Nonlinear Structure Tensor Based Spatial Fuzzy...
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European Journal of Scientific Research
ISSN 1450-216X Vol.80 No.3 (2012), pp.289-302
© EuroJournals Publishing, Inc. 2012
http://www.europeanjournalofscientificresearch.com
Nonlinear Structure Tensor Based Spatial Fuzzy Clustering for
Ultrasound Carotid Artery Image Segmentation with
Texture and IMT Extraction using Hilbert Huang Transform
S. Dhanalakshmi
Department of Electronics and Communication Engineering
Easwari Engineering College, Anna University, Chennai, India
E-mail: [email protected]
C. Venkatesh
Dean, Faculty of Engineering, EBET Group of Institutions
Kangayam, Tamil Nadu, India
E-mail: [email protected]
Abstract
The analysis of the ultrasound carotid artery wall is of highest importance in clinical
practice. In fact, the Intima-Media Thickness of carotid artery wall is an indicator for some
of the most severe and acute cerebro-vascular pathologies like stroke and heart attack.
Ultrasound carotid artery image segmentation is challenging due to the interference from
speckle noise and fuzziness of boundaries. We propose a modified segmentation algorithm
for ultrasound carotid artery images which uses Fuzzy C-Means clustering incorporating
the spatial information and Mahalanobis distance that takes the correlations of the data set
in to account to find cluster distance and centers. Firstly, the nonlinear structure tensor
anisotropic diffusion is applied to image to refine the edges and to extract speckle texture.
Then, a spatial FCM clustering method using Mahalanobis distance is applied to the carotid
image feature space for segmentation. Next the empirical mode decomposition and Hilbert
spectral analysis which provides a new tool of analyzing non-linear and non-stationary time
series data is applied to the fuzzy segmented image to extract texture features that aids in
detecting the Common Carotid Artery thickness to diagnose the disease. An algorithm has
been designed and optimized for the extraction of the boundary of the carotid artery in real-
time. Implementation results indicate the excellent performance of the proposed automated
method that involves minimum human interaction and it is also user independent. In the
experiments with clinical ultrasound images, the proposed method gives more accurate
results than the conventional FCM and other segmentation methods. The area error
obtained by the proposed algorithm is very much less compared to all other methods
reported. We have simulated and tested the algorithm for 350 ultrasound carotid artery
images and the result shows that algorithm works well for both normal and abnormal
images of all types. This automated classification results are also compared with the
manual measurements taken by sonologist. The results of this paper show that the boundary
of artery is exactly detected and Intima media thickness is calculated accurately using the
proposed framework.
Nonlinear Structure Tensor Based Spatial Fuzzy Clustering for Ultrasound Carotid Artery
Image Segmentation with Texture and IMT Extraction using Hilbert Huang Transform 290
Keywords: Ultrasound Carotid Artery, Non-linear structure tensor anisotropic diffusion,
Spatial information, Empirical mode decomposition, Hilbert Huang
Transform, Intima Media Thickness
Abbreviations
CCA Common Carotid Artery
IMT Intima media Thickness
FCM Fuzzy C-Means
SFCM Spatial Fuzzy C-Means
NLSTAD Non- Linear Structure Tensor Anisotropic Diffusion
PSNR Peak signal to Noise Ratio
MSE Mean square Error
HHT Hilbert Huang Transform
IMF Intrinsic Mode Functions
1. Introduction For advanced medical diagnosis and guidance, the efficient and accurate ultrasound image processing
techniques play an important role. Speckle is a multiplicative noise, having a granular pattern which is
the inherent property of ultrasound images. So ultrasound images are treated as textured images and
thus texture feature extraction plays a crucial role. The low quality of image influenced by the speckle
noise and fuzziness of mass boundaries usually makes the segmentation complicated. A high failure
rate of tissue analysis appears because the computerized segmentation failed. Therefore, Segmentation
methods which cope with the speckle noise and fuzziness of mass boundaries are appreciated [1].
The segmentation of the CCA wall is important for the evaluation of the IMT on B-mode
ultrasound images. The accuracy and precision of IMT measurements determined by manual pointing
methods are limited by human variability in operation of the pointing devices and by the resolution of
the displayed ultrasound image. The manual tracing approach, however, is time consuming and based
on subjective operator assessment and therefore inevitably results in inter and intra observer variability.
