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首页> 外文期刊>Journal of Medical Imaging and Health Informatics >Improved Dual-Domain Filtering and Threshold Function Denoising Method for Ultrasound Images Based on Non-Subsampled Contourlet Transform
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Improved Dual-Domain Filtering and Threshold Function Denoising Method for Ultrasound Images Based on Non-Subsampled Contourlet Transform

机译：基于非副取样轮廓变换的超声图像改进的双域滤波和阈值函数去噪方法

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摘要

Ultrasound images reveal pathological signs and are critical for the clinical diagnosis and treatment. Ultrasound image includes lots of pathological information. Noises in images are fundamental issues in the field of ultrasonic image denoising processing. Enhance edges of images efficiently and removal noises in images are fundamental issues in the field of ultrasonic image denoising processing. An improved image denoising method, which combines the ultrasonic image speckle noise and the statistic characteristic of the non-subsampled Contourlet coefficient, is proposed in this study. For the low-frequency coefficients, two modified filters are combined in two domains, while for the high-frequency ones, a refined threshold value is used, with the noise suppression processing being based on the energy condition function. In order to verify the advantages of the proposed algorithm, experiments were performed on simulated images and real ultrasound images. The experimental results obtained in this study strongly indicate that objective indicators and visual quality of image denoising by the proposed method are superior to those of traditional denoising algorithms.
机译：超声图像显示病理迹象，对临床诊断和治疗至关重要。超声图像包括许多病理信息。图像中的噪声是超声图像去噪处理领域的基本问题。有效地增强图像的边缘，并且图像中的去除噪声是超声图像去噪处理领域的基本问题。在本研究中提出了一种改进的图像去噪方法，其结合了超声图像斑点噪声的噪声和非副取样Contourlet系数的统计特征。对于低频系数，两个修改的滤波器在两个域中组合，而对于高频器，使用精细阈值，利用噪声抑制处理基于能量状况。为了验证所提出的算法的优点，对模拟图像和真实超声图像进行实验。本研究中获得的实验结果强烈表明，通过该方法的图像去噪的客观指标和视觉质量优于传统的去噪算法。

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