C. Venkatesh Dean, Faculty of Engineering, EBET Group of Institutions Kangayam, Tamil Nadu, India

Transcription

1 European Journal of Scientific Research ISSN X Vol.80 No.3 (2012), pp EuroJournals Publishing, Inc 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 C. Venkatesh Dean, Faculty of Engineering, EBET Group of Institutions Kangayam, Tamil Nadu, India 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 realtime. 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.

2 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

3 291 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 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 De-speckled Image Segmentation Ultrasound vvvvv Image Gray Stretching Proposed Non-Linear Structure Tensor Anisotropic Diffusion Filter Proposed Spatial FCM using Mahalanobis distance Fuzzy Clusters CCA Thickness Proposed Algorithm IMT Calculation HHT Selecting ROI IEMD Gradient Transform Comparison Reference values of IMT and CCA Texture Feature based Extraction of ROI Normal / Abnormal Carotid Artery Classification Knowledge Database 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

4 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 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

5 293 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) (1) Figure 4 (a): Carotid Artery-Normal Blood Circulation (b) Carotid Stenosis-High-grade Stenosis 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]. 2 K ρ * I x K ρ * I T xi y J = Kρ * ( I I ) = (2) 2 K ρ * I xi y K ρ * I y where, K ρ 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 ] (3) t I(t = 0) = I o (4) The main concept of anisotropic diffusion is diffusion coefficient. Perona and Malik (1990) proposed two options for choosing c(x) 2 x [ - ]. k 1 C (x ) = ; C( x ) = e (x/k) The anisotropic diffusion method can be iteratively applied to the output image: ( n + 1 ) I = I + ε ( n ) (5) [ C ( I ). I + C ( I ). I ( n ) ( n ) ( n ) ( n ) n o r th n o r th e a s t e a s t (6) + [ C ( I ). I + C ( I ). I ( n ) ( n ) ( n ) ( n ) w e s t w e s t s o u th s o u th

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

7 295 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 Frost Gaussian Median Geometric Kuan Lee Wiener 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 = (x 1, x 2.., x N ) 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]. N C m 2 m = i= 1 i= 1 ij i j (7) J u x c where, u ij is the membership function of X i in the cluster j, x i, is the i th measured data, c j is the center of the j th cluster. The exponent m, called fuzzifier, determines the level of cluster fuzziness. The membership functions are constrained to be positive and to satisfy, c u 1 j= 1 ij = (8) The membership functions and cluster centers are updated by the following:

8 Nonlinear Structure Tensor Based Spatial Fuzzy Clustering for Ultrasound Carotid Artery Image Segmentation with Texture and IMT Extraction using Hilbert Huang Transform uij = x C ( ) k = 1 xi Ck And the center is, C j C i j z/( m 1) N m ( u x ) i= 1 = N = m ( u ) i 1 ij ij i (9) (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 U ij = u ij * 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 U ij Map U ij into pixel position and calculate new spatial U ij Compute Objective function and Update the cluster center Is < No Yes Stop

9 297 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: T 1 DM ( 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 Hilbert Huang Transform HHT is a mathematical tool and it is used to extract the region of interest of the nonlinear and nonstationary 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)e jθ(t),where A(t) and θ(t) are the amplitude and phase of x(t). (14) Amplitude A(t) = Phase θ(t) = arctan h( t) 2 2 y t h t ( ) + ( ) (15) y( t) The Hilbert Huang Transform of function y(t) is defined as, 1 y( x) h(t) = PV dt π (17) t - x 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 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 Hilbert Spectral Analysis It is a signal analysis method which is used to find the instantaneous frequency by applying the Hilbert dθ ( t) transform. ω = and finally the original signal is reconstructed as, dt n ( i ω j ( t ) dt ) x( t) = a ( t) e (18) j= 1 j 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. (16)

10 Nonlinear Structure Tensor Based Spatial Fuzzy Clustering for Ultrasound Carotid Artery Image Segmentation with Texture and IMT Extraction using Hilbert Huang Transform 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 h x to denote a horizontal derivative filter (matrix), h y to denote a vertical derivative filter (matrix), and h to denote the arbitrary angle derivative filter (matrix), then: [h Ө ] = cosө [h x ] + sinө [h y ] (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 V pc and Partition entropy V pc are used here [1]. They are defined as follows: V N C j i pc = And, V N u 2 ij N C j i pe = [ u lo g u ] ij ij N 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 V pc is maximal or V pe is minimal. These are tested for normal and abnormal images. Three different cases of abnormalities considered here are (20) (21)

11 299 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)

12 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 CC1 CC2 Vpe Vce Error of clustering Elapsed Time (Sec) IMT (Pixels) Normal Abnormal- Fibro-Fatty Abnormal- Stenosis Abnormal- Narrowing e e Figure 14: Area error of segmented region Area Error Watershed Level set FCM Proposed Algorithm 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) Normal image (40-60) Normal image (>60) Abnormal image (<40) Abnormal image (40-60) Abnormal image (>60) 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

13 301 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. References [1] Yan Xu, A Modified Spatial Fuzzy Clustering Method Based on Texture Analysis for Ultrasound Image Segmentation IEEE International Symposium on Industrial Electronics, July 5-8. [2] C. Liguori, A. Paolillo and A. Pietrosanto, An Automatic Measurement System for the Evaluation of Carotid Intima-Media Thickness, IEEE Trans. on Instr. and Meas., vol. 50, Dec., pp

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