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Vascular and Intravascular Imaging Trends, Analysis, and Challenges, Volume 1

https://t.me/medicina_free

Figure 14.1. Typical examples of images showing the grayscale ring, calcium regional area, vessel regional area, lumen regional area and atheroma regional area from ﬁve different low-risk patients.

14.2 Patient demographics and data acquisition

14.2.1 Patient demographics

From a single case study, twenty-two patients with stable angina pectoris who underwent percutaneous coronary interventions between July 2009 and December 2010 were considered for this study. The study consisted of 22 patients (20 M 2 F) in the age group of 36–81 years (average 66 ± 12 years). In this database, ten patients had a calciﬁed location on the left anterior descending, eight on the right, two on the

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Figure 14.2. Typical examples of images showing the grayscale ring, calcium regional area, vessel regional area, lumen regional area and atheroma regional area from ﬁve different high-risk patients.

left circumﬂex and two on the left main coronary artery. Out of 22 patients, ten had proximal lesions, six in the middle, and six at a distal location. Five patients had a family history of CAD. Furthermore, one patient had a prior myocardial infarction while another had undergone a prior coronary artery bypass grafting. The mean hemoglobin, low-density lipoprotein (LDL), high-density lipoprotein (HDL) and total cholesterol were 5.6 g dL respectively. Ten patients from the pool of 22 patients were smokers.

−1

, 94.4 mg dL−1, 48.7 mg dL−1and 168 mg dL−1,

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14.2.2 Data acquisition

The dataset was approved by the IRB and written informed consent was provided by all the patients. In this study, all the patients have undergone both carotid and coronary ultrasound examinations. Carotid examinations were performed with a scanner (Aplio XV, AplioXG, Xario, Toshiba, Inc., Tokyo, Japan) equipped with a

7.5 MHz linear array transducer. For coronary data acquisition, a 40 MHz IVUS catheter (Atlantis SR Pro; Boston Scientiﬁc, Marlborough, MA, USA) was used. For the carotid database, high-resolution images were acquired as recommended by the American Society of Echocardiography and, for the coronary database, the DICOM image format was used. Durng conversion into AVI movies, these DICOM images (16 bits per pixel) were further compressed to JPEG (8 bits per pixel) images. The mean pixel resolution for the carotid and coronary databases were 0.05 ± 0.01 mm/pixel and 0.0167 mm/pixel, respectively.

14.3 Methodology

14.3.1 IVUS data preparation

The coronary dataset consists of 22 patients with 2109 frames per patient. Our coronary data size preparation involves two assumptions: (a) the head and tail end frames did not contain any morphological information about calcium and (b) the artery is not a straight line and longitudinal and transversal displacements do exist between frames [29, 30]. In this database, the change in calcium was observed in every 10th frame. Using these two assumptions, we accumulated 4930 frames derived from 22 patients. Considering the carotid risk threshold [16, 17, 23], all the patients with a carotid plaque burden higher than or equal to 0.9 mm are categorized into the high-risk pool and the patients with a plaque burden lower than 0.9 mm are categorized in the low-risk pool. Accordingly, 14 patients were categorized as highrisk (∼63.63%) and the other eight patients were in the low-risk pool (∼36.36%). Note that our data size in the ML framework was not 22 subjects, but 4930 frames collected from 22 subjects, with pools consisting of 3043 high-risk frames and 1887 low-risk frames corresponding to 14 high-risk patients and eight low-risk patients, respectively.

14.3.2 Wall region of interest estimation

In this study for the region of interest (ROI) estimation, we have employed an ImgTracer™ system (courtesy of AtheroPoint™, Roseville, CA, USA) recently used by JSS and his team [20, 29–31]. Here, two experts (doctoral students with knowledge of coronary artery disease and IVUS imaging) generated the vessel wall region by manually tracing the internal elastic lamina (IEL) and external elastic lamina (EEL) borders. A typical example of manually traced IEL/EEL borders is shown in ﬁgure 14.3.Inﬁgure 14.3(a), the inner/outer yellow rings indicate IEL/EEL borders. The atherosclerotic wall region treated as the ROI is shown in ﬁgure

14.3(b).

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Figure 14.3. (a) Manually traced internal elastic lamina and external elastic lamina indicated by the inner and outer yellow rings in the vessel wall region obtained by manually tracings using ImgTracer™. (b) Atherosclerotic grayscale ring image used as the ROI. (Courtesy of AtheroPoint™, Roseville, CA, USA. Source: Banchhor et al [

29].)

