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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5835_Библиотеки_им_академика_М_И_Перельмана.pdf
CHAPTER 7
3-D Breast Ultrasound Strain Imaging
Ruey-Feng Chang, Chii-Jen Chen, Jasjit S. Suri, and Wei-Ren Lai
7.1 Introduction
Breast cancer is a common disease that influences the health of women; in fact, it
ranks second among cancer deaths in women. To reduce the morbidity and mortal-
ity associated with breast cancer, early detection becomes a very important job. If
the cancers could be diagnosed through regular breast cancer examinations at an
earlier stage than is currently possible, the survival rate within 5 years would
increase to about 95% [1].
During the past two decades, the ultrasound technique has become an indis-
pensable imaging modality because it is inexpensive for detection and diagnosis of
breast lesions. The main superiority of ultrasound lies in its noninvasiveness and
ability to efficiently capture the tissue properties [2, 3]. Unlike X-ray mammogra-
phy and MRI, ultrasound does not use ionizing radiation; therefore, it has become a
very adjunct tool for real-time interactive visualization. In the past, conventional
2-D ultrasound has been adapted for both diagnosis and visual-based classification
of lesions [4, 5]. The diagnosis of breast pathology was highly dependent on how
the breast radiologists adapted the use of the transducer in 2-D conventional ultra-
sound. However, this is a very subjective task, because the ability to acquire breast
images is based entirely on the knowledge and experience of breast radiologists.
Mentally transforming the conventional 2-D images into 3-D images would affect
the accuracy of diagnosis. Recently, 3-D ultrasound has emerged as a promising
method for breast imaging [6–10].
With the advancement of ultrasound acquisition and the digital nature of 3-D
volume acquisition, computer-aided diagnosis has become even more powerful,
thereby improving the workflow of breast imaging and reducing the physician’s
workload [11–17]. The most important clinical question today in breast imaging
lies in its ability to discriminate benign versus malignant lesions based on US image
features such as shape, margin, and texture [18–21].
To answer the question about the characterization of malignancy types,
researchers recently proposed strategies that involved the nature of tissue property.
An example is the evaluation of tissue change or deformity at different pressure lev-
165
els. Such an evaluation is important because a hard tissue lesion would behave dif-
ferently from a soft tissue lesion at different pressure conditions. This property and
assumption would yield different shapes or volumes for benign and malignant
lesions. Such analysis would come under the field of elastography, which would help
in tissue characterization. Elastography is nothing but computation of the elastic
modulus of breast tissue [22–27]. In elastography, we compare ultrasound
radio-frequency (RF) waveforms obtainedbefore and aftera slight pressure fromthe
ultrasound probe. During pressure, soft tissues tend to flatten more than hard tis-
sues. The use of this characteristic can be very useful for a CAD system.
This research uses CAD tools to collect lesion features in strain imaging frame-
work, and then adapt a classification strategy based on a support vector machine
(SVM) to distinguish benign versus malignant lesions. The novelty of our architec-
ture lies in an application of strain feature extraction that is based on the level
set strategy. Note clearly that our contributions are aimed at development of a
CAD system that takes advantage of the elastic nature of the breast tissues. Thus,
the integration of our strain feature extractor, in precompression and
postcompression pressure modes followed by a classifier, provides an automatic
system that has clinical utility for breast radiologists. Our strain features are com-
puted after image alignment of precompressed and postcompressed volumes.
Because a change in pressure results in deformation of lesions, our goal is to quan-
tify the deformation in the form of change in the lesion volume, change in the lesion
shape, shift of the breast lesion due to pressure, and finally the contour measure-
ments, all within the level set framework. The main advantage of our approach is
its simplicity in measurement of deformation and its accuracy. This comes from the
ability of the level set to capture the topology of benign and malignant lesions rea-
sonably well.
This research describedin thischapter extended theconventional methodto 3-D
ultrasound imaging. The proposed architecture consisted of tumor shape analysis
before and after the compression. This deformation was quantified using the level
set strategy. Four different strain features were computed: volume difference (V
d
),
shape difference (S
d
), contour difference (C
d
), and shift distance (M
d
). All features
were the bases for classification of breast lesions using support vector machines. The
experimental protocol was tested on a data set of 100 tumors with 60 of them being
benign and 40 malignant.
We demonstrated an accuracy rate for the SVM classifier using 3-D strain fea-
tures of 91% and a corresponding ROC area index A
z
of 0.8855. Our 3-D system
has sensitivity and specificity values of 90% and 92.67%, respectively. We also ran
our protocol on ultrasound images acquired using a 2-D conventional framework
and compared our performance index using the 3-D method. This comparative pro-
tocol showed an improvement of 16% in accuracy, 15% in sensitivity, and 15% in
specificity.
