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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5835_Библиотеки_им_академика_М_И_Перельмана.pdf
The exploitation of these simple observations led to the first segmentation algo-
rithms proposed in the literature. These techniques, which will be fully analyzed in
next section, were mainly devoted to computer-assisted measurement of the IMT. In
fact, during a clinical examination, the operator usually measures the IMT directly
on the console of the ultrasound device by freezing a B-mode image and placing two
markers (Figure 5.2): the first one corresponding to the lumen–intima interface, and
the second corresponding to the media–adventitia interface. The device automati-
cally measures the absolute value of the geometric distance of the two markers, pro-
viding the IMT measurement (given in millimeters or inches).
The dependence on the operator of such a measure is clear; previous studies
reported high interoperator and intraoperator variability when measuring IMT. In
1991 a study by Wendelhag and colleagues [9] reported a coefficient of variation for
intraobserver and inter observer variability in the measurement of the mean IMT of
the common carotid artery of approximately 10%. Hence, the possibility of both
reducing the time employed by the operator in determining the IMT through
repeated measurements on the wall and augmenting the reproducibility of the mea-
sured values through image processing algorithms appears to be an attractive
solution.
5.1.2.2 Ultrasound Analysis of Plaques
The basis of many currently used classification schemes for carotid plaques analyzed
by means of ultrasonography is the Gray-Weale scale [10]. According to Gray-
Weale, plaques are subdivided into four classes:
•
Type I plaques are predominantly echolucent, that is, hypoechoic, with a thin
echogenic (fibrous) cap.
114 User-Independent Plaque Segmentation
CCA
JV
Figure 5.2 B-mode ultrasound image of a CCA tract (normal subject). To estimate the IMT, the
operator, via the ultrasound device console, uses the markers placed on the near and far wall. It is
evident that such a measure is punctual and, moreover, user dependent.
•
Type II plaques are substantially echolucent, with small areas of echolucency
(less than 25%).
•
Type III plaques are predominantly echogenic with small areas of echolucency
(less than 25%).
•
Type IV plaques are uniformly echogenic.
Pathological correlation of plaques evaluated following this scheme reveals an
association between intraplaque hemorrhage and/or ulceration with types I and II
and fibrous tissue with types III and IV.
In the Tromso study, plaques were graded on the basis of their echogenicity on a
1 to 4 scale, with 1 signifying echolucent plaques. Plaques were also classified as
heterogeneous if more than 20% of the plaque area corresponded to an
echogenicity of at least two grades different from the rest of the plaque.
Interobserver and intraobserver rates were good on plaque echodensity (k = 0.80
and k = 0.79, respectively), less so on plaque heterogeneity (k = 0.71 and k = 0.54).
Hence, a major problem of these analyses is the need for quantitative measures [11].
In fact, computer-based image processing is expected to further improve the robust-
ness of classification, but introduces new issues related to instrumentation-related
variability, due to the type of equipment and acquisition parameters such as probe
frequency and time gain compensation, which may alter the gray-level distribution
on which the classification is based.
Ultrasound plaque segmentation may be used to:
•
Measure plaque dimensions with respect to the vessel diameter. Even though
this such measure is not accurate enough to calculate the degree of stenosis
and even though plaque dimension itself is insufficient to establish the need
for surgical treatment, nevertheless plaque dimension is an important clinical
parameter to assess the carotid wall degeneration.
•
Perform a gray-scale classification of the plaque. Several approaches have
been proposed for the computer-based measurement of texture features, such
as the gray-level dependence matrices, the gray-level difference statistics, and
first-order statistics [11, 12].
•
Measure border irregularities. Aside from echolucency and heterogeneity,
other ultrasonographic characteristics have been identified as typical of rup-
ture-prone plaques [13]. For instance, irregular borders, which might be
histologically related to ulceration, have been suggested as a possible criterion
for establishing increased cardiovascular risk associated with the imaged
plaque.
Hence, the ultrasoundanalysis ofthe CCA wall is of great importance in clinical
practice, because it represents a low-cost, fast, reliable and quite repeatable tool for
assessing a subject’s cardiovascular risk. Objective and quantitative measurements
are needed to support diagnosis.
