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International Journal of Engineering Trends and Technology (IJETT) – Volume 13 Number 2 – Jul 2014 Cardiac Cycle Phase Estimation in 2-D Echocardiographic Images Using Support Vector Machine Sudeep M S MTech Scholar, Musaliar College of Engineering and Technology Kerala, India Abstract- Proposes a new method to estimate the cardiac cycle phases in 2-D Echocardiographic images is the first step in cardiac volume estimation. Focus on analyzing the Arial systole and diastole events by using the geometrical position of the mitral valve and a set of three image features. The proposed algorithm is based on a tandem of image processing methods and support vector machine as a classifier to robustly extract anatomical information. To recognize the cardiac phases set of image features is proposed and derived. This approach helps to estimate the cardiac phase in double quick time compare to other methods. The algorithm is implemented as computer aided diagnosis (CADi) software. A dataset of 200 images that include both normal and infarct cardiac pathologies were used. Identify an accuracy of 90%and a 2±0.3 s in terms of execution time of CADi application in a cardiac cycle estimation task. The main contribution of this paper is to propose this hybrid method and a set of image features that can be helpful for automatic detection applications without any user intervention. The results of the employed methods are compared with other previous methods in terms of processing time Keywords: - Support Vector Machine, Cardiac Cycle, Computer Aided Diagnosis 1. INTRODUCTION The incidence of chronic heart failure in humans is very high and as a result, the morbidity and mortality are also very high in all countries, regardless of the economic development level. The data on mortality caused by the chronic heart failure reveal that the five-year mortality rate remains at an alarmingly high level of 50%. So Heart failure is a major clinical problem. Echocardiography represents a noninvasive procedure to examine the heart and the surrounding blood vessels. By echocardiography, the physicians visually inspect the four cardiac cavities (left and right atria, left and right ventricle), the inferior vena cava, the aorta, the mitral valve, the aortic valve, the tricuspid valve, and the pulmonary valve. The image of the left ventricle (LV) and the analysis of the cardiac cycles are of great importance to cardiac research and represent a valuable tool to clinically assess cardiac health. ISSN: 2231-5381 Based on the general finding, proposes a new algorithm able to accurately estimate the cardiac cycle phases in Echocardiographic images without involving any manual tracing of the boundaries for segmentation process. The hybrid method proposed in our study is the first approach of such a technique. Attention was mainly focused on the left ventricular cardiac chamber. The purpose of the study was to increase the recognition ability of the algorithm and processing time, so that it correctly discerns between atrial systole and atrial diastole. Detailed cavity geometry and the position of the mitral valve were assessed using denoising, binarization, and edge detection techniques. The denoising operation was performed in the framework of additive Gaussian and multiplicative noise described by Rayleigh distributions. The goal is not to develop a new denoising algorithm but to optimize the formalism for denoising images and to preserve anatomical details relevant from the clinical point of view. To characterize the cardiac cycle phases, an original set of features was proposed and derived. The mitral valve position was described in terms of three image features: 1) the number of boundary pixels belonging to an identified boundary; 2) the height of the mobile rectangle mask which enclosed the useful contour pixels; and 3) the horizontal size of object’s boundary inside the mask. Image features were defined and computed for each image in our dataset by using edge detection. Finally, a support vector machine (SVM) has been trained as a classifier for effective detection of the mitral valve position in the Echocardiographic images. This method does not require the user intervention. Moreover, this hybrid method allows improving the time efficiency. The main reason underlying this study arises from the finding that if the mitral valve annulus fails to properly connect with the leaflets, then the functioning of the heart may be compromised resulting in the mitral valve regurgitation and stenosis heart affection SVM is supervised learning models with associated learning algorithms that analyze data and recognize patterns. Given a set of training examples, each marked as belonging http://www.ijettjournal.org Page 80 