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Vascular and Intravascular Imaging Trends, Analysis, and Challenges, Volume 1
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13.7.2 Parameters of the machine learning system
We studied the sensitivity of the partition protocols for K = 5, K = 10 and K = JK.
Certainly, with an increase in training sets during machine learning, the classification accuracy improved. We also optimized the choice of kernel for the SVM-based
classifier during the training and testing phases. Of all the kernels, Poly-2 performed
the best. We also exhaustively studied the effect of increasing data size on the
classification accuracy (see appendix B, table B7), while keeping the number of
dominant features constant. Our observations show that as the data size increases,
the classification accuracy also increases under all stratification conditions (LD
varying from 5 mm to 8 mm).
13.7.3 A note on wall segmentation validation
In order to evaluate the inter-observer variability [49], the manual tracings are
obtained from the trained observer twice, traced over a period of two weeks. The
observer was not given access to his previous tracings. The observer manually
delineated the lumen as well as adventitia borders using ImgTracer™, commercial
software from AtheroPoint™ (Roseville, CA, USA) [3, 5, 32, 44, 61, 62]. The
borders are delineated by choosing 15–25 edge points proximal to the bulb
depending upon the length of the carotid artery [10]. The observer had the ability
to zoom in on an image in the wall region for better visualization. The output of the
ImgTracer™ is the ordered set of traced (x, y) coordinates. The mean Auto LD and
IAD measurements were 5.86 mm and 8.00 mm, respectively. A reduction in the
Auto LD can be observed due to plaque growth in the far wall region and hence a
decrease in the Auto LD.
13.7.4 Tissue characterization for risk assessment
The key aspect in tissue characterization is in understanding the linear and nonlinear behavior of the plaque characteristics using a combination of the following
texture feature categories: intensity histogram (IH), gray-level run length matrix
(GLRLM) and gray-level co-occurrence matrix (GLCM).
GLCM features: 1—entropy; 2—energy; 3—contrast; 4—homogeneity; GLRLM
features: 5—short run emphasis (SRE); 6—long run emphasis (LRE); 7—gray-level
non-uniformity (GLN); 8—run length non-uniformity (RLN); 9—run percentage
(RP); 10—low gray-level run emphasis (LGRE); 11—high gray-level run emphasis
(HGRE); 12—short run low gray-level emphasis (SRLGE); 13—short run high
gray-level emphasis (SRHGE); 14—long run low gray-level emphasis (LRLGE);
15—long run high gray-level emphasis (LRHGE); and 16—FD feature.
13.7.5 Benchmarking
Several authors have presented stroke risk assessment based on tissue characterization, including work our group has previously performed [31, 34, 36, 37]. The
table of comparison between these techniques and the proposed method is shown in
appendix B, table B10.
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Seven attributes were chosen to compare the previous methods against the
proposed method. They are the following: year of publication, algorithm type,
data size, features adapted, classifier(s) used, cross-validation accuracy, whether or
not ground truth validation was performed, and system performance evaluation.
