CHAPTER 5 EFFICIENT APPROACHES FOR QUANTIFICATION OF AORTIC REGURGITATION USING PROXIMAL ISOVELOCITY SURFACE AREA PROCESS

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1 CHAPTER 5 EFFICIENT APPROACHES FOR QUANTIFICATION OF AORTIC REGURGITATION USING PROXIMAL ISOVELOCITY SURFACE AREA PROCESS 5.1. Introduction Aortic Regurgitation is also known as Aortic Insufficiency (AI). It indicates the inability of the aortic valve. During diastole, the aortic valve allows blood flow in the reverse direction from aorta into the left ventricle. Regurgitation is due to incompetence of the aortic valve or any disorder of the valvular apparatus (e.g., leaflets, annulus of the aorta) ensuing in diastolic flow of blood into the left ventricular chamber. AR force an accurate amount overload to the left ventricle (LV) which results in dilation, eccentric hypertrophy and finally loss of function. The integral part of aorta is aortic valve, which is tubular-like structure. During systole and diastole, the valve apparatus includes three distinct leaflets with definitive passive motion. Due to relatively high systolic and diastolic pressure the aortic valve is challenged with a relatively high mechanical stress and, in terms of morphology, a consideration is to be made of the relation to origin of the coronary arteries. During systole and diastole, the movement of the tube is insufficient in all directions [18]. Echocardiography which is followed by MR imaging (or contrast aortography) produces all valuable parameters. Additionally the key considerations are the diameter of the AV orifice and of the neighboring ascending aorta segment diameter. Of late in clinical cardiology an all-round estimation of Valvular Regurgitation is an essential objective to be carried out by the cardiac surgery. Evaluation of the severity of regurgitation is supreme to clinical decision making in patients with AR., because patients with severe Aortic Regurgitation often need surgical

2 treatment. Semi-quantitative position of Aortic Regurgitation with color and spectral Doppler echo or with angiography is generally used. But both the processes are delayed by some specific restrictions or reasons. Invasive and Noninvasive quantitative evaluation of Regurgitant Volume and Regurgitant Fraction are obtainable, but Regurgitant Volume and Regurgitation Fraction depend on loading circumstances. It was proposed in recent times that noninvasive calculation of aortic ERO was a measure of lesion severity in AR [63]. The EROA is a basic descriptor of Aortic Regurgitation that confirms the effect of AR on the LV and offers information additional to the volume overload measurements like Regurgitation Fraction. But the measurement of the EROA by quantitative Doppler Echocardiography is not always possible to attain high degree of reliability; a grouping of methods is desirable (very much suggested). In order to measure the severity of valvular and congenital heart diseases the clinicians have great interest in PISA methods only. Depending on the conservation of mass, for evaluating (or scheming) the effective orifice area in Valvular Regurgitation the PISA method has been faithfully adopted [64]. In table 5.1, Echocardiographic and Doppler parameters used in the evaluation of Aortic Regurgitation severity are described. Table 5.1 Echocardiographic and Doppler parameters used to evaluate AR severity: Utility, Advantages, and Limitations Utility/Advantages Limitations Left Ventricle size Enlargement sensitive for Enlargement seen in other chronic significant AR. conditions. Normal in acute Normal size virtually excludes significant AR. significant chronic AR. Aortic cusps alterations Simple, usually abnormal in Poor accuracy severe AR; Flail valve denotes severe AR. Doppler parameters

3 Jet width or jet crosssectional Simple, very sensitive, quick Expands unpredictably below area in screen for AR. the orifice. Inaccurate for *LVOT- Color Flow eccentric jets. Vena Contracta Width Simple, quantitative to identify mild or severe AR. Not useful for multiple AR jets. PISA method Quantification both EROA and Rvol. Feasibility limited by aortic valve calcifications. Not valid for multiple jets, less accurate in eccentric jets. Flow quantification-pw Quantitative, valid with Not valid for combined MR multiple jets and eccentric jets. Provides both lesion severity and AR, unless pulmonic site is used. i.e. EROA, RF and volume overload, Rvol. Jet density-cw Simple. Incomplete jet Qualitative. Overlap between compatible with mild AR. moderate and severe AR. Jet deceleration rate Simple Qualitative; affected by (*PHT)- CW changes in LV and aortic diastolic pressures. *LVOT Left Ventricle Outflow Tract, *PHT Pressure Half Time The Proximal Isovelocity Surface Area method [60, 26] depends on the continuity principle and assumes that blood flow converging in the direction of a flat orifice forms hemispherical isovelocity shells. It has been proved that the PISA method is precise and reproducible. This method is frequently applied in medical science because the proximal convergence method can be easily visualized and it is the only probable method currently easy to have it. Despite the hypothetical advantages in comparison to Valvular Regurgitation the PISA is seldom used routinely for the evaluation of AR severity. The chief purpose of the present