The proposed architecture for the 3-D strain imaging system is introduced in
Section 7.2. The 3-D strain feature extraction system is presented in Section 7.3. The
results and data analysis of our experimental protocol are discussed in Section 7.4,
and some special issues are discussed in Section 7.5. Finally, a conclusion is drawn
and future works discussed in Section 7.6.
166 3-D Breast Ultrasound Strain Imaging
7.2 Review of the 2-D Continuous Strain Imaging System
In our previous work, we proposed a novel elasticity analysis technique that was
based on the level set method [28]. The 2-D US image data were acquired with a
Voluson 530 (Kretz Technik, Austria) scanner. On a Voluson 530 scanner, continu-
ous 2-D images can be recorded at 15 images per second. The compression is com-
pleted in 4 seconds and 60 images are obtained.
In this CAD system, the level set segmentation plays a major role that will influ-
ence the results of elasticity analysis. Because the ultrasonic image suffers from
noise, in order to remove the noise in the ultrasound images and to improve the seg-
mentation performance, the proposed preprocessing procedures of anisotropic dif-
fusion filtering and the stick method are also used. The flowchart for the proposed
CAD system is shown in Figure 7.1.
After the segmentation of tumor contours from the continuous ultrasound
images, four features were defined for labeling of tumors: contour difference (C
d
),
shift distance (M
d
), area difference (A
d
), and solidity (S
o
). The first three are related
to the strain, and the fourth is for shape. The four features are briefly introduced as
follows:
1. Contour difference (C
d
): This value is used to evaluate changes in lesion
shape between two images.
2. Shift distance (M
d
): This value uses the motion estimation technique to find
the motion of two image contours.
3. Area difference (A
d
): This feature was used to compare tumor areas between
two images.
4. Solidity (S
o
): Shape values can be used to distinguish between benign and
malignant tumors. Benign tumors usually have smooth shapes, whereas
malignant tumors are irregular. The normalized solidity value [29] was
used in this study. It is defined as
sol
rea rea
Area rea
=
−
−
=
∑
ΑΑ
Α
CT
CT
i
N
N
ii
1
1
(7.1)
where N is the total number of cases, Area
C
is the area of the convex hull of a tumor,
and Area
T
is the area of a tumor, as shown in Figure 7.2.
7.2 Review of the 2-D Continuous Strain Imaging System 167
Ultrasound
image
Diagnosis
result
Anisotropic
diffusion filter
Stick detection
Preprocessing
Segmentation
Level set
Feature
extraction
Strain image analysis
Figure 7.1 The 2-D breast lesion characterization system.
In the experiments, we used all 100 pathology-proven cases, including 60
benign breast tumors and 40 malignant ones, to evaluate the classification accuracy
of the proposed method. The segmentation results in different lesion cases using the
level set framework are shown in Figures 7.3 and 7.4. The mean values of all fea-
tures (C
d
,M
d
,A
d
, and S
o
) for malignant tumors were 3.52% ± 2.12, 2.62 ± 1.31,
1.08% ± 0.85, and 1.70 ± 1.85, respectively; whereas those for benign tumors were
9.72% ± 4.54, 5.04 ± 2.79, 3.17% ± 2.86, and 0.53 ± 0.63, respectively. All of these
statistical data were evaluated by Student’s t-test, and the p-values (<0.0001) can
prove the validity of these statistical data. The accuracy, sensitivity, specificity, PPV,
and NPV values for this CAD system are 87%, 85%, 88%, 82% and 89%, respec-
tively. These sufficient results proved that all performances of the proposed CAD
system are significant.
7.3 Proposed Architecture of the 3-D Strain Imaging System
In this chapter, the strain imaging system is extended into 3-D ultrasound because
2-D ultrasound can only obtain one cross section of a tumor and the tumor may slip
outside when the compression force is slanted. Three-dimensional ultrasound can
168 3-D Breast Ultrasound Strain Imaging
Tumor Tumor
Contour
Convex hull
(a) (b)
Figure 7.2 Drawings depicting (a) the contour of a tumor and (b) the convex hull of a tumor.
The outer polygon is the convex hull of this tumor. The convex hull is the smallest convex set con-
taining the tumor.
Figure 7.3 The segmentation results for a benign case for different image slices (numbers: 1, 17,
33, and 49) with C
d
= 16.90%, M
d
= 7.161, A
d
= 1.71%, and S
o
= 0.1876.
scan the entire tumor and its probe is wider; therefore, the drawbacks of the 2-D US
process can be avoided.
