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behaves like a human operator in segmenting the distal carotid wall in our image
database.
The corresponding values for the near wall were equal to 0.514 for the
lumen–intima interface and 0.3 for the media–adventitia interface. This latter inter-
face is the only one that did not pass the statistical test. Results are in line with the
measurements of the segmentation error: the greater difficulties can be found in the
outlining of the near wall media–adventitia border. The third column of Table 5.1
reports the results for this test.
To our knowledge, none of the previously referenced algorithms for the carotid
artery segmentation were tested by means of the Percent Statistics algorithm; hence,
a direct comparison of the performances is impossible. However, we believe this test
is important because in many practical applications (i.e., image reconstruction,
image fusion, carotid wall texture analysis) it is more important to know that the
CGBs are not different from those obtained by experts than to have a low segmenta-
tion error. Our algorithm passed this statistical test, even though p value was
extremely close to the limit of the acceptance region. Clearly, we are working to
increase the number of human operators segmenting our sample database, since this
would enforce the strength of the statistical test.
5.6.3 Confidence Interval for the Mean Segmentation
Figure 5.17 represents the distribution of the results for the confidence interval. The
CGB was tested against the average segmentation of the four humans. The bars rep-
resent the percentage of times the CGB falls within the confidence interval (
0.05)
for the average LI profiles (left graphs, far wall at the top, near wall at the bottom)
and MA profiles (right graphs, far wall at the top, near wall at the bottom). The
number of tested points was 10,000 (50 points per image). The probability of falling
into the 95% CI for the average segmentation was always quite high, and no signifi-
cant differences were found among LI
NW
, LI
FW
, and MA
FW
in terms of CULEX seg-
mentation. Once again, however, the MA
NW
interface was more problematic; Figure
5.17 shows, in the bottom right histogram, that in 40% of the cases, 40% or fewer
of the pixels fell into the CI. In fact, due to the characteristics of the ultrasound
images, the near wall media–adventitia layer is often difficult to track, even for a
trained expert. At present, our algorithm is capable of performing a segmentation
that is reliable, but it overestimates the real position of the MA
NW
border.
Finally, Figure 5.18 shows an example of segmentation with the CIs overlapped.
Note that, in this image, nearly the 100% of the points of the CGBs are within the
95% CI of the average manual segmentations.
This test is a further step toward ensuring that CULEX performs reasonably like
a human expert when segmenting the CCA walls.
5.7 Plaque Imaging
The availability of a user-independent technique for the segmentation of carotid
wall plaques is of paramount importance in clinical practice as well as in the engi
-
neering field. In fact, most of the time, the segmentation of a plaque is a preliminary
134 User-Independent Plaque Segmentation
5.7 Plaque Imaging 135
Figure 5.18 Example of CULEX segmentation and of the 95% CI. The white lines represent the
borders traced by CULEX; the dashed white lines represent the 95% CI. In this example, nearly
100% of the pixels fall into the CI.
0
0 0.1 0.2
0.3
0.4 0.5 0.6 0.7 0.8 0.9 1.0 0 0.1 0.2
0.3
0.4 0.5 0.6 0.7 0.8 0.9 1.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
LI - near wall MA - near wall
LI - far wall MA - far wall
Percentage of CGB pixels
falling into the CI
Percentage of CGB pixels
falling into the CI
Frequencies (%)
Frequencies (%)
Figure 5.17 Distribution of the pixels falling into the 95% CI for the four interfaces. The abscissa
represents the percentage of pixels falling into the 95% CI in a given image; the bars report the
percentage frequencies over 200 images. The mean percentage values were 92% for the LI
NW
, 81%
for the LI
FW
, and 85% for the MA
FW
interface. The MA
NW
interface presents an average value equal to
52%, confirming that this specific border is the most difficult to track by means of our technique.
step for the application of some other analysis technique, ranging from 3-D morpho-
logical reconstruction to plaque classification. CULEX proved suitable for segment-
ing both fibrous and echolucent type II plaques. We tested CULEX on our second
database, consisting of 100 images of plaques and thickened carotid walls.
When in presence of increased IMT, CULEX provided performances that were
statistically equal to the case of normal carotid wall. ROI selection was effective on
all 30 images in the database, with segmentation errors comparable to the normal
case.
5.7.1 Stable Plaques
Figure 5.19 depicts the CULEX segmentation of an image containing fibrous
plaques. The left panel of the upper row depicts the original image; the right panel
depicts the CULEX segmentations. The image is relative to a subject showing an
important plaque located on the proximal wall (as indicated by the large white
arrows); the far wall is comparable to a normal wall. This image indicates that
CULEX correctly characterizes plaque on the near wall and segment of the distal
wall.
