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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5835_Библиотеки_им_академика_М_И_Перельмана.pdf
•
CT: The second characteristic is related to the region circularity. Because
benign lesions generally have more circular areas compared with malignant
ones, this can also be a good discriminant. Then, we take the ROI’s geometric
center (GC) point and compute the distance from each boundary point (x
i
, y
i
)
to the GC. Malignant lesions tend to have high standard deviations of the
average distances in relation to the benign ones. Also, this feature is normal-
ized by total image area.
•
PT: The third feature is the size distribution of the lobes in a lesion. A bound-
ary’s lobe is a protuberant region on the boundary. We compute the convex
hull of the ROI and the lobe as a protuberance between two valleys. The lobe
areas are computed and only those that are greater than 10% of the lesion area
are considered. This feature is taken as the average area of the lobes. Malig-
nant lesions have high average area in relation to benign ones.
•
HT: The next feature is related to the homogeneity of the lesion. Malignant
lesions tend to be less homogeneous than benign ones. Then, we take the BGS
entropy—taken over the gray-scale histogram—relative to the maximum
entropy as the fourth discriminant feature. The higher the relative entropy, the
more homogeneous the lesion region, and, consequently, the higher the chance
that the lesion is benign.
•
AS: The last feature is related to an interesting characteristic of the lesions: the
acoustic shadow. When benign lesions have many water particles, the forma-
tion of an acoustic reinforcement below it is probable. However, when the
lesion is more solid (a malignant characteristic), there is a tendency to form an
acoustic shadow. Then, by comparing the lesion region and the region below
the lesion, we can get an idea of whether the lesion is a benign or malignant
one. If the region has an acoustic shadow, it tends to be more white than if it
has an acoustic reinforcement. Then, we compute the gray-scale histograms of
both regions and compare them. The darker the region below the lesion, the
greater the acoustic reinforcement and, consequently, the higher the probabil-
ity that the lesion is benign. We have computed the relative darkness between
both areas (tumor’s area and area below the tumor) and use it as the fifth
lesion feature.
These five features are composed into a five-dimensional feature vector. The
space of all feature vectors is then finally input for a nonlinear SVM in order to clas-
sify the lesion area as being a malignant or benign region. SVM is very good
discriminant tool, primarily when we do not have a linear separation between the
data. Because this feature space does not have a linear separation, we have used a
B-spline curve asa kernel. Because some characteristics are better discriminants than
others, we have also combined them into the SVM framework. To justify the use of a
B-spline as a kernel for the SVM in this nonlinear space, we compare in Figure 8.7
the performance of our proposal under three different kernels: B-spline, polynomial,
and exponential. In this figure, the area under the ROC curve (92% of the total) is
clearly superior compared to the polynomial (area 84%) and exponential (area
65%) kernels. Also,Figure 8.8 shows how a polynomial kernel separates our feature
space for two features only: AR and HT.
214 Automatic Classification of Breast Lesions in 3-D Ultrasound Images
8.4 The Proposed Five-Step Methodology 215
Polynominal: Az = 84%
BSpline: AZ = 92%
0
0
0.1
0.1
0.2
0.2
0.3
0.3
0.4
0.4
0.5
0.5
0.6
0.6
0.7
0.7
0.8
0.8
0.9
0.9
1
1
FPF
ROC curve
TPF
Exponential: Az = 65%
Figure 8.7 ROC curves for the proposed CAD system for three different kernel functions used in
the SVM classifier.
Benign lesions
Polynomial kernel
Malignant lesions
Figure 8.8 SVM output with a polynomial kernel. The white line is the polynome separating
malignant tumors (gray circles) from benign ones (black circles). The circles with the white bound
-
aries are those inside the confident region.
In contrast, when we use a B-spline kernel instead of polynomial, we have the
aspect shown in Figure 8.9. In the case of B-spline kernel, note that in Figure 8.9 a
better separation of the feature space is seen compared with the polynomial kernel
shown in Figure 8.8.
Figure 8.10 presentsthree different examples of the proposed method for benign
lesions. Figure 8.10(a) is the original image; Figure 8.10(d) is the result after NESRA
application; Figure 8.10(g) is the result after background extraction with a morpho-
logical chain; and Figure 8.10(j) is the final result after level set application.
Figure 8.11 presents three different examples of the proposed method for malig-
nant lesions. Figure 8.11(a) is the original image; Figure 8.11(d) is the results after
NESRA application; Figure 8.11(g) is the result after background extraction with a
morphological chain; and Figure 8.11(j) is the final result after level set application.
Note in Figure 8.11(g) that the morphological step did not adequately extract the
tumor from the background; however, the final step with level set application did
converge to the desired boundary.
In Figure 8.12 we show five ROC curves for five different combinations of the
defined tumor’s features. According to these curves, the best performance is reached
when the area (AR) + heterogeneity (HT) and acoustic shadow (AS) features are
combined. This combination generated an A
z
of 92%. All other combinations have
resulted in similar performance behavior and A
z
inferior to AR = HT + AS combina-
tion. A better discussion of these results is given in the next section.
216 Automatic Classification of Breast Lesions in 3-D Ultrasound Images
Benign lesions
B-spline kernel
Malignant lesions
Figure 8.9 SVM output with a B-spline kernel. The white line is the polynome separating malig
-
nant tumors (gray circles) from benign ones (black circles). The circles with the white boundaries
are those inside the confident region.
8.5 Performance Evaluation
To test our proposed method we used 50 pathology-proven cases—20 benign and
30 malignant. Each case is a sequence of five images of the same lesion. Then, we
tested 100 images of benign lesions and 150 of malignant ones. Because the detec-
tion of a malignant lesion between five images of the same case indicates a malig-
nant case, it is reasonable to consider 250 different cases.
