A New Framework for Particle Detection in Low-SNR Fluorescence Live-Cell Images and Its Application for Improved Particle Tracking

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.