International Journal of Engineering Trends and Technology (IJETT) – Volume 13 Number 2 – Jul 2014 either of two categories.SVM training algorithm builds a model that assigns new examples into any of the two categories. dropping out the high-frequency components and it is commonly used in edge detection when the algorithms are sensitive to noise II. PROPOSED SYSTEM OVERVIEW ( , )= ( ( , ) . . ) A. Experimental Algorithm Fig. 1 presents the flowchart of the existing algorithm. The algorithm contains three parts. In the first part, the images are processed using the denoising, binarization, and segmentation techniques. A set of image features was extracted in the second part. In the third part, each component of the feature vector was assessed for both the analyzed cardiac phases, and then the images belonging to our database were classified by the instrumentality of an SVM. The cardiac cycle phase estimation is performed in apical two-chamber long-axis 0◦ view (LAX0) of 2-D Echocardiographic images. ( , ) = ( , )= 1 ′ ( , ) ( ( , ) . . ) Here, f(x,y) denotes the processed image, M and N denote the image resolution, F(u,v) is the FT result, H(u,v) is the filter transfer function corresponding to the GLPF, ′(u,v) stands for the result of convolution between F and H functions, g(x,y) is the inverse FT (that is the denoised image), D(u,v) represents the distance from the point (u,v) to center of filter, and represents the parameter that is a nonnegative number associated with the standard deviation. The quality of the denoising process with FT and GLPF is strongly dependent on the value of the parameter. C. Binarization Methods Otsu’s global Binarization method was employed in order to automatically obtain a single optimal binarization threshold. This technique divides the histogram of the image into two classes and calculates the optimum threshold T that separates these two classes so that their combined spread (intraclass variance) to be minimal. D. Image Segmentation and Edge Detection Fig 1 Flow chart of proposed system B. Image Denoising Removal of noise is shown to be a crucial step in imaging cardiac disease diagnosis. An efficient method for accurate noise filtration of the Echocardiographic images is based on the Fourier transform (FT) and Gaussian low-pass filter (GLPF). Here, the image processing is a three-step process: 1) the FT (1) is performed; 2) filtering the frequency components by using the GLPF (2); and 3) the inverse FT (3) is computed in order to reconvert the image to the spatial domain. Smoothing is achieved in the frequency domain by ISSN: 2231-5381 The Canny detector is widely considered as being the optimal boundary detector. The contour map of the studied object is computed using an algorithm consisting of five steps: smoothing (the noise can be removed in an effective way), finding the gradients (we are looking for gradients of large magnitudes), the nonmaximum suppression (edges are marked using the local maxima), double thresholding (the possible edges are identified by thresholding), and the hysteresis (the chosen edges are validated by exclusion of the weak or not connected edges).As is well known, the Canny detector is looking for intensity discontinuities but it provides too many possible http://www.ijettjournal.org Page 81 International Journal of Engineering Trends and Technology (IJETT) – Volume 13 Number 2 – Jul 2014 boundaries. It produces spatially extended outputs and we used these outputs to reduce the search space by selecting a satisfactory contour. A threshold value N is chosen in order to hold the core boundary (when the contour pixel number is higher than N) and to discard those boundaries classified as isolated (for which the contour pixel number is lower than N). By successive removal of isolated boundaries, the image is diluted and simplified. The optimal value of threshold N was established by maximizing the expected accuracy of CADi. The supreme goal is to have an algorithm, which knows where one object stops and another starts. that the set of points x mapped into any hyperplanes can be quite convoluted as a result, allowing much more complex discrimination between sets which are not convex at all in the original space. E. Support Vector Machine 1) Definition Support vector machine constructs a hyperplanes or set of hyperplanes in a high- or infinite-dimensional space, which can be used for classification, regression, or other tasks. Intuitively, a good separation is achieved by the hyperplanes that has the largest distance to the nearest training data point of any class (so-called functional margin), since in general the larger the margin the lower the generalization error of the classifier. Fig 2 Hyper plane separates training examples 2) Optimal Separating Hyper Plane 