4 research is to give an efficient method based on image processing methods. And these can exactly evaluate the EROA using Doppler Echocardiography image with the aid of PISA. Considerable interest is shown in the PISA method to estimate the severity of valvular and congenital heart diseases. In this Part I and part II contain the details of the methodology for quantification of AR that has been carried out. In Part I, in preprocessing stage the RGB color Doppler Echocardiography image has been subjected to Wiener filtering. Then the filtered image has been quantized with the aid of color quantization using NBS / ISCC color space which makes the image more precise. Besides those the PISA method is used for calculating the quantitative parameters like VC, R vol, RF, Effective regurgitant orifice (ERO) and mostly AR. The PFC method is pursued to quantify Aortic Regurgitation by analyzing the converging flow field proximal to estimate the mildness severity and eccentricity of an AR lesion. Similarly in part II, an efficient method for quantifying the EROA in AR using clustering based image segmentation processing methods on Doppler Echocardiographic image and helped by PISA method has been presented. Considerable attention has been received by PISA method by clinicians for assessing the severity of valvular and congenital heart diseases. In the preprocessing stage the Gaussian filter is used to reduce the noise in color Doppler echocardiographic image. Accordingly it has been improved with the support of image contrast enhancement by using Contrast-Limited Adaptive Histogram Equalization (CLAHE). As a sequel, the image is subjected to image segmentation based on Fuzzy k-means clustering to make the quantification more precise. Clustering is a method for grouping an image into units that are reliable to one or more characteristics. Besides those the PISA method is used for computing the quantitative parameters like VC, R vol, RF, ERO and mostly AR. The PFC method is pursued to

5 quantify VR with the help of analyzing the converging flow field proximal to estimate the mildness severity and eccentricity of an AR lesion. This research also offers a survey of qualitative and quantitative parameters for rating AR severity, utility, advantages and limitations of Echocardiography along with Doppler parameters which are made use of in the estimation of MR severity Technical Terms Aortic Regurgitation It indicates the inability of the aortic valve. During diastole, the aortic valve allows blood flow in the reverse direction from aorta into the left ventricle. Wiener Filtering It is one of most significant techniques. This filtering is used for removing the blur in images because of linear motion or unfocussed optics. Color Quantization color image quantization is a method that reduces the number of distinct colors in an image so that the new image will looks like the original image. NBS/ISCC Color Space ISCC NBS System of Color Designation is a system used to name the colors. The colors are named based on a set of 12 basic or fundamental color terms and a few set of adjective modifiers. Gaussian Filtering It is one of the filters whose impulse response is a Gaussian function. The main intention of this filter is to provide no overshoot to a step function input while reducing the rise and fall time. Image Enhancement In the image enhancement, the perception of information in images for human viewers are enhanced and it offers `better' input for further image processing methods. Clustering It is one of the most significant unconfirmed learning problems; it deals with finding a structure in a set of unlabeled data, since other problems are of this kind.

6 Fuzzy k Means Fuzzy K-Means is also known as Fuzzy C-Means. It is an extension of K-Means, the popular simple clustering method.the K-Means discovers hard clusters, where Fuzzy K-Means is more statistically formalized method than K-Means and discovers soft clusters. Hard cluster denotes a point which belongs to only one cluster, but the soft cluster denotes a particular point which belongs to more than one cluster with certain probability Review of Related Researches A brief review of recent researches related to quantification of Aortic Regurgitation is described below. Thomas Wittlinger et al. [65] have presented a study that the MRI was related to both angiography and echocardiography in estimating the RV and AR in patients. At 1.5 T, forty patients were taken under examination. The regurgitant jet was located with the aid of a gradientecho sequence. To compute the left ventricular function, cine measurements were accomplished. For flow estimation, a velocity-encoded breath-hold phase-difference magnetic resonance sequence was made familiar. The severity of AR is calculated by making use of MRI accepted with that of angiography in 28 of 40 patients, and with the Echocardiography result in 80%. As a result the correlation between calculated stroke volume by the two methods that is magnetic resonance cine and flow measurements was very excellent (r > 0.9). Quantification of aortic regurgitation using Echocardiography is still stimulating. In spite of rheological characteristics which, have been granted by Chen Li et al. [33], a novel echocardiographic method called Vector Flow Mapping (VFM) directly measures blood flow volume. They have in turn, projected to estimate the accuracy of VFM in quantifying chronic AR. Therefore for AR quantification the clinicians feel that a highly reproducible parameter with