Furthermore, manual tracing may case drift in measurements overtime. There are also few algorithms
proposed by different authors for segmentation of carotid artery [2].
The Canny technique is an optimal edge detection technique but the increase in the width of the
Gaussian kernel reduces the detector’s sensitivity to noise, at the expense of losing some of the finer
details in the image. The localization error in the detected edges also increases as the Gaussian width is
increased. Segmentation process is normally expected to produce extra contours other than relevant
image objects when watershed transform is applied. Active contours face a number of limitations such
as initial conditions, curve parameterization and the inability to deal with images where the different
structures have many components [3].
FCM clustering method segments the boundary by classifying image pixels into different
clusters and introduces a view of fuzziness for the belongingness of each pixel. Compared with crisp or
hard segmentation methods, FCM is able to retain more information from the original image [2]. But in
FCM, the initial guess for the cluster centers is most likely incorrect. FCM assigns every data point a
membership grade and by iteratively updating the cluster centers and the membership grades for each
data point, FCM iteratively moves the cluster centers to the right location within a data set. However, a
major disadvantage of conventional FCM is not to consider any spatial information in image context,
which makes it very sensitive to noise and other image artifacts [1].
A new framework has been proposed which uses nonlinear structure tensor based spatial FCM
which utilizes both the image intensity and speckle pattern extracted from image texture for
segmentation. Proposed framework is explained in Section 2.This section discusses about ultrasound
images, speckle noise modeling and the capability of nonlinear structure tensors to extract speckle
texture feature. Section 3 briefly highlights the main features of SFCM method and how it is used in
291 S. Dhanalakshmi and C. Venkatesh
the extracted feature space for carotid image segmentation. It also details about HHT and the texture
features extracted using HHT. Section 4 explains the method of extracting the region of interest.
Calculation of IMT and CCA which is needed for classification of images is explained here.
Experimental results of clinical ultrasound images in comparison with some existing schemes are given
in section 5. The paper is concluded with a summary in section 6.
2. Proposed Framework The Ultrasound image is acquired using a linear ultrasound probe connected to a Phillips En Visor C
ultrasound scanner. Ultrasound carotid artery image is first normalized by using gray stretching method
to increase the dynamic range of intensities. Speckle noise that is present in the image is removed by
using the proposed non- linear structure tensor anisotropic diffusion filter which has proved good
PSNR and less mean square error. After de-speckling, gradient transform is applied to the image to
indicate the boundaries of the object and to filter out the less important portions of the image. The
output of the gradient transform is then fed to the proposed spatial modified fuzzy clustering which
uses Mahalanobis distance to yield good segmentation. The fuzzy segmented image is then applied to
Hilbert Huang transform which gives better texture features and better region of interest. Finally the
IMT and CCA thickness is calculated using the proposed algorithm and compared with the standard set
of database values from which the normal and abnormal carotid arteries are classified with high
accuracy. Figure 1 provides the block diagram representation of the proposed method of computer
aided diagnosis.
Figure 1: Block diagram of the proposed work for segmentation and classification of carotid artery
vvvvv
Proposed Non-Linear
Structure Tensor
Anisotropic Diffusion
Filter
Proposed
Spatial
FCM using
Mahalanobis
distance
Gray
Stretching
CCA
Thickness
IMT
Calculation
Ultrasound
Image Fuzzy
Clusters
IEMD
HHT
Comparison Reference values of
IMT and CCA
Normal / Abnormal
Carotid Artery
De-speckled Image
Texture Feature based
Extraction of ROI
Classification
Segmentation
Knowledge
Database
Proposed Algorithm
Gradient
Transform
Selecting ROI
2.1. Normalization of Ultrasound Image
One of the most common degradation in the recorded medical image is its contrast which leads to low
quality image. Poor illumination and lack of dynamic range leads to artifacts and noise in the image.
