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This tight dependence of the two phases (ROI selection and subsequent wall seg-
mentation) is enforced in the media–adventitia segmentation process. In fact, even
though the two steps are conceptually distinct, part of the segmentation procedure
begins at the end of the ROI extraction phase: The media–adventitia contours are
extracted in step 6 of the ROI selection procedure. The subsequent segmentation
does not affect the media–adventitia layer extraction. In other words, we found the
gradient-based segmentation process to be the most suitable technique for evidenc-
ing the border between the media and the adventitia tunicae. The application of a
further step of fuzzy clustering did not improve segmentation. Moreover, by restrict-
ing the pixel classification procedure to the ROI comprised between the two
adventitial walls, we could develop an algorithm that proved suitable for both IMT
measurement and plaque imaging and segmentation.
Due to the differing echogenicity of the tissues, the same observation does not
apply to the lumen–intima segmentation. To accurately trace the LI
NW
and LI
FW
bor-
ders, tissue must be separated from vessel lumen by a fuzzy clustering. This second
step, in fact, is the one that allows us to accurately adhere to the carotid wall in nor-
mal scans and to follow a plaque profile in pathology.
Preliminary tests we made (not reported in this chapter) showed that the classifi-
cation of all pixels of the image without any user interaction is extremely difficult.
The first reason is that in many ultrasound CCA images the jugular vein (JV) is pres-
ent. In that case, for a fuzzy classifier it is impossible to separate pixels belonging to
the carotid lumen from pixels belonging to the JV lumen. Moreover, JV lumen is
often more homogeneous and characterized by lower backscattering than CCA
lumen and this could bias the results of an intensity-based fuzzy classifier. Also, the
relative position of the JV with respect to the CCA is unpredictable, because it
depends on anatomy as well as on the insonation performed by the user.
The second reason is that tissue texture could easily be confounded with plaque
texture. In fact, in many cases, the distribution of the gray tones in tissues surround-
ing the CCA is quite similar to that of an echolucent plaque. Therefore, without a
previous ROI selection step, classification would be extremely complicated.
124 User-Independent Plaque Segmentation
MA
FW
MA
NW
LI
FW
LI
NW
Figure 5.9 Expanded vision of the CULEX segmentation of a tract of the CCA.
5.4 CULEX Characterization and Image Databases
As in many other cases in the medical imaging arena, the ground truth is unavail-
able. Comparison with a manually obtained segmentation is possible, of course, but
interrater agreement, especially in this particular application, may be low, so that
the evaluation of the algorithm performance may be strongly biased on the basis of
who performed the manual segmentation. A partial solution may be to make a com-
parison to the mean of many manual segmentations performed by several experts.
However, this does not lend information on the quality of the segmentation versus
the interrater variability, which is in fact the best indication of which degree of
precision it is possible to achieve.
In the following sections, we will present the results of the validation following
two different approaches, whose common goal is to compare the algorithm’s per-
formance with the performance of human experts.
5.4.1 Percent Statistics Algorithm
The Percent Statistics algorithm for the evaluation of contour extraction was first
proposed by Chalana and Kim [24] and later improved by Alberola-Lòpez et al.
[25]. Its objective is to determine whether “the computer-generated boundaries dif-
fer from the manually outlined boundaries as much as the manually outlined
boundaries differ from one another.” The basic idea is that if the computer-
generated boundary (CGB) behaves like the expert-outlined boundaries (EOB), it
must have the same probability of falling within the interobserver range as the man-
ual segmentations. Defining, for each contour C
i
, the distance D
ij
(i j) from any
other contour C
j
, and the maximum distance D
m
between any two contours:
(
)
DDij
m
ij
ij
=≠max
,
for
A contour falls in the interobserver range if the distances separating it from the
other contours are all less than D
m
. Two different distance metrics have been sug-
gested [24, 25],the Hausdorffdistance and the average distance between the curves.
We considered the average distance.
