Temporal 2D reconstruction of cell nucleus from Fluorescence Confocal Microscopy images with anisotropic filtering

Fluorescence Confocal Microscopy(FCM) is nowadays one of the most important tools in biomedicine research. In fact, it makes possible to accurately study the dynamic processes occurring inside the cell and its nucleus by following the motion of fluorescent molecules along the time. Due to the small amount of acquired radiation and the huge optical and electronics amplification, the FCM images are usually corrupted by a severe type of Poisson noise. This noise may be even more damaging when very low intensity incident radiation is used to avoid phototoxicity. In this paper, a Bayesian algorithm is proposed to remove the Poisson multiplicative noise corrupting the FCM images. The observation are organized in a 3D tensor where each plane is one of the images acquired along the time of a cell using the Fluorescence Loss In Photobleaching (FLIP) technique. The method removes simultaneously the noise by considering different spatial and temporal correlations. This is done by using an anisotropic 3D filter that may be separately tunned in space and time dimensions. Tests using synthetic and real data are described and presented to illustrate the application of the algorithm.