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Aortic Hemodynamics and their Role in Aneurysm Location
167
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In: Aortic Aneurysms ISBN: 978-1-62618-458-9
Editors: Daniel Fischhof and Franz Hatig © 2013 Nova Science Publishers, Inc.
Chapter 8
DECISION-SUPPORT SYSTEM FOR DIAGNOSIS OF
ABDOMINAL AORTIC ANEURYSM AFTER
ENDOVASCULAR REPAIR
G. García
1
, A. Tapia
1
and M. De Blas
2
1
Systems Engineering and Automatic Control Department,
University of the Basque Country UPV/EHU, San Sebastián, Spain
2
Interventional Radiology Department, Donostia Hospital, San Sebastián, Spain
ABSTRACT
The Endovascular Aneurysm Repair (EVAR) is a minimal invasive alternative to
open surgical therapy. From a long term perspective, complications such as prostheses
displacement or leaks inside the aneurysm sac (endoleaks) could appear influencing the
evolution of treatment. The objective of this work is to develop a decision-support
preliminary system for the diagnosis of EVAR progression from computed tomography
angiography CTA images. The system is based on the extraction of texture features from
thrombus aneurysm samples and a posterior classification. Regions of interest (ROIs)
from patients with different post-EVAR evolution were extracted by experienced
radiologists. Three conventional texture-analysis methods such as the gray level co-
occurrence matrix (GLCM), the gray level run length matrix (GLRLM), and the gray
level difference method (GLDM), were applied to each ROI to obtain texture features. In
order to evaluate the discrimination ability of textures features several classification
schemes based on multilayer perceptron neural network (MLP-NN), and support vector
machine (SVM) classifiers were experimented. The performance of classifiers was
evaluated using 10-fold cross validation with the entire dataset. The average of accuracy,
sensitivity, specificity, receiving operating curves (ROC) and area under the ROC curves
(AUC) were calculated for the classification performances of each texture-analysis
method. The study showed that the textural features could help radiologists in the
classification of abdominal aneurysm evolution after EVAR.
Keywords: Aneurysm, EVAR, texture features, multilayer perceptron neural network,
support vector machine
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1. INTRODUCTION
The endovascular prostheses in Abdominal Aortic Aneurysm has proven to be an
effective technique to reduce the pressure and rupture risk of aneurysm, offering shorter post-
operation recovery than open surgical repair. The Endovascular Aneurysm Repair (EVAR)
treatment, is a percutaneous image-guided endovascular procedure in which a stent graft is
inserted into the aneurysm cavity. Once the stent is placed, the blood clots around the metallic
mesh forcing the blood flux through the stent and reducing the pressure on the aneurysm
walls consequently. Nevertheless, in a long term perspective different complications such as
prostheses displacement or leaks inside the aneurysm sac (endoleaks) could appear provoking
a pressure elevation and increasing the danger of rupture consequently. Due to this, periodic
follow-up scans of the prosthesis behaviour are necessary. At present, contrast enhanced
computed tomographic angiography (CTA) is the most commonly used examination for
imaging surveillance [1]. On the other hand, the post operation analysis is quite crude as it
involves manually measuring different physical parameters of the aneurysm cavity [2].
According to these measurements, the evolution of the aneurysm can be split up into two
main categories:
Favourable evolution. A reduction of the diameter of the aneurysm sac can be
observed. This means that the aneurysm has been correctly excluded from the
circulation.
Unfavourable evolution. A growth of the aneurysm diameter is observed. Endoleaks
can sometimes be detected thanks to contrast in the CT images.
Regarding the unfavourable evolution cases we can also distinguish a particular situation.
There are patients in which abdominal aneurysm does not increase or reduce significantly its
volume and endoleaks are not visually detected (endotensión). Even today the cause for this
behaviour remains a source of controversy [3].
According to our previous studies [4], texture thrombus in favourable shrinking
aneurysms differs from unfavourable expanding or stable ones. A decision-support system
based on texture analysis can help clinicians to determine the evolution of mass thrombus
inside the aneurysm sac, particularly in ambiguous aneurysm evolution cases. This is
clinically important because a more complete assessment of EVAR progression would be
useful in re-defining patients management [5].