7 good precision has been discovered RegR evaluated by VFM-as a novel Doppler method that allows quantitative analysis of FV despite the presence of turbulent flow. Leopoldo Pérez de Isla et al. [66] have proposed detailed study so that the measurements of LVOT area are arrived at by using 3D-echo which is highly reliable compared to those which are made by using 2D-echo. Both the methods i.e. 2-D echo and 3-D echo are used to measure the LVOT area and the circularity index was measured using 3D-echo exclusively. Moreover the severity of valvular aortic stenosis was classified using both 2D-echo and 3D-echo. Therefore for assessing the LVOT area, the 3D-echo may be a superior technique. The additional advantage is that the LVOT is elliptical in shape, but not related to its circularity in size. Also for distinguishing the severity of valvular aortic stenosis, the 3D-echo could be supportive. Anne-catherine Pouleur, MD et al. [67] have presented a study to appraise the correctness of multidetector CT compared with cine MR imaging, TTE and TEE in the measurement of aortic valve area (AVA) of patients undergoing cardiac surgery. After examining 48 patients ( out of which 33 are men and 15 are women under ages 62 ±13), the accuracy of multidetector CT for detection of aortic stenosis was compared with that of TTE. The results demonstrated that, the multidetector CT could be an alternative to TTE in patients with poor acoustic windows. The multidetector CT can be used to measure the AVA and detect aortic stenosis at the time of noninvasive coronary imaging with accuracy similar to that of TTE and MRI. An extensive attention is shown to Non-invasive management of AV disease. Actually a lot of current publications have formerly reported its use in clinical practice. The major issue is to obtain considerate pathophysiological processes and, most significantly, extensive experimental activity. In addition to testing of various animal models, technical and material features are also being intensively investigated. Clinicians are dubious about the applicability

8 and durability of this positive improvement whether it can be equated with the standard of present cardiac surgery. Certainly it is justified that the full use of certain models as a tentative measure to help to progress the circulatory status, may not permit the safe surgery. A tiny analysis of the above mentioned issue has been granted by Sochman and Peregrin [18] The Proposed Methodology Part I A major goal of the clinical cardiology is quantifying the severity of AR and it is extremely old clinical decision making. Very often the screening for the existence of AR is preceded with the help of color Doppler flow mapping. Several echocardiographic methods have been published to improve the quantification of valvular incompetence. Although the primary method used for estimating AR severity has been found to be less precise than the latter counterparts and therefore PFC method using color Doppler has been recognized as a precise (exact) and consistent quantitative approach. For the quantification of aortic regurgitant, here we present an efficient mode by uniting image processing method which exactly quantifies the EROA which strengthen the PISA method to estimate the severity of AR lesion. More than this here we deal with the consistency of the PISA method for computing ERO of AR. The present approach mainly includes two modules: i. Preprocessing ii. Quantification using Proximal Isovelocity Surface Area (PISA) Preprocessing Initially in the preprocessing step the color Doppler Echocardiography AR image is subjected to Wiener filtering, that reduces the noise quantity in the image in comparison with an estimation of the preferred noiseless image.

9 Wiener Filtering: The purpose of this filter is to reduce the amount of noise present in a signal. The Wiener filter is the Mean Squared Error optimal stationary linear filter for images despoiled by additive noise and blurring. In order to calculate the Wiener filter, the signal and noise processes are assumed to be second order stationary. Presuming stationary the nature of the involved signals, the average squared distance between filter output and a preferred signal is minimized by calculating the Wiener filter coefficients that can be made perfect in the frequency domain producing easily: Where ( f ) W( f ) ( P ( f ) / P ( f )) (5.1) DY D is the desired signal, S( f ) W( f ) Y( f ) YY is the Wiener filter output, Y( f ) the Wiener filter input and P YY ( f ), P DY ( f ) are the power spectrum of Y ( f ) and the cross power spectrum of Y ( f ), D ( f ) respectively. The AR color flow Doppler TEE image in apical view showing Proximal Flow Convergence is shown in figure 5.1(a), figure 5.1(b) shows its filtered output. Figure 5.1 a) AR Color flow Doppler image (TEE) in apical view showing Proximal FC and b) its filtered output [Image Courtesy: Journal of American College of Cardiology, Clinical Studies article]

