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Dual-tree wavelet based algorithm for speckle reduction and edge enhancement in ultrasound images

机译:基于双树小波的超声图像斑点减少和边缘增强算法

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Ultrasound images are the important bases of disease diagnostic. Unfortunately, the qualities of ultrasound images are generally limited due to speckle noises. Speckle reduction is an important pre-processing step in the ultrasound image feature extraction, analysis and recognition. In this paper, we present an approach for ultrasound image enhancement. It is designed to utilize the three technologies: the separability and multiresolution properties of the wavelet, the local statistics of each subband and the edge enhancement of the shape. The performance of the proposed method has been compared with that of the commonly novel approaches on both synthetic speckle images and real medical ultrasound images. The proposed method reveals superior performance in term of the PSNR value and perceptible quality. Because of the superior performance in noise reduction and edge preservation, the proposed method is more suitable than the other methods in computer-aided diagnosis.
机译:超声图像是疾病诊断的重要基础。不幸的是,由于斑点噪声,超声图像的质量通常受到限制。减少斑点是超声图像特征提取,分析和识别中的重要预处理步骤。在本文中,我们提出了一种超声图像增强方法。它旨在利用三种技术:小波的可分离性和多分辨率特性,每个子带的局部统计信息以及形状的边缘增强。在合成散斑图像和实际医学超声图像上,该方法的性能已与常用方法的性能进行了比较。所提出的方法在PSNR值和可感知的质量方面显示出优异的性能。由于在降噪和边缘保留方面具有优异的性能,因此该方法在计算机辅助诊断中比其他方法更适合。

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