Contrast is defined as the difference between the highest intensity level and its lowest value. Gray
stretching is done to improve the dynamic range of images having low contrast. It applies a scaling
function to all the pixels present in the image which is also called as Normalization. Figure 2(a) and
Nonlinear Structure Tensor Based Spatial Fuzzy Clustering for Ultrasound Carotid Artery
Image Segmentation with Texture and IMT Extraction using Hilbert Huang Transform 292
2(c) shows the gray stretched normal and abnormal carotid artery with its corresponding histogram
shown in figure 2(b) and 2(d) respectively.
Figure 2: (a) Gray Stretched Normal Carotid artery, (b) histogram of a (c) Gray Stretched Abnormal Carotid
artery (d) Histogram of c
(a) (b) (c) (d)
2.2. Nonlinear Model for Speckle Feature Extraction
2.2.1. Ultrasound Carotid Artery Imaging
Each carotid artery is characterized by a longitudinal tract called common carotid, after an enlargement
it bifurcates into two arteries, one internal carotid artery (ICA) and one external carotid artery (ECA),
on the basis of their position in relation to neck skin. Artery walls are made up of three layers or
tunicae: intima, media, and adventitia. The tunica intima is composed of a single layer of flattened
epithelial cells with an underlying basement membrane. The tunica media comprises an inner layer of
elastic fibers and an outer layer of circular smooth muscle. The tunica adventitia is composed of
collagenous fibers. The main symptom of atherosclerosis (found in different ages and races of people)
is the carotid intima layer thickening in proximity to the endothelial lumen surface. This thickening can
be also confined to a short artery segment, and in this case, it is called plaque. It can be detected and
evaluated by measuring intima–media thickness, which can be defined as the distance between intima
and media [4]. Figure 3 shows CCA and IMT of an ultrasound carotid artery. The blood circulation in
the normal carotid artery is shown in figure 4(a). When fatty and inflammatory tissue builds up on the
inside surface of an artery, it forms a plaque. Platelets, fibrin and other blood products can stick to this
as part of a clot. This leads to some degree of blockage of flow through the artery, which is known as
carotid stenosis. If the plaque builds up involves blockage of 70% or more of the inner opening i.e. in
the luminal diameter of the ICA, the stenosis is referred to as "high-grade". The brain may recognize
this via stroke-like symptoms which include vision loss, sensory and muscle function loss, speaking
difficulty, etc. This plaque formation is shown in figure 4(b). Reference values of the IMT as referred
by sonologist are the following:
Figure 3: (a) Ultrasound Carotid Artery showing CCA (b) Carotid Artery showing IMT
293 S. Dhanalakshmi and C. Venkatesh
Normal Carotid Artery: IMT < 1.0 mm, Carotid Artery with Thickening: 1.0 mm < IMT < 1.3
mm. Carotid Artery with Plaque: IMT > 1.3 mm. IMT increases with aging, according to the equation
IMT= (0.005 × age in years) + 0.043 (1)
Figure 4 (a): Carotid Artery-Normal Blood Circulation (b) Carotid Stenosis-High-grade Stenosis
2.2.2. Nonlinear Structure Tensor Anisotropic Diffusion for Speckle Feature Extraction
Speckle is multiplicative noises that reduces both image contrast and detail resolution, degrades tissue
texture, reduces the visibility of small low-contrast lesions and makes continuous structures appear
discontinuous. It is caused by the interference between ultrasound waves reflected from microscopic
scattering through the tissue. It also limits the effective application of automated computer analysis
algorithms. Therefore it is important to despeckle the area of interest prior to segmentation [5].
Anisotropic diffusion is a scale-space technique which creates a homogeneous and clearly
separated region inside an image [6]. It avoids blurring of images at larger scales. Instead of
smoothening the entire image, it processes within the regions determined by the edges which include
borders of the region. The local structure tensor provides a representation of image texture by taking
into account how the gradient changes within the vicinity of any investigated point [7]. For a scalar
image I, the linear structure tensor with a rank of 2 is defined as follows [1].
J * ( I
2
ρ x ρ x yT
ρ 2
ρ x y ρ y
K * I K * I IK I )
K * I I K * I
= ∇ ∇ =
(2)
where, Kρ is a Gaussian kernel with standard deviation ρ, and subscripts of I denote partial derivatives.
This is a classical form of structure tensors, which is a symmetric positive semi-definite matrix.