In recent years, many efforts have been put into the developing of Decision-Support or
Computer-Aided Diagnosis (CAD) systems based on image processing methods. The
principal motivation for the research on this kind of systems has been to assist the clinicians
on the analysis of medical images, [6]. In many occasions this analysis implies the detection
or measurement of subtle differences, usually difficult to appreciate by visual inspection even
for experienced radiologist. CAD systems have been successfully utilized in a wide range of
medical applications [7-9]. A particular field inside the image processing methods for CAD
systems is the so named texture-based analysis. This analysis studies, not only the variation of
the pixel intensity values along the image but also the possible spatial arrangement of them,
and the more or less periodic repetition of such arrangement (primitives). From this point of
view texture analysis can help on the functional characterization of different kind of organs,
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Decision-Support System for Diagnosis of Abdominal Aortic Aneurysm
171
tissues, etc, at the evolution of disease. The textures features obtained from the analysis can
be fed as inputs for a deterministic or probabilistic classifier, which assign each sample with
its specific class.
Textures analysis methods can generally be classified into three categories: statistical
methods, model based methods, and structural methods [10]. In our approach we have
focused on the application of statistical texture methods, specifically, on spatial domain
statistical techniques as the Gray Level Co-ocurrence Matrix (GLCM) [11], the Gray Level
Run Length Matrix (GLRLM) [12][13] and the Gray Level Difference Method (GLDM) [14]
[15]. These three extended methods can capture second or higher order statistics on the
relation between gray values in pixel pairs or groups of pixels in order to estimate their
probability-density functions. Their validity has been proved in many studies [16-19]. In the
last years, multiple classifier architecture has been proposed to improve the performance of
CAD systems [20-23].
Our purpose in this study is to develop a preliminary support system based on textures
analysis which provides clinicians with complementary information for correctly classifying
EVAR treatment evolution. This is clinically important because a more complete assessment
of EVAR progression would be useful in re-defining management pathways for patients,
particularly when new treatment options are available [4].
For obtaining this objective a semi-automated segmentation process to facilitate the
extraction of samples has been developed. Once the samples from patients with different post-
EVAR evolution have been obtained, the texture features from the three methods are
calculated and fed into different classifiers for automatic classification.
The paper is organised as follows: "Materials and Methods" provides with information
about the acquisition of CTA images, the theoretical background on the texture analysis
methods, and the definition of neural network architectures contemplated in the
implementation of the CAD system. The methodology, and results obtained from the
performance evaluation of the classifiers are presented in "Results". Finally, some
conclusions are given in "Conclusion" section.
2. MATERIALS AND METHODS
2.1. Dataset
The CTA image scans used in this work were obtained by a group of 3 experienced
radiologists from the Vascular Surgery Unit and Interventional Radiology Department of the
Donostia Hospital. These CTA images belong to the scan studies of 90 patients with ages
ranging from 65 to 93 years, conducted over a maximum of 5 years in fixed periods of 6-12
months.
The total patient set was selected by each one of the radiologist, unaware of each others
results. In order to generate ground-truth data sets only cases with complete agreement on
classification among all radiologists were selected. Balanced samples sets for training and
testing the classifier system were obtained. A total of 45 CTA studies belonged to the
"favourable evolution" class and 45 to the "unfavourable" one. All the studies were taken
from the abdominal area with a spatial resolution of 512×512 pixels and 12-bit gray-level at
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G. García, A. Tapia and M. De Blas
172
the WW400 y WL40 window and 5 mm thickness in DICOM format. For each patient a
maximum of three ROIs (15x15 pixels) from aneurysm thrombus were manually extracted
from different slices by radiologists, resulting in a total of 270 ROIs. Half of them
corresponded to the "favourable evolution" group and the rest to the "unfavourable" one.
Segmentation process
In order to facilitate the extraction of samples by radiologists a semi-automated
segmenting process of the aneurysm has been implemented. Based on the series of CTA scans
in DICOM format, images are created with a volume format. During this process the
resolution and the spacing of the original images are preserved. The files obtained are used as
inputs for the following 3D processing pipeline. The spinal canal is segmented to use it as
reference, as it deforms only a little and it is relatively easy to segment. The aneurysm is
segmented using a combination of fast marching method
[23] to delineate the thrombus and
confidence connected components [24] to delineate the stent graft. The two segmentations are
then fused together using geodesic active contours [26] and smoothed using a median
filtering. The segmented volume is resliced along the axial plane facilitating the extraction of
thrombus aneurysm samples by specialists.
