Blind Source Parameters for Performance Evaluation of Despeckling Filters.

Nagashettappa Biradar,M L Dewal,Manojkumar Rohit,Sanjaykumar Gowre,Yogesh Gundge

doi:10.1155/2016/3636017

Abstract

The speckle noise is inherent to transthoracic echocardiographic images. A standard noise-free reference echocardiographic image does not exist. The evaluation of filters based on the traditional parameters such as peak signal-to-noise ratio, mean square error, and structural similarity index may not reflect the true filter performance on echocardiographic images. Therefore, the performance of despeckling can be evaluated using blind assessment metrics like the speckle suppression index, speckle suppression and mean preservation index (SMPI), and beta metric. The need for noise-free reference image is overcome using these three parameters. This paper presents a comprehensive analysis and evaluation of eleven types of despeckling filters for echocardiographic images in terms of blind and traditional performance parameters along with clinical validation. The noise is effectively suppressed using the logarithmic neighborhood shrinkage (NeighShrink) embedded with Stein's unbiased risk estimation (SURE). The SMPI is three times more effective compared to the wavelet based generalized likelihood estimation approach. The quantitative evaluation and clinical validation reveal that the filters such as the nonlocal mean, posterior sampling based Bayesian estimation, hybrid median, and probabilistic patch based filters are acceptable whereas median, anisotropic diffusion, fuzzy, and Ripplet nonlinear approximation filters have limited applications for echocardiographic images.

Highlights

Echocardiography is commonly used in the diagnosis of valvular regurgitation and stenosis
This paper evaluates the performance of eleven types of filters based on suppression index (SSI), suppression and mean preservation index (SMPI), β, figure of merit (FoM), and image quality index (IQI) and eleven other parameters along with the visual quality assessment and clinical validation
The wavelet shrinkage techniques such as the multiscale product thresholding (MPT), BayesShrink, OWT, BlockShrink, SURELET, and NSS result in better texture preservation compared to synthetic aperture radar (SAR) filters

Summary

Introduction

Echocardiography is commonly used in the diagnosis of valvular regurgitation and stenosis. It is a noninvasive, safe, and a less expensive technique; but the low contrast, shadowing, and the speckle noise present make it hard for a clinician to read. The noise masks the finer clinical detail present in the image and thereby reduces the human visual ability for detecting the abnormalities. It reduces the potentiality of images in providing crucial and vital information [1]. It is necessary to suppress speckle noise without altering the fine details from the transthoracic echocardiographic (TTE) images

Methods

Results

Conclusion