The 3-D strain imaging architecture is shown in Figure 7.5. In this strain imag-
ing system, the preprocessor is used to remove speckle and enhance the breast
lesions. The main purpose of this enhancement is to help the level set develop strong
stopper capability during the deformation process. Then, the segmentor applies in
segmenting the breast lesions from the ultrasound breast slices. We use the level set
framework in the segmentation process. Note that the segmentor and preprocessor
are cascaded and the outputs of these two processes are segmented breast lesions for
precompressed data and postcompressed data, respectively. The output of the cas-
caded process goes into the feature extraction process, which yields four different
sets of strain features. It is important to understand that it is the combination of
both precompressed and postcompressed data sets that helps in computing the
strain features. The strain features are then fed into the SVM-based classifier, which
helps with the classification of thebreast lesions intobenign andmalignant classes.
7.3.1 Data Acquisition
In this study, the 3-D ultrasound breast images were acquired using a Voluson 730
scanner with a Voluson small part transducer S-VNW5-10. The transducer with a
linear array of crystal elements has the following specifications: 5- to 10-MHz scan
frequency, 4-cm scan width, and 20° to 30° sweep angles. A set of 2-D US images
was obtained by the slow tilt movement of a sector-based mechanical transducer.
After completing the scanning process (on an average of 10 seconds per case), the
7.3 Proposed Architecture of the 3-D Strain Imaging System 169
Figure 7.4 The segmentation results for a malignant case for different image slices (numbers: 1,
17, 33 and 49) with C
d
= 1.20%, M
d
= 1.241, A
d
= 0.85%, and S
o
= 3.6919.
Performance
evaluation
Data acquisition
with strain
Data acquisition
without strain
Classifier
Preprocessor
Segmentor
3-D strain feature
extractor
Contours in
noncompressed
Contours in
compressed
Figure 7.5 3-D breast strain imaging architecture.
3-D US image can be obtained by reconstructing these 2-D US images. This scanning
process of acquiring the successive 2-D US images at regular angular intervals
arranges the set of 2-D image planes in a fanlike geometry.
7.3.2 Image Enhancement Via Sigmoid Mapping
Ultrasound images are dominated by speckle phenomenon or noise. These perturba-
tions may interfere as tissue-related textures during ultrasound image processing
[30, 31]. This interference can affect the lesion boundary estimation processes.
Thus, we need to enhance the boundary lesions and improve the performance of
segmentation [32].
In a breast ultrasound image, different tissues have different intensities, and the
intensity values of malignant and benign tumors mostly tend to be low [33]. For
accurate segmentation of the tumor region, we enhance the tumor boundary by
mapping the intensities based on a sigmoid intensity transform. The sigmoid trans-
form is a nonlinear mapping that maps a specific intensity range into a new intensity
range with a very smooth and continuous transition. This mapping can focus on a
specific set of intensity values and progressively decrease the values outside the cho-
sen range. We will focus on the tumor regions and fade out the other regions. The
following equation represents the sigmoid intensity transformation:
()
~
max
min min
II I
e
I
I
=−
+
⎛
⎝
⎜
⎜
⎞
⎠
⎟
⎟
+
−
−
⎛
⎝
⎜
⎞
⎠
⎟
1
1
β
α
(7.2)
where I and
~
I
are the intensity of the input and output pixels; I
max
and I
min
are the max-
imum and minimum values of the input image; α is the width of the input intensity
range; andβ defines the intensity around which the range is centered. The values of (α,
β) = (10, 25) for the parameters are often adopted in the experiments. However, the
parameters are not the same in each case; they can be slightly adjusted. The results for
different lesion cases subjected to the sigmoid function are shown in Figure 7.6.
7.3.3 Segmentation Subsystem Based on the Level Set Framework
The segmentation technique of the level set is capable of capturing the topology of
shapes in medical images [34, 35]. This technique supports recovery of arbitrarily
complex shapes, such as corners and protrusions, and automatically handles topo-
logical changes such as merging and splitting. Recently, research on large numbers
of implementations for computer vision and medical imaging based on this basic
concept have been published [12, 28, 29, 35–39].
7.3.3.1 Basic Theory of the Level Set
As a starting point and motivation for the level set approach, consider a closed curve
moving in a plane. Let φ(0) be a smooth and closed initial curve in a Euclidean plane
ᑬ
2
, and let φ(t) be the family of curves that is generated by the movement of initial
curve φ(0) along the direction of its normal vector after time t. The main idea of the
170 3-D Breast Ultrasound Strain Imaging
level set approach is to represent the front φ(t) as the level set {Γ=0} of a function Γ.
Thus, given a moving closed hypersurface φ(t), that is, φ(t = 0): [0, ∞) → ᑬ
N
,we
hope to produce a formulation for the motion of a hypersurface propagating along
its normal direction with speed F, where F can be a function of various arguments,
such as curvature or normal direction. The level set methodology and curve evolu-
tion are shown in Figure 7.7. Arrows in Figure 7.7(a) show the propagating direc-
tion of the front. During a period of time t
1
, the front φ(t = t
1
) and hypersurface Γ(x,
y, t = t
1
) are shown in Figure 7.7(b).