Anisotropic diffusion is an efficient nonlinear technique for simultaneously performing contrast
enhancement and noise reduction [6]. It smoothes homogeneous image regions and retains image edges
I div c | I| . I
t[ ( ) ]
∂= ∇ ∇
∂ (3)
I(t = 0) = Io (4)
The main concept of anisotropic diffusion is diffusion coefficient. Perona and Malik (1990)
proposed two options for choosing c(x) 2
x- ].
k
2
1(x x e
1 (x/k)
[
C ) ; C( )
= =
+ (5)
The anisotropic diffusion method can be iteratively applied to the output image: ( 1 )
( )
( ) ( ) ( ) ( )
( ) ( ) ( ) ( )
[ C ( | |) . C ( | |) .
[ C ( | |) . C ( | |) .
n
n
n n n n
n o r th n o r th e a s t e a s t
n n n n
w e s t w e s t s o u th s o u th
I I ε
I I I I
I I I I
+
= +
× ∇ ∇ + ∇ ∇
+ ∇ ∇ + ∇ ∇
(6)
Nonlinear Structure Tensor Based Spatial Fuzzy Clustering for Ultrasound Carotid Artery
Image Segmentation with Texture and IMT Extraction using Hilbert Huang Transform 294
The anisotropic diffusion method gives better contrast while removing speckles effectively. In
fact, because the parameters in anisotropic diffusion method are adjustable, we can control parameters
and choose the best image. With a constant diffusion coefficient, the anisotropic diffusion equations
reduce to the heat equation which is equivalent to Gaussian blurring.Figure 5, 6 (a) - (h) illustrates the
enhanced images, after the removal of speckle noise for both normal and abnormal carotid artery.
Figure 5: Different filters applied to a normal ultrasound carotid artery
(a) Proposed NLSTAD (b) Frost (c) Gaussian (d) Median
(e) Geometric (f) Kuan (g) Lee (h) Wiener
In this study different filter like Kuan, Gaussian, Geometric, Wiener, Frost, Lee, Median filters
are compared with the proposed NLSTAD filter in terms of SNR, PSNR, and MSE.
Figure 6: Different filters applied to an abnormal ultrasound carotid artery
(a) Proposed NLSTAD (b) Frost (c) Gaussian (d) Median
(e) Geometric (f) Kuan (g) Lee (h) Wiener
295 S. Dhanalakshmi and C. Venkatesh
The results are tabulated and the table1 shows that the proposed nonlinear structure tensor
anisotropic diffusion filter has high PSNR in the order of 44.4dB and very less MSE compared to all
other filters reported in the literature survey.
Table 1: Comparison of the performance of different filters applied to ultrasound carotid artery images
Types of Filters used Normal Carotid Artery Image Abnormal Carotid Artery Image
SNR (dB) PSNR (dB) MSE SNR (dB) PSNR(dB) MSE
Proposed NLSTAD 44.09 44.4 1.59 46.11 46.12 1.25
Frost 76.8 37.0 3.56 96.50 35.13 4.46
Gaussian 82.67 36.84 3.66 86.01 35.063 4.501
Median 103 35.19 4.43 98.75 37.28 3.48
Geometric 82.34 -9.22 735.49 85.2114 -11.78 990.08
Kuan 80.73 37.19 3.52 97.89 35.22 4.4082
Lee 80.77 37.19 3.52 96.89 35.23 4.4183
Wiener 83.86 36.94 3.62 88.16 35.08 4.4884
The figure 7 shows that both for normal and abnormal carotid artery, the proposed NLSTAD
gives better PSNR compared to other filters used. It can be also seen that the mean square error is very
much less comparatively. Thus, it shows that the proposed algorithm works well for both normal and
abnormal ultrasound carotid artery images.
Figure 7: Performance analysis of different filters
3. Segmentation using SFCM and HHT 3.1. FCM using Euclidean Norm
FCM is an iterative clustering algorithm with the characteristic that it allows feature vectors to belong
to multiple clusters and the belongingness is described by the grade of membership. Let X = (x1, x2.., xN)
denotes an image with N pixels to be partitioned into C clusters. The conventional algorithm is based
on minimization of the following objective function [8]. 2
1 1|| ||
N C m
m ij i ji iJ u x c
= == −∑ ∑ (7)
where, uij is the membership function of Xi in the cluster j, xi, is the ith
measured data, cj is the center of
the jth
cluster. The exponent m, called fuzzifier, determines the level of cluster fuzziness.
