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Furthermore, Table 4.2 shows that the entropy, which is a measure of the infor-
mation content of the image, was higher for the ATL HDI-5000 in all cases. The
ASM, which is a measure of the inhomogeneity of the image, is lower for the ATL
HDI-5000 in the DS and NDS image cases. Furthermore, the entropy and the ASM
were more influenced by speckle reduction than normalization because they reach
their best values after speckle reduction filtering.
Table 4.3 illustrates the image quality evaluation metrics, for the 80 ultrasound
images recorded from each image scanner, between the NF-DS, NF-N, NF-NDS,
and N-NDS images. Best values were obtained for the NF-N images with lower
RMSE, Err3, and Err4, higher SNR, and PSNR for both scanners. The GAE was
0.00 for all cases, which can be attributed to the fact that the information between
the original and the processed images remains unchanged. Best values for Q and
SSIN were obtained for the NF-N images for both scanners, whereas best values for
SNR were obtained for the ATL HDI-3000 scanner on the NF-N images.
Table 4.3 shows that the effect of speckle reduction filtering was more obvious
on the ATL HDI-3000 scanner, which shows that the ATL HDI-5000 scanner pro-
duces images with lower noise and distortion. Moreover, it was obvious that all
quality metrics presented here are equally important for image quality evaluation.
Specifically, for the most of the quality metrics, better measures were obtained
between the NF-N, followed by the NF-NDS and N-NDS images, for both scanners.
It is furthermore important to note that a higher PSNR (or equivalently, a lower
RMSE) does not necessarily imply a higher subjective image quality, although they
do provide some measure of relative quality.
Furthermore, the two experts visually evaluated 10 B-mode ultrasound images
with different types of plaque [5] by delineating the plaque at the far wall of the
carotid artery. The visual perception evaluation, and the delineations made by the
two experts, showed that the plaque may be better identified on the ATL HDI-5000
scanner after normalization and speckle reduction (NDS), whereas the borders of
the plaque and the surrounding tissue may be better visualized on the ATL
HDI-5000 when compared with the ATL HDI-3000 scanner.
104 Quality Evaluation of Ultrasound Imaging of the Carotid Artery
Table 4.3 Image Quality Evaluation Metrics Between the Original–Speckle Reduction
(NF-DS), Original–Normalized (NF-N), Original–Normalized Speckle Reduction
(NF-NDS) and the Normalized–Normalized Speckle Reduction (N-NDS) Images
Evaluation
Metrics
ATL HDI-3000 ATL HDI-5000
NF-DS NF-N NF-NDS N-NDS NF-DS NF-N NF-NDS N-NDS
MSE 1.4 1.3 2.0 1.3 1.2 0.3 1.9 1.3
RMSE 1.2 0.4 1.4 1.1 1.1 0.5 1.3 1.1
Err3 3.8 0.8 3.9 3.5 3.7 0.8 3.8 3.5
Err4 8.2 1.2 8.0 7.5 8.1 1.3 7.8 7.5
GAE 000 0
0 00 0
SNR 5.0 16.5 4.8 5.4 5.3 15.9 5.1 5.4
PSNR 48.0 59 45.6 44.6 47.4 58.5 46 44.6
Q 0.7 0.93 0.73 0.69 0.72 0.93 0.72 0.71
SSIN 0.9 0.95 0.92 0.83 0.94 0.95 0.91 0.83
Source: [4].
Table 4.4 summarizes the image quality evaluation results of this study, for the
visual evaluation (Table 4.1), the statistical and texture analysis (Table 4.2), and the
image quality evaluation metrics (Table 4.3). A double plus sign in Table 4.4 indi-
cates very good performance, whereas a single plus sign indicates a good perfor-
mance. Table 4.4 can be summarized as follows: (1) The NDS images were rated
visually better on both scanners; (2) the NDS images showed better statistical
and texture analysis results on both scanners, (3) the NF-N images on both scan-
ners showed better image quality evaluation results, followed by the NF-DS
on the ATL HDI-5000 scanner and the NF-DS on the HDI ATL-3000 scanner; and
(4) the ATL HDI-5000 scanner images have considerable higher entropy than those
of the ATL HDI-3000 and thus more information content. However, based on the
visual evaluation by the two experts, both scanners were rated similarly.
