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Vascular and Intravascular Imaging Trends, Analysis, and Challenges, Volume 1
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Table 14.12. DFRP calculation for different PCA-based cut-offs using (a) stand-alone plaque texture-based
features and (b) plaque texture-based features fused with wall-based measurement features.
(a) Using stand-alone plaque texture-based features
Cut-offs
(m and n)
0.90 and
Dominant
features at m
Dominant
features at n
Similar dominant
features (SDF
m−n
)
5 5 5 100.00
Dominant feature
retaining power (in %)
0.91
0.91 and
5 5 5 100.00
0.92
0.92 and
5 6 2 40.00
0.93
0.93 and
6 6 6 100.00
0.94
0.94 and
6 7 6 100.00
0.95
0.95 and
7 7 7 100.00
0.96
0.96 and
7 8 5 71.43
0.97
0.97 and
8 10 8 100.00
0.98
0.98 and
10 13 10 100.00
0.99
(b) Using plaque texture-based features fused with wall-based measurement features
0.90 and
5 6 2 40.00
0.91
0.91 and
6 6 6 100.00
0.92
0.92 and
6 7 5 83.33
0.93
0.93 and
7 7 7 100.00
0.94
0.94 and
7 8 7 100.00
0.95
0.95 and
8 9 8 100.00
0.96
0.96 and
9 10 8 88.89
0.97
0.97 and
10 12 10 100.00
0.98
0.98 and
12 16 11 91.67
0.99
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Table 14.13. Receiver operating characteristic for the highest PCA-based cut-off (= 0.99) and RBF kernel
functions using (a) stand-alone plaque texture-based features and (b) plaque texture-based features fused with
wall-based measurement features.
(a) Using stand-alone plaque texture-based features
Mean sensitivity Mean specificity Mean PPV Mean AUC
84.94 ± 8.44 87.92 ± 4.99 91.71 ± 3.96 0.86 ± 0.07
(b) Using plaque texture-based features fused with wall-based measurement features
90.74 ± 5.22 92.14 ± 3.42 94.86 ± 2.39 0.91 ± 0.04
PPV—Positive predictive value, AUC—area under the curve.
Figure 14.8. Receiver operating characteristic for the optimized PCA-based cut-off of 0.99 using (a) standalone plaque texture-based features and (b) plaque texture-based features fused with wall-based measurement
features.
where N is a set of ten datasets ranging from 493 to 4930 in increments of 493 frames,
and σ and μ correspond to the standard deviation and mean of each dataset computed
for different PCA-based cut-offs ranging from 0.90 to 0.99 for the optimized RBF
kernel function. The overall reliability index using the fusion of plaque texture-based
and wall-based measurement features was higher (= 98.24%) compared to stand-alone
plaque texture-based features (= 94.86%), as shown in table 14.14.
⎛
=− *RI (%) 1 100, (14.13)
N
⎜
⎝
⎞
σ
N
⎟
μ
⎠
N
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Table 14.14. Reliability index (RI) at different data sizes (N) using (a) stand-alone plaque texture-based
features and (b) plaque texture-based features fused with wall-based measurement features.
(a) Using stand-alone plaque texture-based features
Data size 493 986 1479 1972 2465 2958 3451 3944 4437 4930 Average
(%) 100.00 100.00 100.00 100.00 100.00 100.00 88.01 77.58 91.57 91.73 94.86
RI
N
(b) Using plaque texture-based features fused with wall-based measurement features
(%) 100.00 100.00 100.00 100.00 100.00 100.00 96.48 94.08 96.73 95.08 98.24
RI
N
14.5.4 Stability of the cRAS
In this study, we have also analyzed the stability of the system for both (a) standalone plaque texture-based features and (b) plaque texture-based features fused with
wall-based measurement features. The deviation of the accuracy from the mean
accuracy corresponding to all the PCA-based cut-offs for each data size was
computed. The mean deviation for all the data sizes using the fusion of plaque
texture-based with wall-based measurement features was under the tolerance limit of
5% compared to the stand-alone plaque texture-based cRAS, that turned out to be
under the tolerance limit of 15%, as shown in table 14.15.
