Carotid Artery Segmentation in Ultrasound Images and Measurement of Intima-Media Thickness

Abstract

Background:
The segmentation of the common carotid artery (CCA) wall is imperative for the determination of the intima-media thickness (IMT) on B-mode ultrasound (US) images. The IMT is considered an important indicator in the evaluation of the risk for the development of atherosclerosis. In this paper, authors have discussed the relevance of measurements in clinical practices and the challenges that one has to face while approaching the segmentation of carotid artery on ultrasound images. The paper presents an overall review of commonly used methods for the CCA segmentation and IMT measurement along with the different performance metrics that have been proposed and used for performance validation. Summary and future directions are given in the conclusion.

Full text

Hindawi Publishing Corporation
BioMed Research International
Volume , Article ID ,  pages
http://dx.doi.org/.//
Review Article
Carotid Artery Segmentation in Ultrasound Images and
Measurement of Intima-Media Thickness
VaishaliNaik,R.S.Gamad,andP.P.Bansod
Department of Electronics and Instrumentation Engineering, Shri Govindram Seksaria Institute of Technology and Science,
Indore 23, Park Road, Indore 452003, India
Correspondence should be addressed to Vaishali Naik; vainaik@gmail.com
Received  April ; Accepted  May 
Academic Editor: Manuel F. Casanova
Copyright ©  Vaishali Naik et al. is is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background. e segmentation of the common carotid artery (CCA) wall is imperative for the determination of the intima-media
thickness (IMT) on B-mode ultrasound (US) images. e IMT is considered an important indicator in the evaluation of the risk for
the development of atherosclerosis. In this paper, authors have discussed the relevance of measurements in clinical practices and the
challenges that one has to face while approaching the segmentation of carotid artery on ultrasound images. e paper presents an
overall review of commonly used methods for the CCA segmentation and IMT measurement along with the dierent performance
metrics that have been proposed and used for performance validation. Summary and future directions are given in the conclusion.
1. Introduction
Cardiovascular disease (CVD) is one of the leading causes
of deaths in the metropolitan cities. A recent survey by the
World Health Organization revealed that up to . million
people died from CVDs in . Scientically presaged
the upcoming , almost . million human deaths are
resulting from CVDs []. CVD ailments are relative to
atherosclerosis (arterial disease). Atherosclerosis is responsi-
ble for the thickening of the artery walls, and the IMT is used
as a validated measure for the evaluation of atherosclerosis.
Prominently, the increase in IMT jeopardizes the brain
infarction or cardiac attack [,].
Usually, the B-mode ultrasound scan of CCA in its
common tract is used for the evaluation of the artery status
andforthemeasurementoftheIMT.Ultrasoundmethod-
ology has manifest benets of being real-time, noninvasive,
low-cost, reliable, and absolutely safe for the patients. e
essential drawbacks of this methodology are the B-mode
image having low signal-to-noise ratio and ultrasounds are
operator dependent []. Conventionally, the IMT is manually
measured by the trained operator from the US scan images.
is methodology is highly user dependent, time consuming,
tedious,andinfeasibleinpresenceoflargeimagedatabases
[,].Duringthepastyears,severalcomputerizedtech-
niques have been developed for segmentation of CCA. ese
methods can be broadly classied into two categories: rst
category includes techniques that are completely automated,
whereas second category includes those that require user
interaction (semiautomated). is review will focus on the
techniques that have been developed to perform CCA wall
segmentation and IMT measurement in B-mode ultrasound
images in both automated and semiautomated manners.
2. Clinical Significance of Vessel
Wall Segmentation
2.1. Common Carotid Artery Intima-Media ickness. e
CCA longitudinal section is shown in Figure .Itischarac-
terized by a longitudinal tract (common carotid) that, aer
an enlargement (carotid sinus) containing a ow divider,
bifurcates into two arteries, one internal and one external, on
thebasisoftheirpositioninrelationtoneckskin[].
e bifurcation and the internal carotid artery (ICA)
are more threatening to atherosclerosis, due to stronger
hemodynamic stresses in the bifurcation and branching
zones,butitisdiculttovisualizethe“double-line”pattern
in these locations. So an IMT measurement in the CCA is