4.6 Discussion
Normalization and speckle reduction filtering are very important preprocessing
steps in the assessment of atherosclerosis in ultrasound imaging. We have therefore,
investigated the usefulness of image quality evaluation in 80 ultrasound images of
the carotid bifurcation, based on image quality metrics and visual perception after
normalization and speckle reduction filtering using two different ultrasound scan-
ners (ATL HDI-3000 and ATL HDI-5000). Specifically, the images were evaluated
before and after specklereduction, afternormalization, andafter normalization and
speckle reduction filtering. The evaluation was based on visual evaluation by two
experts, statistical and texture features, image normalization, speckle reduction,
and on image quality evaluation metrics. Note that to the best of our knowledge,
there are no other studies found in the literature for evaluating ultrasound image
quality, based on speckle reduction filtering and normalization performed on
carotid artery images, acquired by two different ultrasound scanners.
The main findings of this study can be summarized as follows:
1. The NDS images were rated visually better on both scanners.
2. The NDS images showed better statistical and texture analysis results on
both scanners.
3. Better image quality evaluation results were obtained between the NF-N
images for both scanners, followed by the NF-DS images for the ATL
HDI-5000 scanner and the NF-DS on the HDI ATL-3000 scanner.
4.6 Discussion 105
Table 4.4 Summary Findings of Image Quality Evaluation in Ultrasound Imaging of the
Carotid Artery
Ultrasound
Scanner Visual Evaluation
(Table 4.1)
Statistical and
Texture Analysis
(Table 4.2)
Image Quality Evaluation
(Table 4.3)
NF DS N NDS NF DS N NDS NF-DS NF-N NF-NDS N-NDS
ATL HDI-3000
++ + ++ ++ +
ATL HDI-5000
++ + ++ + ++
Source: [4].
4. The ATL HDI-5000 scanner images have considerably higher entropy than
the ATL HDI-3000 scanner and thus more information content. However,
based on the visual evaluation by the two experts, both scanners were
rated similarly.
It has been shown that normalization and speckle reduction produce better
images. Normalization was also proposed in other studies using blood echogenicity
as a reference and applied in carotid artery images [29]. References [5, 13] showed
that normalization improves the image comparability by reducing the variability
introduced by different gain settings, different operators, and different equipment.
Note that the order in which these processes (normalization and speckle reduction
filtering) are applied affects the final result. Based on unpublished results, we have
observed that by applying speckle reduction filtering first and then normalization,
distorted edges are produced. The preferred method, then, to obtain better results is
to apply normalization first and then speckle reduction filtering.
Recent studies [4, 6, 7] have shown that the preprocessing of ultrasound images
of the carotid artery with normalization and speckle reduction filtering improves the
performance of the automated segmentation of the intima-media thickness [7] and
plaque [6]. More specifically, [7] showed that a smaller variability in segmentation
results was observed when performed on images after normalization and speckle
reduction filtering, compared with the manual delineation results made by two med-
ical experts. Furthermore, in another study [8], we have shown that speckle reduc-
tion filtering improves the percentage of correct classifications score of symptomatic
and asymptomatic images of the carotid. Speckle reduction filtering was also investi-
gated by other researchers on ultrasound images of liver and kidney [30] and on nat-
ural scenery [14], using an adaptive two-dimensional filter similar to the lsmv
speckle reduction filter used in this study. In [14, 30], speckle reduction filtering was
evaluated based only on visual perception evaluation made by the researches.
Verhoeven and Thijssen [31] applied mean and median filtering in simulated
ultrasound images and in ultrasound images with blood vessels. The lesion SNR was
used to quantify the detectability of lesions after filtering. Filtering was applied on
images with fixed and adaptive size windows in order to investigate the influence of
the filter window size. It was shown that the difference in performance between the
filters was small but the choice of the correct window size was important.
Kotropoulos and Pitas [32] applied adaptive speckle reduction filtering in a simu-
lated tissue-mimicking phantom and liver ultrasound B-mode images. They showed
that the proposed maximum likelihood estimator filter was superior to the mean
filter.