14.6 Discussion
14.6.1 Our system
This study demonstrated an ML risk assessment and stratification system, adopting
a fusion of plaque texture-based features with wall-based measurement features. The
ML system fusing plaque texture-based features with wall-based measurement
features outperformed the equivalent using stand-alone plaque texture-based
features. Thus, we validated our hypothesis. Because the atheroma region causes
the IEL and EEL walls to expand bidirectionally [20], there was a clear motivation
to use wall-based measurement features. Further, since the atherosclerotic calcium is
multi-focal [2, 21, 48] and the detection process is well established [ 20], our cRAS
leverages on this burden to improve the overall accuracy of the risk stratification.
Note that our modeling assumes a negligible effect due to heart motion [49], thereby
assuming it to be simple, pragmatic and ensuring high-speed processing due to the
multiresolution approach for calcium detection. Our system demonstrated a high
accuracy of stratification based on the training model that uses the concept of the
genetic make-up of the carotid plaque burden. The carotid plaque burden (considered as a biomarker for stroke risk) can be used as a risk label for patients with
coronary artery disease, which validated our second hypothesis. Finally, we want to
emphasize that the cRAS did take advantage of a selection of the optimized features
using a polling strategy in the PCA paradigm, ensuring best performance. From
figure 14.5, it was observed that for different PCA-based cut-offs, the increase in the
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Table 14.15. Deviation of accuracy from mean accuracy for different data sizes using (a) stand-alone plaque
texture-based features and (b) plaque texture-based features fused with wall-based measurement features.
(a) Using stand-alone plaque texture-based features (in %)
Data size
Cut-offs
0.90 0.00 0.00 0.00 0.00 0.00 0.00 20.02 36.74 13.57 9.88
0.91 0.00 0.00 0.00 0.00 0.00 0.00 20.05 36.64 13.61 9.9
0.92 0.00 0.00 0.00 0.00 0.00 0.00 0.29 6.34 0.15 9.93
0.93 0.00 0.00 0.00 0.00 0.00 0.00 0.31 6.34 0.15 1.45
0.94 0.00 0.00 0.00 0.00 0.00 0.00 4.77 8.37 1.54 1.48
0.95 0.00 0.00 0.00 0.00 0.00 0.00 4.77 8.37 1.49 3.58
0.96 0.00 0.00 0.00 0.00 0.00 0.00 7.01 10.24 4.79 3.57
0.97 0.00 0.00 0.00 0.00 0.00 0.00 7.39 10.78 5.35 5.24
0.98 0.00 0.00 0.00 0.00 0.00 0.00 7.39 11.39 6.39 6.66
0.99 0.00 0.00 0.00 0.00 0.00 0.00 8.19 11.53 7.34 7.74
Average 0.00 0.00 0.00 0.00 0.00 0.00 8.02 14.67 5.44 5.94
SD 0.00 0.00 0.00 0.00 0.00 0.00 6.92 11.75 5.00 3.38
(b) Using plaque texture-based features fused with wall-based measurement features (in %)
0.90 0.00 0.00 0.00 0.00 0.00 0.00 5.88 10.48 4.93 5.25
0.91 0.00 0.00 0.00 0.00 0.00 0.00 5.80 10.51 4.89 6.15
0.92 0.00 0.00 0.00 0.00 0.00 0.00 0.95 0.90 1.54 6.16
0.93 0.00 0.00 0.00 0.00 0.00 0.00 0.27 0.91 1.56 0.07
0.94 0.00 0.00 0.00 0.00 0.00 0.00 0.24 1.78 0.76 0.07
0.95 0.00 0.00 0.00 0.00 0.00 0.00 1.97 3.02 0.70 0.53
0.96 0.00 0.00 0.00 0.00 0.00 0.00 2.67 3.08 2.24 2.03
0.97 0.00 0.00 0.00 0.00 0.00 0.00 2.79 3.73 2.73 4.44
0.98 0.00 0.00 0.00 0.00 0.00 0.00 2.81 3.78 2.99 5.07
0.99 0.00 0.00 0.00 0.00 0.00 0.00 2.87 3.77 3.45 5.31
Average 0.00 0.00 0.00 0.00 0.00 0.00 2.63 4.20 2.58 3.51
SD 0.00 0.00 0.00 0.00 0.00 0.00 1.98 3.50 1.52 2.55
493 986 1479 1972 2465 2958 3451 3944 4437 4930
number of dominant features is higher for the fusion of plaque texture-based and
wall-based measurement features compared to the stand-alone plaque texture-based
paradigm. Also, in table 14.9, the selection of coronary calcium area, coronary
lumen area and coronary wall thickness as dominant features proves that wall-based
measurement features are as important as plaque texture-based features in coronary
artery risk assessment.