BioMed Research International
Internal
External
Skin
Bifurcation
Common
1 2 3
NW
4 5 6
FW
0.5–1.0 cm
1.0 cm
1.0 cm
F : e carotid artery view with the interfaces: ()
periadventitia-adventitia (NW), () adventitia-media (NW), ()
intima-lumen (NW), () lumen-intima (FW), () media-adventitia
(FW), and () adventitia-periadventitia (FW) [].
preferred in the development of segmentation algorithms and
in clinical practice [].
Classically, IMT is dened as a double-line pattern
visualized by echotomography on both walls of the CCA in
a longitudinal image. It consists of two parallel anatomical
boundaries referred to as the lumen-intima and media-
adventitia interfaces []. US waves are reected dierently
by blood (vessel lumen) and wall layers because of their
dierences in density and elasticity. US waves are not reected
by vessel lumen and tunica media, thus allow detection of the
lumen-intima (LI) and media-adventitia (MA) interfaces, as
depicted in Figure  B-mode ultrasound CCA image [].
2.2. Importance of Carotid Arterial Intima-Media ickness.
e increased IMT reects early stages of atherosclerosis and
cardiovascular risk. Higher blood pressure and changes in
shear stress are the potential causes of intimal thickening.
Changes in shear stress and blood pressure may cause a
local delay in lumen transportation of potentially atherogenic
particles, which favors the accumulation of particles in the
arterial wall and consequent plaque formation []. It is
found that type  diabetes is a signicant risk factor for
increased carotid IMT in children []. It is conrmed that
theincreaseinIMTisdirectlyassociatedwithanincreaserisk
of myocardial infarction and stroke in older adults without
a history of cardiovascular disease []. e assessment of
IMT in prediction of the degrees of atherosclerosis and the
risk of stroke and CVDs has been demonstrated by a lot of
studies [–]. It is found that obesity especially abdominal
obesity in childhood and adolescence is closely related to IMT
[]. e studies conrmed that the increase of the IMT value
above .-. mm is indicative of a signicant increase of
CVD risk when taking into account the population of healthy
elderly [,]. In the following sections, we will discuss the
diculties confronted in the intima and media detection and
review the recent advancements in CCA segmentation.
3. Difficulties of Intima and
Adventitia Detection
e diculties encountered in detecting intima and adventi-
tia layers are as follows []:
(i)presenceofspecklesinUSimage;
(ii) the structure of IM complex or the intimal layer
changes due to diseases such as atherosclerotic
plaques;
(iii) variation in echo characteristic on intima and adven-
titia on images with the variation in sonographic
instrumentations.
4. Segmentation Techniques
Plethora of ultrasound-segmentation techniques have been
reviewed in recent surveys by Noble and Boukerroui []and
Molinari et al. []. ereby authors are steadfast in reporting
on recent techniques, used for the segmentation of the CCA
on US images. ese studies on the segmentation of the
carotid artery boundaries include the application of dynamic
programming, deformable snakes, hough transforms, and
classication approaches to detect the carotid boundaries on
longitudinally oriented images [,–]. For each method-
ology, the authors have described principles, performance,
advantages, and limitations. Table  gives and summarizes the
technique that is mentioned below.
4.1. Dynamic Programming Techniques (DP). DP technique is
concisely used to solve optimization problems, where desired
segmentation minimizes the cost function dened by the
particular application. Local measurements of echo intensity,
edgestrength,andboundarycontinuityareincludedas
weighted terms in a cost function. All possible set of spatially
consecutive points forming a polyline is being considered,
andfavorisgiventothatwhichminimizesthecostfunction
[].
e polylines are represented as a vector:
=1,
2,...,
𝑖−1,
𝑖,...,
𝑁,()
where is the horizontal pixel position, 𝑖−1 and 𝑖are
neighbor points, and is the horizontal length of the search
region. A vertical search window of size ×1pixels is used to
scan the boundary from le to right at horizontal positions
as shown in Figure .Atscanposition, the boundary point,
𝑖,in() can be any pixel in this window. e optimized
connection is searched for each point in this window and the
cost accumulated. At the end of the scanning, the optimal
polyline is the one that minimizes the cost function:
Sum =𝑁

𝑖=1
𝑖. ()
e local cost is a weighted sum of cost terms:

𝑖=
11𝑖+
22𝑖+
33𝑖−1,
𝑖, ()

BioMed Research International 
Anatomy echo zones
Lumen diameter Z4
I5
I6I7
Intima-media thickness (IMT): Z5-Z6
Media thickness: Z6
Intima thickness: Z5
I2
I3
Adventitia Z1
Media Z2
Intima Z3
Intima Z5
Media Z6
Adventitia Z7
F:B-modeCCAimage.emedialayerthickness(MLT)isdenedasthedistancebetweentheintima-mediaandthemedia-adventitia
interface [].
where 1,2,and3are weighting factors, 1,2,and
3are the echo intensity, intensity gradient, and boundary
continuity cost terms, respectively. Based on DP techniques
in  Gustavsson et al. []introducedaprocedurefor
automaticultrasonicmeasurementsofthecarotidartery,
and lumen diameter (LD) and IMT were computed. Inter-
method(autoversusmanual)variabilityaswellasinter-
and intraobserver variability was studied by computing the
conventional coecient of variation (CV). A major advantage
of this methodology was complete automation and low
computational complexity, thus suitable for clinical purposes.
is method requires interactive tools for manual tracing in
order to correct the remaining detection errors. e major
limitation of this technique is the need for training of the
system. In , Gustavsson et al. []havecomparedfour
algorithms: the dynamic programming, the maximum gradi-
ent, the model-based, and the matched lter algorithm and
conrmed that the DP algorithm provides superior perfor-
manceintermsofaccuracyandrobustness.In Liuetal.
[] proposed a segmentation method in which the energy
denition of active contour model was used and DP was
employed to search the shortest path. To reduce the eects
of speckle noise, anisotropic diusion method was adopted.
It is advantageous as it requires less manual input. Holdfeldt
et al. [] proposed a method based on DP for boundary
detection in ultrasound image sequences. According to the
author, this method gives favorable results on both synthetic
and real ultrasound data. Cheng and Jiang []proposed
a novel dual dynamic programming (DDP) technique that
detectedintimalandadventitiallayersoftheCCAoftheB-
mode US images. In this, the robustness against the speckles
was increased by embedding the anatomical knowledge into
its structure. erefore, the researcher reported that the DDP
technique achieved a detection performance comparable to
manual segmentation.
4.2. Hough Transform (HT). HTtechniqueusedtodetect
straight lines. A straight line at a distance and orientation
canberepresentedby
=cos +sin . ()
e HT of this line is just a point in the (,)plane;thatis
all the points on this line map into a single point. is fact
is utilized to detect straight lines in a given set of boundary
points. If given boundary points are (𝑖,
𝑖),  = 1,...,
for some selected quantized values of parameter and ,
map each (𝑖,
𝑖)intothe(,)spaceandcount(, ),the
number of edge points that map into the location (,); that
is, set
𝑘,
1=
𝑘,
1+1
if 𝑖cos +
𝑖sin =
𝑘for =
𝑖.()
en the local maxima of (,) give the dierent straight
line segments through the edge points. Generalized HT can
be used to detect curves other than the straight lines [].
Segmentation algorithm based on the HT was demonstrated
by Golemati et al. []intosegmentbothlongitudinal
and transverse images. e HT is eective in detecting lines
(longitudinal images) or circles (transverse images), but it
may fail in detecting curved vessels. In , Stoitsis et al. []
proposed the HT-initialized active contour methodology. In
, Petroudi et al. []proposedafullyautomatedmethod
that was proposed for the delineation of the intima-media
complex (IMC). In this technique aer speckle removal
andHTusedforboundarydetectionfollowedbyimage
normalization, the corresponding results were used to pro-
vide the initial statistical information needed for a Markov
random eld (MRF). In , Matsakou et al. []proposed
a method in which an HT-based methodology was used for
thedenitionoftheinitialsnakefollowedbyagradient
vector ow (GVF) snake deformation for the nal contour
detection. e author reported that the sensitivity, specicity,
and accuracy were ., . and ., respectively, for both
diastolic and systolic cases. Recently Xu et al. []proposeda
segmentation method using HT and dual snake model; two
contours are initialized from line segments generated by HT.
Author admits that the technique is not suitable for irregular
boundaries and decimate minor details.
4.3. Nakagami Mixture Modelling. In , Destrempes et
al. [] introduced a segmentation technique based on

