基于改进高斯模糊隶属函数的超声图像混合降噪方法

Priyankar Biswas, K. K. Halder, Arnab Sarkar
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引用次数: 0

摘要

在生物医学成像中,大多数图像通常会受到各种形式的噪声的影响,如高斯噪声、散斑噪声等。因此,从医学图像中去除这些噪声是正确诊断和更好地分析人体内部器官、身体组织等的必要条件。本研究提出了一种抑制医学超声图像中高斯噪声和散斑噪声的新策略。该方法建立在一个模糊滤波器的基础上,该滤波器遵循一个改进的高斯隶属函数。实验结果表明,即使在更高密度的情况下,该滤波器也能有效地降低组合噪声,与现有方法相比具有一定的竞争力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modified Gaussian Fuzzy Membership Function for Mixed Noise Reduction from Ultrasound Images
In biomedical imaging, most of the images are usually distorted by various forms of noises such as Gaussian noise, speckle noise, etc. Therefore, removing these noises from the medical images is required for proper diagnosis and better analysis of human inner organs, body tissues, and many more. A new strategy for suppressing combined Gaussian noise and speckle noise in medical ultrasound images has been proposed in this research. This methodology is founded on a fuzzy filter which follows a modified Gaussian membership function. The experimental findings show that the proposed filter is competent in reducing the combined noises, even with higher densities, and is competitive with the existing methods.
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