A speckle reduction filter using wavelet-based methods for medical imaging application

Su Cheol Kang, S. Hong
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引用次数: 17

Abstract

One of the most significant features for diagnostic echocardiographic images is to reduce speckle noise and improve image quality. We propose a simple and effective filter design for image denoising and contrast enhancement based on a multiscale wavelet method. Wavelet threshold algorithms replace small magnitude wavelet coefficients by zero and keep or shrink the other coefficients. This is basically a local procedure, since wavelet coefficients characterize the local regularity of a function. After we estimate the distribution of noise within an echocardiographic image, we apply it to a fitness wavelet threshold algorithm. A common way of estimating the speckle noise level in coherent imaging is to calculate the mean-to-standard-deviation ratio of the pixel intensity, often termed the equivalent number of looks (ENL), over a uniform image area. Unfortunately, this measure is not very robust, mainly due to the difficulty of identifying a uniform area in a real image. For this reason, we only use the S/MSE ratio, which corresponds to the standard SNR in case of additive noise. We have simulated some echocardiographic images by specialized hardware for a real-time application; processing of 512/spl times/512 images takes about 1 min. Our experiments show that the optimal threshold level depends on the spectral content of the image. With high spectral content, the noise standard deviation estimation performed at the finest level of the DWT tends to be over-estimated. Hence a lower threshold parameter is required to get the optimal S/MSE. The standard WCS theory predicts a threshold that depends only on the number of signal samples.
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基于小波的散斑减少滤波器在医学成像中的应用
超声心动图诊断图像最重要的特点之一是降低斑点噪声,提高图像质量。提出了一种基于多尺度小波方法的简单有效的图像去噪和对比度增强滤波器设计。小波阈值算法将小波系数替换为零,保留或缩小其他系数。这基本上是一个局部过程,因为小波系数表征了函数的局部正则性。在估计超声心动图图像中的噪声分布后,我们将其应用于适应度小波阈值算法。相干成像中估计散斑噪声水平的一种常用方法是计算均匀图像区域上像素强度的平均与标准偏差比,通常称为等效外观数(ENL)。不幸的是,这种方法不是很健壮,主要是由于难以识别真实图像中的均匀区域。因此,我们只使用S/MSE比,它对应于加性噪声情况下的标准信噪比。我们用专门的硬件模拟了一些超声心动图图像,用于实时应用;处理512/spl次/512张图像大约需要1分钟。我们的实验表明,最佳阈值水平取决于图像的光谱含量。在高光谱含量的情况下,在DWT的最精细水平上进行的噪声标准偏差估计往往会被高估。因此,需要一个较低的阈值参数来获得最佳的S/MSE。标准WCS理论预测的阈值仅取决于信号样本的数量。
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