14.6.2 A note on population size
One of the requirements in the ML framework is large enough population size for
achieving generalization while maintaining satisfactory accuracy for risk
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stratification. We had approached our design strategy based on the number of
frames rather than the number of patients as the population pool during the crossvalidation protocol design. In our ML system design, even though we have a low
population size of 22 subjects, we had a total of 4930 frames. These 4930 frames
were stratified into 3043 high-risk frames and 1887 low-risk frames derived from 14
high-risk patients and 8 low-risk patients, respectively. Our analysis demonstrated
that our cRAS system has a sufficient pool of frames to design an accurate risk
assessment system.
14.6.3 A note on kernel functions
In table 14.10, we can clearly observe that the lowest classification accuracy is
obtained for linear and polynomial order 1 kernel functions as the optimum
separating hyper-plane cannot separate all of the database into two distinct classes.
The classification accuracy of both the linear and polynomial order 1 functions is the
same as there is not much difference between their kernel functions. With the
increase in the order of the polynomial kernel functions, we observe an increase in
the classification accuracy, as now the size of the function class increases. Finally,
because of its Gaussian contribution, it was observed that the highest classification
accuracy is achieved with the RBF kernel function.
14.6.4 A note on performance evaluation of our cRAS
A prerequisite for an ML system is to understand the variability due to (i) PCAbased dominance feature selection and (ii) the type of cross-validation protocol used
while fusing the wall-based features with grayscale morphological characteristics of
the plaque region derived from the IVUS video frames. It is thus imperative to
evaluate the cRAS by understanding the metrics which evaluate the dynamics of the
performance evaluation. We therefore took special steps in computing four different
performance parameters, namely (a) dominant feature retaining power, (b) receiver
operating characteristic curves, (c) reliability index, and (d) stability under both (i)
stand-alone plaque texture-based features and (ii) plaque texture-based features
fused with wall-based measurement features. The results of these analyses are shown
in tables 14.12–14.15, respectively. The above curves/tables demonstrate encouraging results for the design for risk assessment.
14.6.5 Comparison against current literature and benchmarking
IVUS is one of the speedily emerging medical imaging modalities showing promising
signs towards high-resolution imaging [50]. This opens new doors for many medical
image analysis methods and techniques to extract and quantify plaque [20, 23, 29]
and finally, to risk characterize the plaque into low- and high-risk bins using MLbased strategies [16, 17]. Not much work has been done so far in the area of IVUSbased CAD risk stratification; however, several studies in the literature have
reported on carotid plaque characterization and risk stratification. Table 14.16
shows the comparison between these techniques using eight different attributes such
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Cross-validation
accuracy
MNN 73.1%
Feature selection
technique (s) Classifier (s)
features
Total
Feature
(s) type
Distance
AdaBoost—81.7%
SVM—82.4%,
AdaBoost
7 T test SVM 91.7%
Tex
16 N/A EAI 77%
mixture
NW—98.00%
NW—98.83%
65 PCA SVM Only Tex—86.08%,
Tex fused with
fused with
wall—91.28%
wall
size
Population
Arterial
type
Table 14.16. Survey of risk stratification techniques in the literature.