Acharya et al (2011) in [63] used machine learning based on tissue characterization with three features for symptomatic versus asymptomatic plaque classification. The authors used an SVM-based classifier for training and testing, resulting in
a classification accuracy of 83.77%. The ROI was manually segmented unlike this
study where everything was automated. The same authors perform a research, in
[38], with the same configuration, except that tissue characterization was performed
using 32 texture features. Their accuracy was 90.66%. Also, another paper was
published with the same purpose, however, the authors adapted two different sets of
data (plaque and far wall data), for their risk stratification [34]. Eight and four
features were used in this approach, respectively. LBP
LBP
features, while LBP
Ene, LBP
216
Ene, LTE5Ene, LTE6Ene and LTE8Ene were the plaque
324
Ene, LTE2Ene, LTE8Ene and IMTV
324
Ent, LBP18Ene, LBP
18
were the wall
poly
216
Ent,
features. The accuracy for the plaque region was 83%, while the accuracy for the
far wall region was 89.5%. In 2013, Acharya et al [64] demonstrated another
combination of features for the Atheromatic™ (trademarked by AtheroPoint, CA,
USA) system to classify symptomatic versus asymptomatic plaques. Seven features
were used in this study, three of which were texture features, two were discrete
wavelet transforms (DWTs) and two were higher order spectral (HOS) features. The
accuracy achieved in their results was 91.77%. In [37], Acharya et al adapted the
same approach with two sets of data, 346 and 146 images, consisting of machine
learning with 16 and 12 features, respectively. Their accuracy in this study was
85.3% and 93.1% for both datasets, respectively. In 2014, Pedro et al developed a
risk stratification strategy [33] that had a similar motivation to Acharya’s work, but
addressed the vulnerability of plaque rupture, using a tissue characterization
approach (AtheroRisk™, trademarked by AtheroPoint, CA, USA). The system
using a total of the following 16 features: Rayleigh parameter (4th, 5th, 6th),
GLCM, wavelet, percentile (10, 50), degree of stenosis, echogenic cap, appearance,
mean, skewness, mixture components and plaque disruption. Their classifier was an
enhanced activity index based on the Bayes factor. The accuracy for their results was
77%. Our group’s prior best accuracy using his Atheromatic™ system was published
in 2015, in which a value of 99.1% was obtained [43]. A combination of non-linear
HOS features and two wall features under four different classifiers allowed these
results to be achieved. However, the system did not demonstrate any evidence of
performance evaluation and system validation, nor did it discuss machine learning
systems for the near wall or combined near and far wall.
The fundamental novelties of the proposed method compared to previous
methods are:
(i) The proposed study combines the use of automated segmentation of both
the near and far walls for stroke risk assessment, while previous methods
have evaluated stroke risk without the use of automated segmentation.
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(ii) None of the past methods have explored the role of tissue characterization
for the near wall of the common carotid artery.
(iii) In addition to feature estimation, we also optimize the criteria for feature
selection.
(iv) The demonstration of stroke risk assessment by jointly taking into
consideration the near and far walls of the common carotid artery.
(v) Comprehensive design of an online cascaded system, consisting of a far and
near wall segmentation block and stroke risk assessment via a tissue
characterization block.
(vi) Comprehensive validation of the cascaded system through comparison to
the manual strategy.
We believe that one of the most remarkable results of our study is the extremely
high cross-validation accuracy achieved under the proposed method. Such high
accuracy is obtained through the optimization of the feature selection process. In
machine learning, feature selection determines the thought process of the machine.
Optimization of the features which are given to the machine avoids the selection of
noisy and redundant features, which would likely decrease the accuracy [42]. In the
proposed method, dominant features are determined using the differences between
the mean values of each feature of the two classes (high risk and low risk). The larger
the difference between these mean values, the higher the dominance level of the
feature. The features are then arranged in descending order of their dominance level.
In this way, a total of 16 feature combinations are constructed from highest
dominant feature to lowest dominant feature with the increment of one feature in
each feature combination.
13.7.6 Strengths and weaknesses
A sRAS is presented based on tissue characterization of the near, far and combined
walls. Our results show that the near wall is equally important for stroke risk
assessment compared to the far wall. Additionally, the tissue characterization system
gave the highest cross-validation accuracy, compared to previous published results,
with all three partition protocols: K = 5, K = 10 and K = JK. These results are
consistent with the current literature. The online system is completely automated,
where the near and far walls are automatically segmented in carotid scans.
In the validation of our risk assessment system, a histological approach would
have yielded more accurate validation. However, this is very tedious and expensive,
and therefore was not feasible for this study. Our approach for validation is the
standard in machine learning, and gave encouraging results. Even though the results
are encouraging, more sophisticated feature selection techniques such as principal
component analysis (PCA) or functional data analysis (FDA) can be adopted.
Further, intra- and inter-observer studies can be conducted on manual tracings of
the LD and labeling of the ground truth risks.
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13.8 Conclusions
In this study, a system for comprehensive stroke risk assessment based on tissue
characterization of the near, far and combined walls is designed and developed. This
system consists of (i) automated segmentation of near and far walls followed by (ii) a
machine learning paradigm with three partition protocols. The selected features are
optimized based on their statistical distribution. Three different kinds of experiments
are conducted: first, the selection of the best kernel in the SVM-based classifier;
second, studying the effect of dominant features on classification accuracy; and
third, to study the effect of data size on this cross-validation accuracy. All these
experiments are independently conducted for the near, far and combined walls to
understand the relative risk of plaque in the carotid walls. The system shows
consistent, stable and reliable results throughout. The system is fully novel and adds
value to the field of stroke risk assessment.
