THYROID SEGMENTATION IN ULTRASOUND IMAGES USING SUPPORT VECTOR MACHINE

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1 International Journal of Neural Networks and Applications, 4(1), 2011, pp THYROID SEGMENTATION IN ULTRASOUND IMAGES USING SUPPORT VECTOR MACHINE D. Selvathi 1 and V. S. Sharnitha 2 Mepco Schlenk Engineering College, Sivakasi 1 selvathi_d@gail.co, 2 sharni.ece38@gail.co Abstract: Ultrasound iaging is currently the ost popular diagnostic tool. In this paper, the autoatic syste is developed to segent the thyroid gland in ultrasound iages using wavelet transfor and Support Vector Machine with polynoial kernel. The goal of thyroid gland segentation is to accurately estiate the volue of the thyroid horone fro the segented area by which the abnoral syptos of thyroid gland can be detected. The preprocessing steps such as Adaptive Weighted Median Filter (AWMF), orphological operations and gray level copensation of the thyroid region are used. These steps were to enhance the ultrasound iage by reducing the speckles. After preprocessing, the statistical features such as ean and variance are extracted in spatial doain and wavelet transfored doain. The features are used to train the SVM and this trained SVM segents the thyroid region based on pixel classification. The results are copared with the ground truth iages obtained fro the radiologist and the perforance easure such as accuracy is evaluated. Keywords: Support Vector Machine (SVM), thyroid segentation, ultrasound (US) iages, wavelet transfor. I. INTRODUCTION In clinical environent, edical iages of a specific organ or part of the body are obtained for clinical exaination for the diagnosis of a disease or pathology. However, edical iaging tests are also perfored to obtain iages and inforation to study anatoical and functional structures for research purposes with noral as well as pathological subjects. The significance of edical iaging paradig is its direct ipact on the healthcare through diagnosis, treatent evaluation, intervention and prognosis of a specific disease. Ultrasound (US) iaging is currently the ost popular diagnostic tool. Physicians usually diagnose the pathology of the thyroid gland by its volue. The thyroid glands are found and the shapes are hand-arked fro ultrasound (US) iages. This approach relies heavily on the experience of the physicians and is very tie consuing. US iages contain echo perturbations and speckle noise, which can ake diagnosis difficult. Techniques to process US iages are continuously being developed. Several ethods for segenting anatoical objects fro US iages have been presented, such as those for segenting the prostate [1], [2] tuors in the breast [3], the carotid artery [4], [5] and the thyroid nodule [6], [7]. Aong these segentation ethods, active contour odels (ACMs) [8] have attracted attention due to their high perforance. However, ost active contour ethods are sensitive to the gradient of the edge, and physicians are required to outline the rough contour of the thyroid gland. This is a tieconsuing procedure, and an inaccurately outlined contour seriously affects the segentation results. The syste perforance of SVM was copared with the quadratic least squares iniu distance classifier (QLSMD) and quadratic Bayesian classifier [9] for assessing thyroid nodule alignancy risk on US iages. Here the accuracy of SVM in classifying the low and high risk nodules is 96.7% where QLSMD classifier is 92.5% and QB classifier is 92%.Thus it is evident that the other two classifier had to eploy an extra feature to enhance their perforance. In [10] Hierarchical classification ethod based on SVM and decision tree algorith can be used for segentation of thyroid nodule in US iages. But this ethod requires too any orphological operations. The RBF neural network is used to segent the thyroid gland [11]. The SVM is efficient than RBF neural network i.e. the learning task is in sensitive