3) Linear SVM Classifiers Whereas the original problem may be stated in a finite dimensional space, it often happens that the sets to discriminate are not linearly separable in that space. For this reason, it was proposed that the original finite-dimensional space be mapped into a much higher-dimensional space, presumably making the separation easier in that space. To keep the computational load reasonable, the mappings used by SVM schemes are designed to ensure that dot products may be computed easily in terms of the variables in the original space, by defining them in terms of a kernel function K(x, y) selected to suit the problem.The vectors defining the hyperplanes can be chosen to be linear combinations with parameters of images of feature vectors that occur in the data base. With this choice of a hyperplanes, the points in the feature space that are mapped into the hyper plane are defined by the relation: ∑ ∝ ( , ) = Note that if K(x, y) becomes small as y grows further away from x, each term in the sum measures the degree of closeness of the test point to the corresponding data base point . In this way, the sum of kernels above can be used to measure the relative nearness of each test point to the data points originating in one or the other of the sets to be discriminated. Note the fact ISSN: 2231-5381 Let ( , ) 1≤ i ≤N be a set of training examples, ∈ Rn and belongs to class labeled by ∈ {−1, 1}.The aim is to carry out the equation of a hyperplanes which divides the set of examples such that all the points with the same label are on the same side of the hyperplanes. This means find w and b such that (w. + b) > 0, i = 1. . . N 1 If there exists a hyper plane satisfying eq (1), the set is said to be linearly separable. In this case, it is always possible to rescale w and b such that min 1 < i < N (w. + b) > 0, i = 1. . . N 2 i.e. such that the closest point to the hyper plane has a distance of 1/ ||w||. Then, eq (1) becomes (w. + b) ≥ 1 Among the separating hyperplanes, the one for which the distance to the closest point is maximal is called optimal separating hyper plane (OSH). Since the distance to the http://www.ijettjournal.org Page 82 International Journal of Engineering Trends and Technology (IJETT) – Volume 13 Number 2 – Jul 2014 closest point is 1/ ||w||, finding the OSH amounts to solve the following problem: 1/2 w.w The quantity 2/ ||w|| are called the margin and thus, the OSH is the separating hyper plane which maximizes the margin. The margin can be the measure of difficulty of the problem: the smaller the margin is the more difficult the problem. If the margin is larger, expect better generalization. (Figure 2) 4) Non Linear SVM Classifiers Precision = Here P denotes the total number of “positive” images and N the total number of “negative” images. Here, we considered the diastole cases as “positive condition” and the systole cases as “negative condition.” The correct diagnosis of a diastole image as “diastole” is a true-positive case (TP) and the incorrect diagnosis of diastole image as “systole” is a false-negative case (FN). III. EXPERIMENTAL RESULTS The linear SVM can be readily extended to a nonlinear classifier by first using a nonlinear operator Ф(.) to map the input pattern into a higher dimensional space Ή. The nonlinear SVM classifier so obtained is defined as In terms of the transformed data it is linear Ф(x), but nonlinear in terms of the original data x Ɛ . The experimental images were divided into two sets corresponding to two analyzed cardiac cycles (systole and diastole) by one expert radiologist physician. The expert manually labeled the images. This activity is labor-intensive and tiring. Note that the expert’s accuracy was 100%. hundred systolic images and hundred diastolic images were used. The experimental Echocardiographic images came from a blend of healthy and cardiac patients that suffer from myocardial infarction. Following nonlinear transformation, the parameters of the decision function f(x) are determined by the following minimization: All experimental images were denoised using the FT and GLPF. Fig. 3 shows the denoised images using FT and GLPF f(x) = Ф(x) +b min J(w,) = ½ ||w.w|| + C ∑ subject to ( Ф( ) +b) >= 1- >=0; i=1, 2 …l F. CADi Application The CADi applications are tools developed to help physicians to detect various diseases and to reduce the workload of radiologists. One of most used feature of the CADi applications is the efficiency. The following parameters are considered when the efficiency of the CADi is evaluated: the sensitivity, the specificity, the precision and accuracy sensitivity = Specificity = Accuracy = Fig. 3. Denoising results using FT and GLPF. (a) Atrial diastole. (b) Atrial systole Fig. 4 presents the binarized images using the automatic Otsu