10 After that the filtered color Doppler Echocardiographic image is subjected to color quantization. Then the color quantization fetches down the numerous colors used in an image, usually with the intention that the new image has to be as visually similar as possible to the original image. Color Quantization: Color quantization is the technique used for minimizing the number of colors in a digital image by changing them with a specific color selected from a palette [68]. It is broadly used nowadays because it decreases the work load of huge image data on storage and transmission bandwidth in several multimedia applications. Accordingly the main inducement of color image quantization is for mapping the set of colors in the original color image to a smaller set of colors in the quantized image so that this mapping reduces the variation between the original and the quantized images, as mentioned earlier [45]. With the help of NBS/ISCC color space color quantization is carried out as our contribution.. Imagine that the color Doppler Echocardiographic image as I i where, 0 i ni 1. Consequently, to accomplish the quantization, the RGB color space values of the image are represented by vectors, that is, I, I resample into and I B i Ri G i and I correspondingly with size M N. Where M N is B i R R images (generally R =192). Therefore, from the color space images I R i, I Gi, the sampled image S, R i SG i and S B i are obtained. After that, with the help of S, S, R i G i S B i and Color Look Up Table (CLUT), the color quantization is implemented. The matrix representation of the CLUT can be specified as

11 C lt R0 R1 R2 Rn G G G G B B B B 1 n1 n (5.2) From the above matrix representation, the CLUT has n different probable colors created by various, R x, G y and B z transformations. After that by manipulating the Euclidean distance between every pixel value of the re-sampled color space images and the images are color quantized. As a result the quantized image C lt value, the database Q I is obtained. Then the quantized image as Q consists of quantized RGB color space values Q, Q and I Q R G Q B which are formulated R ( a, b) min ( SR ( a, b) R j ) ( SG ( a, b) G j ) ( SB ( a, b) B j ) (5.3) where, 0 j n 1, 0 a r 1 and 0 b r 1. This is the same for the quantization of other color space images Q G and Q also (i.e. Q B R Q Q ). G B

12 Figure 5.2 Color Quantization Outputs a) Quantized view b) Binary output c) Segmented output Quantification using Proximal Isovelocity Surface Area After preprocessing stage the color Doppler Echocardiographic image is subjected to efficient quantification of AR along with the help of PISA method. The PISA method which is derived from the analysis of the Flow Convergence (FC) region proximal to the regurgitant orifice and from the conservation of mass has already been previously illustrated in reference sited [69 71]. The regurgitant jet size of LV cavity, LVOT width of the jet and the pressure half-time are calculated by using CW Doppler. The Echocardiography calculates R vol or RF as

13 the total stroke volume through the AV is equal to the forward stroke volume plus R vol. Basing on these methods the degree of severity of AR can be understood as mild, moderate, severe or eccentric. The Qualitative and quantitative standard parameters that are used in grading of severity is illustrated in the table 5.2. Table 5.2 Qualitative and quantitative parameters useful in grading Aortic Regurgitation severity Structural Parameters Mild Moderate Severe LA size Normal* Normal / dilated Generally dilated Aortic leaflets Normal / Doppler Parameters Jet width in LVOT-Color Flow ξ abnormal Small in central jets Normal / abnormal Intermediate Abnormal/flail, or wide coaptation defect Large in central jets; variable in eccentric jets Jet density-cw Incomplete Thick/Dense Thick/Dense Jet deceleration rate-cw Slow>500 Medium Steep<200 (PHT, ms) ψ Quantitative Parameters φ VC width, cm ξ < >0.6 Jet width/lvot width, % ξ < Jet CSA/LVOT CSA, % ξ < R vol, ml/beat < RF, % < EROA, cm 2 <

