Biradar Nagashettappa, Dewal M L, Rohit ManojKumar, Gowre Sanjaykumar, Gundge Yogesh
Bheemanna Khandre Institute of Technology, Bhalki 58532, India.
Indian Institute of Technology Roorkee, Roorkee 247667, India.
Int J Biomed Imaging. 2016;2016:3636017. doi: 10.1155/2016/3636017. Epub 2016 May 19.
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.
散斑噪声是经胸超声心动图图像固有的。不存在标准的无噪声参考超声心动图图像。基于诸如峰值信噪比、均方误差和结构相似性指数等传统参数对滤波器进行评估，可能无法反映滤波器在超声心动图图像上的真实性能。因此，可以使用散斑抑制指数、散斑抑制和均值保持指数（SMPI）以及β指标等盲评估指标来评估去斑性能。使用这三个参数克服了对无噪声参考图像的需求。本文从盲和传统性能参数以及临床验证方面，对11种用于超声心动图图像的去斑滤波器进行了全面的分析和评估。使用嵌入了斯坦无偏风险估计（SURE）的对数邻域收缩（NeighShrink）有效地抑制了噪声。与基于小波的广义似然估计方法相比，SMPI的效果要好三倍。定量评估和临床验证表明，非局部均值、基于后验采样的贝叶斯估计、混合中值和基于概率补丁的滤波器等滤波器是可接受的，而中值、各向异性扩散、模糊和Ripplet非线性逼近滤波器在超声心动图图像中的应用有限。