BioMed Research International
T : Overview of recent CCA IMC segmentation techniques for ultrasound imaging.
Name,
year, and
ref. no.
Common carotid artery
IMT segmentation
technique
Advantages & limitations of the methods
Selection
method of ROI
(SA/FA)
Performance
metric
Processing
time/frame avg
IMT error
(mm) 
Ilea et al., 
[]
Model-based approach,
video tracking procedure:
spatially coherent
algorithm
Advantages: Method can deal with inconsistencies in the
appearance of the IMC over the cardiac cycle. Robustness with
respect to data captured under dierent imaging conditions.
FA MA D
 sec
( sec in st frame
&secin
tracking 
frames)
(−.) ±
. ∗
Xu et al.,
,
[]
Houghtransformanddual
snake model
Advantages: It is less likely to be aected by noise, compensates
the holes or missing boundaries, and can estimate the missing
LI interface boundary.
Limitations: e method would work ne for early thickening of
IMC but fails for irregular boundaries in the presence of plaques
and eliminates minor details.
SA MAD . sec . ±. 
Molinari et al.,
,
[]
Multiresolution edge
snapper
Advantages: Complete automation, robustness to noise, and
real-time computation.
Limitations: Robustness with respect to noise, but the LI/MA
representation is less accurate.
FA MAD Less than  sec . ±. 
Destrempes et al.,
,
[]
Nakagami distributions,
Bayesian model
Advantages: Robust to a reasonable variability in the
initialization, lowest tracing error for LI & MA, method is not
sensitive to the degree of stenosis or calcication.
Limitations: Depends on initial segmentation
SA MAD
HD  sec — 
Petroudi et al.,
, []
Active contours & active
contours without edges
Advantages: Fully automated, fast, does not require any user
interaction, and works well for noisy images. FA MA D — .   ±. 
Destrempes et al.,
,
[]
Nakagami distributions,
stochastic optimization
Advantages: Reasonable average computation time, robust to
the estimation procedures.
Limitations: Method suitable for healthy arteries, extensive
tuning & training, so computational cost is high, for dierent
scanner requires retraining & retuning.
SA MAD
HD  sec — 
Faita et al.,
, []
First-order absolute
moment edge operator
Advantages: Suited for fast real-time implementation, operator
can have immediate feedback on the quality of the images.
Limitations: Depends on ROI selection.
SA MAD — . ±
.

Liang et al.,
,
[]
Multiscale dynamic
programming
Advantages: No initial human setting, capable of processing
images of dierent quality, ambiguous cases user can intervene,
and reduced interobserver variability.
Limitations: Training required, for dierent scanner retraining
needed, searched LI & MA interfaces may cross each other.
FA MAD . min . ±. 

BioMed Research International 
T : Continu ed.
Name,
year, and
ref. no.
Common carotid artery
IMT segmentation
technique
Advantages & limitations of the methods
Selection
method of ROI
(SA/FA)
Performance
metric
Processing
time/frame avg
IMT error
(mm) 
Gustavsson et al.,
,
[]
Dynamic programming
Advantages: Fully automated, low computational complexity;
suitable for clinical purposes, human correction allowed.
Limitations: Initial human setting & training required, fails for
slanting IMC with weak boundary.
SA MAD — . ±
. 
𝑁: number of imag es/cases, SA : semiautomated, FA: fu lly automated, SD: st andard deviati on, HD: Hausdor di stance, MAD: mean absolut e distance, ∗video sequences, 𝑇avg: average processing time/ frame or image.