14.3.3 Wall- and texture-based feature computation

In this study, we have computed six different wall-based measurement features, namely coronary calcium area, coronary vessel area, coronary lumen area, coronary atheroma area, coronary wall thickness and coronary wall thickness variability [20]. Coronary calcium area is computed using a well-established threshold-based segmentation technique [29, 31]. However, coronary vessel area, coronary lumen area and coronary atheroma area was computed by generating their corresponding binary mask images using the IEL/EEL borders manually traced by experts using the ImgTracer™ system [20]. Further, using the bidirectional concept of the polyline distance method [32, 33], we have computed the coronary wall thickness. Finally, coronary wall thickness variability is estimated by computing the standard deviation of the coronary wall thickness over all the frames. For this study, we have also extracted 59 different plaque texture-based features [16, 17], thus having a total set of 65 features. Detailed derivations of all the plaque texture-based features are as shown below.

14.3.3.1 Gray-level co-occurrence matrix (GLCM)

The GLCM matrix indicates the joint probability of the occurrence of the gray-level between neighboring pixels [34]. Let us assume a 2D image containing pixels with gray levels

0, 1, , 1)

occurring in the neighborhood of gray-levelj. Here the (i, j)th entry of GLCM is denoted as

ij(, ),

the sum of all the elements in the matrix. and columns at location

for the rows and columns at location are given in table 14.1 [35] which reﬂects the spatial distribution of intensity values of an image.

…−N

. A GLCM indicates the probability of gray-level

which is normalized by dividing each element in the matrix by

. Similarly

ij(, )

and

represents the mean for the rows

and

represent the standard deviation

. The 18 features derived from GLCM

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Table 14.1. Features of the GLCM.

Feature name Equation

μμ=·

∑∑

=−=

iNj

010

Mean (

Standard deviation (

)

μ,

σσ,

∑∑

=−=

iNj

010

σμ

=−·

∑∑

=−=

iNj

010

)

σμ

=−·

∑∑

=−=

iNj

010

Autocorrelation (ACorr)

Correlation (Corr)

Contrast (Cont)

Corr

=−·

Cluster prominence (CPro)

Cluster Shade (CSha)

=∣−∣·

DijPij(, )

∑∑

Dissimilarity (Dij)

Energy (Eng)

Entropy (Ent)

Homogeneity (Homo)

ij df

=−=

iNjN010

=− ·

Homo

−

iPij

() (, )

−

jPij

(). (, )

−

iPij

()(,)

−

iPij

()(,)

=·

∑∑

=−=

iNjN010

−

∑∑

=−=

iNjN010

−

∑∑

=−=

iNjN010

−

∑∑

=−=

iNjN010

−

∑∑

=−=

ioNjN10

−

PijEng ( , )

∑∑

=−=

iNjN010

−

∑∑

=−=

iNjN010

∑∑

=−=

iNjN010

−

ij P i jACorr ( ) ( , )

·−

ij P i j

() (, )

σσ

ij PijCont ( ) ( , )

ij PijCPro ( ) ( , )

ij PijCSha ( ) ( , )

Pij PijEnt ( , ) log{ ( , )}

df df

−

1( )

μμ=+−−·

Pij

(, )

+−

μμ

Maximum probability (MPro)

Sum of squares (SoS)

= PijMPro max ( , )

oS ( ) ( , )

ij,

−

μ=−·

iPij

∑∑

=−=

iNjN010

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Sum average (SAvg)

Sum entropy (SEnt)

Sum variances (SVar)

Difference variance (DVar)

Difference entropy (DEnt)

−

2( 1)

=·

Avg ( )

=− ·

Ent ( ) log{ ( )}

=−·

Var ( SEnt) ( )

iP i

∑

iN0

2( 1)

∑

iN0

−

2( 1)

∑

iN0

−

=−′·

∑

iN0

=− ·

∑

iN0

−

Pi Pi

xy xy

iPi

iPiDVar ( DVar ) ( )

