Nonlinear Structure Tensor Based Spatial Fuzzy...

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 nonlinear 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



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


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)



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


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:



/( 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


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.



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


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)



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


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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