The membership functions are constrained to be positive and to satisfy,
11
c
ijju
==∑ (8)
The membership functions and cluster centers are updated by the following:
Nonlinear Structure Tensor Based Spatial Fuzzy Clustering for Ultrasound Carotid Artery
Image Segmentation with Texture and IMT Extraction using Hilbert Huang Transform 296
/( 1)
1
1
|| ||( )|| ||
ijC i j z m
ki k
ux C
x C
−
=
=−
−∑
(9)
And the center is,
1j
1
( )C
( )
N m
ij ii
N m
iji
u x
u
=
=
=∑∑
(10)
3.2. SFCM using Mahalanobis Distance
One of the important characteristics of an image is that neighboring pixels are highly correlated. This
spatial relationship is important in clustering, but it is not utilized in a standard FCM algorithm, where
the noise may lead a misclassification. A SFCM algorithm was proposed by altering the membership
weighting of each cluster [9], [10]. Referring to that idea of incorporating spatial information, the
spatial membership function is defined as follows:
New spatial Uij = uij * w(u) (11)
ω(u) is a spatial weight function which can be defined as a two dimensional median filter.
Euclidean norm is usually used as the similarity measure between vector-valued data in conventional
FCM to find distance between the clustered pixels. But it does not take in to account of spatial
relationship between the pixels. The figure 8 gives the flowchart for the proposed Spatial FCM
algorithm using Mahalanobis distance.
Figure 8: Proposed Spatial FCM using Mahalanobis distance
Start
Initialize the number of clusters and also
initialize the centers for every cluster
Calculate Cluster centers and belongingness of
clusters using Mahalanobis distance and also find Uij
Map Uij into pixel position and calculate new spatial Uij
Compute Objective function and Update the cluster center
Is ∥ ���� − �� ∥
< �
Stop
Yes
No
297 S. Dhanalakshmi and C. Venkatesh
We have proved that Mahalanobis distance gives best result compared to all distance measures
when applied for ultrasound carotid artery images. It is a statistic value which measures the distance of
a single data point from the sample mean or centroid in the space of the independent variables used to
fit a multiple re-gression model. Mahalanobis distance can be defined as dissimilarity measure between
two random vectors x & µ , of the same distribution with the covariance matrix S: 1( ) ( ) ( )T
MD x,µ x - µ S x - µ
−=� � �� � �
(12)
where µ is the corresponding mean from the class and S its covariance matrix. Mahalanobis distance is
based on correlations between variables by which different patterns can be identified and analyzed. It is
a useful way of determining similarity of an unknown sample set to a known one. It differs from
Euclidean distance in that it takes into account the correlations of the data set and is scale-invariant, i.e.
not dependent on the scale of measurements. Another important use of the Mahalanobis distance is the
detection of outliers.
3.3. Hilbert Huang Transform
HHT is a mathematical tool and it is used to extract the region of interest of the nonlinear and non-
stationary ultrasound images [11], [12]. HHT decomposes a signal into intrinsic mode functions to get
the instantaneous frequency components. HHT is an empirically based data analysis method and is
very adaptive [12]. The analytical signal x(t) is represented as,
x(t) = y(t) + jh(t) ,where y(t) is the real part and h(t) is the imaginary part. (13)
In polar coordinates, x(t) = A(t)ejθ(t)
,Where A(t) and θ(t) are the amplitude and phase of x(t).
(14)
Amplitude A(t) = 2 2( ) ( )y t h t+ (15)
Phase θ(t) = arctan ( )
( )
h t
y t
(16)
The Hilbert Huang Transform of function y(t) is defined as,
–
1 ( )h(t) PV dt
π
y x
t - x
∞
∞= ∫ (17)
PV indicates the Cauchy principle value i.e. h(t) is an improper integral and it becomes
undefined, when we assigning x=t.