2 8 International Journal of Neural Networks and Applications to the relative nuber of training saples in positive and negative classes [12]. Hence in this work SVM is used to segent the thyroid gland in ultrasound iages. The deterination of features which represent the tissues best is still a serious proble which affects the results of segentation directly. There are any types of feature extraction ethods for edical iages in literature. However, there is not any unique ethod that fits all tissue types. Wavelet is one aong the ost popular feature extraction ethod. Two-diensional (2D) fast Fourier transfor (FFT) and 2D continuous wavelet transfor (CWT) were coputed in order to for the feature vectors of US bladder and phanto iages [13]. In this work, ultrasound Iages with thyroid gland are used as inputs. In the training phase, the physicians ust anually outline the rectangular regions of interest (ROI) fro the thyroid gland and non thyroid tissues. Two features such as ean and variance are extracted fro the ROIs after perforing Haar wavelet transfor [14] are used to train the SVM classifier with polynoial kernel. The trained SVM classifier can then autoatically segent the thyroid regions based on pixel classification fro the US iages. The results obtained are copared with ground truth obtained fro radiologist and the perforance easure such as accuracy is calculated. The chapter 2 describes about proposed ethodology. The chapter 3 explains about the results obtained. results. A preprocessing step is thus required to enhance and locate the probable thyroid region. The steps for iage enhanceent are (1) Locating the probable thyroid region using horizontal projection (2) Applying an Adaptive Weighted Median Filter (AWMF) to reduce speckles (3) Applying two orphological operations, opening and closing to enhance the filtering result by reoving the redundancy enhanced by the filter (4) Copensating for different US iages according to the intensity teplate of the thyroid region. II. PROPOSED METHODOLOGY The basic steps of the proposed ethodology are shown in Fig.1. The various stages of proposed technique are US database (obtained fro radiologist), iage enhanceent, feature extraction, segentation using SVM. Figure 1: Proposed Methodology A. Ultrasound Iages Physicians often diagnosis the abnoral syptos of the thyroid gland. Soe of the US iages of thyroid gland. Soe of the US iages of thyroid are shown in Fig. 2 the iages used in this work are obtained fro the website referred in [11]. The iages used are noral thyroid iages. B. Iage Enhanceent In thyroid US iages, low visual quality greatly affects the segentation and the volue estiation Figure 2: Ultrasound Iages of Thyroid Gland (1) Locating Probable thyroid Region In a thyroid US iage, the thyroid gland is always in the iddle, below the bright part and above the dark part of the iage. Two reference values (R 1 and R 2 ) are defined to locate the probable thyroid region. The horizontal and vertical histogras are used to deterine these reference values. Here horizontal projection histogra is used to deterine the two reference values. R 1 is the row index with the largest

3 Thyroid Segentation in Ultrasound Iages using Support Vector Machine 9 average intensity in the horizontal projection of the US iage. R 2 is the first row index with an average intensity of zero fro the top to botto in the horizontal projection of the US iage. The probable thyroid region is located between the th row and the th row of the US thyroid iage. Projection histogra is defined as an operation that aps an iage into a one-diensional array called a histogra or projection profile. The values of the histogra are the sus of the gray values along a particular direction. Two types of histogras are defined. They are at 0-degrees (horizontal projection histogra) and 90-degrees (vertical projection histogra) with respect to the horizontal axis y. A horizontal projection histogra h(x) of an iage ƒ(x,y) is the su of gray values projected onto the vertical axis x. A vertical projection histogra v(y) of an iage ƒ(x,y) is the su of gray values projected onto the horizontal axis. Horizontal projection histogra P H (y) and vertical projection histogra P V (x) are given in Eqn(1) and Eqn (2) P= 1 PH ()( y =,) I x y (1) x= 0 P= 1 PV ()( x =,) I x y (2) y= 0 (2) Adaptive Weighted Median Filer Iage quality is of central iportance to the success of an ultrasonic exaination. However these iages suffer fro a type of acoustic noise called speckle, which represents one of the ajor sources of iage quality degradation, speckle tends to ask the presence of low-contrast lesions and reduces the ability of a huan observer to resolve