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Performance
https://t.me/medicina_free
evaluation
Validation
against GT
Cross
validation
Vascular and Intravascular Imaging Trends, Analysis, and Challenges, Volume 1
N/A N/A
ACC:
83.7%
SVM
PPV:
81.8%
Sn: 80%,
Sp: 86.4%
N/A N/A
ACC:
90.66%
Sn:
83.33%
Sp:
95.39%
N/A N/A
83.0%
(a) ACC:
GMM, DT,
(a) SVM,
Sn:
87.4%
KNN,
NBC,
Sp:
79.7%
RBPNN,
Fuzzy
89.5%
(b) ACC:
KNN,
(b) SVM,
Sn:
89.6%
Sp:
RBPNN
88.9%
Table B10. Comparison of various tissue characterization techniques from literature against our proposed work.
Wall plaque/wall
segmentation Data size Features Classifier
SN Author (year) Algorithm
346 Total of 3 features:
Manual
Risk assessment in
1 Acharya et al
DWT-based average (Dh1),
average (Dv1), energy (E)
segmentation
for plaque
carotid plaque
images
(2011)
region
(Atheromatic™)
160 Total of 32 features: texture SVM
segmentation
Manual
characterization
Carotid plaque tissue
(2012a)
2 Acharya et al
for plaque
region
and classification
(Atheromatic™)
Ent
Ene
18
18
LBP
LBP
(a) Total of 8 features:
plaque
(a) 346
(b) 342 wall
segmentation
for plaque
(a) Manual
using texture-
based features
Risk stratification
(2012b)
3 Acharya et al
Ent
Ene
216
216
LBP
LBP
region;
(b) Automated
(Atheromatic™)
Ene
Ene
5
324
LBP
LTE
segmentation
for wall using
Ene
6
LTE
AtheroEdge™
Ene
8
LTE
(b) Total of 4 features:
Ene
324
LBP
Ene
2
LTE
Ene
8
LTE
poly
IMTV
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Vascular and Intravascular Imaging Trends, Analysis, and Challenges, Volume 1
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± 0.223
and 6.67
± 0.186
N/A SACI*: 7.04
ACC:
91.7%
SVM
Sn: 97%
Sp: 80%
N/A N/A
85.3%
Sn:
84.4%
(a) ACC:
SVMDTFuzzy
Sp:
85.9%
(b) ACC:
93.1%
Sn:
99.0 %
Sp:
80%
N/A
against
Done
ACC:
77%
activity
Enhanced
GT
Sn: 70%
index based
Sp:
on Bayes
(Continued)
80.13%
factor
features (3), DWT (2) and
146 Total of 7 features: texture
segmentation
Manual
carotid plaque
Classification of
(2013a)
4 Acharya et al
HOS features (2)
for plaque
region
images by tissue
characterization
16 (UK dataset)
12 (Portugal dataset)
FGLCM: E, contrast,
entropy, correlation,
correlation,
homogeneity
FRLM: SRE,
GLNU, ASM, mean,
LRE, RLNU, RP
parameter (4th, 5th, 6th),
GLCM, wavelet, percentile
(10, 50), DoS, echogenic cap,
appearance, mean, skewness,
mixture components (5th, 6th,
No.), and plaque disruption
Total features:
346
146
segmentation
for plaque
risk assessment in
carotid plaque
(2013b)
region
images
(Atheromatic™)
Manual
(Atheromatic™)
Segmentation and
5 Acharya et al
146 Total of 16 features: Rayleigh
segmentation
Manual
characterization
Tissue
(2013)
6 Pedro et al
for plaque
region
for stroke risk
(AtheroRisk™)
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Performance
evaluation
Validation
against GT
N/A N/A
Cross
validation
ACC:
SVM,
91.8%
Sn: 83.3%
KNN,
RBPNN,
6 HOS features and
2 wall features (IMT and
Sp: 95%
DT
IMTVpoly)
95.86%
PoM:
against
Done
98%
SVM Far wall:
GLCM, GLRLM
1; Chaotic features: FDi
16 texture features:
GT
98%
Near wall:
with feature
wall:
99%
Combined
optimization
118 Total of 7 features:
Wall plaque/wall
segmentation Data size Features Classifier
Table B10. (Continued )
SN Author (year) Algorithm
wall
Automated far
characterization
Tissue
(2015)
7 Acharya et al
segmentation
for far wall
407
segmentation
for wall region
Automated
(Atheromatic™)
segmentation and
risk assessment in
Automated
method
8 Proposed
carotid far wall
and near wall
ACC = Accuracy; Sn: Sensitivity; Sp: Specificity.
