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Abstract
A method has been developed to measure multi-parametric colocalization of color- or channel-specific light intensities in 3D space using a sequence of planar confocal microscopic images. A region growing algorithm has been implemented that analyzes the 3D image stack produced by a confocal microscope and identifies regions of interest. This algorithm selects threshold values from control images and analyzes the histogram information from each image to intelligently select and categorize object pixels from background pixels. A new technique for measuring the ratio of colocalization has also been proposed in conjunction with the region growing algorithm that results in an intuitive and easily understandable metric for characterization of the spatial colocalization throughout the whole x-y-z image set. This research successfully extends the currently available colocalization metrics by modifying the original equations to work on multi-parametric inputs. The consistency of the results on a range of input channel values is demonstrated via Monte Carlo simulation. A new spatial colocalization metric has also been proposed and implemented which results in a colocalization ratio that includes object information from all images in the colocalization experiment.