Assuming then that the EOBs are independent and identically distributed and
that the CGBbehaves like the EOBs, the probability of the computer-generated con-
tour falling into the interobserver range is equal to:
()
PC IR
n
n
0
1
1
∈=
−
+
where C
0
is the CGB, n 1 is the total number of contours (n EOBs + 1 CGB), and
n − 1 is the number of “internal” contours (i.e., the total number of contours minus
the two contours who are distant D
m
). Denoting then as X
j
the event “the
computer-generated boundary lies within the interobserver range for the jth
image,” for j ranging from 1 to N, we have that X
j
is a Bernoulli distributed vari-
able, with parameter p
(n − 1)/(n 1), q 1 − p.
5.4 CULEX Characterization and Image Databases 125
Let us now consider the random variable
ZXN
j
j
N
=
=
∑
/
1
. Because we are trying to
determine whether the CGB falls outside the interobserver range more frequently
than the EOBs, we seek the one-sided confidence interval for variable Z, that is, the ε
value for which P(p − Z
) , which is 1 – the confidence level of the interval.
For N sufficiently large, we can apply the De Moivre-Laplace approximation and
assume that Z is normally distributed, with mean p and standard variance pq/N.
Then,
ε
α
=
−
pq
N
z
1
, where z
1−α
is the value of a normal standard distribution leaving
an area equal to 1−
to its right. The acceptance region for this test is the region in
which Z has a value greater than p −
.
5.4.2 Confidence Interval for the Average Segmentation
In this approach, we consider for each column the points corresponding to the
EOBs. The basic assumption is that the operator-traced points follow a Gaussian
distribution, whose mean corresponds to the true value of the lumen–intima and
media–adventitia interfaces.
We then verify on each column whether or not the CGB falls within the confi-
dence interval for the mean. Because the variance of the distributionis unknown,the
t-Student distribution is used. Thus, we require:
EOB CGB EOB
j
nj
j
n
t
s
N
t
N
−<<+
−−αα21 21
2
,,
where
EOB
j
is the sample mean of the EOB’s ordinate values for the jth column,
CGB
j
is the CGB ordinate value for the jth column, s is the sample variance, N
is the sample size (i.e., the number of operators), and t
/2,n–1
is the value for which
H(t
/2,n–1
) α/2, where H( ) is the distribution functionof a t-Student variable, with n
1 degrees of freedom. The percentage of times that the CGB falls within the confi-
dence interval for the mean is then computed for each image.
5.4.3 Segmentation Error
An estimation of the CULEX segmentation error can be obtained by comparing the
algorithm’s lumen–intima and media–adventitia borders to the average borders
traced by the human operators. Considering the ith image in the database, we
defined the segmentation error for each column of the image as follows:
() () ()
ec B c B c
B
ii i
=−
CULEX OP
(5.2)
where
•
B
i
CULEX
is the border contour generated by CULEX.
•
B
i
OP
is the average border segmented by the four human operators.
126 User-Independent Plaque Segmentation
•
c is an index spanning through the columns of the image.
We evaluated the segmentation error for all the four borders in the image: LI
NW
,
MA
NW
, LI
FW
, and MA
FW
.
5.4.4 Image Database and Ultrasound Equipment
To test our algorithm, we used two different databases: The first one consists of 200
images relative to 50 healthy individuals (age range = 35 to 89 years; mean ± STD =
63 ± 7.5 years) without previous history of cardiovascular or cerebrovascular dis-
eases, the secondone is made of 100images relative to 20 patients (age range = 41 to
89 years; mean ± STD = 56 ± 4.6 years). Thirty images were relative to
intima–media thickening (IMT values ranging from 0.09 to 0.59 cm), 30 to homo-
geneous echogenic (stable) carotid plaques, and 40 to inhomogeneous echolucent
type II (unstable) plaques.
All of the images were relative to the common tract of the carotid artery,
insonated according to internationally recognized standards [1]. Patients were ran-
domly selected among those who performed ultrasound examinations in the Neu-
rology Division of the Presidio Sanitario Gradenigo of Torino, Italy. All of them
signed an informed consent before being enrolled in this study.