Year Authors
2003 Christodoulou et al [8] Carotid 230 Tex 61 Mean, SD,
2007 Mongiakakou et al [10] Carotid 54 SF, Law’s 21 ANOVA HNN 99.1%
2005 Kyriacou et al [9] Carotid 274 Tex, Wall 10 N/A NN 71.2%
2012 Acharya et al [12] Carotid 346 Tex 4 T test SVM,
2009 Kyriacou et al [11] Carotid 274 Tex 10 N/A SVM 73.7%
2012 Acharya et al [13] Carotid 346 Tex, Wall 3 T test SVM 83%
2013 Acharya et al [14] Carotid 492 DWT, HOS,
2014 Pedro et al [15] Carotid 146 Rayleigh
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2016 Araki et al [16] Coronary 2865 Tex 56 N/A SVM 94.95%
2016 Araki et al [17] Coronary 2865 Tex 56 PCA SVM 98.43%
2017 Araki et al [51] Carotid 407 Tex 16 N/A SVM FW—98.00%,
2017 Saba et al [52] Carotid 407 Tex 16 PCA SVM FW—98.55%,
2017 Proposed Coronary 4930 Only Tex, Tex
Tex—plaque texture-based; Wall—wall-based; NN—neural network; SF—statistical feature; SD—standard deviation; MNN—modular neural network; HNN—
hybrid neural network, SVM—support vector machine; EAI—enhanced activity index; PCA—principal component analysis; DWT—discrete wavelet transform;
HOS—higher order spectra; NW—near wall; FW—far wall; N/A—not applicable.
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as year, artery type, population size and feature type, the number of features, feature
selection techniques, classifier and cross-validation accuracy.
It was very recently that Araki et al [51] in 2017 performed a risk stratification on
a database of 407 carotid B-mode ultrasound images. Here, the SVM classifier
trained by 16 texture features provides a cross-validation accuracy of 98% for both
the near and far wall, respectively. The same group [52] upgraded their system by
utilizing a PCA-based pooling strategy for the dominant feature selection and
obtained high accuracies of 98.55% and 98.83% for the carotid near wall and carotid
far wall, respectively.
While the above benchmarking used carotid plaque classification for risk
estimation, our team has been attempting to model coronary plaque risk stratification by fusing the coronary and carotid atherosclerotic genetic make-up concepts
[22–28]. This study brings a novel approach of introducing coronary wall-based
measurement features along with grayscale coronary plaque texture-based features
for risk stratification. We, therefore, showed the cRAS for both (a) stand-alone
plaque texture-based features and (b) plaque texture-based features fused with wallbased measurement features. The fused system showed an improvement of 5.69%.
To the best of our knowledge, this is the first ML-based CADx system which utilizes
a fusion of plaque texture-based and wall-based measurement features for coronary
artery risk stratification.
14.6.6 Carotid plaque burden as a gold standard for the training phase in ML design
One of the important components of the cRAS design is the choice of the gold
standard during the training phase. The idea behind the choice of the gold
standard is to ensure that we have an indicator which has a strong link to the
coronary artery disease while maintaining the low-cost design of the cRAS. Surely,
one can consider the histology-based [53] or the calcium score [54] using CT as a
gold standard. However, the CT results are not real-time, and it is difficult to
reconstruct them as a 3D image [55]. They are not easily affordable, being very
expensive and tedious protocols. Furthermore, the focus of our paper is solely
based on ultrasound. The second option is to adopt the concept of genetic make-up
between carotid and coronary atherosclerosis disease, which is now well established [22–28]. Pr evious studies had proven that cIMT influences the disease and
death rates, which proves the relationship between plaque burden and cIMT in the
coronary and carotid arteries [56–61]. Ogata et al [62]alsoshowedthecorrelation
of cIMT with the plaque accumulation in the left main coronary artery.