Although in this study, speckle has been considered as noise, in other studies
speckle, which was approximated by the Rayleigh distribution, was used to support
automated segmentation. Specifically, in [17], an automated luminal contour seg-
mentation method based on a statistical approach was introduced, whereas in [19]
ultrasound intravascular images were segmented using knowledge-based methods.
Furthermore, in [18] a semiautomatic segmentation method for intravascular ultra-
sound images, based on gray-scale statistics of the image, was proposed in which the
lumen, IMT, and plaque were segmented in parallel by utilizing a fast marching
model.
106 Quality Evaluation of Ultrasound Imaging of the Carotid Artery
Some statistical measures, as shown in the upper part of Table 4.2, were better
after normalization, whereas others, shown in the bottom part of Table 4.2, were
better after speckle reduction. Table 4.2 also shows that the contrast was higher for
the NF and N images on both scanners and was significantly different (S) after nor-
malization and speckle reduction filtering. All other measures presented in Table
4.2 were comparable and showed that better values were obtained on the NDS
images. Moreover, it was shown that the entropy that is a measure of the informa-
tion content of the image [22] was higher for both scanners in the cases of the NDS
and DS images. Significantly different entropy values were obtained mostly after
normalization and speckle reduction filtering. Low-entropy images have low con-
trast and large areas of pixels with the same or similar gray-level values. An image
that is perfectly flat will have a zero entropy. On the other hand, high-entropy
images have high contrast and thus higher entropy values [25]. The ATL HDI-5000
scanner, therefore, produces images with higher information content.
The entropy was also used in other studies to classify the best liver ultrasound
images [33], where it was shown that the experts rated images with higher entropy
values better. In [21], entropy and other texture features were used to classify
between symptomatic and asymptomatic carotid plaques for assessing the risk of
stroke. It has also been shown [13] that asymptomatic plaques tend to be brighter,
have higher entropy, and are more coarse, whereas symptomatic plaques tend to be
darker, have lower entropy (i.e., the image intensity in neighboring pixels is more
unequal), and are less coarse. Furthermore, note that texture analysis could also be
performed on smaller areas of the carotid artery, such as the plaque, after
segmentation [6, 7].
In previous studies [2, 3, 9–11, 26, 27], researchers evaluated image quality on
natural scenery images using either only the visual perception by experts or some of
the evaluation metrics presented in Table 4.3. In this study, MSE and RMSE values
were in the range of 0.4 to 2.0, for all cases; Err3, Err4, SNR, PSNR, Q, and SSIN
were better between the NF-N images for both scanners, showing that normaliza-
tion increases the values of these measures. In [15], speckle reduction filtering was
investigated on ultrasound images of the heart. The MSE values reported after
speckle reduction for the adaptive weighted median filtering, wavelet shrinkage
enhanced filter, wavelet shrinkage filter, and nonlinear coherence diffusion were
289, 271, 132, and 121, respectively. Loupas et al. [34] applied an adaptive
weighted median filter for speckle reduction in ultrasound images of the liver and
gallbladder and used the speckle index and the MSE for comparing the filter with a
conventional mean filter. The filter improved the resolution of small structures in
the ultrasound images. References [3, 26, 35] also documented that the MSE,
RMSE, SNR, and PSNR measures are not objective for image quality evaluation
and that they do not correspond to all aspects of the visual perception nor they
correctly reflect artifacts [24, 27].
Recently the Q [3], and SSIN [26] measures for objective image quality evalua-
tion have been proposed. The best values obtained in this study were Q = 0.95 and
SSIN = 0.95 and were obtained for the NF-N images for both scanners. These
results were followed with Q = 0.73, and SSIN = 0.92 in the case of NF-NDS for the
HDI ATL-3000 scanner, and Q = 0.72 and SSIN = 0.94 in the case of NF-DS for the
HDI ATL-5000 scanner. In [3], where natural scenery images were distorted by
4.6 Discussion 107
speckle noise, the values for Q reported were 0.4408, whereas the values for Q after
contrast stretching were 0.9372.
Finally, in other studies [36, 37], a natural scene statistical model was proposed
for assessing the quality of JPEG2000 compressed images blindly, without having
the reference image available.