A neurologist, a cardiologist, and two radiology technicians independently seg-
mented all the images in our database by means of a graphical user interface (GUI)
we developed. The manually traced profiles were used for the performance evalua-
tion of CULEX. The GUI was developed in a MATLAB (The MathWorks, Natick,
Massachusetts) framework and an example of the GUI’s appearance is shown in
Figure 5.10. This GUI allows for segmenting of the images and for reviewing saved
tracings. New operators can be added automatically. The near and far wall sections
are kept separate in order to allow the user to focus on a specific target. The images
are stored in a structure and it is possible to complete segmentation in different
sessions.
All of the tests were conducted on a G4 dual-processor, 2.5-GHz PowerMac,
equipped with 4 GB of RAM memory and a 30-inch flat screen.
For acquisition of the images, we used two different devices: a Philips HDI
ATL-5000 and a General Electric LOGIQ 500. The devices were equipped with
10-MHz (Philips) and 9-MHz (GE) linear probes. All of the images were transmit-
ted by the US device to a host computer in DICOM format, discretized on 8 bits,
and represented by means of a grayscale linear mapping of the pixel intensity.
In the following section, we will present the results in terms of the segmentation
capabilities of the CULEX algorithm. To provide a full and comprehensive presen-
tation of the algorithm’s performances in the various scenarios, we separated the
results and the discussions relative to ROI extraction, IMT measurement, and
plaque imaging and segmentation.
As discussed inthe previous section, because the ground truth is unavailable, we
relied on manually traced contours to assess the quality of the computer-generated
segmentations.
5.4 CULEX Characterization and Image Databases 127
5.5 ROI Detection and Robustness to Noise
CULEX correctly extracted the ROI in 295 images out of 300. All 200 images of the
first database were correctly processed, whereas in 5 images of the second database
(i.e., the one relative to patients), the ROI was incorrectly chosen due to image arti-
facts. The evaluation of the correctness of the ROI extraction was done by visual
inspection of the images.
At the current stage of development, an error in the ROI selection precludes the
correct segmentation of the carotid wall and cannot be recovered.
Generally, different noise sources and artifacts characterize ultrasound images.
Some of these sources are caused by instrumentation (i.e., speckle noise [26]), some
result from the biological environment (i.e., artifacts produced by air, bones, high
density deposits), and some derive from incorrect use of the device (i.e., excessive
blood backscattering, low contrast, details out of focus). Our tests revealed that
blood backscattering represents the major threat to a suitable ROI extraction. In
fact, an excessive level of gray tones dispersed into the vessel lumen may produce
two effects: (1) the gray tone distribution is considered part of a partially echolucent
plaque, and (2) gradient-based segmentation (i.e., step 6 of the CULEX algorithm)
fails in determining the adventitia layer due to poor contrast of the wall with the
lumen. Figure 5.11 (left panel) represents an image in which the ROI was incorrectly
chosen due to excessive blood backscattering.
128 User-Independent Plaque Segmentation
Figure 5.10 GUI developed in the MATLAB environment to evaluate CULEX performance and
allow operators to segment the images. The GUI also allows for reviewing of the results: The image
databases are stored in a structure together with the operators’ manual segmentations. The GUI
also allows new segmentations to be added and previous tracings can be reviewed.
Note that if lumen and MA
FW
are correctly detected, the adventitia layer of the
near wall is not extracted properly. Specifically, the leftmost part of the image
shows the MA
NW
profile falling into the vessel lumen, whereas immediately after it
exceeds the artery boundaries. Both of these effects are due to insufficient contrast
between the adventitia and carotid lumen. Moreover, in the central portion, this
image shows a wide zone in which backscatter is even higher; obviously, in that
region ROI extraction is unreliable given the loss of detail in the image itself. Figure
5.11 (right panel) shows the segmentation performed by CULEX. Note how the
portion of the image that is unaffected by blood backscattering is correctly
segmented.
