Contrast enhancement based denoising method in diffusion tensor imaging

Solwin Johnson, Arun A. Balakrishnan
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Abstract

In this paper, a new denoising and contrast enhancement method for DTI is proposed. Noise removal is given priority in existing denoising methods. In order to increase the visibility of structural details, contrast enhancement methods has to be used. In proposed method, a non-linear adaptive Gaussian denoising filter removes noises from the DTI. To increase the visibility of filtered micro structural details, a contrast enhancement method is also used. The proposed method is compared with the existing scalar Partial Differential Equation (PDE) and non local means (NLM) denoising methods. Quantitative measures are used for validating the efficiency of the proposed contrast enhancement method. The experiment results shows that the proposed method outperforms the scalar PDE method and NLM method.
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扩散张量成像中基于对比度增强的去噪方法
本文提出了一种新的DTI去噪和对比度增强方法。在现有的去噪方法中,去噪是优先考虑的问题。为了增加结构细节的可见性,必须使用对比度增强方法。该方法采用非线性自适应高斯去噪滤波器去除DTI中的噪声。为了增加过滤后微观结构细节的可见性,还使用了对比度增强方法。将该方法与现有的标量偏微分方程(PDE)和非局部均值(NLM)去噪方法进行了比较。定量测量用于验证所提出的对比度增强方法的效率。实验结果表明,该方法优于标量偏微分方程方法和NLM方法。
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