Conflict of interest
Dr Jasjit S Suri has a relationship with AtheroPoint™ (Roseville, CA, USA) which
is dedicated to atherosclerosis disease management, including stroke and cardiovascular imaging.
Contributions
Tadashi Araki, MD: Support in image data collection.
Pankaj K Jain, MTech: Statistical plots, analysis and programming.
Harman S Suri: Understanding and writing the original draft of the machine
learning manuscript.
Narendra D Londhe, PhD: Advice and support in arranging IT resources.
Nobutaka Ikeda, MD, PhD: Support in clinical demographics collection.
Ayman El-Baz, PhD: Physics of vascular imaging protocols.
Vimal K Shrivastava, MTech: Support in programming.
Luca Saba, MD: Clinical discussions, manual tracings and validations.
Andrew Nicolaides, PhD: Clinical discussions on carotid imaging.
Shoaib Shafique, MD: Clinical application and risk scores.
John R Laird, MD: Clinical discussions on the link between the carotid and
coronary.
Ajay Gupta, MD: Support in clinical writing of the manuscript.
Jasjit S Suri, PhD, MBA, Fellow AIMBE: Principal Investigator of the project.
Acknowledgements
Reprinted from Araki T, Jain P K, Suri H S, Londhe N D, Ikeda N, El-Baz A,
Shrivastava V K, Saba L, Nicolaides A, Shafique S and Laird J R 2017 Stroke risk
stratification and its validation using ultrasonic Echolucent Carotid Wall plaque
morphology: a machine learning paradigm Comput. Biol. Med. 80 77–96, with
permission from Elsevier.
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We acknowledge Toho Hospital, Tokyo, Japan, for support in providing patient
datasets to AtheroPoint™ (Roseville, CA, USA). We thank our clinical team for the
manual tracings of the lumen and cIMT borders. We also acknowledge Sumit K
Banchhor, Research Scholar at NIT Raipur for support in formatting and proofreading the manuscript.
Appendix A Grayscale features
Table A1. Features of the gray-level co-occurrence matrix (GLCM).
Feature name Equation
−
Contrast (Con)
Energy (Eng)
Entropy (Ent)
Homogeneity (HOM)
L
ggg
=∣−∣=
∑∑∑
=
n
0
∑∑
=
=−
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ij
∑∑
ij
∑∑
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PijEng ( , )
LjL
1
2
nPijijnCon ( , )
{}
==
i
11
2
d
Pij PijEnt ( , )log( ( , ))
dd
1
++ij
1( )
d
PijHOM
(, )
d
2
Table A2. Features of the gray-level run length matrix.
Feature name Equation
1
==
Short run emphasis
Long run emphasis
Gray-level non-uniformity
Run length non-uniformity
Run percentage
Low gray-level run emphasis
High gray-level run emphasis
Short run low gray-level emphasis
Short run high gray-level emphasis
Long run low gray-level emphasis
RE
l
=·=·
==
LN
==
l
t
RP
=
l
q
LGRE
==
=·=·
RLGE
RHGE
LRLGE
Long run high gray-level emphasis
()
L
(, )
∑∑ ∑
== =
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Appendix B Statistical results
Table B1. Patient demographics of the 204 patients.
Population 157 males and 47 females
Mean age 69 ± 11 years ranging from 29 to 88 years
Lesion location in CCA 108 patients had proximal lesions, 67 patients had lesions in the
middle and 29 had lesions in the distal locations of the CCA
HbA1c 6.30 ± 1.1 (mg dl
LDL cholesterol 101.61 ± 31.55 (mg dl
HDL cholesterol 50.66 ± 15.22 (mg dl
Total cholesterol 175.82 ± 37.97 (mg dl
Smokers 40% of the patients
Table B2. Distribution of high-risk (HR) and low-risk (LR) images in our population based on the LDT.
Italics represent low risk while bold represents high risk.
