an accuratesegmentation can also be realized in the short-axis view by
optimizing the optimum cardiac phase and number of frames
for the calculation of MSC. Moreover, AM ﬁltering was
effective, particularly in the short-axis view, in eliminationof spotlike regions with low MSCs. Furthermore, the MTI
ﬁltering applied to a limited range resulted in the reduction of
clutter components in the right ventricle while preserving
MSC in the heart wall. Through these optimizations, it was
shown that accurate segmentation was possible in both longaxis and short-axis views by the proposed method.

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07KF09-9© 2014 The Japan Society of Applied Physics