Unsupervised noise removal algorithms for 3-D confocal fluorescence microscopy

摘要

Fast algorithms are presented for effective removal of the noise artifact in 3-D confocal fluorescence microscopy images of extended spatial objects such as neurons. The algorithms are unsupervised in the sense that they automatically estimate and adapt to the spatially and temporally varying noise level in the microscopy data. An important feature of the algorithms is the fact that a 3-D segmentation of the field emerges jointly with the intensity estimate. The role of the segmentation is to limit any smoothing to the interiors of regions and hence avoid the blurring that is associated with conventional noise removal algorithms. Fast computation is achieved by parallel computation methods, rather than by algorithmic or modelling compromises. The noise-removal proceeds iteratively, starting from a set of approximate user-supplied, or default initial guesses of the underlying random process parameters. An expectation maximization algorithm is used to obtain a more precise characterization of these parameters, that are then input to a hierarchical estimation algorithm. This algorithm computes a joint solution of the related problems corresponding to intensity estimation, segmentation, and boundary-surface estimation subject to a combination of stochastic priors and syntactic pattern constraints. Three-dimensional stereoscopic renderings of processed 3-D images of murine hippocampal neurons are presented to demonstrate the effectiveness of the method. The processed images exhibit increased contrast and significant smoothing and reduction of the background intensity while avoiding any blurring of the neuronal structures.