BioMed Research International
M
M
Region
Scan window
N
Cost
Cost
Y
Y
O
O
F : Detecting interfaces I and I in an artery image.
Nakagami mixture modeling and stochastic optimization.
e echogenicity of the region of interest (ROI) comprising
the intima-media layers, the lumen, and the adventitia in
an ultrasonic B-mode image is modeled by a mixture of
three Nakagami distributions. In a rst step the expectation
maximization (EM) algorithm was used to compute the
maximum A posterior (MAP) estimator of the proposed
model, then computes the optimal segmentation based on
the estimated distributions as well as a statistical prior for
disease-free IMT using a variant of the exploration/selection
(ES) algorithm. is method requires minimal manual ini-
tialization. Destrempes et al. []proposedamethodfor
segmentation of plaques in sequences of ultrasound B-mode
images of carotid arteries based on motion estimation and
Nakagami distributions. In it, a local geometrical smoothness
constraintandanoriginalspatiotemporalcohesioncon-
straint were incorporated, envisaging the segmented plaque
based on motion eld estimation. In , Destrempes et al.
[] proposed a method for the segmentation of plaques in
the sequence of ultrasound B-mode images of carotid arteries
based on motion estimation and a Bayesian model. Authors
have reported that the algorithm was not sensitive to the
degree of stenosis or calcication.
4.4. Active Contour. e basic concept of active contour
modelistotacontourtolocalimageinformation,for
example, gradient. ere exist several implementations of
thisbasicideasuchassnakes[], discrete dynamic contour
model [], and level sets []. Based on the involved feature
image, they can be categorized as edge based [], region
based [,], and higher level knowledge based [,].
Several studies have adopted the traditional snake model as
proposed by Williams and Shah []. Snakes are also called
active parametric contours, which have been widely used in
medical image segmentation. e major limitations of this
method are sensitive to noise, depends on the initial contour
that is provided by the user, need for optimization of the
parameters. In , Moursi and El-Sakka []proposed
an active contour-based segmentation technique, in which
user only requires to place seed points in the ROI with
the aim of reducing user interaction. Author admitted that
the computational time depends on the size of the carotid
artery and the location of the seed point. In , Bastida-
Jumilla et al. [] used geodesic active contours for IMC
detection. In , Petroudi et al. []proposedthefully
automated segmentation algorithm based on active contours,
andactivecontourswithoutedgeswereproposedinwhich
anatomical information was incorporated to achieve accurate
segmentation. e segmented regions were used to auto-
matically achieve image normalization, which is followed
byspeckleremoval.eresultingsmoothedLIboundary
combined with anatomical information provides an excellent
initialization for parametric active contours that provide the
nal IMC segmentation. No information about an inter- and
intraobserver variability and its eect on segmentations was
given by author.
4.5. Edge Detection and Gradient-Based Techniques. e edge
detection methods could detect the variation of gray levels,
butitissensitivetonoiseandmaysuerfromthefocusing
artifact. In , Liguori et al. [] proposed the segmentation
technique based on an edge detection, in which image
gradient was used. In this method, for each column of the
image the gradient of the intensity prole has been computed.
It was assumed that pixels belonging to lumen were black and
that the carotid wall layers originate with gradient transitions.
It is a semiautomatic method; ROI is selected by the user.
e pattern recognition, edge detection (PRED) algorithm,
and the measurement algorithm were used for carotid IMT
measurement. Its main task is to nd out all the pixels
belongingtothetworequiredinterfaces(LIandMA)for
each wall. e measured intensity gradient was dierent
from the theoretical one, due to noise. In order to reduce
the eect of noise, a statistical thresholding was adopted
before computing the image gradient. Robustness of the
edge detection algorithm had been evaluated with respect
to the ROI. e gradient-based segmentation mainly suers
from the problem of superimposed noise, which precludes
a proper individuation of the LI and MA transitions. In
, Faita et al. [] proposed a method in which the
gradient performance was improved by the use of a rst-
order absolute moment edge operator (FOAM) and a pat-
tern recognition approach. e overall performance of this
methodology was very high: IMT measurement error was
equal to 10.0 ± 38.0m. Moreover, FOAM operator and
intelligent procedure determines maxima, ensuring a good
robustness to noise. As the technique is real-time, it suits
well to clinical application. It is a semiautomatic technique.
Recently, Mahmoud et al. []introducedamethod,which
employs a multistep gradient-based algorithm. is method
principally uses intensity, intensity gradient, and interface
continuity of pixels to determine the ultrasound interface.
Author reported that this technique eliminates subjectivity
associated with conventional manual tracing and semiauto-
mated gradient methods that employ seed point selection.
4.6. Combined Approaches. Delsanto et al. []proposed
a combined approach for classication and a snake-based

