Quantification of aortic regurgitation using proximal isovelocity surface area: an effective segmentation approach based on fuzzy clustering

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.