In this way, the actual object contour can be obtained using the level set
method. The details of the level set segmentation methodology are described next.
Let Γ(x, t = 0), where x is a point in ᑬ
N
, be defined by Γ(x, t = 0) =±d, where d is
the signed distance from x to φ(t = 0), and a plus (minus) sign is chosen if the point x
is outside (inside) the initial front. The goal is to produce an equation for the evolv-
ing function Γ(x, t), which contains the embedded motion of φ(t) as the level set
{Γ=0}. Let x(t), for t ∈ [0, ∞) be the path of a point on the propagating front; that is,
x(t = 0) is a point on the initial front φ(t = 0). Because the evolving function Γ is
always 0 on the propagating front φ(t), then
()
()
Γ x tt, = 0
(7.3)
By the chain rule [35],
ΓΓ
tx
i
i
N
i
t
x+=
=
∑
0
1
(7.4)
7.3 Proposed Architecture of the 3-D Strain Imaging System 171
(b)
(a)
Figure 7.6 Results from sigmoid filtering on (a) benign and (b) malignant cases. The images in
the left panel are raw images; the images on the right are enhanced by sigmoid mapping with
parameters α = 10, β = 25.
where
x
i
t
is the ith component of x at time t, and N is the number of components.
Since
(
)
()
Γ ΓΓΓ Γ
x
i
i
N
xxx x
N
i
t
N
t
tt t
x xxx x
F
=
∑
=⋅
=
1
123
123
,,,, , , ,,KK
x
()
()
t ∇Γ
(7.5)
where F(x(t)) is the speed function with the front for all components of x at time t.
Combining (7.4) and (7.5), the final curve evolution equation is given as:
()
∂
∂
κ
Γ
Γ
t
F=∇
(7.6)
where Γ is the level set function and F(κ) is the speed with which the front (or
zero-level curve) propagates. This fundamental equation describes the time evolu-
tion of the level set function Γ in such a way that the zero-level curve of this evolving
function is always identified with the propagating interface [38]. The preceding
equation is also called a Eulerian Hamilton-Jacobi equation [34, 38]. Equation (7.6)
for 2-D and 3-D cases can be generalized as follows:
()
()
∂
∂
∂
∂
κ
κ
Γ
Γ
Γ
Γ
t
Fxy
t
Fxyz
=∇
=∇
,
,,
and
(7.7)
where (x, y) and (x, y, z) are the points on the propagation directions, and F (x, y)
and F
(x, y, z) are curvature-dependent speed functions in two and three dimensions,
respectively [38].
The level set can be used for image segmentation by using image-based features
such as mean intensity, gradient, and edges in the governing differential equation. In
172 3-D Breast Ultrasound Strain Imaging
tt=
1
t 0=
X
Y
Γ<(, ,0) 0xy
Γ=(, , ) 0xyt
1
Zt=
(a) (b)
Figure 7.7 The level set methodology and curve evolution. (a) Arrows show the propagating
direction of the front. (b) The level set hypersurface.
a typical approach, a contour is initialized by a user and then evolved until it fits the
topology of an anatomic structure in the image [35]. There are challenges to using
the level set framework by itself on the raw images. This is because the propagation
of the fronts can sometimes lead to bleeding of the fronts due to noise structures
present in the images [40]. One way to handle this is by enhancing the structures to
be segmented. This effect will assist the level set front propagations to recognize the
edges of the structures, thereby bringing the speed close to zero at the structure
edges. Keeping this in mind, we have developed our breast lesion identification
chain. The segmentation examples with and without the preprocessing block by
using the level set method are illustrated in Figure 7.8. These conspicuous results
also prove the performance of our proposed CAD architecture.
7.3.3.2 3-D Strain Imaging and Segmentation
In this system, 3-D ultrasound is used to generate two datasets with and without the
pressure of a probe to provide additional information for tissue characterization of
stiffness. The aim is to differentiate between different tissues based on their elastic-
ity. For each tumor, a normal 3-D ultrasound image dataset is first obtained, and
then the physician applies slight pressure to the probe to obtain the second 3-D
ultrasound strain image dataset. Then, the level set method is used to extract
the contour of each dataset, and the contours of the precompressed and
postcompressed ultrasound images are aligned first and compared by using the pro-
posed strain features. In Figures 7.9 and 7.10, we illustrate the segmentation results
of different lesion cases for precompressed and postcompressed ultrasound images
using the proposed level set framework. According to previous examples, the shape
7.3 Proposed Architecture of the 3-D Strain Imaging System 173
(b)
(a)
Figure 7.8 The segmentation results on (a) benign and (b) malignant cases with and without the
preprocessing block of the 3-D strain imaging architecture. In each case, the first image is the raw
image; the middle column is obtained by running the level set without the sigmoid enhancer (pre
-
processing block), and the last column shows the results using the entire architecture.