BioMed Research International 
segmentation to perform IMT measurement. Completely
user-independent layer extraction based on signal analysis
(CULEXsa) is a completely user independent algorithm for
IMT measurement. Firstly ROI is identied automatically fol-
lowed by gradient-based initial segmentation, and then active
contour technique is used for segmentation renement. For
improving segmentation performance, Molinari et al. []
have combined the three IMT segmentation methods: (i)
signal processing approach, combined with snakes and fuzzy
clustering, (ii) integrated approach based on seed and line
detection, followed by probability-based connectivity and
classication, and (iii) morphological approach and fused
the resulting boundaries using a greedy method described
by the “ball and basket” to minimize the system error. In
, Molinari et al. [] proposed a completely automated
layer extraction technique (named CALEXia). e IMT
measurement error was equal to 0.87 ± 0.56 pixels (0.054 ±
0.035mm). Author admitted that CALEXia showed limited
performance in segmenting the LI interface. Meiburger et
al. [] introduced the Carotid Automated Double-Line
Extraction System based on the Edge Flow (CADLES-EF). It
is characterized and validated by comparing the output of the
algorithm with CALEXia and CULEXsa. Validation was per-
formed on a multi-institutional database of  longitudinal
B-mode carotid images with normal and pathologic arteries.
CADLES-EF showed an IMT bias of 0.043 ± 0.097mm
in comparison with CALEXia and CULEXsa that showed
0.134 ± 0.088mm and 0.74 ± 0.092mm, respectively. e
system’s Figure of Merit (FoM) showed an improvement
when compared with CALEXia and CULEXsa, leading to
values of .%, .%, while CADLES-EF performed the best
with .%. In , Molinari et al. []proposedamethod
called CARES . (a patented technology). CARES . is
completely automated and adopts an integrated approach for
segmentationofcarotidarteryintheimageframe.eFoM
of CARES . was .%. In , Molinari et al. []proposed
completely automated multiresolution edge snapper (named
CAMES). In it carotid artery is recognized automatically
using a scale-space and statistical classication in a multires-
olution framework. Recently, Ilea et al. []proposedafully
automated segmentation and tracking of the intima-media
thickness in ultrasound video sequences of the CCA. For
the video tracking procedure, a spatially coherent algorithm
is introduced, which prevents the tracking process from
converging to wrong arterial interfaces. Author reported that
method can deal with inconsistencies in the appearance of the
IMC over the cardiac cycle.
5. Validation/Quantitative Performance
Assessment
Validation experiments are necessary in order to quantify
the performance of a segmentation method. Validation is
usually performed using truth models such as phantom
studies, animal model studies, simulation, comparing the
automated segmentation method with manually obtained
segmentations. Following are the most used performance
metrics to validate IMT measurements and computer traced
boundaries []:
(i)Meanabsolutedistance(MAD),
(ii) Hausdor distance (HD),
(iii) Polyline distance metric (PDM),
(iv) Percent statistic test,
(v) Reproducibility of manual procedures. It is assessed
by calculating intraoperator and interoperator vari-
ability using either of CV, MAD, HD, regression
analysis, and Bland-Altman statistics,
(vi) Manual and computer-measured IMT (intermethod).
Comparison between the two sets was done using
correlation or Bland-Altman plot.
6. Conclusion
is paper reports an extensive review of ultrasound
carotid artery IMT segmentation techniques. Active con-
tours, dynamic programming, and integrated approaches
have been presented to segment the carotid wall and trace
the boundaries of the LI and MA interfaces. None of the
existing techniques were overwhelmingly good in all aspects.
Characterization and validation studies will be required in
order to carefully assess the eect of such variability on
segmentation performance. Finally, we recognize that in the
future, more work is likely to be done in segmentation
based on adaptive segmentation for determination of IMT
in ultrasound images of CCA with high IMT measurement
accuracy, robustness, automation and reducing processing
time.
As in the case of fully automatic techniques, detection
is not reliable, since it may detect the jugular veins edges.
In addition performance of semiautomatic segmentation
techniques is better than fully automatic segmentation tech-
niques. erefore, in future we will develop techniques in
which human operator will select an ROI manually, and
methodology will be based on adaptive segmentation with
the aim of high accuracy, great robustness, and with reducing
processing time. e proposed accuracy of detection of IMT
algorithm falls within the inter- and intraobserver variability
for the manual determination.
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