method. The binarization produces satisfying results and the myocardium walls are optimally highlighted. TP N TN N TP + TN P+N Fig.4. Images binarized using the Otsu method. (a) Atrial diastole. (b) Atrial systole. ISSN: 2231-5381 http://www.ijettjournal.org Page 83 International Journal of Engineering Trends and Technology (IJETT) – Volume 13 Number 2 – Jul 2014 Fig. 5 shows the edges detected by using the canny method. Most of the major edges were detected but also lots of details have been emphasized and this can hamper the subsequent processing. In order to identify the position of the mitral valve, these images were scanned with a horizontal rectangle window having height H. Fig.5. Edge information by canny detector and the rectangular scanning window. (a) Diastole. (b) Systole. Using the rectangular mask two values are calculated p1 and p2.this rectangular mask is considered as ROI. ROIs considered in this analysis contain the maximum number of contour pixels (p1 value). For diastolic phases, this condition is always accomplished when the mask scans the mitral valve in closed position. In the case of the systolic phase, this condition makes the mask not to stop when it scans the valve in open position. The mask will detect those regions containing the higher number of contour pixels. This drawback is minimized by using the second feature, p2, which helps the SVM to classify the images as diastole for higher value of p2 and as systole for lower value of p2. Significantly smaller p2 and significantly larger p1 values were observed. An ideal recognition process of diastolic versus systolic phases must be identified. The recognition and classification results are strongly influenced by N (the threshold of canny edge detector) and H (the height of sliding window) parameters. In existing system time taken to identify the cardiac phase is very high but in proposed system processing time is very less compared existing system. It is shown in fig 6. Fig 6 Difference in processing time with ANN and SVM and CADi efficiency. ISSN: 2231-5381 This study shows that the SVM enables an accurate distinction between diastole and systole. During the recognition process, 200 test images were investigated and they were equally divided between diastolic and systolic phases. 96 diastolic images were correctly recognized as TP, 4 diastolic images were incorrectly recognized as FN, 95 systolic images were correctly recognized as TN, and 5 systolic images were incorrectly recognized as FP. 95 diastolic images were correctly recognized as TP, 5 diastolic images were incorrectly recognized as FN, 96 systolic images were correctly recognized as TN, and 4 systolic images were incorrectly recognized as FP. The recognition failing rate is almost equally distributed between healthy and infarcted patients so that a possible pathology does not affect the accuracy of results provided by CADi. When taking into account only the diastolic phase, in the first scenario the diagnostic performance of SVM for the differentiation is 95% accurate. In the case of systole, the differentiation is 95.5% accurate. For the second scenario, the sensitivity is 95% and the specificity is 96% (shown in fig 6). This finding is readily explained by the entirely different appearance of the mitral valve in closed position. On the other hand, the performance of SVM for differentiating systolic phase is more challenging, because of the more similar appearance of various small detected contour types. At systole, these small independent contours can be easily mistaken as mitral valve, and thus, the false-negative diagnosis of systolic phase is made. IV. CONCLUSION A new possible approach in the field of heart disease remote monitoring could offer patients a more individually focused care and, thus, an improved quality of life. The main goal of this system was to develop a method capable of realtime estimate the cardiac cycle in LV Echocardiographic image sequences. In a first step, the developed system used the denoising, the Binarization, and the segmentation techniques. Then, an original method dealing with new proposed image feature sets for an analysis task was designed. In the second step, the SVM technique was employed followed by a CADi efficiency study. In order to obtain a computationally fast, efficient and processing time for estimation of cardiac cycle phase recognition this method is use full. ACKNOWLEDGMENT The authors would like to thank anonymous reviewers for their insightful comments and valuable suggestions. http://www.ijettjournal.org Page 84 International Journal of Engineering Trends and Technology (IJETT) – Volume 13 Number 2 – Jul 2014 REFERENCE [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] W. B. 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