14 *Unless there are other reasons for LV dilation. Normally 2D values; LV minor axis 2.8cm/m 2, LV end-diastolic volume 82ml/m 2 (2).; ξ With a Nyquist limit of cm/s. ψ PHT is shortened with increasing LV diastolic pressure and vasodilator therapy, and may be lengthened in chronic adaptation to severe AR. (AHA, ACC and ESC recommended values). Understanding the hemispheric shape of the PISA, the diastolic aortic regurgitant flow R Flow, is measured as R 2 Flow r V r 2 (5.4) Where in early diastole, the radius r of the FC is calculated and equivalent aliasing velocity. The aortic regurgitant ERO area is then calculated as V r represents the ERO ( PISA) R Flow / R Vel (5.5) Here R Vel represents the maximal velocity of the aortic regurgitant jet in early diastole recorded with continuous wave Doppler Echocardiography from the apical, par apical or right parasternal transducer position. Color-flow methods comprise the measurement of the maximal anteroposterior diameter (height) of the regurgitant jet at the junction of the LV Outflow Tract (LVOT) and the aortic annulus in parasternal long-axis view, and the maximum height of the LV outflow tract at the same site. Continuous Doppler-wave imaging of AR allows quantification of both the slope and pressures half-time. Regurgitant Volume ( R Vol ) which is calculated as: R aortic flow - mitral flow (5.6) Vol 2 A ortic flow (DLVOT *0.785)R Vel (5.7)

15 Here D LVOT represents the diameter of LV Outflow Tract (LVOT). Regurgitant Fraction ( RF ) was calculated as RF R Vol aortic flow. A Regurgitant Fraction above 40% to 50 % accuses more severe AR. The quantitatively obtained values of mild and eccentric (figure 5.2) are shown in table 5.3. Table 5.3 Measured parameter values of Mild and Eccentric Aortic Regurgitation Quantitative Parameters Mild Eccentric Radius r (cm) Vena Contracta Width (cm) Jet Width (cm) LVOT width (cm) Regurgitant Flow Rate (cm 2 ) EROA (cm 2 ) Aortic flow (cm 3 ) R vol (cm 3 ) Regurgitant Fraction (%) The Proposed Methodology Part II In part II also it is restated that the PFC method using color Doppler has been recognized as a faithful and accurate quantitative approach for quantifying AR by combining different image processing techniques which duly quantify the EROA in assessing the degree of severity

16 of an aortic regurgitant lesion. The approach which is offered for the quantification of AR has three modules: i. Preprocessing ii. Image Segmentation iii. Quantification using Proximal Isovelocity Surface Area (PISA) Preprocessing In preprocessing stage, primarily the color Doppler Echocardiography AR image is subjected to Gaussian filtering which is used for reducing the noise present in the image. After filtering make use of image enhancement for enhancing and improving the excellence i.e contrast and brightness of the image for human viewing Gaussian Filtering It is one of the linear filters used in different contexts of image processing. A Gaussian filter merges both differential and low pass filtering which identify the edges or computing orientation of features in digital images. If the Gaussian filter is used for low-pass filtering, then the smooth impulse response is obtained which approximates the Gaussian derivative. The weighted mean of the input values gives the output of the Gaussian filter at any time t. The weights are specified by the formula 2 ( ) ( ).exp w C ;..., 1,0, 1,..., (5.8) 2 2 Here represents distance in time lapsed from the current moment, is the Gaussian filter parameter and the sum of all weights is made equal to unit value by the normalization constant. C ( ). The parameter represents the whole Gaussian filter.

17 Figure 5.3 shows a central severe Aortic Regurgitation image in short axes view and its filtered output. Figure 5.3 Aortic Regurgitation image along with their filtered output [Image Courtesy : (timed and dated 09:30, )] Image Enhancement Image enhancement is used for the benefit of human viewers to understand or perceive the information in images and it offers better input for further image processing methods. After filtering, the image is subjected to image contrast enhancement. Contrast enhancement is commonly done by converting an image to a color space where image intensity is one of the main components. One such color space is L *a *b* [72]. Firstly, therefore, we have to create a color transformation structure. The said structure decides the specific color space conversion by using color transform functions to transform the image from RGB to L*a *b* color space, and afterwards work on the luminosity layer 'L*' of the image. The values of luminosity which can distance a wide range from 0 to 100 is scaled between [0 1] as shown below, 100 (I Lab ) L i 100 (5.9) i0

