Zong X, Laine A F, Geiser E A
Department of Computer and Information Science and Engineering, University of Florida, Gainesville 32611, USA.
IEEE Trans Med Imaging. 1998 Aug;17(4):532-40. doi: 10.1109/42.730398.
This paper presents an algorithm for speckle reduction and contrast enhancement of echocardiographic images. Within a framework of multiscale wavelet analysis, we apply wavelet shrinkage techniques to eliminate noise while preserving the sharpness of salient features. In addition, nonlinear processing of feature energy is carried out to enhance contrast within local structures and along object boundaries. We show that the algorithm is capable of not only reducing speckle, but also enhancing features of diagnostic importance, such as myocardial walls in two-dimensional echocardiograms obtained from the parasternal short-axis view. Shrinkage of wavelet coefficients via soft thresholding within finer levels of scale is carried out on coefficients of logarithmically transformed echocardiograms. Enhancement of echocardiographic features is accomplished via nonlinear stretching followed by hard thresholding of wavelet coefficients within selected (midrange) spatial-frequency levels of analysis. We formulate the denoising and enhancement problem, introduce a class of dyadic wavelets, and describe our implementation of a dyadic wavelet transform. Our approach for speckle reduction and contrast enhancement was shown to be less affected by pseudo-Gibbs phenomena. We show experimentally that this technique produced superior results both qualitatively and quantitatively when compared to results obtained from existing denoising methods alone. A study using a database of clinical echocardiographic images suggests that such denoising and enhancement may improve the overall consistency of expert observers to manually defined borders.
本文提出了一种用于减少超声心动图图像斑点并增强对比度的算法。在多尺度小波分析框架内，我们应用小波收缩技术来消除噪声，同时保留显著特征的清晰度。此外，对特征能量进行非线性处理，以增强局部结构内和物体边界沿线的对比度。我们表明，该算法不仅能够减少斑点，还能增强具有诊断重要性的特征，如从胸骨旁短轴视图获得的二维超声心动图中的心肌壁。在对数变换后的超声心动图系数上，通过在更精细的尺度级别内进行软阈值处理来收缩小波系数。通过非线性拉伸，然后在选定的（中间范围）空间频率分析级别内对小波系数进行硬阈值处理，来实现超声心动图特征的增强。我们阐述了去噪和增强问题，引入了一类二进小波，并描述了我们对二进小波变换的实现。我们减少斑点和增强对比度的方法受伪吉布斯现象的影响较小。我们通过实验表明，与仅从现有去噪方法获得的结果相比，该技术在定性和定量方面都产生了更好的结果。一项使用临床超声心动图图像数据库的研究表明，这种去噪和增强可能会提高专家观察者对手动定义边界的总体一致性。