HHT has two steps:
1. Empirical Mode Decomposition 2. Hilbert Spectral Analysis
3.3.1. Empirical Mode Decomposition
The fundamental part of the HHT is the empirical mode decomposition method. Using the EMD
method, any complicated data set can be decomposed into a finite and often small number of
components, which is a collection of IMF. IMF is a function which has equal number of extrema points
and zero crossings, with its envelopes being symmetric with respect to zero [11].The process of
extracting IMF is called sifting.
3.3.2. Hilbert Spectral Analysis
It is a signal analysis method which is used to find the instantaneous frequency by applying the Hilbert
transform. ω = ( )d t
dt
θ and finally the original signal is reconstructed as,
( ( ) )
1( ) ( ) j
n i ω t dt
jjx t a t e
=
∫=∑ (18)
This equation is used to represent the amplitude and instantaneous frequency components as a
function of time and the amplitude is contoured on frequency time plane. This frequency time
distribution of the amplitude is called as Hilbert amplitude spectrum or simply Hilbert spectrum.
Nonlinear Structure Tensor Based Spatial Fuzzy Clustering for Ultrasound Carotid Artery
Image Segmentation with Texture and IMT Extraction using Hilbert Huang Transform 298
4. Identification of Region of Interest 4.1. Gradient Transform
As an image is a function of two (or more) variables it is necessary to define the direction in which the
derivative is taken. For the two-dimensional case we have the horizontal direction, the vertical
direction, or an arbitrary direction which can be considered as a combination of the two. If we use hx to
denote a horizontal derivative filter (matrix), hy to denote a vertical derivative filter (matrix), and h to
denote the arbitrary angle derivative filter (matrix), then:
[hӨ ] = cosӨ [hx] + sinӨ [hy] (19)
In our case, the horizontal gradient can be neglected as it is redundant in the method of
processing used here. The vertical gradient is the crucial input for the column-wise computation we
performed. The magnitude of the vertical gradient takes large values when there are strong edges in the
image. Hence for easy computation it is necessary to normalize the values of the gradient. Once
normalized, the values of the gradient lie between 0 and 1. Figure 9(a) and (b) shows the gradient
transformed image and ROI respectively.
Figure 9: (a) Gradient Transformed Image (b) ROI
4.2. Measurement of IMT
Most crucial part of the algorithm is to calculate the thickness of the intima-media layer, which is
critical to predicting the risk of cardiac disorders in patients. The unique characteristic of the artery
wall, when compared to others parts of the image, is exploited. IMT values are calculated and
compared with the values obtained by radiologist.
5. Experimental Results and Discussions The performance of the modified spatial FCM using Mahalanobis distance that has been proposed in
this paper is investigated with simulations. We have tested our algorithms with 350 real-time
ultrasound images. Two types of cluster validity functions like partition coefficient Vpc and Partition
entropy Vpc are used here [1]. They are defined as follows: 2N C
ijj i
pc
uV
N=∑ ∑
(20)
And,[ ]
N C
ij ijj i
pe
u lo g uV
N
−=∑ ∑
(21)
The idea of these validity functions is that the partition with less fuzziness means better
performance. As a result, the best clustering is achieved when the value Vpc is maximal or Vpe is
minimal. These are tested for normal and abnormal images. Three different cases of abnormalities
considered here are
299 S. Dhanalakshmi and C. Venkatesh
Fibro-fatty abnormal carotid artery image, Stenosis abnormal carotid artery image and
Narrowing abnormal carotid artery image
Figure 10,11,12,13 (a),(b),(c) displays the output of SFCM for normal , fibro-fatty , stenosis
and narrowing carotid artery respectively.