5.1 Ultrasound Imaging of the Carotid Wall 115
5.2 Algorithms for the Segmentation of Carotid Artery Ultrasound
Images
The problem of carotid artery image segmentation, as will be more extensively clari-
fied in the following, is composed of two conceptually different steps. In the first
step, identification of the region of interest (ROI) is performed; that is, the region of
the image corresponding to the carotid artery, the object of the segmentation, is
identified. In the second step, the intimal and medial layers of the carotid artery wall
are distinguished from the carotid lumen, from pixels belonging to the adventitial
wall, and from pixels belonging to surrounding tissues.
5.2.1 Different Approaches for ROI Extraction
This first step is of paramountimportance because it defines the portionof the image
in which the carotid wall is contained. The extraction of a proper ROI: (1) enables
the selection of parameters for the segmentation phase and (2) facilitates the seg-
mentation procedure, avoiding further misclassification of pixels with characteris-
tics similar to those of the vessel wall, but belonging to other structures.
In previously proposed algorithms, this step is almost always performed with a
certain level of user interaction.
In the approach proposed by Liguori et al. [14], the user had to draw a rectangle
on the image in which the carotid wall was contained. The authors developed a
graphical user interface that could also be used for some preprocessing of the image
(i.e., smoothing, gain calibration, data storage). Similarly, a ROI containing the
carotid wall had to be selected by the human operator in the algorithm by Gutierrez
et al. [15].
Cheng et al. [16] proposed an automated algorithm for the intimal and
adventitial layers detection of the CCA based on snakes. In their solution, segmenta-
tion was performed by means of an active contour that required initialization.
Hence, the user had to provide the algorithm with the coordinates of two points
located just above the intima and in the carotid lumen. This initialization also
restricted the portion of the image to be segmented and allowed for the avoiding of
other confounding borders that could attract the snake (i.e., the jugular vein). A sim-
ilar initialization is required for the gradient-based segmentation method described
by Touboul et al. [17]. Regarding Cheng’s technique, note that it is capable of seg-
menting only the distal border of the CCA.
All previous methods have the major drawback of requiring a certain degree of
user interaction. In fact, once the starting points or rectangles are selected, segmen-
tation will be performed only within those boundaries. This may be a major limita-
tion in the presence of plaque if the ROI is not accurately selected, since a portion of
the lesion may be ignored by the segmentation procedure. Moreover, consistency of
results in function of the selected ROI is questionable: As an example, the selection
of different starting points for the algorithm in [16] would lead to different profiles
of the intimal and adventitial layers.
The major advantage of human-based ROI selection is insensitivity to superim-
posed noise. One of the major problems when segmenting B-mode ultrasound vas
-
cular images is the presence of noise, usually given by blood backscattering. An
116 User-Independent Plaque Segmentation
example is illustrated in Figure 5.3. International guidelines and clinical practice
indicate that the gain of the echographic channels should be reduced as much as
possible to avoid excessive noise. However, in some patients, lowering the gain
makes the carotid wall tunicae less visible. When the operator delimits the ROI, the
problem of noise is lessened, because the expert operator always draws the ROI in
the portion of the image where he can more easily distinguish the carotid wall; we
could say that human ROI selection usually corresponds to the portion of the
ultrasound image where the SNR is higher.
We developed a completely user-independent algorithm for layer extraction of
the CCA wall from ultrasound images. Hence, our technique allows for the auto-
matic detection of the ROI. Section 5.3 describes the automatic procedure for ROI
detection as well as the overall structure of the algorithm. Sections 5.4 to 5.7 show
the performances on a large image database.
A major advantage of our approach is that its results are completely independ-
ent of the user. This ensures that:
•
Results are consistent since there is no way to have different starting condi-
tions on the same image;
•
Segmentation is performed throughout the entire length of the image with no
restrictions;
•
The procedure is suitable for the automatic processing of large database sets,
since there is no need for human interaction with the computer program.
5.2.2 Approaches to Image Segmentation
Once the ROI has been determined in the first step, the pixels belonging to the
intima-media layer must be identified. Because the pixels belonging to the intimal
and medial layers are separated from pixels belonging to the lumen and the
5.2 Algorithms for the Segmentation of Carotid Artery Ultrasound Images 117
CCA
Figure 5.3 B-mode ultrasound image of a CCA tract (normal subject). During this examination,
excessive gain of the echographic channels determined blood backscattering. The CCA lumen is
not black and homogeneous, but it is textured. The SNR of such an image is poor and its segmen
-
tation may be problematic.
adventitia by a relatively simple interface (especially in normal cases), boundary
detection techniques (as opposed to other classification techniques such as cluster-
ing) have been the methods of choice for performing the intima–lumen
segmentation.