Distribution of patients in HR and LR with change in manual LDT
−1
)
−1
)
−1
)
−1
)
SN
LDT (mm)
threshold
# of images
in HR
# of images
in LR
% of images
in HR
% of images
in LR
18 395 12 97.05 2.95
2 7.8 389 18 95.58 4.42
3 7.6 381 26 93.61 6.39
4 7.4 374 33 91.89 8.11
5 7.2 358 49 87.96 12.04
67 342 65 84.03 15.97
7 6.8 326 81 80.1 19.9
8 6.6 302 105 74.2 25.8
9 6.4 269 138 66.09 33.91
10 6.2 234 173 57.49 42.51
11 6 198 209 48.65 51.35
12 5.8 165 242 40.54 59.46
13 5.6 121 286 29.73 70.27
14 5.4 95 312 23.34 76.66
15 5.2 71 336 17.44 82.56
16 5 46 361 11.3 88.7
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Table B3. Accuracy comparison for five different kernels for the three different partition protocols K = 5,
K = 10 and K = JK, using N = 407 images.
Fold/Kernel Linear RBF Poly-1 Poly-2 Poly-3
K = 5 93.44 91.27 98.54 98.74 95.03
K = 10 93.46 91.58 98.49 98.80 96.01
K = JK 98.77 94.35 98.77 99.02 96.56
Table B4. Percentage accuracy for different feature combinations for the different partition protocols, K = 5,
K = 10 and K = JK, for 20 trials (T ).
Far wall Near wall Combined wall
Features-combination
FC1 88.13 88.15 88.27 78.69 78.74 78.64 74.90 74.93 75.03
FC2 89.57 89.58 89.56 83.05 83.04 83.06 76.39 76.41 76.40
FC3 90.58 90.57 90.63 84.06 84.07 84.03 77.27 77.24 77.29
FC4 91.09 91.15 91.20 87.26 87.28 87.33 79.66 79.71 79.76
FC5 91.71 91.75 91.86 90.97 91.00 91.05 82.51 82.60 82.59
FC6 93.32 93.37 93.38 93.40 93.48 93.55 89.40 89.48 89.47
FC7 95.34 95.46 95.59 94.30 94.40 94.41 93.51 93.61 93.69
FC8 96.04 96.15 96.22 96.51 96.61 96.61 96.90 96.97 97.04
FC9 96.93 97.02 97.01 97.41 97.50 97.54 97.00 97.16 97.24
FC10 97.36 97.44 97.47 97.86 97.94 97.99 97.56 97.70 97.85
FC11 97.62 97.78 97.83 97.97 98.11 98.33 97.91 98.08 98.10
FC12 98.15 98.32 98.50 98.09 98.22 98.31 98.20 98.29 98.36
FC13 98.45 98.50 98.59 98.63 98.70 98.80 98.62 98.72 98.71
FC14 98.59 98.61 98.68 98.61 98.68 98.79 98.79 98.90 99.00
FC15 98.60 98.66 98.74 98.78 98.85 98.89 98.91 99.04 99.14
FC16 98.74 98.80 98.82 98.96 99.03 99.05 99.16 99.25 99.31
K = 5 K = 10 K = JK K = 5 K = 10 K = JK K = 5 K = 10 K = JK
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Table B5. Automated versus manual mean accuracies for three partition protocols, K = 5, K = 10 and K = JK,
using N = 407 images for 20 trials (T ).
sRAS: Automated IMT region and automated LD
K = 5 K = 10 K = JK
Far wall 95.01 95.08 95.15
Near wall 93.41 93.48 93.52
Combined wall 91.04 91.13 91.18
mRAS: Manual IMT region and manual LD
Far wall 93.96 94.07 94.16
Near wall 91.92 92.04 92.11
Combined wall 91.91 92.02 92.08
Table B6. Names of the features for various feature combinations for the far wall, LD = 6.4 mm.