Because our database is small, we have improved the results through a
cross-validation method. Then, these ultrasonic images are randomly divided into
five groups. We first set the first group as a testing group and use the remaining four
groups to train the SVM. After training, the SVM is then tested on the first group.
Then, we set the second group as a testing group and the remaining four groups are
trained and then the SVM is tested on the second. This process is repeated until all
five groups have been set in turn as the testing group.
To estimate the performance of the experimental result, five objective indices
are used: accuracy, sensitivity, specificity, PPV, and NPV. In our experiment, the
accuracy of the SVM with B-spline kernel for classification malignancies is 95.2%
8.5 Performance Evaluation 217
(a) (b) (c)
(d) (e) (f)
(g) (h) (i)
(j) (k) (l)
Figure 8.10 (a–l) The first three steps of the proposed methodology. Each column represents a
different example of a benign lesion.
(238/250), the sensitivity is 97% (97/100), the specificity is 94% (141/150), the PPV
is 91.51% (97/106), and the NPV is 97.92% (141/144).
2
Figure 8.12 shows the SVM classification output for our methodology when we
consider two dimensions only.
8.6 Discussion and Conclusions
Geometrical and textured information from the lesion area in ultrasound images
provide important discriminants for CAD systems. Because ultrasound images gen-
erally have complex characteristics between pixels, it is interesting to study them
from the point of view of nonextensive entropy. Because the traditional Shannon
entropy provides image segmentation between foreground and background only, it
does not guarantee that transition areas (which normally occur on the boundary of
218 Automatic Classification of Breast Lesions in 3-D Ultrasound Images
(a) (b) (c)
(d) (e) (f)
(g) (h) (i)
(j) (k) (l)
Figure 8.11 (a–l) The first three steps of the proposed methodology. Each column represents a
different example of a malignant lesion.
2. TP = true positive; TN = true negative; FP = false positive; FN = false negative; Accuracy = (TP+TN)/
(TP+TN+FP+FN); Sensitivity = TP/(TP+FN); specificity = TN/(TN+FP); positive predictive value = TP/
(TP+FP); and negative predictive value = TN/(TN+FN).
such lesions) will be adequately analyzed. So our proposed algorithm is a good
option to initial segmentation. This algorithm, NESRA, includes the same results as
the traditional entropy calculations, but with the improvement that it is able to iso-
late intermediate regions, such as the lesion’s boundary transitions. This is one of
the advantages of NESRA when applied on this kind of image. A CAD system can
decide if the transition area will be included or not in the ROI based on a careful
analysis.
On the other hand, the use a level set framework helps to find the correct
boundary position without increasing the computational time, but with an
improvement in system precision.
Regarding the chosen features, this chapter does not discuss why some feature
combinations, such as AR + HT + AS, generate better CAD system performance
than others. We chose to test empirically which combination is better, which can be
seen in the graphic of Figure 8.12.
Our work proposes an SVM diagnostic system that uses as input five-dimen-
sional feature space characteristics. These characteristics are based on geometrical
and textured information and should be combined in order to tune the system. In
our experiments, the best combination was achieved when only area, heterogeneity,
and shadow were used. All of these features are affected by the segmentation pro-
cess. For example, if the region of a benign lesion will not be adequately extracted
from the background, it can be present as an area (AR), which is greater than the
reality, yielding the inference that it is a malignant case. The same may occur with
the circularity (CT) or protuberance (PT) features. In all cases, the use of a level set
framework helps to revert a bad segmentation situation. For example, suppose that
a lesion was erroneously segmented to include parts of background in the lesion
8.6 Discussion and Conclusions 219
Feature nomenclature
AR = Area
CT = Circularity
HT = Heterogeneity
PT = Protuberance
AS = Acoustic shadow
AR AS: Az = 83%
AR PT AS: Az = 84%
AR CT HT PT AS: Az = 88%
AR HT PT AS: Az = 81%
AR HT AS: Az = 92%
ROC curve
0
0
0.1
0.1
0.2
0.2
0.3
0.3
0.4
0.4
0.5
0.5
0.6
0.6
0.7
0.7
0.8
0.8
0.9
0.9
1
1
FPF
TPF
Figure 8.12 ROC curves for the proposed CAD system for several combinations of tumor
features.
region. In this case, because the segmented region is near the boundary, the level set
approach has great chances to stop over the real boundary.
Another aspect (not addressed in this chapter) is the use of a dual approach for
the level set framework, such as in Giraldi et al. [56, 57]. In this case, two embedded
curves evolve toward the lesion boundary, one from inside and the other outside the
lesion region.
Finally, the use of SVM with a B-spline kernel is one of the main contributions of
our paper and one of the main features responsible for the system’s performance (up
to 92%), because this kind of curve allows more flexibility over our feature space, as
seen in Figure 8.9. The use of a B-spline clearly improves the classification, as seen in
Figure 8.7.
Actually, the greater challenge of the CAD systems is the delimitation of an ini-
tial region, which is normally rectangular. This delimitation is manually accom-
plished by radiologists. A possible future direction is to investigate tracking
approaches. For example, an ultrasound analysis will be done over a frame
sequence. In the first frame, several candidate regions can be automatically
extracted, and then, a tracking system is accomplished over the remainder of the
frames, where each candidate region is separately analyzed.
Acknowledgments
The authors are grateful to Coordenaçao de Aperfeiçoamento de Pessoal de Nível
Superior, Conselho Nacional de Desenvolvimento Científico e Tecnològico, and
FAPERJ, Brazilian agencies for scientific financing, for the support of this work.
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