We would like to remark once again that this segmentation is completely user
independent, and the four profiles are extracted in a single shot, without the need to
136 User-Independent Plaque Segmentation
MA
FW
MA
NW
LI
FW
LI
NW
MA
FW
CCA
CCA
JV
JV
MA
NW
LI
FW
LI
NW
Figure 5.19 CULEX segmentation of stable plaques. The upper row shows the case of a fibrous
plaque located on the near CCA wall; the lower row shows a CCA with a distal and a proximal
plaque. Left images are the raw ultrasound B-mode images. Large white arrows point to plaques.
The right images report CULEX segmentation of the plaques, where we can see that this technique
allows for excellent characterization of the plaque profile.
tune the algorithm or select parameters. Specifically, in this image, CULEX was
capable of segmenting contemporarily a plaque and a normal carotid wall.
The left panel of the lower row in Figure 5.19 shows two fibrous plaques: one
on the near wall and one on the far wall. Again, CULEX was able to characterize
plaques (right image).
To determine how CULEX performed on the plaque database, we calculated
the percentage of misclassified pixels in relation to the total number of pixels corre-
sponding to plaque (i.e., an error expressed as percentage of misclassification over
the plaque area). For the 30 images constituting our database, we found an error
equal to 8% ± 5% (mean ± STD).
We also compared the maximum stenosis diameter of the CCA lumen as
detected by CULEX and compared it to the one derived from human tracings; we
found a relative error lower than 1% (STD = 1%).
In this case too, a direct comparison of the CULEX performances with previous
proposed techniques is not possible. Some approaches are not suitable for plaque
characterization [14, 15], whereas other algorithms fail to segment proximal wall
malformations [16]. Besides being completely user independent, our approach
allows for direct extraction of a plaque contour, independent of its location.
5.7.2 Unstable Plaques (Echolucent Type II)
Figure 5.20 illustrates the CULEX segmentation of two echolucent plaques located
on the distal CCA wall. The left panel shows the raw images, whereas the right
panel depicts the segmentations. Note that echolucent type II plaques can also be
correctly characterized by our technique. Of the 40 images in our database, the seg-
mentation error was equal to 12% ± 4% (mean ± STD). The difference between
maximum the stenotic diameter was, in this case, about 10% (STD = 3%).
The development of an algorithm capable of also segmenting echolucent
plaques poses a problem of SNR. Specifically, if the image is too noisy or character-
ized by excessive blood backscattering, the lumen–intima interface segmentation
may become difficult. Some pixels belonging to the lumen, in fact, could be classi-
fied as plaque due to the fact that they represent light-gray spots in the black of the
vessel lumen. Similarly, pixels belonging to an echolucent plaque may be classified
as lumen if the SNR is poor. A partial solution could be to filter the image, but such
an operation enhances the signal power, but decreases the image detail and con-
trast, resulting in greater bias in the automatically segmented profiles. Hence, to
correctly segment echolucent plaques, the image must be of good quality.
Again, no other technique that we are aware of allows for the segmentation of
echolucent plaques in a user-independent fashion. Most of the previously described
techniques are not capable of distinguishing echolucent plaques from image noise
[14–16]. Hence, despite a segmentation error that is of the order of one-tenth of the
plaque area, our approach allows for the individuation and characterization of
echolucent plaques withoutbeing instructed by the user. It must be considered, also,
that with this structure it is possible to segment a CCA ultrasound image in a com-
pletely blind fashion: CULEX proved suitable for normal scans as well as for
pathological carotid images.
5.7 Plaque Imaging 137
5.8 Discussion and Conclusions
The CULEX algorithm can be proficiently used for the segmentation of ultrasound
images of the common carotid artery wall. This algorithm has the advantage of
being completely user independent; that is, the raw US image in DICOM format can
be processed without any interaction with the operator. Moreover, the CULEX
algorithm is able to characterize both echogenic and echolucent plaques in the
carotid wall. Pixel fuzziness and user independence are preserved and the obtained
IMT design is extremely close to the gold-standard tracing.
On normal carotids, the characterization of the algorithm showed that the con-
tour traced by the CULEX is statistically comparable to that of a human trained
operator. Moreover, the average error the algorithm commits in segmenting the
lumen–intima and the media–adventitia interfaces is lower than 1 pixel for the far
wall, equal to 1.2 pixels for the lumen–intima interface of the near wall, and equal to
2.1 pixels for the media–adventitia layer of the near wall.