fine detail. Hence, speckle suppression by eans of digital iage processing should iprove iage quality and possibly the diagnostic potential of edical ultrasound. In this work the odified filter such as an Adaptive Weighted Median Filter (AWMF) is used for reducing speckle noise in edical Ultrasonic iages The AWMF paraeters have been selected experientally in ters of the clinical quality of the processed iage. In this work the window size, which deterines the axiu noise reduction, is 9 by 9. The scaling constant g and the central weight w (K+1, K+1), which deterine the filter s ability to preserve iage detail are equal to 0.25 and 10, respectively. AWMF is conducted on a fixed oving ask with the weights adjusted according to the local statistics. For ask with size M x M, the weight coefficient w(i, j) at position (i,j) is given by W ( i,) j gd = W σ 0 µ x, y 2 x, y (3) Where g is the scaling constant, µ x,y and σ 2 x, y the local ean and variance inside the M by M window, D is the distance of the point (i, j) fro the centre of the window. (3) Morphological Operations Thus the opening and closing operations are ost coonly used techniques.the purpose of orphological processing is priarily to reove iperfections added during segentation. Using the basic operations we can perfor opening and closing. A set of 3 x 3 closing and opening operations are applied to reove the redundancy enhanced by AWMF. (4) Gray Level Copensation The variation of the gray level of the thyroid region in the US iage greatly affects the segentation results. A gray-level copensation technique is thus applied to adjust the intensity of the probable thyroid region. In thyroid US iages, the intensities of skin/fat are larger than those of other tissue. In general, the skin area occupied 20% of a thyroid US iage. Hence, the noral-reference gray level GLN is defined as the half gray level of an 8-bit iage (GLN = 128). The average value of the top 20% pixels (in intensity) in the test iage T (x,y) is regarded as the test-reference gray level GLT. Accordingly, the intensity of the test iage is adjusted by the value between the noral-reference gray level and the test-reference gray level using 255,( ift,) x y >255 GL T '( x,) y = 0,ifT(x,y)- GL <0 T(x,y)- GL,otherwise (4) Where GL = GLN GLT, x = 1, 2... H, and y = 1, 2..., W, where H and W denote the height and width of the probable thyroid region, respectively. C. Feature Extraction The Haar wavelet features are significant features for segentation in US iages. The Two

4 10 International Journal of Neural Networks and Applications discriinative statistical features that are extracted fro the selected ROIs using Haar wavelet are the ean and variance. These features are calculated for each pixel in the approxiation subband after applying first level of Haar wavelet transfor. The ean and variance are given by 1 Mean; µ x, y = I( x,) y 2 M (5) ( x,) y B 2 1 Variance; σ x, y = (( I x,)( y,)) µ x y 2 M (6) ( x,) y B Where I(x, y) denotes the intensity of a pixel (x, y) in the ROI which has passed through the Haar transfor, and B denotes the approxiation subband. For coparative analysis the sae features are extracted in iage doain. D. Support Vector Machine In this project the Support Vector Machine with polynoial kernel is used to segent the thyroid gland in ultrasound iages. The statistical features that are obtained fro feature extraction are used to train the SVM. Ideally all feature saples at hand should be eployed, but since ost of the are redundant due to utual correlations, an optiu nuber of the are selected to achieve highest accuracy. Thus the SVM is trained using these feature saples. The trained SVM is then used for testing the input iages which classifies the thyroid region based on pixel classification. Let vector x R n denote a pattern to be classified, and let scalar y denote its class label (i.e. y {±1}. In addition, Let {(x i, y i ), i = 1,2,..l} denote a given set of l training exaples. Referring to the siplest case, in which the training patterns are linearly separable. That is, there exists a linear function of the for f(x) = W T x + b (7) Such that for each training exaple x i, the function yields f(x i ) 0 for y i = ±1, and f(x i ) < 0 for y i = 1. In other words, training exaples fro the two different classes are separated