2.2. Texture Analysis - Feature Extraction
Gray Level Co-ocurrence Matrix (GLCM)
The gray level co-occurrence matrix (GLCM) [11] is an estimation of a second order
joint conditional probability density function f(i,j
spatial interrelationships of the gray values in an image. The values of the co-occurrence
matrix elements represent the probability of going from grey level i to grey level j given that
45°, 90°, and 135°). For computing
the co-ocurrences accumulated in the GLCM. GLCM features are extracted for 'n×n'
primitive template matrix in the directions 0°, 45°, 90°, and 135°, and in some cases,
averaging is done to make them direction invariant.
In the present application, GLCM features have been calculated at distance 1 due to the
reduced size of the aneurysm samples. Initially, the assumption of an isotropic texture
distribution inside the aneurysm sac was considered, consequently averaging over the four
angular directions was computed. To reduce the influence of random noise on texture
features, the number of gray levels was reduced to 16 prior to the accumulation of the matrix.
From GLCM matrix a set of features are obtained to classify the kind of texture analysed. In
this study, 13 features have been evaluated: Energy, correlation, inertia, entropy, inverse
difference moment, sum average, sum variance, sum entropy, difference average, difference
variance, difference entropy and two information measures of correlation.
The Gray Level Run Length Matrix (GLRLM)
The gray level run length matrix (GLRLM) [12] higher order statistical texture
information. For a given image, a run length matrix is a two-dimensional matrix in which
each element p(i,j
represents the total number of runs with pixels of gray value i and run
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Decision-Support System for Diagnosis of Abdominal Aortic Aneurysm
173
length j in a certain direction . The number of gray levels in the image is usually reduced by
re-quantization before the accumulation of the matrix. Averaging over the four angular
directions was computed and the number of gray levels has been kept in 16, equal than in the
GLCM method in order to make both methods comparable.
Various textures features can then
be extracted from the run length matrix. In our case the following 11 features has been
calculated: 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, and long run high gray-level emphasis.
Gray Level Difference Method
The Gray Level Difference method (GLDM) [13] based on the occurrence of two pixels
which have a given absolute difference in gray level and which are separated by a specific
displacement , estimated by the probability-density function D(i
).
In this analysis, four
possible forms of the vector will be considered: (0, d), (-d , d), (d, 0), and (d , -d) where d is
the intersample spacing distance. Due to the reduced size of the aneurysm samples, d distance
was considered equal to 1. Five textural features are measured from D(i): contrast, angular
second moment, entropy, mean, and inverse difference moment. As with the GLCM method,
the assumption of an isotropic texture distribution inside the aneurysm sac was considered,
consequently averaging over the four angular directions was computed.
2.3. Classification
Two different computer-aided diagnosis strategies (Figure 1) were developed for
comparative purposes. The first one (Figure 1a), consisted of three independent neural
networks [27], each one using as input one of the three texture features vector types. A
multilayer perceptron neural network with 13 input nodes for GLCM method, another with 11
input nodes for GLRLM method, and another one with 5 input nodes for GLDM method were
implemented.
The type of neural network employed was a three-layer backpropagation [28]. The
structure for each neural network consisted of the mentioned number of neurons on the input
layer, one hidden layer, and an output layer with one neuron. A nonlinear sigmoid function
with 0 and 1 saturation values is used as the activation function for each neuron, both the
hidden layer and the output layer. The backpropagation algorithm with adaptive learning rate
and momentum was used for neural networks training. In order to find the appropriate
number of hidden neurons, and the values of learning rate and momentum for each neural
network, a trial-and-error process was applied. Finally, a hidden layer of 20 neurons for the
GLCM and the RLM features and 10 neurons for the GLDM features were found appropriate.
To evaluate the network performances during the training and testing processes, the criterion
was the mean square error given in Eq. (1)
N
i
ii
to
N
MSE
1
2
)(
1
(1)
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G. García, A. Tapia and M. De Blas
174
where o
i
and t
i
are the output value and the target value for the ith input pattern, respectively,
and N is the total number of training patterns. The training process is stopped when the MSE
error no decreases for a significant number of iterations or when it starts increasing.
In the second alternative (Figure 1b), Support Vector Machines (SVM) [29] have been
utilized as texture basic classifiers. Support vector machine classifier is a powerful machine
learning tool which that generalizes well to a wide range of real-world applications [30] [31].