The methodology presentedin this studymay also be applicable in future studies
to the evaluation of new ultrasound and telemedicine systems in order to compare
their performance. It is also important to note that the methodology consists of a
combination of subjective and objective measures that should be combined to arrive
at a proper image quality evaluation result [27].
In conclusion, the results of this study showed that normalization and speckle
reduction filtering are important processing steps favoring image quality. Addition-
ally, the usefulness of the proposed methodology based on quality evaluation met-
rics combined with visual evaluation in ultrasound systems and in wireless
telemedicine systems needs to be further investigated.
Acknowledgments
This work was partly funded through the Integrated System for the Support of the
Diagnosis for the Risk of Stroke (IASIS) project of the Fifth Annual Program for the
Financing of Research of the Research Promotion Foundation of Cyprus
sound Imaging of the Carotid Artery (TALOS) project of the Program for Research
and Technological Development 2003–2005 of the Research Promotion
Foundation of Cyprus.
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110 Quality Evaluation of Ultrasound Imaging of the Carotid Artery
CHAPTER 5
User-Independent Plaque Segmentation
and Accurate Intima-Media Thickness
Measurement of Carotid Artery Wall
Using Ultrasound
Filippo Molinari, Silvia Delsanto, Pierangela Giustetto, William Liboni, Sergio
Badalamenti, and Jasjit Suri
Cardiovascular diseases (CVDs) are responsible for one-third of all global deaths.
Among all CVDs, the ischemic stroke and the cardiac attack represent the most dan-
gerous threat to health. A recent study by the World Health Organization revealed
that by 2010 cerebrovascular and cardiac diseases would be responsible for 20%
and 26% of all global deaths, respectively. Moreover, if nowadays CVDs are a spe-
cific problem of industrialized countries, by 2020 they will also strongly affect
emerging and third world countries.
Interestingly, CVDs are a problem of both the sexes: Cerebrovascular accidents
are prevalent in men under 65 years, but then, after 65 years of age, they tend to be
represented more in women. Early prevention is believed to be the key to success-
fully lowering the impact of CVD on health.
Several studiesprovide evidence of the relationships between carotid artery wall
status and CVDs [1]; specifically, an increased intima-media thickness (IMT) is cor-
related with an augmented risk of brain infarction or cardiac attack. Moreover, the
presence of carotid plaques has been correlated notonly toCVDs, butalso to degen-
erative pathologies such as vascular dementia and Alzheimer’s disease [2]. Hence,
the assessment of carotid wall status is also essential for the early identification of
risk conditions in asymptomatic patients.
A suitable clinical examination for the early detection of carotid wall patholo-
gies has to be noninvasive, low cost, fast, highly repeatable, user independent, and
safe for the subjects. Ultrasound scanning is a technique that possesses all these
characteristics; in clinical practice, in fact, the status of the carotid artery is usually
assessed by means of B-mode longitudinal images.
This chapter presents an innovative algorithm for the accurate segmentation of
ultrasound images of the common tract of the carotid artery. Besides being com-
pletely user independent, this technique allows for accurate IMT measurements and
for carotid plaque characterization.
111
5.1 Ultrasound Imaging of the Carotid Wall
5.1.1 Carotid Artery Anatomy and Pathology
Arteries and veins present a common structure constituted by three coaxial tunicae,
or layers. Proceeding from the external tissue to the inner vessel, these three layers
are named, respectively, adventitia, media, and intima [3].
The inner coat is constituted by a layer of pavement endothelium, with polygo-
nal, oval, or fusiform cells, with very distinct round or oval nuclei, followed by a
subendothelial layer, formed by delicate connective tissue with branched cells lying
in the interspaces of the tissue, and finally an elastic or fenestrated layer, composed
by a network of elastic fibers, named fenestrated membrane. This last part repre-
sents the main component of the intimal wall.
The middle coat is composed mainly of muscular tissue, and it is characterized
by a transversal arrangement of fibers, as opposed to the longitudinal direction of
those of the intimal layer. In smaller arteries, the muscular fibers are arranged in
lamellae and arranged circularly around the vessel, whereas in larger arteries, such
as the iliac, femoral, and carotid, layers of muscular fibers alternate with elastic
fibers. The elastic component is even more present in the aorta and the innominate
arteries, where bundles of white connective tissue have also been found. The muscle
fiber cells constituting the media are around 50 μm and contain well-marked,
rod-shaped nuclei.