Another major problem for most segmentation algorithms is the presence of
ultrasound artifacts. In CCA clinical examination, one of the most important arti-
facts is the presence of shadow cones. Hard tissues, usually calcium deposits or hard
plaques, originate in these regions of poor image quality. A previous version of our
algorithm [27] was unable to deal with such artifacts. In this version, ROI selection
is also used to restrict image segmentation: if in some parts of the image ROI is
incomplete (i.e., one among the lumen, MA
FW
, and MA
NW
cannot be identified), seg-
mentation in those specific portions of the image does not take place. Hence, the
remaining portion of the image that is unaffected by image artifacts can be correctly
processed. An example is shown in Figure 5.12, where the large white arrow indi-
cates a calcium deposit present in the near wall media tunica that projects a shadow
cone onto the image. CULEX provides suitable segmentation of the remaining part
of the image.
5.5 ROI Detection and Robustness to Noise 129
MA
FW
MA
FW
ROI
L
MA
NW
MA
NW
LI
FW
LI
NW
Figure 5.11 Segmentation of an image with high blood backscattering. The left image shows the
ROI extraction: The lumen (ROI
L
) and MA
FW
are correctly individuated, but MA
NW
extraction is prob-
lematic due to backscattering. This profile falls into the lumen vessel (leftmost part of the image)
and then explodes beyond the adventitia tunica. Segmentation in this region is not feasible. The
right image shows the segmentation performed by CULEX in the remaining portion of the image.
Note that even if part of the image is corrupted by noise, the unaffected part can nevertheless be
correctly segmented.
5.6 Segmentation of the Lumen–Intima and Media–Adventitia
Interfaces and IMT Measurement
The evaluation of the CULEX performances in terms of segmentation accuracy were
characterized using the methods defined in Section 5.4. Results herein presented are
relative to the image database containing images of normal carotid walls.
We asked four experts to manually segment the images. However, due to differ-
ent support for the segmentations, we decided to evaluate the segmentation errors at
50 points per image. Hence, results are relative to a total amount of 10,000 points
(50 points × 200 images).
5.6.1 Absolute Segmentation Errors and IMT Measurements
The segmentation errors defined in (5.2) were computed on each image of the data-
base for the lumen–intima and media–adventitia interfaces of the near and far wall.
Figure 5.13 depicts the distribution of the segmentation error for the four interfaces.
The abscissa reports the absolute error value, expressed in pixels, and the vertical
axis reports the percent of time a specific error value occurred in the 10,000 evalu-
ated points. Note that the segmentation error is always very low, except for the
MA
NW
that presents a greater error.
The boxplot in Figure 5.14 reports the distribution of the segmentation errors
for the interfaces of the far wall. The crosses represent the outliers, estimated using a
confidence level equal to 95%. It can be seen that the average segmentation error on
both the interfaces is lower than a pixel. Figure 5.15 reports the boxplot for the near
wall: The LI
NW
interface is segmented with a very low average error, in this case
lower than a pixel, whereas the MA
NW
is segmented with an average equal to
approximately 2 pixels.
The second column of Table 5.1 reports in a synthetic way the segmentation
performances in terms of average segmentation error and standard deviation.
130 User-Independent Plaque Segmentation
MA
FW
MA
NW
LI
FW
LI
NW
Figure 5.12 B-mode image of a CCA showing a calcium deposit in the near wall media tunica.
The large white arrow indicates the calcium and the shadow cone the deposit projects onto the
image. Segmentation performed by CULEX is, however, correct in the portion of the image unaf-
fected by the artifact.
Figure 5.16 reports the relative error committed in the IMT measurement. Data
are relative only to the distal border of the images, because several studies reported
how it could be clinically misleading and difficult to estimate IMT from the proxi-
mal artery wall. The average error on the IMT measurement is equal to 9% ± 9%,
which corresponds, in our image database, to approximately 1 pixel.
5.6 Segmentation of the Lumen–Intima and Media–Adventitia Interfaces and IMT Measurement 131
0
0−11
2
3456 78910
0.1
0.2
0.3
0.4
0
0.1
0.2
0.3
0.4
LI - near wall MA - near wall
LI - far wall MA - far wall
Frequencies (%)
Frequencies (%)
Absolute error (pixel)
Absolute error (pixel)
0−11
2
3456 78910
Figure 5.13 Distribution of the segmentation errors estimated on 10,000 points (50 points × 200
images). The histograms are relative to the LI
FW
(upper left), to the MA
FW
(upper right), to the MA
NW
(lower right), and to the LI
NW
(lower left). The abscissa reports the absolute value of the segmenta-
tion error [defined in (5.1)] and the bars represent the percentage of times that specific error
occurs. A summary of the mean errors is shown in the second column of Table 5.1.