−

Pi PiDEnt () log{ ()}

−−

xy xy

−

where

−

= =+= … −

Pk Pijkij N

() (,), {0,1, 2( 1)}

Pk Pijk ij N

−

∑∑

=−=

iNj

010

−

==∣−∣=…−

() (,), {0,1, 2( 1)}

∑∑

=−=

iNj

010

14.3.3.2 The gray-level run length matrix (GLRLM)

In the GLRLM, the gray intensity of the pixel is measured from the reference pixel and run length is deﬁned as the set of pixels having the same gray levels in a speciﬁc direction. In this grayscale texture feature, runs of length y at reference gray level

deﬁnes the maximum run length,tis the total number of runs andqis the number

Fx y(, )

. Here, GL deﬁnes the number of gray levels,

deﬁnes the set of pixels of successive

of pixels in the image. From the GLRLM, 11 features are extracted, as shown in table

14.2 [35], which measure the joint distribution of gray-level and run length distribution.

14.3.3.3 Intensity histogram

In the intensity histogram, the following measurements are computed: mean energy, variance, entropy, skewness and kurtosis. Mean, variance and skew measure the brightness, contrast and the allocation of the gray level in an image. Entropy is the converse of skew. Here

deﬁnes the number of pixel intensities of valuej. Table

j()

14.3 shows the formulas for extracting seven features using the intensity histogram

of a given image [35].

14.3.3.4 Gray-level difference statistics (GLDS)

The GLDS algorithm measures the absolute difference between the pairs of the gray levels for a given displacement

=∣ − +Δ +Δ ∣

xy Ixy Ix xy y(, ) (, ) ( , )

. In this analysis, four features are extracted as shown in table 14.4 for displacement

=−(0, 3), (3, 0), (3, 3),(3, 3)

. For any given displacement

and

be the probability density function

=Δ Δxy(, )

and their average values are considered [36].

,let

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Table 14.2. Features of the GLRLM.

Feature name Equation

Short run emphasis (SRE)

Long run emphasis (LER)

Gray-level non-uniformity (GLNU)

LNU

Run length non-uniformity (RLNU)

RPer

Run percentage (RPer)

Low gray-level run emphasis (LGRE)

LGRE

High gray-level run emphasis (HGRE)

Short run low gray-level emphasis

RLGE

(SRLGE)

Short run high gray-level emphasis

RHGE

(SRHE)

Long run low gray-level emphasis

LRLGE

(LRLGE)

Long run high gray-level emphasis

(LRHGE)

Fx y

∑∑

== =

1GL1

∑∑

== =

∑∑

(, )

Fx y y

((, ) )

1GL1

GL1RL

()

== =

GL1RL

()

== =

Fx y

== =

1GL1

Fx y x

((, ) )

== =

1GL1

xy1GL1

∑

·=

Fx y

((, ))

Fx y

((, ))

(, )

·=

(, )

Fx y

Fx y x

Fx y y

Fx y x yLRHGE

(, )

··

∑

()

∑

Fy yLER

()

FyRLNU

()

Fy xHGRE

()

14.3.3.5 The neighborhood gray tone difference matrix (NGTDM)

Essentially, NGTDM is a column matrix magnitude of the difference between the mean of the pixels in the neighborhood and the pixel under assessment. Here value

. Busyness, contrast, complexity, coarseness and texture length are the ﬁve

texture features that are derived from the NGTDM, as shown in table 14.5 [37, 38].

14.3.3.6 Invariant moment

In an invariant moment, the raw moments and the central moments were utilized for the feature extraction [39]. The seven features extracted using raw moments central moments

=pq, 0,1,2,3

scale, rotation and translation.

wherein each ith value is the

Ki()

is the probability of occurrence of gray-level

and central moments after scaling normalization

for

are shown in table 14.6. All the seven moments are invariant to

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Table 14.3. Features of the intensity histogram.

Feature name Equation

−

=·

MjIj()

Mean (M)

Energy (E)

Standard deviation (SD)

Variance (Var)

Entropy (Ent)

Skewness (Skew)

Kurtosis (Kurt)

∑

jN0

−

EIj()

=−·

D()()

=−

ar ( ) ( )

kew ( ) ( )

∑

jN0

−

∑

jN0

−

∑

jN0

−

N01

Ij IjEnt ( )log ( )

∑

−

π=−

−

π=−−

∑

iM Ij

jMIj

−

jMIj

∑

jN0

−

jMIjKurt ( ) ( ) 3

jN0

Table 14.4. Features of GLDS.