When estimating the IMT, the average error is equal to 9%: note that this per
-
formance is in line with that obtained by means of user-dependent techniques.
138 User-Independent Plaque Segmentation
MA
FW
MA
NW
LI
FW
LI
NW
MA
FW
CCA
CCA
JV
JV
MA
NW
LI
FW
LI
NW
Figure 5.20 CULEX segmentation of unstable plaques. The upper row shows the case of a big
echolucent type II plaque located on the near CCA wall; the lower row shows a plaque image in
which the operator fixed four markers for the delimitation of the plaque area. The images in the
left panel are the raw ultrasound B-mode images. Large white arrows point to plaques. The images
on the right depict CULEX segmentation of the plaques, in which we can see that this technique
allows for excellent characterization of the plaque profile. Specifically, the percentage of
misclassified pixels with respect to the plaque area is lower than 9% in these examples.
On stable plaques and on increased IMT images, the CULEX algorithm per-
formed a segmentation substantially equal to that obtained on normal images.
When processing echolucent plaques, the segmentation error increased slightly
because of the weak difference in gray tones between pixels belonging to the wall
and pixels belonging to the lumen.
This algorithm is a step forward in the completely automatic detection of the LI
and MA interfaces in an ultrasound B-mode carotid scan, suitable for healthy as
well as pathological subjects.
One of the major advantages of this technique, in fact, is that it can also be used
in presence of pathology. The algorithm proved effective in automatically detecting
and segmenting echolucent type II plaques, echogenic plaques, and carotid walls
with increased IMT. Despite a small bias in the estimation of the plaque dimensions,
we found the performance of our algorithm to be extremely useful for the assess-
ment of the carotid media layer, as a computer aid to the early diagnosis of wall
degeneration in asymptomatic patients.
This system may be extremely helpful when segmentation is just a preliminary
step for further processing such as coregistration with other imaging techniques,
3-D reconstruction, and carotid wall assessment. As an example, this automatic
algorithm could be used for plaque identification before performing plaque texture
analysis, especially when the effects of several image processing steps need to be
validated.
The results are very promising and the system is being integrated into clinical
setups for automatic pathological carotid wall analysis.
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140 User-Independent Plaque Segmentation
PART III
2-D/3-D Breast Ultrasound Imaging
CHAPTER 6
Breast Lesion Classification Using 3-D
Ultrasound
Ruey-Feng Chang, Chii-Jen Chen, Kau-Yih Chiou, Woo Kyung Moon, Dar-Ren
Chen, and Jasjit S. Suri
6.1 Introduction
Breast cancer is a progressive disease, and the probability of developing invasive
breast cancer in women is not the same at different age intervals [1]. According to
recent studies [2, 3], early detection of breast cancer can reduce morbidity and mor-
tality rates. In the past, mammography has been the most widely used screening
modality, but it has a low false-negative rate [4]. False-negatives can be attributed to
the existing technological limitations of mammography, human error, and quality
assurance failures. Many researchers have found that only 5.1% of malignancies
(193 of 3,753) were detected in women with an abnormal or benign finding on the
mammogram [5]. Recently, it has been shown that the rates of false-negatives can
be reduced by using a different modality, such as ultrasound [6].
Because ultrasound is widely available and relatively inexpensive to perform, it
has become a valuable diagnostic adjunct to mammography [7–9]. Zonderland et
al. [10] reported a sensitivity of 91% and specificity of 98% for ultrasound com-
pared to mammography (83% and 97%, respectively) based on their experiment.
Although different ultrasound operators can produce different US images, ultra-
sound is still preferred due to its convenience, noninvasiveness, and low cost. To
reduce the effect of operator dependence in ultrasound, several computerized meth-
ods have been proposed to help the classification between benign and malignant
tumors [11–18].
The general idea of computer-aided diagnosis (CAD) has been developed to
assist physicians in diagnosing ultrasonic abnormalities suspicious for breast can-
cer. With the assistance of CAD, physicians can efficiently detect lesions, such as
spiculations, and discriminate more accurately between begin and malignant
tumors [11, 16, 19–25]. Sawaki et al. [11] proposed a CAD system that uses fuzzy
inference for breast sonography and adopted six different criteria to classify lesions:
lesion shape, border, edge shadows, internal echoes, posterior echoes, and halo.
However, their system’s accuracy, sensitivity, and specificity were only 60.3%,
82.1%, and 42.9%, respectively.
143