An adaptive non-local means filter for denoising live-cell images and improving particle detection.
An adaptive non-local means filter for denoising live-cell images and improving particle detection.
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DOI：
10.1016/j.jsb.2010.06.019
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发表时间：
2010-12
影响因子：
3
通讯作者：
Lu, Weiping
中科院分区：
文献类型：
--
作者：
Yang, Lei;Parton, Richard;Ball, Graeme;Qiu, Zhen;Greenaway, Alan H.;Davis, Ilan;Lu, Weiping
活细胞中动态过程的荧光成像通常导致低信噪比。我们提出了一种新的保持特征的非局部均值方法来消除这些图像的噪声，以提高特征恢复和粒子检测。通常使用的非局部均值滤波器对于包含感兴趣的小特征的有噪声的生物图像不是最佳的，因为图像噪声阻止准确确定用于平均的正确系数，导致过度平滑和其他伪影。我们的自适应方法解决了这个问题，通过构建一个粒子特征概率图像，这是基于Haar特征提取。然后，粒子概率图像用于改进用于平均的正确系数的估计。我们表明，该滤波器实现了更高的峰值信噪比去噪图像，并有更大的能力，在识别弱粒子时，应用于合成数据。我们已经将这种方法应用到活细胞图像中，从而增强了对光敏果蝇组织中动态延伸微管的末端结合蛋白1焦点的检测。我们表明，我们的特征保持非局部均值滤波器可以降低阈值的成像条件所需的获得有意义的数据。
Fluorescence imaging of dynamical processes in live cells often results in a low signal-to-noise ratio. We present a novel feature-preserving non-local means approach to denoise such images to improve feature recovery and particle detection. The commonly used non-local means filter is not optimal for noisy biological images containing small features of interest because image noise prevents accurate determination of the correct coefficients for averaging, leading to over-smoothing and other artifacts. Our adaptive method addresses this problem by constructing a particle feature probability image, which is based on Haar-like feature extraction. The particle probability image is then used to improve the estimation of the correct coefficients for averaging. We show that this filter achieves higher peak signal-to-noise ratio in denoised images and has a greater capability in identifying weak particles when applied to synthetic data. We have applied this approach to live-cell images resulting in enhanced detection of end-binding-protein 1 foci on dynamically extending microtubules in photo-sensitive Drosophila tissues. We show that our feature-preserving non-local means filter can reduce the threshold of imaging conditions required to obtain meaningful data.
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