Various techniques have been proposed in the literature for this purpose. The
simpler approach is the gradient-based technique [14, 18], which is sometimes com-
bined with a model-based technique [18]. Carotid segmentation had also been per-
formed by means of active contours [16] or by means of dynamic programming [18,
19].
Some of these proposedalgorithms achieved quite good results. Cheng et al. [16]
reported an average segmentation error lower than a pixel on a database of 32
images. The core of their algorithm is a snake driven by the Macleod operator as an
external force. An interesting feature of this approach is that geometric and topolog-
ical considerations about carotid morphology are taken into account to improve
segmentation: A gradient filter is used to enhance the edge having a black region
above and a white region below it (i.e., the adventitia layer). This simple consider-
ation exploits geometrical characteristics into pixel fuzziness that is useful for
segmentation.
Finally, Gutierrez et al. [15] used a deformable model proposed by Lobregt and
Viergever [20]. Their technique is based on a geometrical deformable model made of
points that can shift toward the image discontinuities under the effect of external
dynamic forces. This implementation is very close to a snake, with the advantage
that its formulation of the internal and external forces is simpler. This technique
proved effective in segmenting the carotid artery wall, despite a sensible
overestimation of the IMT.
Among all of the described algorithms, a thorough characterization of the per-
formances of the techniques in terms of plaques is missing. In the following, we will
show that our technique proves effective in estimating IMT and in segmenting
fibrous and type II echolucent plaques.
5.3 Structure of the CULEX Algorithm
We propose a technique for the completely user-independent layer extraction
(CULEX) of the CCA wall from ultrasound images. As discussed earlier, our algo-
rithm consists of two distinct parts: (1) automatic ROI extraction, and (2) segmenta-
tion of the pixels in the ROI by means of a fuzzy C-means approach.
Figure 5.4 shows a block diagram of the CULEX structure. The meaning of the
blocks is fully described in the following sections. Conceptually, the algorithm first
automatically determines the region in which the carotid walls are located, then per-
forms a fuzzy classification of the pixels belonging to the ROI. The output of the
fuzzy classifier is then postprocessed in order to extract the layers contour.
5.3.1 ROI Extraction
ROI extraction is an iterative algorithm made of six steps. To correctly identify this
region, morphological considerations are crucial: The basis of our ROI identifica
-
118 User-Independent Plaque Segmentation
tion procedure is the observation that the vessel lumen is characterized by pixels
with low intensity and relatively low variance (low ultrasound echoes of the blood
cells and relative homogeneity of the medium), surrounded by high-intensity pixels
belonging to the carotid walls. Hence, the basic idea is to search for high-intensity
pixels possibly belonging to the distal adventitial wall, starting from the deepest
imaged layers of the scan. Then, considering the image column-wise and proceeding
from the bottom of the image to the top, the lumen vessel and the near adventitial
wall are extracted.
To perform this identification, we compute a bidimensional histogram of the
image. For each pixel in the image we consider a 10 × 10 square neighborhood and
then derive two measures: the mean intensity and the variance of the group of pix-
els. All of the mean values and variances of the neighborhoods are grouped into
classes in a bidimensional histogram. It can be observed that the bidimensional dis-
tribution of these two variables for the pixels belonging to the lumen is condensed in
the lowest classes. Figure 5.5 (right panel) reports an example of the distribution of
the neighborhood mean and variance for the CCA image (left panel) of a healthy
subject; the black pixels represent lumen pixels, whereas the gray pixels are all the
other pixels in the image. Similar results are reported in [21].