Feature Equation

−

=·

Ci iPD ( )

Contrast (C

)

Angular second moment (Φm)

Entropy (E1)

Mean (M1)

∑

iN0

−

Φ=

∑

iN0

=− ·

EiiPD ( ) log{PD ( )}

∑

iN0

−

=·

MiiPD ( )

∑

iN0

iPD ( )

−

γγ

14.3.3.7 The statistical feature matrix (SFM)

The values of the statistical properties of pixel pairs at various locations were stored in the SFM. coarseness computation. matrix, respectively.

is the normalization factor, usually considered to be 100 for

and

depicts the displacement vectors and

represent the dissimilarity and contrast

CON

Nms

deﬁnes the

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Table 14.5. Features of the NGTDM.

Feature name Equation

Coarseness (Coar)

Contrast (Cont)

Busyness (Bus)

Bus

Complexity (Comp)

Texture strength (TS)

Table 14.6. Features of the invariant moment.

−

⎡ ⎢

pK iCoar ( )

∑

⎢

⎣

iN0

(1)

−

∑

NjN

101

∑∑

=−=

−

∑∑

=−=

iNjN010

NjN

∑∑

=−=

010

−

⎤ ⎥ ⎥

⎦

−

()

pp i j

∑∑ ∑

=−=

iNj

010

−

()

pK i

−

()

−

ip jp

⎧

⎪⎪

∣−∣

⎨

⎪⎪

np p

()

⎩

−

+−

ppij

()()

−

∑

()

−·

⎫ ⎬

·+

pK i pK jComp

{() ()}

⎭

−

()

KiCont

Feature name Equation

=+fM M

20 02

=+ +fMM M()4

20 02

=− + −fM M MM(3)(3 )

=− ++fMM MM()()

=− + + − +fM MMMM M MM( 3 )( )[( 3 ) 3( ) ]

+− + +−+MMMM MM MM(3 )( )[3( ) ( )

=− + −− + + +fMMMM MM MMMMM( )[( ) ( ) ] [4 ( )( )]

=+ + + − +fMMMMMM MM()()[()3()

−− + + −+M MMM MM MM( 3 )( )[3( ) ( )

12 30 12

where

−

∑∑

RM ( , );

xrwy

010

=−=

∑∑

xrwy

=−=

(1 ( )/ 2)

010

·· = =

xyfxyx y

−

()()(,);

−·−·

xx yy fxy

number of elements in the setDV.

represents the mean of

fractal dimensions in the

andydirections are denoted by

shown in table 14.7 [40].

21 03

and

21 03

represents SFM for the dissimilarity matrix,

represents the deepest valley of

RM RM

;

RM RM

xsf()

12 21 03

and

. The

ysf()

,as

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Table 14.7. Features of statistical feature matrix.

Feature name Equation

DM( , )/Nms

ij DV(, )

−MM

ddv

CON( , )

∑

ij(, ) DV

()

Coarseness (Coa)

Periodicity (Perio)

Contrast (Cont)

Roughness (Roug)

Table 14.8. Features of the MGFM.

Coa

erio

Cont

Roug

∑

(FD FD )

Feature name Equation

∑

σμ=

Mean grayscale value (μ

)

Mean grayscale standard deviation (σGS)

()

∑

()

−X

14.3.3.8 Mean grayscale feature matrix (MGFM)

Let

be a matrix of all the intensities of a grayscale (GS) image I, consisting of

number of elements. Then, the mean (μGS) and standard deviation (σGS) of the image I are as shown in table 14.8.

14.3.4 Principal component analysis with polling contribution

For this study, we have extracted 59 different plaque texture-based features [16, 17] along with six wall-based measurement features, thus having a total set of 65 features. However, since there are a large number of features (= 65), it is ﬁrst necessary to extract the dominant features from the pool consisting of plaque texture-based features fused with wall-based measurement features. Since PCA is mainly used for dimensionality reduction, a PCA-based polling strategy is used in this study [41]. Here, we use eigenvectors to evaluate the signiﬁcance of different feature components of the original samples. The algorithm of feature selection is as follows:

Step 1. First, we compute the covariance matrix of the dataset. Step 2. Now, we compute the eigenvectors (V) and eigenvalues (λ) of the

covariance matrix.

Step 3. Now, we select the largest eigenvalues and arrange the eigenvectors

according to the selected eigenvalues.

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