A Bayesian method for 3D estimation of subcellular particle features in multi-angle TIRF microscopy

Multi-angle total internal reflection fluorescence microscopy (MA-TIRFM) is a relatively new and powerful tool to study subcellular particles near cell membrane due to its unique illumination mechanism. We present a MAP-Bayesian method to automatically estimate features of individual particles in MA-TIRF images, including 3D positions, relative sizes, and relative amount of fluorophores. Using the MAP criterion, the optimal values of the features can be obtained by maximizing a nonlinear functional. Initial feature values are estimated by using image filters and clustering algorithms. The method is evaluated on synthetic data and results show that it has high accuracy. The result on real data from our initial experiments is also presented.