The basic idea of SVM classifier is to determine a separating hyperplane that distinguishes
between two classes. Given a set of labeled training data, the input data are transformed into
high-dimensional feature space with the use of kernel functions, so that the transformed data
becomes more separable compared to the original ones. The SVM attempts to minimize a
bound on the generalization error and therefore it tends to perform well when applied to data
outside the training set. Lots of variety kernels can be employed for mapping input data but
some of the most frequently used kernel functions are Gaussian, Polynomial and Sigmoid
kernels. In this study, the Gaussian radial basis function (RBF) kernel was utilized because of
its good results in many practical applications. Three SVMs with different number of inputs
were implemented. A SVM with 13 inputs for GLCM, another with 11 inputs for GLRLM,
and another one with 5 inputs for GLDM. All the textural features were normalized by the
sample mean and standard deviation of the data set before being fed to the SVM. In our case,
the best classification was obtained with a penalty factor value of +4.7 and a sigma value of
+12.5 for cross correlation protocol.
(a)
(b)
Figure 1. Implemented CAD schemes, (a) Multilayer perceptron neural network, and (b) Support vector
machine scheme.
GLCM
ROI
Selectio n
GLRLM
GLDM
MLP-1
MLP-2
MLP-3
Aneu rysm
Evo lutio n
Aneu rysm
Evo lutio n
Aneu rysm
Evo lutio n
GLCM
ROI
Selection
GLRLM
GLDM
SVM-1
SVM-2
SVM-3
Aneurysm
Evolution
Aneurysm
Evolution
Aneurysm
Evolution
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Decision-Support System for Diagnosis of Abdominal Aortic Aneurysm
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Table 1. Mean and standard deviation values for classification accuracy (in %) for
training and testing sets of the three neural network classifiers
Classifier
Accuracy (%) - training
Accuracy (%) - testing
MLP
GLCM
93.70 0.20
91.53 0.32
GLRLM
80.23 0.35
79.42 0.26
GLDM
90.11 0.16
88.38 0.20
SVM
GLCM
95.70 0.19
93.61 0.15
GLRLM
86.03 0.35
84.05 0.47
GLDM
92.11 0.16
91.41 0.23
3. RESULTS
In order to evaluate the potential of texture analysis based CAD systems to classify
between the two types of aneurysm evolution the development and validation of the neural
network structures has been based on the 10-fold cross validation method. To improve the
evaluation of performance of the feature sets, the 10-fold cross validation was repeated 6
times, averaging the results. The average of total percent of correctly classified cases
(accuracy) for all the trials was used as an estimate of the performance of each classifier.
Table 2 shows the accuracy values (mean standard deviation) estimated by the 10-fold cross
validation of the training and testing sets for each classifier strategy.
From the table 1 it is shown that all texture analysis methods supplied the neural network
and the support vector machine classifiers with enough discriminative information to
differentiate between aneurysm evolutions. The best performing features in terms of correctly
classified cases for the testing sets corresponds to the GLCM method applied to a support
vector machine classifier (93.61 0.15) followed by the GLCM feature set applied to a neural
network (91.53 0.32). The second best performing features set corresponds to the GLDM
texture method applied to a support vector machine classifier (91.41 0.23) followed by the
GLCM feature set applied to a neural network (88.38 0.20). Finally, the GLRLM method
applied to a support vector machine classifier (84.05 0.47) and the GLRLM feature set
applied to a neural network (79.42 0.26).
According to the results in Table 1 GLCM feature set can be considered as the most
discriminative information to differentiate between aneurysm evolutions, independently of the
type of classifier selected. The other two methods, GLDM and GLRLM could also be
considered as significant alternatives. It is worth noting that support vector machine
alternatives perform better than neural network classifiers on their respective texture method.
Regarding accuracy, it is worth noting that support vector machine alternative performs
always better than multilayer perceptron neural network classifier if we compare similar
texture feature sets. Nevertheless GLCM feature set applied to a neural network (91.53
0.32) works better than GLDM texture method applied to a support vector machine classifier
(91.41 0.23) and GLRLM method applied to a support vector machine classifier (84.05
0.47).
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Receiver Operating Characteristics (ROC) curve (Figure 2 and Figure 3) and area under
the ROC curve (A
z
) were also used as a measure of the classification performance.
Table 2 presents the A
z
values (mean standard deviation) calculated for training and
testing feature sets using the 10-fold cross validation for each classifier strategy.
Figure 1. ROC curves for MLP-NN classifier with GLCM (▬▬), GLDM(◊◊◊), and GLRLM(---)
texture methods.
Figure 3. ROC curves for SVM classifier with GLCM (▪▪▪), GLDM(---), and GLRLM(xxx) texture
methods.
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
false positive rate
true positive rate
roc curve
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
false positive rate
true positive rate
roc curve
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