18 Later the luminosity layer is changed with the processed data and is converted back to the RGB color space with the help of Contrast-Limited Adaptive Histogram Equalization. This CLAHE functions on small data regions instead of on the entire image unlike the histogram. The contrast of each region is improved and so the histogram of each output region almost nearly equals the specified histogram. (uniform distribution by default). In order to avoid the amplifying noise that is present in the image, the contrast enhancement can be limited. Figure 5.4 shows color space conversion output and image contrast enhancement output. (a) (b) Figure 5.4 a) Color Space Conversion Output b) Image contrast Enhancement Output Image Segmentation using Clustering Image segmentation is comprised as a significant part in low-level vision, image analysis, and so on. More over the image segmentation decides the quality of the final results of image analysis, and it is a hard and challenging task in image processing. Normally in image segmentation, the processes of partitioning an image into several regions are uniform or similar. But the union of any two adjacent regions is not homogenous (i.e., regions that belong to

19 individual surfaces by clustering pixels) [73]. In packages, numerous image segmentation methods are used as per availability. Here based on the Fuzzy K means clustering, the image segmentation is employed. Fuzzy K means clustering is a pixel-based segmentation that groups objects which are more similar to each other. The fuzzy clustering algorithm is one of the most important techniques which is used in unsupervised pattern recognition. In this way the first step was taken by Ressini after that Zadeh defined the fuzzy sets. A wide range of applications besides fuzzy control and machine vision utilized this technique in recent times. K-means is the most famous technique out of the many fuzzy clustering methods. The main aim of fuzzy clustering (or grouping) is to analyze the data S { Y 1, Y n } R based on minimum distance measure criterion into a few c clusters as shown below, J n c p m m( u, v) k i ik Yk v 1 1 i (5.10) Here the fuzzification parameter is m which obtains a value greater than unity. v i is cluster center. To each cluster, ik [0,1 ] is the degree of membership of data and p is the power of Euclidian distance. By use of optimization algorithm, the optimal values m ik V { V, i 1, 2,, c}, U { } are computed. i th i

20 Figure 5.5 Segmentation Output Quantification using Proximal Isovelocity Surface Area For the efficient quantification of Aortic Regurgitation, the preprocessed color Doppler image was utilized with the aid of PISA method. The evaluation of the flow convergence region close to the regurgitant orifice and the theory of conservation of mass, lead to the growth of PISA method [69 71]. The severity of AR was calculated by means of M-mode, 2-D and Doppler echocardiography and it was graded reliable with the recommendations of the American Society of Echocardiography (ASE). In Doppler echocardiography, Aortic Regurgitation is approximated by the size of the regurgitant jet in the LV cavity, the jet width in the LVOT and the pressure half-time measured by the continuous wave Doppler. Calculation of R vol or RF is also possible by Echocardiography, because the sum of forward stroke volume and R vol provides the total stroke volume through the aortic valve. Basing on these methods, the clinicians can grade the severity of AR whether mild, moderate, severe or eccentric (See detailed in section 5.4.2).

21 Table 5.4 Measured values of the above mentioned parameters of Mild and Eccentric Aortic Regurgitation Quantitative Parameters Mild Eccentric Radius r (cm) Aliasing Velocity (cm/s) Vena Contracta Width (cm) Jet Width (cm) LVOT width (cm) Jet width/lvot width (%) Regurgitant Flow Rate (cm 2 ) EROA (cm 2 ) Aortic flow (cm 3 ) R vol (cm 3 ) Regurgitant Fraction (%) Conclusions In this chapter the researcher has presented an efficient quantification of Aortic Regurgitation. For quantification the Doppler Echocardiographic image is processed by utilizing image processing techniques. The greater precision was achieved rationally while quantifying AR Doppler image. In part I, we have engaged Weiner filtering and color quantization in the preprocessing stage and quantification of AR is done with the help of PISA method. Similarly in part II, for improving the accuracy of AR to a greater extent we have used Gaussian filtering and

22 contrast image enhancement in the preprocessing stage. The Aortic Regurgitation quantification is made very exact by using image segmentation. The preprocessed color Doppler Echocardiographic image is segmented using fuzzy K-means clustering method. The enhancements in the quantifying of Valvular Regurgitation are finally led based on improvements in imaging technologies which assist to improve the measurement of flow convergence, Vena Contracta and regurgitant jet. Therefore from our research we can conclude that it is advantageous to find out cardiac output non-invasively by Doppler Echocardiography with the assistance of flow convergence method. These experimental results are found correlated with several other procedures that exist for measuring cardiac output.