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Angular feature vector based speckle denoising technique for ultrasound medical images

机译:基于角度的超声医学图像散斑去噪技术

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An ultrasound image provides the radiologist with noninvasive, low cost, and real-time images that can help them in diagnosis, planning and therapy. However, although the human eye is able to derive the meaningful information from these images, automatic processing is very difficult because of the noise and artifacts present in the image. This paper proposes different filtering techniques based on feature vector based statistical methods for the removal of speckle noise. In this work, we propose to extend the current hybrid median filter technique to deal with the speckle noise present in the Ultrasound images. Metrics like Mean Square Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Root Mean Square Error (RMSE) and Structural Similarity Index Measurement (SSIM) are used to evaluate the efficiency of the proposed work. Experimental results and performance analysis shows promising results when compared with existing methods.
机译:超声图像为放射科医师提供无侵入性,低成本和实时图像,可以帮助它们诊断,规划和治疗。然而,尽管人眼能够从这些图像中获得有意义的信息,但由于图像中存在的噪声和伪像,自动处理非常困难。本文提出了基于特征向量的统计方法来提出了不同的过滤技术,用于去除散斑噪声。在这项工作中,我们建议扩展当前的混合中值滤波技术,以处理超声图像中存在的斑点噪声。指标等平均方误差(MSE),峰值信噪比(PSNR),均方根误差(RMSE)和结构相似性指数测量(SSIM)用于评估所提出的工作的效率。实验结果和性能分析显示与现有方法相比的有希望的结果。

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