by the hyperplane f(x) = W T x + b = 0 (8) For a given training set, there ay exist any hyperplanes that axiizes the separating argin between the two classes. SVM finds the hyperplane that causes the largest separation between the decision function values for the borderline exaples fro the two classes. Matheatically, this hyperplane can be found by iniizing the cost function 1 T 1 2 f () W = W W = W (9) 2 2 The data with linear separability ay be analyzed with a hyperplane, linearly non separable data are analyzed with kernel functions. The kernel function used in this work is polynoial kernel of order 2, given as, k(x, z) = (τ + x T z) d (10) The input to the SVM classifier can be in vector for as x = [ f, f,... f ] (11) i i,1 i,2 i, where f i, is the th feature of the ith block of size M by M. All features are noralized before use by subtracting their ean value, and then dividing the difference by their standard deviation. The th noralized feature of the ith block is obtained by f ' i, = f i, σ µ (12) Where µ is the ean and σ is the standard deviation of the feature. these noralized feature vectors are then regarded as the training vectors of the SVM. This trained SVM is used for testing the input iages. E. Perforance Measure In order to illustrate the segentation perforance standard easureent such as accuracy is adopted. In this work noral thyroid iages are used. The thyroid segentation results are copared with the respective ground truth thyroid segentations that are anually segented by radiologist. Quantitative easureent of segentation accuracy is calculated in ters of true positive (TP) with respect to the ground truth. The accuracy is given by the Eqn. (13). A accuracy = A TP P + A + A TN N (13) Where A P = Total nuber of actual positive pixels. A N = Total nuber of actual negative pixels. A TP = Nuber of pixels in the actual thyroid gland region segented in this work. A FP = Nuber of pixels of

5 Thyroid Segentation in Ultrasound Iages using Support Vector Machine 11 non thyroid region, which are falsely segented as pixels of thyroid gland region. Thus the true negative pixels A TN and false negative pixels A FN are given as A TN = A N + A FP and A FN = A P A TP. training the SVM.For segentation, the thyroid region of ground iage is assigned as 1 and non thyroid region is assigned as 0. III. RESULTS AND DISCUSSIONS The iages used in this work are obtained fro the online website entioned in [11] the iages used are noral thyroid iages. These iages are anually segented by radiologist and used as ground truth iages. In this work, the US data are divided into two groups naely training and testing data sets. The classifiers are trained using training set and the test sets are used to evaluate the perforance of the trained classifier. The preprocessing result of one US thyroid iage is shown in Fig 3. The sae steps are repeated for other iages. The features are extracted fro the preprocessed iages. Figure 4: (a), (b), (c) input iage1, segented result, and ground truth. (d), (e), (f) input iage2, segented result, and ground truth. (g), (h), (i) input iage3, segented result, and ground truth Table 1 Quantitative Results of Segented Region Iages used Accuracy for results Accuracy for results using wavelet using without transfor wavelet transfor Figure 3: (a) Input iage of thyroid gland (b) horizontal projection (c) region of interest (d) after applying AWMF (e) after applying orphological operation, (f) Haar wavelet transfor first level Haar wavelet transfor is applied. The two statistical features such as ean and variance are deterined for each pixel in the approxiation subband at the first level of decoposition. The results for wavelet transfor for training and testing iages are shown in Fig. 3. These saples are noralized using Eqn. (12) before applying to the Support Vector Machine. All feature vectors are noralized by subtracting their ean value and then dividing the difference by their standard deviation. The noralized features values of the training iage and the ground truth iages are used for Input iage Input iage Input iage Input iage Using the trained SVM the thyroid region is segented in the test iages. The saple testing iages and the segented iages are shown in Fig.4. Siilarly the thyroid region is segented for any other testing iages. The quantitative easureent is tabulated for various testing iages is given in table 1. IV. CONCLUSION This work proposes a coplete solution to autoatically segent the thyroid gland in Ultrasound iages. The proposed ethod includes iage enhanceent processing to reove speckle noise, which greatly affects the segentation results of the thyroid gland region obtained fro US iages The probable thyroid gland region is located