The histogram allowed us to differentiate between lumen pixels and pixels belong-
ing to the tissues.The extractionof the adventitial layers was performed considering
the intensity profile of each column. Specifically, the peak intensities that can be
observed in the intensity profile of each column derive from the adventitia layers,
which are the most echogenic surfaces in the image. Figure 5.6 shows an example of
5.3 Structure of the CULEX Algorithm 119
2D
histogram
Lowpass
filtering
MA
extraction
FW
MA
extraction
NW
Lumen
extraction
Wall
class
Post-
processing
Fuzzy classifier
Lumen
class
LI
NW
LI
FW
ROI extraction
Image
Figure 5.4 Schematic block diagram of the CULEX algorithm. The algorithm is made up of two
distinct parts. The ROI extraction phase (dashed rectangle) deals with local and punctual measures
on the input image; its outputs are the indication of the CCA lumen, the profile of the far wall
media–adventitia (MA
FW
) and the profile of the near wall media–adventitia (MA
NW
). The second
phase consists of a fuzzy classifier that separates lumen pixels from wall pixels and produces the
final contours of the far and near wall lumen–intima interfaces (LI
FW
and LI
NW
, respectively).
an intensity profile for a CCA image: The deepest peak corresponds to the far
adventitial wall, the carotid lumen is characterized by a minimum value of intensity,
and the near adventitial layer corresponds to the peak intensity closer to skin sur-
face. The combination of punctual measures (i.e., depth and intensity of a pixel)
with local measures (i.e., average intensity of the neighborhood and standard devia-
tion) allowed for an effective determination of the ROI.
In detail, the fundamental steps of ROI identification are as follows:
120 User-Independent Plaque Segmentation
CCA
0
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
Standard deviation of pixel
neighborhood intensity
0.2 0.4 0.6 0.8
1
Mean pixel neighborhood intensity
Figure 5.5 Distribution of mean and standard deviation (right panel) of a 10 × 10 square neigh-
borhood for the pixels belonging to a B-mode ultrasound image of a normal CCA (left panel). The
black pixels represent the points in the vessel lumen, whereas the gray pixels are all the other pix-
els in the image. The lumen pixels are characterized by low values for both the neighborhood
mean and standard deviation. This neat discrimination of the pixels located in the lumen of the
CCA is used by the algorithm for automatic ROI selection (i.e., to ensure the vessel lumen has been
correctly located). (From: [8]. © 2007 IEEE. Reprinted with permission.)
Adventitia tunica
Carotid
lumen
Intensity (a.u.)
Depth (i.e., column index)
0
1
Figure 5.6 Intensity profile relative to a column of a B-mode CCA image. The abscissa represents
depth (i.e., column index of the image) increasing from left to right; the vertical axis represents
pixel brightness. The deepest peak in the intensity profile is the adventitia tunica; the previous
minimum is the vessel lumen. This intensity profile is used to automatically determine the ROI
(both the adventitia border, then the carotid lumen). (From: [8]. © 2007 IEEE. Reprinted with per-
mission.)
1. Lowpass filtering of the image (Gaussian filter of the 50th order, STD equal
to 10) in order to increase the SNR.
2. Determination of the far adventitial wall from the intensity profile of the
image considered column-wise. Specifically, for each column of the image,
we identify the pixels possibly belonging to the adventitia as those local
maxima whose intensity is in the 90th percentile of the intensity distribution
(see Figure 5.6 as an example).
3. Individuation of the carotid lumen as the minimum point on the intensity
profile whose mean intensity and variance of the neighborhood belong to
the lowest classes of the bidimensional histogram. The minimum is searched
descending the intensity profile from the maxima found in step 2. When the
minimum is reached, the corresponding point is taken as a marker of the
carotid lumen and the starting maximum as a marker of the adventitia.
4. Identification of the successive maximum as the adventitia layer of the near
wall is accomplished.
5. Iteration of steps 2, 3, and 4 is done for all columns of the image.
6. The near wall media–adventitia interface is determined by step 4. To extract
the far wall media–adventitia interface, a gradient-based contour extraction
is performed and successively refined by applying a snake.
We adopted a simple snake to adjust border detection. Specifically, considering
the curvilinear representation of a parametric model:
() () ()
[]
[]
svs x s y s
s
:,
,
=
∈ 01
the energy functionalof the snake, which tendsto modify the curvilinear representa-
tion in the bidimensional plane, at a given profile may be expressed as follows:
() ()
()
EvsEvsds
ext
=′+
∫
1
2
2
0
1
αγ
(5.1)
Details, implementations, and extensions of this algorithm can be found in [21].
We used a value of
equal to 0.1 and a value of equal to 0.01.
As an example, Figure 5.7 represents an ultrasound CCA scan with ROI over-
lapped: The white continuous line labeled ROI
L
represents the vessel lumen, the
white continuous line at the bottom of the image corresponds to the far wall
media–adventitia layer (MA
FW
), and the white continuous line at the top of the
image is relative to the near wall media–adventitia layer (MA
NW
).
