基于拉普拉斯混合偏微分方程的医学诊断超声图像去斑

S. Kalaivani Narayanan1, R. Wahidabanu
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引用次数: 1

摘要

在本文中,我们提出了一种有效的降噪方法,可以用来减少斑点和共同增强边缘信息,而不仅仅是抑制平滑。该方法利用基于混合偏微分方程的非线性扩散对带通超声图像在拉普拉斯金字塔域进行滤波去除斑点。在每个金字塔层中,使用鲁棒中值估计器自动估计梯度阈值。采用两个相邻扩散步骤之间的平均绝对误差(MAE)作为停止准则。在合成数据和仿真模型上的定量结果表明,与目前的方法相比,所提出的方法具有良好的性能。实验结果表明,该方法能较好地保留图像的边缘和结构细节。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Despeckling of medical diagonostic ultrasound images via Laplacian based mixed PDE
In this paper, we propose an efficient noise reduction method that can be used to reduce speckle and jointly enhancing the edge information, rather than just inhibiting smoothing. In this method speckle is removed by filtering of band pass ultrasound images in Laplacian pyramid domain by using mixed PDE based nonlinear diffusion. In each pyramid layer, a gradient threshold is estimated automatically using robust median estimator. The mean absolute error (MAE) between two adjacent diffusion steps is used as stopping criterion. Quantitative results on synthetic data and simulated phantom show the performance of the proposed method compared to state of the art methods. Results on real images demonstrate that the proposed method is able to preserve edges & structural details of the image.
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