Department of Physics, EPS, Heriot-Watt University, Edinburgh, EH14 4AS, U.K.
IEEE Trans Biomed Eng. 2012 Jul;59(7):2040-50. doi: 10.1109/TBME.2012.2196798. Epub 2012 Apr 27.
Image denoising and signal enhancement are two common steps to improve particle contrast for detection in low-signal-to-noise ratio (SNR) fluorescence live-cell images. However, denoising may oversmooth features of interest, particularly weak features, leading to false negative detection. Here, we propose a robust framework for particle detection in which image denoising in the grayscale image is not needed, so avoiding image oversmoothing. A key to our approach is the new development of a particle enhancement filter based on the recently proposed particle probability image to obtain significantly enhanced particle features and greatly suppressed background in low-SNR and low-contrast environments. The new detection method is formed by combining foreground and background markers with watershed transform operating in both particle probability and grayscale spaces; dynamical switchings between the two spaces can optimally make use the information in images for accurate determination of particle position, size, and intensity. We further develop the interacting multiple mode filter for particle motion modeling and data association by incorporating the extra information obtained from our particle detector to enhance the efficiency of multiple particle tracking. We find that our methods lead to significant improvements in particle detection and tracking efficiency in fluorescence live-cell applications.
图像去噪和信号增强是提高低信噪比 (SNR) 荧光活细胞图像中粒子对比度的两种常用方法。然而，去噪可能会过度平滑感兴趣的特征，特别是弱特征，导致假阴性检测。在这里，我们提出了一种用于粒子检测的稳健框架，其中不需要对灰度图像进行图像去噪，从而避免图像过度平滑。我们方法的一个关键是基于最近提出的粒子概率图像开发新的粒子增强滤波器，以在低 SNR 和低对比度环境中获得显著增强的粒子特征和大大抑制背景。新的检测方法是通过将前景和背景标记与在粒子概率和灰度空间中操作的分水岭变换相结合形成的；两个空间之间的动态切换可以最优地利用图像中的信息，准确确定粒子的位置、大小和强度。我们通过将从我们的粒子探测器获得的额外信息结合到粒子运动建模和数据关联的交互多模式滤波器中，进一步开发了用于提高多粒子跟踪效率的方法。我们发现，我们的方法在荧光活细胞应用中显著提高了粒子检测和跟踪效率。