The ROI extends from the near wall media–adventitia layer (MA
NW
) to the far
wall media–adventitia layer (MA
FW
). The carotid lumen is taken as a check of the
feasibility of the segmentation: In some cases ROI extraction fails (a detailed
description of the robustness of the CULEX algorithm is provided in Section 2.5)
because lumen cannot be correctly identified. The columns in which one or more of
the three components of the ROI is missing are excluded by the segmentation pro
-
cess. This choice allowed us to develop an algorithm that is less susceptible to spa
-
5.3 Structure of the CULEX Algorithm 121
tial variations of SNR, because if noise corrupts a portion of the image, making ROI
detection impossible, the remaining part can still be correctly processed.
5.3.2 Segmentation of the Lumen and Carotid Wall
As previously discussed, we chose to perform image segmentation by means of a
fuzzy c-means approach. Only the pixels comprised between the near and far
media–adventitia walls are classified. We used the pixel intensity as feature and
allowed two classes: one relative to the carotid wall and the other to the lumen. The
class memberships were in the range of [0,1], with 0 meaning no membership and 1
meaning full membership in the specific cluster. Decisions were made using a thresh-
old value equal to 0.1: Pixels with a wall-class membership of greater than 0.1 are
considered to belong to the wall.
At this stage, two final postprocessing steps are required in order to come to the
final segmentation:
1. Successive dilation and erosion operations (disk structure element) on the
binary image obtained as output of the (defuzzified) clustering process and
filling of the holes in the regions belonging both to the lumen and the walls
[23];
2.
Contour refinement by means of a snake [as in (5.1) with
= 0.1 and =
0.01]. Near and far wall media–adventitia interfaces (indicated by MA
NW
and
MA
FW
, respectively) and near and far wall lumen–intima interfaces (indicated
by LI
NW
and LI
FW
, respectively) are available.
Figure 5.8 shows an example of the resulting automatic segmentation per-
formed by CULEX on four CCA ultrasound images of healthy individuals.
122 User-Independent Plaque Segmentation
CCA
MA
MA
ROI
NW
FW
L
JV
Figure 5.7 Example of ROI extraction performed by CULEX on a CCA image of a healthy subject.
The ROI is determined by the pixels comprised between the near wall media–adventitia interface
(MA
NW
) and the far wall media–adventitia interface (MA
FW
). The white line in the center of the
image is the carotid lumen (ROI
L
) that we use as a check.
The images in Figure 5.8 are different in terms of superimposed noise, spatial
resolution, and presence of signs, but CULEX segmentation is still correct. This
technique also proved effective on recorded images that had been previously pro-
cessed by an operator who added markers, written parts, measures, and so on.
Figure 5.9 represents a zoomed vision of the CCA of a healthy subject. This fig-
ure allows us to evaluate the quality of CULEX segmentation.
5.3.3 Notes About the CULEX Segmentation Strategy
The basic idea of the CULEX algorithm is the preservation of pixel fuzziness, which
is used for both automatic ROI extraction and for segmentation. Depending on the
purpose, pixel features (i.e., intensity value, average intensity of a regional neigh-
borhood, standard deviation of the neighborhood and depth) are combined in dif-
ferent fashions to allow for proper classification/clustering. For instance, to
proficiently detect the ROI in which the CCA walls are comprised, CULEX needs to
take into account at least three pixel features; whereas to perform fuzzy c-means
classification, we observed that pixel intensity alone gives good results, provided the
ROI has been selected properly.
5.3 Structure of the CULEX Algorithm 123
MA
NW
MA
FW
MA
FW
MA
FW
MA
NW
MA
NW
MA
NW
LI
FW
LI
FW
LI
FW
LI
FW
LI
NW
LI
NW
LI
NW
LI
NW
MA
FW
Figure 5.8 CULEX segmentation of four CCA images relative to healthy subjects. MA
NW
indicates
the near wall media–adventitia interface, MA
FW
indicates the far wall media–adventitia interface,
LI
NW
indicates the near wall lumen–intima interface, and LI
FW
indicates the far wall lumen–intima
interface. The four images are quite different in terms of image resolution, level of noise, and pres-
ence of markers or written parts on the image itself. CULEX provides suitable segmentations in all
of the images; hence, it also proves effective on previously recorded images that have already been
processed.