Figure 10: (a) Normal Carotid image (b) De-Speckled image (c) SFCM –Segmented Image
(a) (b) (c)
Figure 11: (a) Fibro-Fatty image (b) De-Speckled image (c) SFCM –Segmented image
(a) (b) (c)
Figure 12: (a) Stenosis image (b) De-Speckled image (c) SFCM –Segmented image
(a) (b) (c)
Figure 13: (a) Narrowing image (b) De-Speckled image (c) SFCM –Segmented image
(a) (b) (c)
Nonlinear Structure Tensor Based Spatial Fuzzy Clustering for Ultrasound Carotid Artery
Image Segmentation with Texture and IMT Extraction using Hilbert Huang Transform 300
The features like cluster centers, evaluation parameters, error of clustering and the elapsed time
has been determined for all types of abnormal and normal images. The result are tabulated and table 2
shows that error of clustering is very less for all types of images considered. Time consumed for
analyzing the normal image is less compared to abnormal cases. The area error is used to measure the
misclassification rate and it is defined as, Error = Nm / Tm, where
Nm=Number of misclassified pixels and Tm=Total number of pixels
Table 2: Evaluation Parameters for different cases of carotid
Image Type
Cluster Centers Evaluation Parameters Error of
clustering
Elapsed
Time (Sec)
IMT
(Pixels) CC1 CC2 Vpe Vce
Normal 8.0581 87.9239 0.1847 0.0126 0.0024 0.0024 2.32
Abnormal-
Fibro-Fatty 3.9377 80.7726 0.1346 0.0049 4.9794e-004 34.511633 2.83
Abnormal-
Stenosis 7.6618 95.3451 0.1589 0.0096 6.02474e-004 42.345443 2.55
Abnormal-
Narrowing 16.0878 89.7045 0.1557 0.0209 0.0022 53.341633 2.71
Figure 14: Area error of segmented region
0.138
0.079 0.0575
0.00143
Watershed Level set FCM Proposed
Algorithm
Area Error
It is observed from the figure 14 that the area error of the proposed method is very less
compared to all other methods reported in the literature. Using the proposed method, ROI is segmented
and then IMT is calculated and the results are tabulated and compared with the manual measurement
taken by the sonologist.
Table 3: Comparison of IMT values for manual and automated algorithm
Nature of the image Proposed Automated measurement of IMT Manual measurement of IMT
pixels Centimeters pixels Centimeters
Normal image (<40) 2.203 0.0583 2.268 0.06
Normal image (40-60) 1.848 0.0489 1.89 0.05
Normal image (>60) 2.993 0.0792 3.024 0.08
Abnormal image (<40) 4.0824 0.108 3.78 0.10
Abnormal image (40-60) 6.87 0.182 6.426 0.17
Abnormal image (>60) 4.4226 0.117 4.536 0.12
From the table 3 and figure 15, it is clear that automated IMT values are very much close to the
manual measurement done by specialized and experienced sonologist. We have classified the images in
to three age groups like less than 40, between 40 to 60 and greater than 60. We have simulated and
301 S. Dhanalakshmi and C. Venkatesh
tested our algorithm for all these types of images and found that it works well for both normal and
abnormal carotid artery images of all age groups.
Figure 15: Graph of comparison of Automated and IMT values
6. Conclusion An efficient automatic segmentation algorithm has been designed and optimized for the extraction of
the boundary of ultrasound carotid artery images. Generally manual tracing of the IMT layer
boundaries gives higher deviation in results since it is a factor which is dependent on the equipment
operator and sonologist. We proposed a largely automatic and user-independent algorithm for the
extraction of the intima and media thickness from the ultrasound images of the carotid artery.
Independent of the ultrasonic image quality, the accuracy in IMT tracing has been reported in this
paper. The proposed nonlinear structure tensor anisotropic diffusion method is more tolerant to noise
than the conventional filters. Using HHT the texture and intensity information is extracted to get an
accurate result. Based on the image intensity and the speckle texture extracted using HHT, the
fuzziness of boundaries in ultrasound images is detected exactly and it results in good segmentation.
From the segmented region i.e. ROI, the thickness of intima and media is calculated using the proposed
algorithm. Based on the IMT values determined, the images are classified as normal and abnormal
carotid artery. The result shows the excellent performance of the proposed system of classifying
ultrasound carotid artery images.
Acknowledgement We sincerely acknowledge the Bharat Scans, Chennai for rendering the ultrasound carotid artery
images for our work. We specially thank Dr. Divyan Paul, consultant Radiologist for extending his
technical guidance.
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Ultrasound Image Segmentation” IEEE International Symposium on Industrial Electronics,
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Evaluation of Carotid Intima-Media Thickness”, IEEE Trans. on Instr. and Meas., vol. 50,
Dec., pp. 1684-1691
Nonlinear Structure Tensor Based Spatial Fuzzy Clustering for Ultrasound Carotid Artery
Image Segmentation with Texture and IMT Extraction using Hilbert Huang Transform 302
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