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Echocardiography is mainly to assess valvular regurgitation and get valuable information on the severity of aortic regurgitation (AR). It has several applications, but this paper focuses only on its use in the quantitative evaluation of AR. Proximal isovelocity surface area (PISA) evaluates the severity of AR. The quantification of the effective regurgitant orifice area (EROA) in AR is presented utilising Doppler echocardiography aided by clustering based image segmentation and PISA techniques. Pre-processing is done subjecting the colour Doppler echocardiography image to Gaussian filtering which improves the signal to noise ratio of the image. Subsequently, the image was enhanced with the aid of an image contrast enhancement method that utilises contrast-limited adaptive histogram equalisation. Then this image is segmented by using fuzzy-k means clustering to enable more precise quantification of the AR. PISA method is employed for calculating the quantitative parameters of AR such as, EROA, regurgitant volume (RV), regurgitant fraction (RF), etc. The proximal flow convergence method is used to quantify valvular regurgitation by analysing the converging flow field proximal to the mild, severe or eccentric AR lesion. Experimental evaluation on the commonly accessible dataset illustrates the enhanced performance of the proposed approach effectively.

Heart disease is the foremost reason for death and also the main source of incapability in the developed nations. Mitral regurgitation (MR) is a typical heart disease that does not bring about manifestations until its end position. In view of the hidden etiologies of heart distress, functional MR can be partitioned into two subgroups, ischemic and no ischemic MR. A procedure is progressed for jet area separation and quantification in MR evaluation in arithmetical expressions. Thus, a strategy that depends on echocardiography recordings, image processing methods, and artificial intelligence could be useful for clinicians, particularly in marginal cases. In this research paper, MR segmentation is analyzed by the optimal histogram equalization (OHE) system used to segment the jet area. For a better execution of the work, threshold in HE was improved with the help of the krill herd optimization (KHO) strategy. With the MR quantification procedure, this segmented jet area was supported by the proximal isovelocity surface area (PISA); in this procedure, a few parameters in the segmentation were evaluated. From the results, this proposed methodology accomplishes better accuracy in the segmented and quantification method in contrast with the existing examination.

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