Table 5.1 Performance of the CULEX Technique in Terms of
Segmentation Error (Second Column), of the Percent Statistics
Test (Third Column), and of the Confidence Interval (Fourth
Column)
Interface
Segmentation
Error (Pixel)
Mean
STD
Percent
Statistics
Z Score
Confidence Interval:
Percentage Points Falling
into the 95% Confidence
Interval (Mean
STD)
LI
NW
1.2/0.9 0.514* 92/14
MA
NW
2.1/1.9 0.300 52/28
LI
FW
0.9/0.8 0.570* 81/18
MA
FW
0.8/0.7 0.800* 85/21
* For the percent statistics test, the asterisk indicates that the test has been passed
(i.e., it is possible to accept the hypothesis that CULEX behaves like a human
operator in segmenting that specific interface).
Comparing the performance of CULEX to previously developed algorithms, we
found that the segmentation error is in line with other operator-dependent tech-
niques [15, 16] andis slightly better than other segmentation approaches [13]. How-
ever, a direct comparison of human-dependent techniques with CULEX is not
straightforward, because there may be a correlation of the segmentation error with
the manual ROI selection. In fact, the presence of an operator-dependent error in the
132 User-Independent Plaque Segmentation
0
−1
2
1
3
4
Far wall
Absolute error (pixel)
5
LI MA
Figure 5.14 Boxplot of the segmentation errors for the LI (left box) and MA (right box) interfaces
of the far wall. Mean errors and standard deviations are comparable. The crosses represent the
outliers (calculated by considering a confidence level equal to 95%).
0
4
8
12
16
Near wall
Absolute error (pixel)
20
LI MA
Figure 5.15 Boxplot of the segmentation errors for the LI (left box) and MA (right box) interfaces
of the near wall. Note that the algorithm commits a greater error in the MA segmentation. The
crosses represent the outliers (calculated by considering a confidence level equal to 95%).
computer-generated boundaries (e.g., in the case of nonoptimal selection of the ROI
in partially automated approaches) introduces a further element of variability,
which complicates the performance evaluation. More importantly still, a
well-known fact is that the characteristics of the image database may be fundamen-
tal in determining the quality of the computer-generated contour. In some cases,
such as in [14], this is due to the fact that the algorithm was developed on the basis
of specific hypotheses, for example, the fact that the CCA remains horizontal in the
viewed image. More generally, the segmentation performances are always related to
the image quality and the segmentation error per se does not take this factor into
account. In this sense, a comparison with the variability of the segmentations per-
formed by the operators is fundamental, because it yields important information on
the quality of the guess of the contour made by the operators, which is strongly
dependent on factors such as image noise.
Our technique is thus interesting in that CULEX allows a completely user-
independent extraction of the lumen–intima and media–adventitia profiles with a
segmentation error (compared to the average segmentation of four expert opera-
tors) that is acceptable in clinical practice, without restrictions on the orientation of
the CCA in the image.
5.6.2 Percent Statistics
For the Percent Statistics test, choosing 0.05, we find that the p has a value of
0.513. The Z value for the far wall lumen–intima interface was equal to 0.57,
whereas the one for the media–adventitia was equal to 0.8. Hence, we may say that
Z falls within the 95% confidence interval (CI), that is, that CULEX statistically
5.6 Segmentation of the Lumen–Intima and Media–Adventitia Interfaces and IMT Measurement 133
0
0
0.1
0.2
0.3
0.4
0.1 0.2
0.3
Relative error
Frequencies (%)
0.4 0.5 0.6 0.7 0.8 0.9
Figure 5.16 Distribution of the absolute percentage error on the IMT estimation obtained on
10,000 points. The abscissa reports the IMT measurement error in terms of percentage; the bars
represent the percentage of times that specific error occurs. The average error is equal to 9% ± 9%
(mean ± STD).