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首页> 美国卫生研究院文献>Elsevier Sponsored Documents >An adaptive non-local means filter for denoising live-cell images and improving particle detection
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An adaptive non-local means filter for denoising live-cell images and improving particle detection

机译：自适应非局部均值滤波器用于对活细胞图像进行去噪并改善粒子检测

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摘要

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.
机译：活细胞中动态过程的荧光成像通常会导致信噪比低。我们提出了一种新颖的特征保留非局部均值方法来对此类图像进行降噪，以改善特征恢复和粒子检测。对于包含感兴趣的小特征的嘈杂生物图像，常用的非局部均值滤波器并不是最佳选择，因为图像噪声会阻止准确确定用于平均的正确系数，从而导致过度平滑和其他伪影。我们的自适应方法通过构造基于Haar样特征提取的粒子特征概率图像来解决此问题。然后，将粒子概率图像用于改进对平均系数的估计，以进行平均。我们表明，该滤波器在去噪图像中实现了更高的峰值信噪比，并且在应用于合成数据时具有识别弱粒子的更大能力。我们已将这种方法应用于活细胞图像，从而增强了对光敏果蝇组织中动态延伸的微管上末端结合蛋白1病灶的检测。我们表明，我们的特征保留非局部均值滤波器可以降低获取有意义数据所需的成像条件的阈值。

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