Features
set
Total no of
features Name of the features
ACC (%)
K = 5
ACC (%)
K = 10
ACC (%)
K = JK
FC1 1 16 88.71 88.74 88.70
FC2 2 16,9 90.71 90.69 90.66
FC3 3 16,9,12 90.92 90.96 91.15
FC4 4 16,9,12,1 91.18 91.28 91.65
FC5 5 16,9,12,1,4 91.22 91.11 91.40
FC6 6 16,9,12,1,4,8 93.06 92.93 93.12
FC7 7 16,9,12,1,4,8,10 94.64 94.80 94.84
FC8 8 16,9,12,1,4,8,10,2 95.36 95.53 95.58
FC9 9 16,9,12,1,4,8,10,2,15 95.23 95.66 95.33
FC10 10 16,9,12,1,4,8,10,2,15,11 95.53 95.69 95.58
FC11 11 16,9,12,1,4,8,10,2,15,11,13 95.47 95.62 95.58
FC12 12 16,9,12,1,4,8,10,2,15,11,13,14 98.06 98.29 98.28
FC13 13 16,9,12,1,4,8,10,2,15,11,13,14,7 98.11 98.15 98.03
FC14 14 16,9,12,1,4,8,10,2,15,11,13,14,7,5 98.37 98.49 98.53
FC15 15 16,9,12,1,4,8,10,2,15,11,13,14,7,5,6 98.64 98.78 98.77
FC16 16 16,9,12,1,4,8,10,2,15,11,13,14,7,5,6,3 98.62 98.73 98.53
GLCM features: 1—entropy; 2—energy; 3—contrast; 4—homogeneity; GLRLM features: 5—short run
emphasis (SRE); 6—long run emphasis (LRE); 7—gray-level non-uniformity (GLN); 8—run length nonuniformity (RLN); 9—run percentage (RP); 10—low gray-level run emphasis (LGRE); 11—high gray-level
run emphasis (HGRE); 12—short run low gray-level emphasis (SRLGE); 13—short run high gray-level
emphasis (SRHGE); 14—long run low gray-level emphasis (LRLGE); 15—long run high gray-level emphasis
(LRHGE); 16: fractal dimension (FD) feature.
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Table B7. Change in percentage mean accuracy for different data size for three partition protocols, K = 5,
K = 10 and K = JK, and total trials (T = 20).
Data size (in %) # of patient images K = 5 K = 10 K = JK
10 40 79.21 80.46 97.17
20 80 85.70 87.00 96.66
30 120 88.14 89.44 96.24
40 160 92.51 93.42 96.79
50 200 94.54 95.16 97.15
60 240 96.54 96.77 97.54
70 280 97.31 97.48 97.86
80 320 98.61 98.74 98.75
90 360 98.54 98.61 98.62
100 407 98.72 98.81 98.82
Table B8. PoM for far, near and combined walls for three partition protocols: K = 5, K = 10 and K = JK and
total trials (T = 20) for three different dominant feature (D) conditions.
PoM for D < 10
PoM Far wall Near wall Combined
K = 5 98.05 97.53 97.2
K = 10 98.05 97.57 97.11
K = JK 98.05 97.62 97.25
PoM for D > 10
K = 5 99.52 99.15 99.42
K = 10 99.62 99.21 99.44
K = JK 99.67 99.22 99.53
PoM for all D
K = 5 98.88 98.38 98.95
K = 10 98.92 98.43 98.99
K = JK 98.95 98.47 98.94
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Table B9. Mean sensitivity (recall), mean specificity, precision (PPV) and mean AUC for automated RAS versus manual RAS for total feature combinations to 16.
Automated LD
Far wall Near wall Combined wall
Sens. Spec. PPV AUC Sens. Spec. PPV AUC Sens. Spec. PPV AUC
K = 5 97.05 94.65 98.27 0.96 98.39 94.77 99.06 0.97 97.41 97.40 99.07 0.97
K = 10 96.92 94.87 98.31 0.96 98.59 94.72 99.08 0.97 97.87 97.48 99.31 0.98
K = JK 96.78 95.26 98.43 0.96 98.63 94.50 99.11 0.97 98.28 97.58 99.38 0.98
Manual LD
Far wall Near wall Combined wall
Sens. Spec. PPV AUC Sens. Spec. PPV AUC Sens. Spec. PPV AUC
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K = 5 97.45 90.79 97.74 0.94 97.05 94.65 98.27 0.96 97.62 90.55 98.64 0.94
K = 10 97.67 91.07 97.96 0.94 97.52 93.82 98.42 0.96 98.04 90.99 98.96 0.95
K = JK 97.88 91.41 98.15 0.95 97.93 94.74 98.79 0.96 98.10 91.16 98.98 0.95
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