The tunica adventitia is composed of bundles of connective tissue and of elastic
fibers, except in thesmallest arteries. The elastic tissue is located prevalently near the
middle coat and is sometimes described as forming a special layer, the tunica elastica
externa of Henle. This layer is especially evident in arteries of medium size.
The term atherosclerosis literally means “hardening of the arteries” and is
derived from the Greek words athera, meaning “porridge,” with reference to the
foamy appearance of plaques under high-power ultralight microscopy, and sclero-
sis, meaning “hardening.” Clinically this term indicates an ensemble of pathological
conditions having the common factors of thickening and elasticity loss in the arterial
walls. This pathology is the most common cause of death and morbidity in the
Westernized world.
For more than 40 years, the mechanisms involved in the development of this
pathology have been one of the most fiercely debated arguments in the medical
arena. Scientists now generally agree that atherosclerosis is a chronic inflammatory
process, triggered by a series of risk factors, the most notable of which is an elevated
level of cholesterol in blood serum, apparently unique in its ability to induce athero-
sclerosis even in the absence of the other known risk factors. Many hypotheses have
been formulated throughout the years, each emphasizing a different aspect of the
atherogenetic process, at times presented as the major cause in the development of
the pathology [4].
The progression of atherosclerosis may cause the appearance of plaques on the
artery wall. In 1994–1995 the American Heart Association (AHA) proposed the
well-known classification of plaques into six categories on the basis of their compo-
sition and morphology. Further studies revealed that plaque type is associated with
the patient’s clinical course [5, 6]. Hence, assessment of the status of the carotid wall
is of paramount importance in clinical practice to detect early signs of
112 User-Independent Plaque Segmentation
atherosclerotic pathology, tofollow up onthe progressionof existing lesions, and to
select the proper therapy.
5.1.2 Principles and Validity of Ultrasound Analysis of the Carotid Artery
The B-mode ultrasound scan of the common tract of the carotid artery (CCA) is a
widely adopted clinical exam for the assessment and diagnosis of several patholo-
gies. In fact, in the presence of a disease, the CCAtract may be subjected to morpho-
logical changes (such as stenosis or plaques) that can perturb the normal blood
circulation. In asymptomatic subjects it may represent a risk index for some severe
pathologies such as acute stroke or cardiac infarction. It is well known that
increased thickness of the CCA wall is correlated with a higher incidence of cardio-
vascular and cerebral acute events.
5.1.2.1 Normal Carotid Wall
The principles and the validity of the carotid analysis performed by means of ultra-
sound B-mode images was firstly described by Pignoli et al. [7]. Common features are
observable in B-mode carotid images: The carotid artery wall appears as a double-
layered structure made of two lines of different echogenicity. The first line of echoes is
generated by the interface between the vessel lumen and the intima layer; the second
line of echoes is generated by the interface between the media and the adventitia
layers.
Figure 5.1 reveals an intrinsic limitation of the ultrasound carotid analysis: The
intima layer is not distinguishable from the media layer. Considering the distal wall
of the common carotid artery, note that the artery wall is made of a first layer of
variable gray tones that depicts the progressive variation of the acoustic impedance
in the media layer; then, a sharp-edged layer of high echogenicity is found at the
media–adventitia interface.
5.1 Ultrasound Imaging of the Carotid Wall 113
Intima-lumen
interface
Scanning depth
Media-adventitia
interface
Far wall
Carotid lumen
Near wall
Figure 5.1 B-mode ultrasound image of a CCA tract (normal subject). The probe is positioned on
the top of the figure and the direction of propagation of the ultrasound beam is downward. The
far wall of the carotid artery is observable at the bottom of the figure. Specifically, the carotid
tunica is characterized by two stripes that originate ultrasonic echoes: The first gray line is relative
to the intimal and medial layers, underlined by the bright stripe corresponding to the adventitial
layer. Note that in this kind of imaging, the intimal layer cannot be distinguished by the medial
one. (From: [8]. © 2007 IEEE. Reprinted with permisson.)