6 12 International Journal of Neural Networks and Applications in the US iage, and then, SVM is used to segent the region into thyroid and nonthyroid gland areas. Thus it is evident fro the Table 1 that the accuracy for segentation is higher by perforing wavelet transfor deterines the feature vectors. The accuracy can be increased by choosing the appropriate feature saples for training phase and different types of features. REFERENCES [1] N. Hu, D. B. Downey, A. Fenster, and H. M. Ladak, Prostate boundary segentation fro 3d ultrasound iages, Medical Physics, Vol. 30, No. 7, pp , [2] B. Chiu, G. H. Freean, M. M. A. Salaa, and A. Fenster, Prostate segentation algorith using dyadic wavelet transfor and discrete dynaic contour, Phys. Med. Biol., Vol. 49, No. 21, pp , [3] R. Chen, R. F. Chang, W. J. Wu, W. K. Moon, and W. L. Wu, 3-D breast ultrasound segentation using active contour odel, Ultrasound Med. Biol., Vol. 29, No. 7, pp , [4] C. Baillard C. Barillot, P. Bouthey, Robust Adaptive Segentation of 3D Medical Iages With Level Sets, Institut National de Recherche en Inforatique et en Autoatique (INRIA), Le Chesnay Cedex, France, Tech. Rep. 4071, [5] J. C. R. Seabra, L. M. Pedro, J. F. e Fernandes, and J. M. Sanches, A 3-D Ultrasound-Based Fraework to Characterize the Echo Morphology of Carotid Plaques, IEEE Trans. Bioed. Eng., Vol. 56, pp , [6] D. E. Maroulis, M. A. Savelonas, D. K. Iakovidis, S. A. Karkanis, and N. Diitropoulos, Variable Background Active Contour Model for Coputer- Aided Delineation of Nodules in Thyroid Ultrasound Iages, IEEE Trans. Inf. Technol. Bioed., Vol. 11, No. 5, pp , [7] E. G.Keraidas, D. K. Iakovidis, D. Maroulis, and S. Karkanis, Efficient and Effective Ultrasound Iage Analysis Schee for Thyroid Nodule Detection, in Lecture Notes in Coputer Science, Vol Heidelberg: Springer, pp , [8] M. Kass, A. Witkin, and D. Terzopoulos, Snakes: Active Contour Models, Int. J. Coput. Vision, Vol. 1, No. 4, pp , [9] Stavros Tsantis, Dionisis Cavouras, Ioannis Kalatzis, Nikos Piliouras, Nikos Diitropoulos, and George Nikiforidis, Developent of a Support Vector Machine-Based Iage Analysis Syste for Assessing the Thyroid Nodule Malignancy Risk on Ultrasound Ultrasound in Med. & Biol., Vol. 31, No. 11, pp , [10] Chuan-Yu Chang,Hsin-Cheng Huang, and Shao-Jer Chen, Thyroid Nodule Segentation and Coponent Analysis in Ultrasound Iages, proceedings of 2009, APSIPA Annual Suit And Conference, Sapporo, Japan, October 4-7, [11] Chuan-Yu Chang Yue-Fong Lei, Chin-Hsiao Tseng, and Shyang-Rong Shih, Thyroid Segentation and Volue Estiation in Ultrasound Iages IEEE Trans. on Bioedical Engineering, Vol. 57, No. 6, [12] Datong Chen and Jean-Marc Odobez, Coparison of Support Vector Machine and Neural Network for Text Texture Verification, IDIAP,Switzerland. [13] Zafer İşcan, Mehet Nadir Kurnaz, Züray Dokur, and Taer Ölez, Ultrasound Iage Segentation by Using Wavelet Transfor and SelfOrganizing Neural Network Vol. 10, Nos. 8-9, Aug.-Sept [14] C. M.Wu, Y. C. Chen, and K. S. Hsieh, Texture features for classification of ultrasonic liver iages, IEEE Trans. Med. Iag., Vol. 11, No. 2, pp , Jun [15] Constantine Kotropoulos, Ioannis Pitas, Segentation of Ultrasonic Iages Using Support Vector Machines, Departent of Inforatics, Aristotle University of Thessaloniki, Greece. [16] W. K. Pratt, Digital Iage Processing. New York: Wiley, 1978, pp [17] Ioannidis, D. Kazakos, and D. D. Watson, Application of edian filtering on nuclear edicine scintigra iages. in Proc. 7th Inr. Conf. Pattern Recognition, Montreal, Canada, pp , [18] E. R. Ritenour, T. R. Nelson, and U. Raff, Applications of the edian filter to digital radiographic iages, in Proc. IEEE Int. Conf. Acousr. Speech, Signal Processing, San Diego, CA, pp , [19] Evgeniou, T., Pontil, M., Poggio, T., 2000b. Regularization Networks and Support Vector Machines. In: Sola, A., Bartlett, P., Sch olkopf, B., Schuurans.