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Speckle Noise Removal Using Spatial and Transform Domain Filters in Ultrasound Images

机译:超声图像中使用空间和变换域滤波器的散斑噪声删除

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Noise in the Ultrasound (US) images creates difficulties in interpreting the actual information in the image. Various studies have been done in this direction to retain useful information from the images. Speckle noise is an important noise type present in ultrasound images in the area of Synthetic Aperture Radar (SAR), active Radar, Optical coherence tomography (OCT) and medicine. The granular pattern of speckle noise is formed due to the simultaneous processing of backscattered signals from dispersed targets. This noise limits the quality and contrast of the obtained images resulting in poor understanding of the underlying facts. This work focuses on the various denoising techniques in spatial and transform domain for speckle noise removal in ultrasound images. The performance evaluation of these techniques was done with metrics like Speckle suppression index (SSI), Peak signal-to-noise ratio (PSNR) and Structural similarity index measure (SSIM). From the experiments, it is evident that SRAD algorithm in spatial domain really out performed all other filters with better SSI, PSNR and SSIM values.
机译:超声(US)图像中的噪声在解释图像中的实际信息时会产生困难。在此方向上完成了各种研究以保留图像中的有用信息。斑点噪声是在合成孔径雷达(SAR),主动雷达,光学相干断层扫描(OCT)和药物中的超声图像中存在的重要噪声类型。由于同时处理来自分散的目标的反向散射信号,形成斑点噪声的粒状图案。这种噪声限制了所获得的图像的质量和对比导致对潜在事实的理解不良。这项工作侧重于空间和变换域中的各种去噪技术,用于超声图像中的散斑噪声清除。这些技术的性能评估是用散斑抑制指数(SSI),峰值信噪比(PSNR)和结构相似度指标(SSIM)的度量等度量进行的。从实验中,很明显,空间域中的SRAD算法真正脱掉了具有更好SSI,PSNR和SSIM值的所有其他滤波器。

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