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Vascular and Intravascular Imaging Trends, Analysis, and
Challenges, Volume 1
Stent applications
Petia Radeva and Jasjit S Suri
Chapter 14
An improved framework for IVUS-based
coronary artery disease risk stratification by
fusing wall-based and texture-based features
during a machine learning paradigm
Sumit K Banchhor, Narendra D Londhe, Tadashi Araki, Luca Saba, Petia Radeva,
John R Laird and Jasjit S Suri
The planning of percutaneous interventional procedures involves pre-screening
andriskstratification of the coronary artery disease (CAD). Current screening
tools use stand-alone plaque texture-based features and therefore lack the ability to
stratify the risk. This institutional research board (IRB) approved study presents a
novel strategy for CAD risk stratification using an amalgamation of intravascular
ultrasound (IVUS) plaque texture-based and wall-based measurement features. As
it is a common genetic plaque make-up, the carotid plaque burden was chosen as a
gold standard for risk labels during the training phase of the machine learning
(ML) paradigm. A cross-validation protocol was adopted to compute the accuracy
of the ML framework. A set of 59 plaque texture-based features was padded with
six wall-based measurement features to show the improvement in stratificat ion
accuracy. The ML system was executed using a principle component analysis
(PCA)-based framework for dimensionality reduction and uses a support vector
machine (SVM) classifier for the training and testing phases. The ML system
produced a stratification accuracy of 91.28%, demonstrating an improvement of
5.69% when wall-based measurement features were combined with plaque texturebased features. The fused system showed an improvement in mean sensitivity,
specificity, positive predictive value and area under the curve (AUC) by 6.39%,
4.59%, 3.31% and 5.48%, respectively, wh en compared to the stand-alone system.
In addition to meeting the stability criterion of 5%, the ML system also showed a
high average feature retaining power and mean reliability of 89.32% and 98.24%,
doi:10.1088/2053-2563/ab01fach14 14-1 ª IOP Publishing Ltd 2019
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respectively. The ML system showed an improvement in risk stratification
accuracy when the wall-based measurement features were fused with the plaque
texture-based fea tures.
14.1 Introduction
Atherosclerotic cardiovascular disease accounts for the largest number of deaths in
the USA [1]. The disease of atherosclerosis over time causes calcium to build-up in
the coronary arteries [2]. During the advanced stage of the disease, the combined risk
of the patient includes higher plaque growth leading to plaque rupture. This also
includes the risk of developing different plaque components such as fibro-fatty,
macrophages, calcium and fatty tissue. When these components increase in size,
there is a risk of an increase in stenosis and stress on the fibrous cap thickness, which
can cause the risk of rupture leading to myocardial infarction (MI). All of the above
can be categorized as the ‘ risk of arterial wall rupture’ or ‘risk of MI’. Thus, rupture
of the arterial wall cap can cause calcium to dislodge, blocking the oxygen-rich
blood flow in the arteries, leading to myocardial infarction or stroke [3]. Current
screening methods such as computed tomography (CT) and magnetic resonance
imaging (MRI) [4, 5] suffer from excess radiation and magnetic interference,
respectively. Further, these devices take a long time to reconstruct the images,
thus they lack a real-time interface [4]. IVUS screening, on the other hand, has lowradiation exposure, is economic compared to MR/CT, is ergonomic and offers realtime diagnosis [6, 7].
Prior to stenting and percutaneous interventional procedures, cardiologists are
interested in performing pre-screening and risk stratification of CAD. Studies for
risk stratification of cardiovascular events are mainly categorized into two groups.