Establishing this concept, we thus leveraged our gold standard choice to be the
carotid artery and its plaque burden. One way to establish this gold standard is to
measure the media wall thickness as an indicator, which can be computed using
cIMT measurem ents [51, 52]. JSS and his team have shown numerous studies for
cIMT measurement and its link to various cardiovascular risk events, such as
ankle–brachial index [ 25], syntax score [26], etc. Since this study collected dual
information, such as cIMT and c oronary IVUS images, we therefore used cIMT
measurements as the risk label for our cRAS.
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14.6.7 A note on time computation for online risk prediction
An ML system is characterized by its training (or offline) and testing (online) phases.
The time complexity of the risk assessment system is based on the hardware
components, such as processor speed and computer RAM. The PC configuration
of our system had the following: HP Compaq Elite 8300 with an Intel Core i7–3770
Processor, 3.40 GHz and 2 GB RAM, MATLAB 2013a software and the Windows7 Operating System. Typically, the offline system is not accounted for in the overall
time complexity of the ML design, however, our single frame training and testing
times of the proposed cRAS were 0.0435 s and 0.0083 s, respectively, which can be
considered as reasonably fast by looking at the previous risk assessment systems
designed for carotids and coronary applications [17].
14.6.8 Strength, weakness and extensions
We demonstrated a machine learning system for risk assessment based on
grayscale plaque morphology c ombined with wall-based features. We can characterize the benefits into two categories: primary benefits and secondary benefits.
The key benefit of our design is the generalized system for risk assessment which
can be extended to a more complex design by adding meaningful features which
are clinically more relevant. The successful main idea of coronary risk dependence
based solely on genetic make-up can also be further extended into other designs,
such as histology-based or CT-based biomarkers. This is another powerful strength
of our cRAS. The ability to fuse the wall-based measurement features with
grayscale morphologic plaque texture-based features provides another platform
for fusion of the feature paradigm. The secondary benefits are the ability to obtain
higher classification accuracy using an SVM-based classifier, integration of a PCAbased polling strategy for dominant feature extraction, the ability to optimize the
best kernel function, the ability to optimize the data to obtain the best accuracy,
and the high mean sensitivity, specificity, positive predictive value and AUC. T he
system also has a high average feature retaining power and is reliable and stable.
Despite the above strengths, the study suffers from some limitations. The study
utilizes manual tracings of IVUS images for ROI generation. The dataset used in the
current study is controlled as all the patients come from a diabetic cohort. Intra/interobserver variability could have been tried but is outside the scope of the current study, as
tracing all the frames is very expensive. DICOM images with gating and registration
schemes can be incorporated with a large dataset for extensive evaluations. Optical
coherence tomography (OCT) is a high-resolution optical imaging technology and
provides more accurate arterial cross-sections compared to IVUS [63]. We understand
that there is a need for further OCT/histology-based validation and automated
segmentation of IVUS walls [7], however, the current results are encouraging.
14.7 Conclusion
The coronary artery disease risk stratification tool based on IVUS wall grayscale
morphological characterization, when fused with wall-based measurement features,
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showed superior performance using ML-based techniques. The system computed six
novel wall-based measurement features: coronary calcium area, coronary vessel
area, coronary lumen area, coronary atheroma area, coronary wall thickness and
coronary wall thickness variability, which were fused with grayscale features, gave
an improvement of ∼6% in the accuracy for predicting the class label of the plaque
type as high-risk or low-risk. All performance parameters showed similar behavior.
Our cRAS also showed improvement in stability and reliability. Since the ML
system was automated, it can be adopted one step closer to clinical use for
cardiovascular imaging laboratories
Acknowledgments
Reprinted from Banchhor S K, Londhe N D, Araki T, Saba L, Radeva P, Laird J R,
Suri J S 2017 Wall-based measurement features provides an improved IVUS
coronary artery risk assessment when fused with plaque texture-based features
during machine learning paradigm 91 198–212, with permission from Elsevier.
Funding
This research did not receive any specific grant from funding agencies in the public,
commercial, or not-for-profit sectors.
Conflicts of interest
The authors declare no conflict of interest.
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