The first group attempts to predict the risk by quantifying the plaque characteristics
(i.e. texture-based features) while the second group predicts the risk by quantifying
wall-based measurement features [8–13]. Christodoulou et al [8], in 2003, proposed a
neural network for carotid plaque classification. Ten dominant texture feature sets
were selected from a total of 61 texture features showing a low accuracy of 73.10%
on a data size of 230 images. Two years later, Kyriacou et al [9] showed a carotid
classification system that used a neural network classifier with ten different textures
and carotid wall-based features and achieved even a slightly lower accuracy 71.2%
on a data size of 274 images. The same group in 2009 applied a SVM classifier on the
same database using only the texture features and showed an improvement in
accuracy of 2.5%. In a hybrid neural network approach on B-mode carotid
ultrasound images, Mongiakakou et al [10] in 2007 used 21 statistical and law
features on 108 images. The neural network was trained on the combined use of
genetic algorithms and back-propagation with momentum and adaptive learning
rate and showed an accuracy of 99.10%.
Our team, led by author JSS, has been working on the characterization of carotid
plaque. Acharya et al [12], in 2012, proposed an Atheromatic™ system for plaque
stratification into symptomatic and asymptomatic plaques showing an accuracy of
82.40% and 81.70%, using SVM and AdaBoost classifiers, respectively. The same
14-2
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group [13], in 2012, obtained an accuracy of 83% by fusing the plaque texture-based
and wall-based measurement features using an SVM classifier. A year later, the same
group [14] obtained a further high accuracy of 85.3% by fusing discrete wavelet
transform, higher order spectra and textural features on a large data size consisting
of 492 images. A year later, Pedro et al [15] fused the clinical and texture features for
the classification of carotid plaque. An enhanced activity index was proposed and
was correlated with the presence or absence of ipsilateral appropriate ischemic
symptoms. Leave-one-patient-out was applied to 146 carotid plaques obtained from
99 patients and a cross-validation accuracy of 77% was obtained. Araki et al [16]
showed a CADx system using the SVM that demonstrated a training and testingbased ML system using plaque texture features for coronary artery risk assessment.
Later, the same group [17] modified and improved their CADx system by introducing the PCA-based polling technique for selection of the grayscale dominant features
for improving the stratification accuracy. These prior studies had ignored how
plaque growth affects the walls of the arteries and lacked the prominent features
contributed by the wall-based parameters. This study is an extension of the above
studies using an amalgamation of IVUS plaque texture-based and wall-based
measurement features. This is motivated by the current strategy by JSS and his
team in stroke imaging where carotid intima–media thickness (cIMT) variability
was fused with carotid longitudinal grayscale features to improve the stroke risk
stratification [18, 19]. But in our current study, circular wall parameters along with
the plaque calcium are derived as measurement features from IVUS coronary walls.
Thus, the objective is to demonstrate the importance of wall-based measurement
features and their integration with plaque texture-based grayscale features for better
ML system design.
Calcium accumul ations always occur in the atheroma region which lies
betweenthelumen(innerwallorinternalelasticwall)regionandvessel(outer
wall or external elastic wall) region [20]. Therefore, an expansion of the walls is
purely a reflection of the growth of calcium in the arteries. Moreover, due to the
multi-focal nature of calcium [21], the wall thickness can vary along the circular
walls of the coronary artery. Figures 14.1 and 14.2 show typical examples of
images showing a g rayscale ring, along with calcium, lumen, vessel and athero ma
regional areas corresponding t o five different low-risk and high-risk patients,
respectively. Furthermore, our study is based on two hypotheses: (i) fusion of
plaque texture-based and wall-based measurement feat ures can offer an improvement in the coronary artery risk stratification; (ii) due to the genetic make-up of
the plaque, carotid plaque burden, which is considered as a biomarker for stroke
risk [22–28], can be used as a risk label for patients with coronary artery disease
[16, 17].
The novelty of this study is to demonstrate an improvement in the accuracy of the
CADx system built for the coronary artery risk assessment by fusing plaque texturebased features with wall-based measurement features compared to a stand-alone
system consisting of only plaque texture-based features. Our objective in this paper is
to predict the class label of the plaque type as high-risk or low-risk.
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